PROJECT REPORT: THE CROWN PROSECUTION SERVICE CASE OUTCOMES
Shedrach Ojodomo Agbo - 4212651
2023-01-11
EXECUTIVE SUMMARY
Background
The Crown Prosecution Service (CPS) prosecutes criminal cases that have been investigated by the police and other investigative organisations in England and Wales. The CPS claims to be independent make her decisions independently of the police and government.
Their duty is to make sure that the right person is prosecuted for the right offence, and to bring offenders to justice wherever possible, hence her core values are fairness, objectivity and independence. According to the CPS website , outcomes are broken down into two categories: convictions and unsuccessful outcomes. The reports sourced show the number (No) and the proportion (%) of defendants falling into each category. There are 13 various principal offences categories which are “Homicide”, “Offences against the person”, “Sexual offences”, “Burglary offences”, “Robbery”, “Theft and handling”, “Fraud and forgery”, “Criminal damage”, “Drugs offences”, “Public order”, “All other offences excluding motoring”, “Motoring offences”, and “Administrative finalisations”.
Project Objective:
The objective of this project is to analyse the datasets from a five-year period of Crown Prosecution Service Case Outcomes by Principal Offence Category by developing a prediction model using using one linear regression technique, one clustering technique and one classification technique.
Data Report:
For the purpose of this project, the dataset was sourced from the data.gov.uk website https://data.gov.uk/dataset/89d0aef9-e2f9-4d1a-b779-5a33707c5f2c/crown-prosecution-service-case-outcomes-by-principal-offence-category-data). This comprised of 2,193 crime cases across 42 areas in the United Kingdom (UK) in a five-year period, between 2014 to 2018.
The R programming language and some of its relevant libraries/packages were used for analysing the dataset for this project. Various packages and libraries, such as “tidyverse”, “ggplot”, “rmarkdown”,“corrplot”, “inspectdf”, etc were installed for the purpose of importing, cleaning, modifying, analysing the dataset and generating the project report.
Analysis Assumptions & Limitation:
The data collected seem to be having some missing period for year 2015, 2016, 2017 and 2018. There is one missing month (November) in 2015, two months (February and March) missing in 2016, three months (April, May, June) in 2017 and also three months (April, May, June) in 2018. Hence, for the data analysis is carried out by Areas (Counties) and not dates.
For the purpose of analysis, the Principal Offence Category “Offences against the person” was adopted as the dependent variable, while the other Principal Offence Categories are adopted as the independent variables.
Project Analytical Techiques:
The dataset used is an integration of various data stored and arranged in monthly csv files and organised in yearly folders (year 2014 to year 2018). Different techniques related to descriptive and predictive analytics were adopted and applied on the integrated dataset. The variables understand consideration were assessed for correlation using tabular and visualization techniques. Statiscal analysis were conducted through hypothesis testing. Also, visualization tools such as bar charts, correlation charts, etc were used for graphically presenting the data for insights.
The Exploratory and descriptive analytics techniques applied include: - imported and merged the various csv files into a single dataset - Viewed and set the structure of the dataset - Checked for missing values (NA) and treating them - modified the variables in the dataset for proper analysis - Conducted a univariate analysis to understand patterns - Conducted a bivariate analysis between variables to gain insight of any existing relationship. - viewed statistical data
The predictive analytics techniques applied include: - Linear Regression - K-mean Clustering - Decision Tree
EXPLORATORY DATA ANALYSIS (EDA)
Exploratory data analysis (EDA) involves using graphics and visualizations to explore and analyze a data set. The goal is to explore, investigate and learn, as opposed to confirming statistical hypotheses
Loading Libraries
The directories in R where the packages are stored are called the libraries. The terms package and library are sometimes used synonymously. You can also embed plots
library(tinytex)
library(latexpdf)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(inspectdf)
library(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ purrr 1.0.0
## âś” tidyr 1.2.1 âś” stringr 1.5.0
## âś” readr 2.1.3 âś” forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## âś– dplyr::filter() masks stats::filter()
## âś– dplyr::lag() masks stats::lag()library(gridExtra)##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combinelibrary(plotly)##
## Attaching package: 'plotly'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layoutlibrary(cluster) # algorithms for clustering
library(factoextra) # algorithms & visualization for clustering## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(Hmisc)## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
##
## The following object is masked from 'package:plotly':
##
## subplot
##
## The following objects are masked from 'package:dplyr':
##
## src, summarize
##
## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(corrplot)## corrplot 0.92 loadedlibrary(ggcorrplot)
library('rpart')
library('rpart.plot')
library(Metrics)
library(RWeka)
library(rmarkdown)
library(ggplot2)
options (warn = - 1)Setting and checking the working directory
The working directory in R is the folder where you are working. Hence, it’s the place (the environment) where you have to store your files of your project in order to load them or where your R objects will be saved.
setwd ("/Users/newuser/Desktop/Uniglos Project") #To set the Working directory
getwd() #To confirm the directory is properly set.## [1] "/Users/newuser/Desktop/Uniglos Project"Importing Data - Integrating the entire CSV files from 2014 to 2018
Each folder represents month data recorded for each year on a monthly basis. Hence, for the purpose of this project analysis, the entire csv files are integrated into a single data set.
#To integrate all the CSV files in the folders into a single dataset file
CrimeCases_data = list.files(full.names = TRUE, recursive = TRUE, path="/Users/newuser/Desktop/Dataset - Assignment") %>%
lapply(read_csv, col_types = cols( .default = col_character())) %>%
bind_rows## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## • `` -> `...1`To view the Data Dimension
This is to reveal the number of columns and rows of a matrix, array or data frame. Here the dim() function was applied to achieve this.
dim(CrimeCases_data)## [1] 2193 51Integrating the entire csv files gave rise to 2,193 rows (observations) with 51 columns (variables)
Converting the integrated dataset to a data frame.
A data frame is a list of variables of the same number of rows with unique row names, given class “data.frame”. It is the most common way of storing data in R and, generally, is the data structure most often used for data analyses.
To be able to keep the data in the appropriate structure for this analysis, the data imported was converted to a dataframe.
CrimeCases_data = as.data.frame(CrimeCases_data)
class(CrimeCases_data)## [1] "data.frame"This confirms that the integrated dataset is now a dataframe.
Data frame Check
In order to confirm that the entire data as captured have been imported for analysis, a view of the first and last 6 rows of the data set were made.
head(CrimeCases_data)## ...1 Number of Homicide Convictions
## 1 National 81
## 2 Avon and Somerset 1
## 3 Bedfordshire 0
## 4 Cambridgeshire 0
## 5 Cheshire 1
## 6 Cleveland 0
## Percentage of Homicide Convictions Number of Homicide Unsuccessful
## 1 85.3% 14
## 2 100.0% 0
## 3 - 0
## 4 - 0
## 5 50.0% 1
## 6 - 0
## Percentage of Homicide Unsuccessful
## 1 14.7%
## 2 0.0%
## 3 -
## 4 -
## 5 50.0%
## 6 -
## Number of Offences Against The Person Convictions
## 1 7,805
## 2 167
## 3 69
## 4 99
## 5 140
## 6 85
## Percentage of Offences Against The Person Convictions
## 1 74.1%
## 2 78.8%
## 3 75.0%
## 4 81.1%
## 5 74.9%
## 6 67.5%
## Number of Offences Against The Person Unsuccessful
## 1 2,722
## 2 45
## 3 23
## 4 23
## 5 47
## 6 41
## Percentage of Offences Against The Person Unsuccessful
## 1 25.9%
## 2 21.2%
## 3 25.0%
## 4 18.9%
## 5 25.1%
## 6 32.5%
## Number of Sexual Offences Convictions
## 1 698
## 2 36
## 3 5
## 4 6
## 5 17
## 6 11
## Percentage of Sexual Offences Convictions
## 1 72.2%
## 2 81.8%
## 3 83.3%
## 4 66.7%
## 5 85.0%
## 6 73.3%
## Number of Sexual Offences Unsuccessful
## 1 269
## 2 8
## 3 1
## 4 3
## 5 3
## 6 4
## Percentage of Sexual Offences Unsuccessful Number of Burglary Convictions
## 1 27.8% 1,470
## 2 18.2% 37
## 3 16.7% 16
## 4 33.3% 8
## 5 15.0% 26
## 6 26.7% 25
## Percentage of Burglary Convictions Number of Burglary Unsuccessful
## 1 86.7% 226
## 2 94.9% 2
## 3 94.1% 1
## 4 100.0% 0
## 5 89.7% 3
## 6 71.4% 10
## Percentage of Burglary Unsuccessful Number of Robbery Convictions
## 1 13.3% 517
## 2 5.1% 9
## 3 5.9% 4
## 4 0.0% 6
## 5 10.3% 1
## 6 28.6% 5
## Percentage of Robbery Convictions Number of Robbery Unsuccessful
## 1 81.7% 116
## 2 75.0% 3
## 3 100.0% 0
## 4 85.7% 1
## 5 100.0% 0
## 6 71.4% 2
## Percentage of Robbery Unsuccessful Number of Theft And Handling Convictions
## 1 18.3% 10,045
## 2 25.0% 266
## 3 0.0% 98
## 4 14.3% 107
## 5 0.0% 206
## 6 28.6% 254
## Percentage of Theft And Handling Convictions
## 1 92.3%
## 2 92.7%
## 3 91.6%
## 4 91.5%
## 5 98.1%
## 6 88.8%
## Number of Theft And Handling Unsuccessful
## 1 840
## 2 21
## 3 9
## 4 10
## 5 4
## 6 32
## Percentage of Theft And Handling Unsuccessful
## 1 7.7%
## 2 7.3%
## 3 8.4%
## 4 8.5%
## 5 1.9%
## 6 11.2%
## Number of Fraud And Forgery Convictions
## 1 666
## 2 11
## 3 8
## 4 7
## 5 16
## 6 6
## Percentage of Fraud And Forgery Convictions
## 1 86.0%
## 2 100.0%
## 3 80.0%
## 4 100.0%
## 5 88.9%
## 6 75.0%
## Number of Fraud And Forgery Unsuccessful
## 1 108
## 2 0
## 3 2
## 4 0
## 5 2
## 6 2
## Percentage of Fraud And Forgery Unsuccessful
## 1 14.0%
## 2 0.0%
## 3 20.0%
## 4 0.0%
## 5 11.1%
## 6 25.0%
## Number of Criminal Damage Convictions
## 1 2,259
## 2 54
## 3 20
## 4 21
## 5 35
## 6 32
## Percentage of Criminal Damage Convictions
## 1 85.2%
## 2 90.0%
## 3 76.9%
## 4 95.5%
## 5 79.5%
## 6 80.0%
## Number of Criminal Damage Unsuccessful
## 1 391
## 2 6
## 3 6
## 4 1
## 5 9
## 6 8
## Percentage of Criminal Damage Unsuccessful
## 1 14.8%
## 2 10.0%
## 3 23.1%
## 4 4.5%
## 5 20.5%
## 6 20.0%
## Number of Drugs Offences Convictions Percentage of Drugs Offences Convictions
## 1 4,536 94.2%
## 2 135 98.5%
## 3 45 95.7%
## 4 40 95.2%
## 5 75 88.2%
## 6 63 90.0%
## Number of Drugs Offences Unsuccessful
## 1 279
## 2 2
## 3 2
## 4 2
## 5 10
## 6 7
## Percentage of Drugs Offences Unsuccessful
## 1 5.8%
## 2 1.5%
## 3 4.3%
## 4 4.8%
## 5 11.8%
## 6 10.0%
## Number of Public Order Offences Convictions
## 1 3,549
## 2 68
## 3 29
## 4 45
## 5 86
## 6 74
## Percentage of Public Order Offences Convictions
## 1 84.4%
## 2 86.1%
## 3 82.9%
## 4 83.3%
## 5 92.5%
## 6 73.3%
## Number of Public Order Offences Unsuccessful
## 1 654
## 2 11
## 3 6
## 4 9
## 5 7
## 6 27
## Percentage of Public Order Offences Unsuccessful
## 1 15.6%
## 2 13.9%
## 3 17.1%
## 4 16.7%
## 5 7.5%
## 6 26.7%
## Number of All Other Offences (excluding Motoring) Convictions
## 1 2,640
## 2 66
## 3 11
## 4 6
## 5 50
## 6 28
## Percentage of All Other Offences (excluding Motoring) Convictions
## 1 83.7%
## 2 80.5%
## 3 64.7%
## 4 75.0%
## 5 89.3%
## 6 84.8%
## Number of All Other Offences (excluding Motoring) Unsuccessful
## 1 513
## 2 16
## 3 6
## 4 2
## 5 6
## 6 5
## Percentage of All Other Offences (excluding Motoring) Unsuccessful
## 1 16.3%
## 2 19.5%
## 3 35.3%
## 4 25.0%
## 5 10.7%
## 6 15.2%
## Number of Motoring Offences Convictions
## 1 8,283
## 2 188
## 3 40
## 4 79
## 5 209
## 6 124
## Percentage of Motoring Offences Convictions
## 1 86.3%
## 2 83.6%
## 3 88.9%
## 4 92.9%
## 5 94.6%
## 6 87.9%
## Number of Motoring Offences Unsuccessful
## 1 1,314
## 2 37
## 3 5
## 4 6
## 5 12
## 6 17
## Percentage of Motoring Offences Unsuccessful
## 1 13.7%
## 2 16.4%
## 3 11.1%
## 4 7.1%
## 5 5.4%
## 6 12.1%
## Number of Admin Finalised Unsuccessful
## 1 718
## 2 24
## 3 16
## 4 4
## 5 1
## 6 10
## Percentage of L Motoring Offences Unsuccessful
## 1 100.0%
## 2 100.0%
## 3 100.0%
## 4 100.0%
## 5 100.0%
## 6 100.0%tail(CrimeCases_data)## ...1 Number of Homicide Convictions
## 2188 Thames Valley 4
## 2189 Warwickshire 0
## 2190 West Mercia 6
## 2191 West Midlands 11
## 2192 West Yorkshire 5
## 2193 Wiltshire 0
## Percentage of Homicide Convictions Number of Homicide Unsuccessful
## 2188 80.0% 1
## 2189 - 0
## 2190 100.0% 0
## 2191 91.7% 1
## 2192 71.4% 2
## 2193 - 0
## Percentage of Homicide Unsuccessful
## 2188 20.0%
## 2189 -
## 2190 0.0%
## 2191 8.3%
## 2192 28.6%
## 2193 -
## Number of Offences Against The Person Convictions
## 2188 333
## 2189 65
## 2190 220
## 2191 609
## 2192 446
## 2193 85
## Percentage of Offences Against The Person Convictions
## 2188 76.4%
## 2189 80.2%
## 2190 78.6%
## 2191 78.0%
## 2192 85.9%
## 2193 86.7%
## Number of Offences Against The Person Unsuccessful
## 2188 103
## 2189 16
## 2190 60
## 2191 172
## 2192 73
## 2193 13
## Percentage of Offences Against The Person Unsuccessful
## 2188 23.6%
## 2189 19.8%
## 2190 21.4%
## 2191 22.0%
## 2192 14.1%
## 2193 13.3%
## Number of Sexual Offences Convictions
## 2188 46
## 2189 9
## 2190 20
## 2191 66
## 2192 71
## 2193 12
## Percentage of Sexual Offences Convictions
## 2188 75.4%
## 2189 69.2%
## 2190 69.0%
## 2191 74.2%
## 2192 68.3%
## 2193 100.0%
## Number of Sexual Offences Unsuccessful
## 2188 15
## 2189 4
## 2190 9
## 2191 23
## 2192 33
## 2193 0
## Percentage of Sexual Offences Unsuccessful Number of Burglary Convictions
## 2188 24.6% 58
## 2189 30.8% 13
## 2190 31.0% 25
## 2191 25.8% 63
## 2192 31.7% 84
## 2193 0.0% 7
## Percentage of Burglary Convictions Number of Burglary Unsuccessful
## 2188 96.7% 2
## 2189 92.9% 1
## 2190 83.3% 5
## 2191 82.9% 13
## 2192 94.4% 5
## 2193 87.5% 1
## Percentage of Burglary Unsuccessful Number of Robbery Convictions
## 2188 3.3% 7
## 2189 7.1% 3
## 2190 16.7% 4
## 2191 17.1% 30
## 2192 5.6% 15
## 2193 12.5% 0
## Percentage of Robbery Convictions Number of Robbery Unsuccessful
## 2188 70.0% 3
## 2189 100.0% 0
## 2190 66.7% 2
## 2191 76.9% 9
## 2192 93.8% 1
## 2193 0 0
## Percentage of Robbery Unsuccessful
## 2188 30.0%
## 2189 0.0%
## 2190 33.3%
## 2191 23.1%
## 2192 6.3%
## 2193 0
## Number of Theft And Handling Convictions
## 2188 233
## 2189 42
## 2190 112
## 2191 446
## 2192 243
## 2193 58
## Percentage of Theft And Handling Convictions
## 2188 91.4%
## 2189 82.4%
## 2190 93.3%
## 2191 94.5%
## 2192 94.2%
## 2193 98.3%
## Number of Theft And Handling Unsuccessful
## 2188 22
## 2189 9
## 2190 8
## 2191 26
## 2192 15
## 2193 1
## Percentage of Theft And Handling Unsuccessful
## 2188 8.6%
## 2189 17.6%
## 2190 6.7%
## 2191 5.5%
## 2192 5.8%
## 2193 1.7%
## Number of Fraud And Forgery Convictions
## 2188 36
## 2189 10
## 2190 16
## 2191 59
## 2192 34
## 2193 8
## Percentage of Fraud And Forgery Convictions
## 2188 87.8%
## 2189 90.9%
## 2190 80.0%
## 2191 84.3%
## 2192 85.0%
## 2193 100.0%
## Number of Fraud And Forgery Unsuccessful
## 2188 5
## 2189 1
## 2190 4
## 2191 11
## 2192 6
## 2193 0
## Percentage of Fraud And Forgery Unsuccessful
## 2188 12.2%
## 2189 9.1%
## 2190 20.0%
## 2191 15.7%
## 2192 15.0%
## 2193 0.0%
## Number of Criminal Damage Convictions
## 2188 73
## 2189 9
## 2190 41
## 2191 89
## 2192 70
## 2193 13
## Percentage of Criminal Damage Convictions
## 2188 89.0%
## 2189 69.2%
## 2190 89.1%
## 2191 84.8%
## 2192 84.3%
## 2193 92.9%
## Number of Criminal Damage Unsuccessful
## 2188 9
## 2189 4
## 2190 5
## 2191 16
## 2192 13
## 2193 1
## Percentage of Criminal Damage Unsuccessful
## 2188 11.0%
## 2189 30.8%
## 2190 10.9%
## 2191 15.2%
## 2192 15.7%
## 2193 7.1%
## Number of Drugs Offences Convictions
## 2188 98
## 2189 21
## 2190 56
## 2191 211
## 2192 137
## 2193 25
## Percentage of Drugs Offences Convictions
## 2188 93.3%
## 2189 91.3%
## 2190 96.6%
## 2191 94.2%
## 2192 96.5%
## 2193 100.0%
## Number of Drugs Offences Unsuccessful
## 2188 7
## 2189 2
## 2190 2
## 2191 13
## 2192 5
## 2193 0
## Percentage of Drugs Offences Unsuccessful
## 2188 6.7%
## 2189 8.7%
## 2190 3.4%
## 2191 5.8%
## 2192 3.5%
## 2193 0.0%
## Number of Public Order Offences Convictions
## 2188 81
## 2189 19
## 2190 75
## 2191 253
## 2192 154
## 2193 21
## Percentage of Public Order Offences Convictions
## 2188 89.0%
## 2189 82.6%
## 2190 92.6%
## 2191 86.9%
## 2192 91.1%
## 2193 87.5%
## Number of Public Order Offences Unsuccessful
## 2188 10
## 2189 4
## 2190 6
## 2191 38
## 2192 15
## 2193 3
## Percentage of Public Order Offences Unsuccessful
## 2188 11.0%
## 2189 17.4%
## 2190 7.4%
## 2191 13.1%
## 2192 8.9%
## 2193 12.5%
## Number of All Other Offences (excluding Motoring) Convictions
## 2188 32
## 2189 4
## 2190 11
## 2191 69
## 2192 24
## 2193 7
## Percentage of All Other Offences (excluding Motoring) Convictions
## 2188 97.0%
## 2189 100.0%
## 2190 84.6%
## 2191 82.1%
## 2192 75.0%
## 2193 87.5%
## Number of All Other Offences (excluding Motoring) Unsuccessful
## 2188 1
## 2189 0
## 2190 2
## 2191 15
## 2192 8
## 2193 1
## Percentage of All Other Offences (excluding Motoring) Unsuccessful
## 2188 3.0%
## 2189 0.0%
## 2190 15.4%
## 2191 17.9%
## 2192 25.0%
## 2193 12.5%
## Number of Motoring Offences Convictions
## 2188 318
## 2189 78
## 2190 190
## 2191 280
## 2192 236
## 2193 64
## Percentage of Motoring Offences Convictions
## 2188 88.8%
## 2189 78.8%
## 2190 87.6%
## 2191 80.5%
## 2192 91.8%
## 2193 97.0%
## Number of Motoring Offences Unsuccessful
## 2188 40
## 2189 21
## 2190 27
## 2191 68
## 2192 21
## 2193 2
## Percentage of Motoring Offences Unsuccessful
## 2188 11.2%
## 2189 21.2%
## 2190 12.4%
## 2191 19.5%
## 2192 8.2%
## 2193 3.0%
## Number of Admin Finalised Unsuccessful
## 2188 30
## 2189 11
## 2190 12
## 2191 92
## 2192 51
## 2193 4
## Percentage of L Motoring Offences Unsuccessful
## 2188 100.0%
## 2189 100.0%
## 2190 100.0%
## 2191 100.0%
## 2192 100.0%
## 2193 100.0%Viewing Column Names
This is to view the variables under observation and to ensure that the expected variables are imported.
names(CrimeCases_data)## [1] "...1"
## [2] "Number of Homicide Convictions"
## [3] "Percentage of Homicide Convictions"
## [4] "Number of Homicide Unsuccessful"
## [5] "Percentage of Homicide Unsuccessful"
## [6] "Number of Offences Against The Person Convictions"
## [7] "Percentage of Offences Against The Person Convictions"
## [8] "Number of Offences Against The Person Unsuccessful"
## [9] "Percentage of Offences Against The Person Unsuccessful"
## [10] "Number of Sexual Offences Convictions"
## [11] "Percentage of Sexual Offences Convictions"
## [12] "Number of Sexual Offences Unsuccessful"
## [13] "Percentage of Sexual Offences Unsuccessful"
## [14] "Number of Burglary Convictions"
## [15] "Percentage of Burglary Convictions"
## [16] "Number of Burglary Unsuccessful"
## [17] "Percentage of Burglary Unsuccessful"
## [18] "Number of Robbery Convictions"
## [19] "Percentage of Robbery Convictions"
## [20] "Number of Robbery Unsuccessful"
## [21] "Percentage of Robbery Unsuccessful"
## [22] "Number of Theft And Handling Convictions"
## [23] "Percentage of Theft And Handling Convictions"
## [24] "Number of Theft And Handling Unsuccessful"
## [25] "Percentage of Theft And Handling Unsuccessful"
## [26] "Number of Fraud And Forgery Convictions"
## [27] "Percentage of Fraud And Forgery Convictions"
## [28] "Number of Fraud And Forgery Unsuccessful"
## [29] "Percentage of Fraud And Forgery Unsuccessful"
## [30] "Number of Criminal Damage Convictions"
## [31] "Percentage of Criminal Damage Convictions"
## [32] "Number of Criminal Damage Unsuccessful"
## [33] "Percentage of Criminal Damage Unsuccessful"
## [34] "Number of Drugs Offences Convictions"
## [35] "Percentage of Drugs Offences Convictions"
## [36] "Number of Drugs Offences Unsuccessful"
## [37] "Percentage of Drugs Offences Unsuccessful"
## [38] "Number of Public Order Offences Convictions"
## [39] "Percentage of Public Order Offences Convictions"
## [40] "Number of Public Order Offences Unsuccessful"
## [41] "Percentage of Public Order Offences Unsuccessful"
## [42] "Number of All Other Offences (excluding Motoring) Convictions"
## [43] "Percentage of All Other Offences (excluding Motoring) Convictions"
## [44] "Number of All Other Offences (excluding Motoring) Unsuccessful"
## [45] "Percentage of All Other Offences (excluding Motoring) Unsuccessful"
## [46] "Number of Motoring Offences Convictions"
## [47] "Percentage of Motoring Offences Convictions"
## [48] "Number of Motoring Offences Unsuccessful"
## [49] "Percentage of Motoring Offences Unsuccessful"
## [50] "Number of Admin Finalised Unsuccessful"
## [51] "Percentage of L Motoring Offences Unsuccessful"This shows every column has been named except for the first column which was not named, hence there is a need to name it. Also the default names to the other columns would be change for seamless identification of the variables during analysis.
DATA CLEANING
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.
In order to arrive at the reasonable level of quality information in the analysis, it is important to clean the data, hence the following tasks.
Column Renaming.
As aforementioned, for seamless identification of variables, the column names will be changed for the purpose of analysing the dataset.
CrimeCases_data = CrimeCases_data %>%
rename(
"Area" = "...1",
"No_Homicide_Conv" = "Number of Homicide Convictions",
"Per_Homicide_Conv" = "Percentage of Homicide Convictions",
"No_Failed_Homicide" = "Number of Homicide Unsuccessful",
"Per_Failed_Homicide" = "Percentage of Homicide Unsuccessful",
"No_Conv_Offence" = "Number of Offences Against The Person Convictions",
"Per_Conv_Offence" = "Percentage of Offences Against The Person Convictions",
"No_Failed_Conv_Offence" = "Number of Offences Against The Person Unsuccessful",
"Per_Failed_Conv_Offence" = "Percentage of Offences Against The Person Unsuccessful",
"No_Sex_Offence_Conv" = "Number of Sexual Offences Convictions" ,
"Per_Sex_Offence_Conv" = "Percentage of Sexual Offences Convictions",
"No_Failed_Sex_Offence" = "Number of Sexual Offences Unsuccessful",
"Per_Failed_Sex_Offence" = "Percentage of Sexual Offences Unsuccessful",
"No_Burg_Conv" = "Number of Burglary Convictions",
"Per_Burg_Conv" = "Percentage of Burglary Convictions",
"No_Failed_Burg_Conv" = "Number of Burglary Unsuccessful",
"Per_Failed_Burg_Conv" = "Percentage of Burglary Unsuccessful",
"No_Rob_Conv" = "Number of Robbery Convictions",
"Per_Rob_Conv" = "Percentage of Robbery Convictions",
"No_Failed_Rob_Conv" = "Number of Robbery Unsuccessful",
"Per_Failed_Rob_Conv" = "Percentage of Robbery Unsuccessful",
"No_TheftAndHandling_Conv" = "Number of Theft And Handling Convictions",
"Per_TheftAndHandling_Conv" = "Percentage of Theft And Handling Convictions",
"No_Failed_TheftAndHandling" = "Number of Theft And Handling Unsuccessful",
"Per_Failed_TheftAndHandling" = "Percentage of Theft And Handling Unsuccessful",
"No_FraudAndForgery_Conv" = "Number of Fraud And Forgery Convictions",
"Per_FraudAndForgery_Conv" = "Percentage of Fraud And Forgery Convictions",
"No_Failed_FraudAndForgery" = "Number of Fraud And Forgery Unsuccessful",
"Per_Failed_FraudAndForgery" = "Percentage of Fraud And Forgery Unsuccessful",
"No_CrimeDamage_Conv" = "Number of Criminal Damage Convictions",
"Per_CrimeDamage_Conv" = "Percentage of Criminal Damage Convictions",
"No_Failed_CrimeDamage" = "Number of Criminal Damage Unsuccessful",
"Per_Failed_CrimeDamage" = "Percentage of Criminal Damage Unsuccessful",
"No_DrugOffences_Conv" = "Number of Drugs Offences Convictions",
"Per_DrugOffences_Conv" = "Percentage of Drugs Offences Convictions",
"No_Failed_Drug_Offence" = "Number of Drugs Offences Unsuccessful",
"Per_Failed_Drug_Offence" = "Percentage of Drugs Offences Unsuccessful",
"No_PublicOrderOffences_Conv" = "Number of Public Order Offences Convictions",
"Per_PublicOrderOffences_Conv" = "Percentage of Public Order Offences Convictions",
"No_Failed_PublicOrderOffences" = "Number of Public Order Offences Unsuccessful",
"Per_Failed_PublicOrderOffences" = "Percentage of Public Order Offences Unsuccessful",
"No_Others_Ex_Motoring" = "Number of All Other Offences (excluding Motoring) Convictions",
"Per_Others_Ex_Motoring" = "Percentage of All Other Offences (excluding Motoring) Convictions",
"No_Failed_Others_Ex_Motoring" = "Number of All Other Offences (excluding Motoring) Unsuccessful",
"Per_Failed_Others_Ex_Motoring" = "Percentage of All Other Offences (excluding Motoring) Unsuccessful",
"No_Motoring_Offences_Conv" = "Number of Motoring Offences Convictions",
"Per_Motoring_Offences_Conv" = "Percentage of Motoring Offences Convictions",
"No_Failed_Motoring_Offences" = "Number of Motoring Offences Unsuccessful",
"Per_Failed_Motoring_Offences" = "Percentage of Motoring Offences Unsuccessful",
"No_Failed_AdminFinalised_Conv" = "Number of Admin Finalised Unsuccessful",
"Per_Failed_AdminFinalised_Conv" = "Percentage of L Motoring Offences Unsuccessful"
)
names(CrimeCases_data) #To confirm the renamed columns## [1] "Area" "No_Homicide_Conv"
## [3] "Per_Homicide_Conv" "No_Failed_Homicide"
## [5] "Per_Failed_Homicide" "No_Conv_Offence"
## [7] "Per_Conv_Offence" "No_Failed_Conv_Offence"
## [9] "Per_Failed_Conv_Offence" "No_Sex_Offence_Conv"
## [11] "Per_Sex_Offence_Conv" "No_Failed_Sex_Offence"
## [13] "Per_Failed_Sex_Offence" "No_Burg_Conv"
## [15] "Per_Burg_Conv" "No_Failed_Burg_Conv"
## [17] "Per_Failed_Burg_Conv" "No_Rob_Conv"
## [19] "Per_Rob_Conv" "No_Failed_Rob_Conv"
## [21] "Per_Failed_Rob_Conv" "No_TheftAndHandling_Conv"
## [23] "Per_TheftAndHandling_Conv" "No_Failed_TheftAndHandling"
## [25] "Per_Failed_TheftAndHandling" "No_FraudAndForgery_Conv"
## [27] "Per_FraudAndForgery_Conv" "No_Failed_FraudAndForgery"
## [29] "Per_Failed_FraudAndForgery" "No_CrimeDamage_Conv"
## [31] "Per_CrimeDamage_Conv" "No_Failed_CrimeDamage"
## [33] "Per_Failed_CrimeDamage" "No_DrugOffences_Conv"
## [35] "Per_DrugOffences_Conv" "No_Failed_Drug_Offence"
## [37] "Per_Failed_Drug_Offence" "No_PublicOrderOffences_Conv"
## [39] "Per_PublicOrderOffences_Conv" "No_Failed_PublicOrderOffences"
## [41] "Per_Failed_PublicOrderOffences" "No_Others_Ex_Motoring"
## [43] "Per_Others_Ex_Motoring" "No_Failed_Others_Ex_Motoring"
## [45] "Per_Failed_Others_Ex_Motoring" "No_Motoring_Offences_Conv"
## [47] "Per_Motoring_Offences_Conv" "No_Failed_Motoring_Offences"
## [49] "Per_Failed_Motoring_Offences" "No_Failed_AdminFinalised_Conv"
## [51] "Per_Failed_AdminFinalised_Conv"This confirms that all the column names have been renamed as expected for seamless identification.
Data Structure View
A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently.
This is to confirm that the data are in their appropriate data types for properly analysis
str(CrimeCases_data)## 'data.frame': 2193 obs. of 51 variables:
## $ Area : chr "National" "Avon and Somerset" "Bedfordshire" "Cambridgeshire" ...
## $ No_Homicide_Conv : chr "81" "1" "0" "0" ...
## $ Per_Homicide_Conv : chr "85.3%" "100.0%" "-" "-" ...
## $ No_Failed_Homicide : chr "14" "0" "0" "0" ...
## $ Per_Failed_Homicide : chr "14.7%" "0.0%" "-" "-" ...
## $ No_Conv_Offence : chr "7,805" "167" "69" "99" ...
## $ Per_Conv_Offence : chr "74.1%" "78.8%" "75.0%" "81.1%" ...
## $ No_Failed_Conv_Offence : chr "2,722" "45" "23" "23" ...
## $ Per_Failed_Conv_Offence : chr "25.9%" "21.2%" "25.0%" "18.9%" ...
## $ No_Sex_Offence_Conv : chr "698" "36" "5" "6" ...
## $ Per_Sex_Offence_Conv : chr "72.2%" "81.8%" "83.3%" "66.7%" ...
## $ No_Failed_Sex_Offence : chr "269" "8" "1" "3" ...
## $ Per_Failed_Sex_Offence : chr "27.8%" "18.2%" "16.7%" "33.3%" ...
## $ No_Burg_Conv : chr "1,470" "37" "16" "8" ...
## $ Per_Burg_Conv : chr "86.7%" "94.9%" "94.1%" "100.0%" ...
## $ No_Failed_Burg_Conv : chr "226" "2" "1" "0" ...
## $ Per_Failed_Burg_Conv : chr "13.3%" "5.1%" "5.9%" "0.0%" ...
## $ No_Rob_Conv : chr "517" "9" "4" "6" ...
## $ Per_Rob_Conv : chr "81.7%" "75.0%" "100.0%" "85.7%" ...
## $ No_Failed_Rob_Conv : chr "116" "3" "0" "1" ...
## $ Per_Failed_Rob_Conv : chr "18.3%" "25.0%" "0.0%" "14.3%" ...
## $ No_TheftAndHandling_Conv : chr "10,045" "266" "98" "107" ...
## $ Per_TheftAndHandling_Conv : chr "92.3%" "92.7%" "91.6%" "91.5%" ...
## $ No_Failed_TheftAndHandling : chr "840" "21" "9" "10" ...
## $ Per_Failed_TheftAndHandling : chr "7.7%" "7.3%" "8.4%" "8.5%" ...
## $ No_FraudAndForgery_Conv : chr "666" "11" "8" "7" ...
## $ Per_FraudAndForgery_Conv : chr "86.0%" "100.0%" "80.0%" "100.0%" ...
## $ No_Failed_FraudAndForgery : chr "108" "0" "2" "0" ...
## $ Per_Failed_FraudAndForgery : chr "14.0%" "0.0%" "20.0%" "0.0%" ...
## $ No_CrimeDamage_Conv : chr "2,259" "54" "20" "21" ...
## $ Per_CrimeDamage_Conv : chr "85.2%" "90.0%" "76.9%" "95.5%" ...
## $ No_Failed_CrimeDamage : chr "391" "6" "6" "1" ...
## $ Per_Failed_CrimeDamage : chr "14.8%" "10.0%" "23.1%" "4.5%" ...
## $ No_DrugOffences_Conv : chr "4,536" "135" "45" "40" ...
## $ Per_DrugOffences_Conv : chr "94.2%" "98.5%" "95.7%" "95.2%" ...
## $ No_Failed_Drug_Offence : chr "279" "2" "2" "2" ...
## $ Per_Failed_Drug_Offence : chr "5.8%" "1.5%" "4.3%" "4.8%" ...
## $ No_PublicOrderOffences_Conv : chr "3,549" "68" "29" "45" ...
## $ Per_PublicOrderOffences_Conv : chr "84.4%" "86.1%" "82.9%" "83.3%" ...
## $ No_Failed_PublicOrderOffences : chr "654" "11" "6" "9" ...
## $ Per_Failed_PublicOrderOffences: chr "15.6%" "13.9%" "17.1%" "16.7%" ...
## $ No_Others_Ex_Motoring : chr "2,640" "66" "11" "6" ...
## $ Per_Others_Ex_Motoring : chr "83.7%" "80.5%" "64.7%" "75.0%" ...
## $ No_Failed_Others_Ex_Motoring : chr "513" "16" "6" "2" ...
## $ Per_Failed_Others_Ex_Motoring : chr "16.3%" "19.5%" "35.3%" "25.0%" ...
## $ No_Motoring_Offences_Conv : chr "8,283" "188" "40" "79" ...
## $ Per_Motoring_Offences_Conv : chr "86.3%" "83.6%" "88.9%" "92.9%" ...
## $ No_Failed_Motoring_Offences : chr "1,314" "37" "5" "6" ...
## $ Per_Failed_Motoring_Offences : chr "13.7%" "16.4%" "11.1%" "7.1%" ...
## $ No_Failed_AdminFinalised_Conv : chr "718" "24" "16" "4" ...
## $ Per_Failed_AdminFinalised_Conv: chr "100.0%" "100.0%" "100.0%" "100.0%" ...This reveals that the data are not in their appropriate data types as they are all in Character. Column 2 to Column 51 are expected to be integers and not character, hence, there is need to convert them into integer for the purpose of this analysis.
Duplicate Check
This was done on the Area column (variable) using the unique() function. This is to correct any inconsistency in the Area variable.
unique(CrimeCases_data$Area)## [1] "National" "Avon and Somerset" "Bedfordshire"
## [4] "Cambridgeshire" "Cheshire" "Cleveland"
## [7] "Cumbria" "Derbyshire" "Devon and Cornwall"
## [10] "Dorset" "Durham" "Dyfed Powys"
## [13] "Essex" "Gloucestershire" "GreaterManchester"
## [16] "Gwent" "Hampshire" "Hertfordshire"
## [19] "Humberside" "Kent" "Lancashire"
## [22] "Leicestershire" "Lincolnshire" "Merseyside"
## [25] "Metropolitan and City" "Norfolk" "Northamptonshire"
## [28] "Northumbria" "North Wales" "North Yorkshire"
## [31] "Nottinghamshire" "South Wales" "South Yorkshire"
## [34] "Staffordshire" "Suffolk" "Surrey"
## [37] "Sussex" "Thames Valley" "Warwickshire"
## [40] "West Mercia" "West Midlands" "West Yorkshire"
## [43] "Wiltshire"This confirms that any duplicate found in the data frame have been removed.
Converting all dash (-) values in the dataset to Zero (0) value
For the purpose of analysis, the dash values anywhere in the dataframe are being replaced with Zero.
CrimeCases_data[CrimeCases_data == '-'] = 0.00
print(head(CrimeCases_data))## Area No_Homicide_Conv Per_Homicide_Conv No_Failed_Homicide
## 1 National 81 85.3% 14
## 2 Avon and Somerset 1 100.0% 0
## 3 Bedfordshire 0 0 0
## 4 Cambridgeshire 0 0 0
## 5 Cheshire 1 50.0% 1
## 6 Cleveland 0 0 0
## Per_Failed_Homicide No_Conv_Offence Per_Conv_Offence No_Failed_Conv_Offence
## 1 14.7% 7,805 74.1% 2,722
## 2 0.0% 167 78.8% 45
## 3 0 69 75.0% 23
## 4 0 99 81.1% 23
## 5 50.0% 140 74.9% 47
## 6 0 85 67.5% 41
## Per_Failed_Conv_Offence No_Sex_Offence_Conv Per_Sex_Offence_Conv
## 1 25.9% 698 72.2%
## 2 21.2% 36 81.8%
## 3 25.0% 5 83.3%
## 4 18.9% 6 66.7%
## 5 25.1% 17 85.0%
## 6 32.5% 11 73.3%
## No_Failed_Sex_Offence Per_Failed_Sex_Offence No_Burg_Conv Per_Burg_Conv
## 1 269 27.8% 1,470 86.7%
## 2 8 18.2% 37 94.9%
## 3 1 16.7% 16 94.1%
## 4 3 33.3% 8 100.0%
## 5 3 15.0% 26 89.7%
## 6 4 26.7% 25 71.4%
## No_Failed_Burg_Conv Per_Failed_Burg_Conv No_Rob_Conv Per_Rob_Conv
## 1 226 13.3% 517 81.7%
## 2 2 5.1% 9 75.0%
## 3 1 5.9% 4 100.0%
## 4 0 0.0% 6 85.7%
## 5 3 10.3% 1 100.0%
## 6 10 28.6% 5 71.4%
## No_Failed_Rob_Conv Per_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 116 18.3% 10,045
## 2 3 25.0% 266
## 3 0 0.0% 98
## 4 1 14.3% 107
## 5 0 0.0% 206
## 6 2 28.6% 254
## Per_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 1 92.3% 840
## 2 92.7% 21
## 3 91.6% 9
## 4 91.5% 10
## 5 98.1% 4
## 6 88.8% 32
## Per_Failed_TheftAndHandling No_FraudAndForgery_Conv Per_FraudAndForgery_Conv
## 1 7.7% 666 86.0%
## 2 7.3% 11 100.0%
## 3 8.4% 8 80.0%
## 4 8.5% 7 100.0%
## 5 1.9% 16 88.9%
## 6 11.2% 6 75.0%
## No_Failed_FraudAndForgery Per_Failed_FraudAndForgery No_CrimeDamage_Conv
## 1 108 14.0% 2,259
## 2 0 0.0% 54
## 3 2 20.0% 20
## 4 0 0.0% 21
## 5 2 11.1% 35
## 6 2 25.0% 32
## Per_CrimeDamage_Conv No_Failed_CrimeDamage Per_Failed_CrimeDamage
## 1 85.2% 391 14.8%
## 2 90.0% 6 10.0%
## 3 76.9% 6 23.1%
## 4 95.5% 1 4.5%
## 5 79.5% 9 20.5%
## 6 80.0% 8 20.0%
## No_DrugOffences_Conv Per_DrugOffences_Conv No_Failed_Drug_Offence
## 1 4,536 94.2% 279
## 2 135 98.5% 2
## 3 45 95.7% 2
## 4 40 95.2% 2
## 5 75 88.2% 10
## 6 63 90.0% 7
## Per_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 5.8% 3,549
## 2 1.5% 68
## 3 4.3% 29
## 4 4.8% 45
## 5 11.8% 86
## 6 10.0% 74
## Per_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 1 84.4% 654
## 2 86.1% 11
## 3 82.9% 6
## 4 83.3% 9
## 5 92.5% 7
## 6 73.3% 27
## Per_Failed_PublicOrderOffences No_Others_Ex_Motoring Per_Others_Ex_Motoring
## 1 15.6% 2,640 83.7%
## 2 13.9% 66 80.5%
## 3 17.1% 11 64.7%
## 4 16.7% 6 75.0%
## 5 7.5% 50 89.3%
## 6 26.7% 28 84.8%
## No_Failed_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 1 513 16.3%
## 2 16 19.5%
## 3 6 35.3%
## 4 2 25.0%
## 5 6 10.7%
## 6 5 15.2%
## No_Motoring_Offences_Conv Per_Motoring_Offences_Conv
## 1 8,283 86.3%
## 2 188 83.6%
## 3 40 88.9%
## 4 79 92.9%
## 5 209 94.6%
## 6 124 87.9%
## No_Failed_Motoring_Offences Per_Failed_Motoring_Offences
## 1 1,314 13.7%
## 2 37 16.4%
## 3 5 11.1%
## 4 6 7.1%
## 5 12 5.4%
## 6 17 12.1%
## No_Failed_AdminFinalised_Conv Per_Failed_AdminFinalised_Conv
## 1 718 100.0%
## 2 24 100.0%
## 3 16 100.0%
## 4 4 100.0%
## 5 1 100.0%
## 6 10 100.0%This confirms that the replacement of the dash value with Zero has been effected in the dataframe if found.
Removing Percentage (%) signs
This is important for the purpose of data analysis
NoPercent <- function(val) {
if (is.character(val)) {
return (gsub("%", "", val))
}
return (val)
}
CrimeCases_data = as.data.frame(lapply(CrimeCases_data, NoPercent))
print(head(CrimeCases_data))## Area No_Homicide_Conv Per_Homicide_Conv No_Failed_Homicide
## 1 National 81 85.3 14
## 2 Avon and Somerset 1 100.0 0
## 3 Bedfordshire 0 0 0
## 4 Cambridgeshire 0 0 0
## 5 Cheshire 1 50.0 1
## 6 Cleveland 0 0 0
## Per_Failed_Homicide No_Conv_Offence Per_Conv_Offence No_Failed_Conv_Offence
## 1 14.7 7,805 74.1 2,722
## 2 0.0 167 78.8 45
## 3 0 69 75.0 23
## 4 0 99 81.1 23
## 5 50.0 140 74.9 47
## 6 0 85 67.5 41
## Per_Failed_Conv_Offence No_Sex_Offence_Conv Per_Sex_Offence_Conv
## 1 25.9 698 72.2
## 2 21.2 36 81.8
## 3 25.0 5 83.3
## 4 18.9 6 66.7
## 5 25.1 17 85.0
## 6 32.5 11 73.3
## No_Failed_Sex_Offence Per_Failed_Sex_Offence No_Burg_Conv Per_Burg_Conv
## 1 269 27.8 1,470 86.7
## 2 8 18.2 37 94.9
## 3 1 16.7 16 94.1
## 4 3 33.3 8 100.0
## 5 3 15.0 26 89.7
## 6 4 26.7 25 71.4
## No_Failed_Burg_Conv Per_Failed_Burg_Conv No_Rob_Conv Per_Rob_Conv
## 1 226 13.3 517 81.7
## 2 2 5.1 9 75.0
## 3 1 5.9 4 100.0
## 4 0 0.0 6 85.7
## 5 3 10.3 1 100.0
## 6 10 28.6 5 71.4
## No_Failed_Rob_Conv Per_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 116 18.3 10,045
## 2 3 25.0 266
## 3 0 0.0 98
## 4 1 14.3 107
## 5 0 0.0 206
## 6 2 28.6 254
## Per_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 1 92.3 840
## 2 92.7 21
## 3 91.6 9
## 4 91.5 10
## 5 98.1 4
## 6 88.8 32
## Per_Failed_TheftAndHandling No_FraudAndForgery_Conv Per_FraudAndForgery_Conv
## 1 7.7 666 86.0
## 2 7.3 11 100.0
## 3 8.4 8 80.0
## 4 8.5 7 100.0
## 5 1.9 16 88.9
## 6 11.2 6 75.0
## No_Failed_FraudAndForgery Per_Failed_FraudAndForgery No_CrimeDamage_Conv
## 1 108 14.0 2,259
## 2 0 0.0 54
## 3 2 20.0 20
## 4 0 0.0 21
## 5 2 11.1 35
## 6 2 25.0 32
## Per_CrimeDamage_Conv No_Failed_CrimeDamage Per_Failed_CrimeDamage
## 1 85.2 391 14.8
## 2 90.0 6 10.0
## 3 76.9 6 23.1
## 4 95.5 1 4.5
## 5 79.5 9 20.5
## 6 80.0 8 20.0
## No_DrugOffences_Conv Per_DrugOffences_Conv No_Failed_Drug_Offence
## 1 4,536 94.2 279
## 2 135 98.5 2
## 3 45 95.7 2
## 4 40 95.2 2
## 5 75 88.2 10
## 6 63 90.0 7
## Per_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 5.8 3,549
## 2 1.5 68
## 3 4.3 29
## 4 4.8 45
## 5 11.8 86
## 6 10.0 74
## Per_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 1 84.4 654
## 2 86.1 11
## 3 82.9 6
## 4 83.3 9
## 5 92.5 7
## 6 73.3 27
## Per_Failed_PublicOrderOffences No_Others_Ex_Motoring Per_Others_Ex_Motoring
## 1 15.6 2,640 83.7
## 2 13.9 66 80.5
## 3 17.1 11 64.7
## 4 16.7 6 75.0
## 5 7.5 50 89.3
## 6 26.7 28 84.8
## No_Failed_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 1 513 16.3
## 2 16 19.5
## 3 6 35.3
## 4 2 25.0
## 5 6 10.7
## 6 5 15.2
## No_Motoring_Offences_Conv Per_Motoring_Offences_Conv
## 1 8,283 86.3
## 2 188 83.6
## 3 40 88.9
## 4 79 92.9
## 5 209 94.6
## 6 124 87.9
## No_Failed_Motoring_Offences Per_Failed_Motoring_Offences
## 1 1,314 13.7
## 2 37 16.4
## 3 5 11.1
## 4 6 7.1
## 5 12 5.4
## 6 17 12.1
## No_Failed_AdminFinalised_Conv Per_Failed_AdminFinalised_Conv
## 1 718 100.0
## 2 24 100.0
## 3 16 100.0
## 4 4 100.0
## 5 1 100.0
## 6 10 100.0This confirms that the percentages (%) in the data frame have been removed.
Removing Comma signs
This is important for the purpose of data analysis as csv files do not need to have commas for the numeric values.
NoComma = function(val) {
if (is.character(val)) {
return (gsub(",", "", val))
}
return (val)
}
CrimeCases_data = as.data.frame(lapply(CrimeCases_data, NoComma))
print(head(CrimeCases_data))## Area No_Homicide_Conv Per_Homicide_Conv No_Failed_Homicide
## 1 National 81 85.3 14
## 2 Avon and Somerset 1 100.0 0
## 3 Bedfordshire 0 0 0
## 4 Cambridgeshire 0 0 0
## 5 Cheshire 1 50.0 1
## 6 Cleveland 0 0 0
## Per_Failed_Homicide No_Conv_Offence Per_Conv_Offence No_Failed_Conv_Offence
## 1 14.7 7805 74.1 2722
## 2 0.0 167 78.8 45
## 3 0 69 75.0 23
## 4 0 99 81.1 23
## 5 50.0 140 74.9 47
## 6 0 85 67.5 41
## Per_Failed_Conv_Offence No_Sex_Offence_Conv Per_Sex_Offence_Conv
## 1 25.9 698 72.2
## 2 21.2 36 81.8
## 3 25.0 5 83.3
## 4 18.9 6 66.7
## 5 25.1 17 85.0
## 6 32.5 11 73.3
## No_Failed_Sex_Offence Per_Failed_Sex_Offence No_Burg_Conv Per_Burg_Conv
## 1 269 27.8 1470 86.7
## 2 8 18.2 37 94.9
## 3 1 16.7 16 94.1
## 4 3 33.3 8 100.0
## 5 3 15.0 26 89.7
## 6 4 26.7 25 71.4
## No_Failed_Burg_Conv Per_Failed_Burg_Conv No_Rob_Conv Per_Rob_Conv
## 1 226 13.3 517 81.7
## 2 2 5.1 9 75.0
## 3 1 5.9 4 100.0
## 4 0 0.0 6 85.7
## 5 3 10.3 1 100.0
## 6 10 28.6 5 71.4
## No_Failed_Rob_Conv Per_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 116 18.3 10045
## 2 3 25.0 266
## 3 0 0.0 98
## 4 1 14.3 107
## 5 0 0.0 206
## 6 2 28.6 254
## Per_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 1 92.3 840
## 2 92.7 21
## 3 91.6 9
## 4 91.5 10
## 5 98.1 4
## 6 88.8 32
## Per_Failed_TheftAndHandling No_FraudAndForgery_Conv Per_FraudAndForgery_Conv
## 1 7.7 666 86.0
## 2 7.3 11 100.0
## 3 8.4 8 80.0
## 4 8.5 7 100.0
## 5 1.9 16 88.9
## 6 11.2 6 75.0
## No_Failed_FraudAndForgery Per_Failed_FraudAndForgery No_CrimeDamage_Conv
## 1 108 14.0 2259
## 2 0 0.0 54
## 3 2 20.0 20
## 4 0 0.0 21
## 5 2 11.1 35
## 6 2 25.0 32
## Per_CrimeDamage_Conv No_Failed_CrimeDamage Per_Failed_CrimeDamage
## 1 85.2 391 14.8
## 2 90.0 6 10.0
## 3 76.9 6 23.1
## 4 95.5 1 4.5
## 5 79.5 9 20.5
## 6 80.0 8 20.0
## No_DrugOffences_Conv Per_DrugOffences_Conv No_Failed_Drug_Offence
## 1 4536 94.2 279
## 2 135 98.5 2
## 3 45 95.7 2
## 4 40 95.2 2
## 5 75 88.2 10
## 6 63 90.0 7
## Per_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 5.8 3549
## 2 1.5 68
## 3 4.3 29
## 4 4.8 45
## 5 11.8 86
## 6 10.0 74
## Per_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 1 84.4 654
## 2 86.1 11
## 3 82.9 6
## 4 83.3 9
## 5 92.5 7
## 6 73.3 27
## Per_Failed_PublicOrderOffences No_Others_Ex_Motoring Per_Others_Ex_Motoring
## 1 15.6 2640 83.7
## 2 13.9 66 80.5
## 3 17.1 11 64.7
## 4 16.7 6 75.0
## 5 7.5 50 89.3
## 6 26.7 28 84.8
## No_Failed_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 1 513 16.3
## 2 16 19.5
## 3 6 35.3
## 4 2 25.0
## 5 6 10.7
## 6 5 15.2
## No_Motoring_Offences_Conv Per_Motoring_Offences_Conv
## 1 8283 86.3
## 2 188 83.6
## 3 40 88.9
## 4 79 92.9
## 5 209 94.6
## 6 124 87.9
## No_Failed_Motoring_Offences Per_Failed_Motoring_Offences
## 1 1314 13.7
## 2 37 16.4
## 3 5 11.1
## 4 6 7.1
## 5 12 5.4
## 6 17 12.1
## No_Failed_AdminFinalised_Conv Per_Failed_AdminFinalised_Conv
## 1 718 100.0
## 2 24 100.0
## 3 16 100.0
## 4 4 100.0
## 5 1 100.0
## 6 10 100.0Character Data Type to Integer Conversion
This is to convert all relevant columns [2:51] (variables) from Character data types to Integer for relevant analysis.
CrimeCases_data[2:51] = lapply(CrimeCases_data[2:51], FUN = function(y){as.numeric(y)})
str(CrimeCases_data)## 'data.frame': 2193 obs. of 51 variables:
## $ Area : chr "National" "Avon and Somerset" "Bedfordshire" "Cambridgeshire" ...
## $ No_Homicide_Conv : num 81 1 0 0 1 0 0 0 1 0 ...
## $ Per_Homicide_Conv : num 85.3 100 0 0 50 0 0 0 100 0 ...
## $ No_Failed_Homicide : num 14 0 0 0 1 0 0 0 0 0 ...
## $ Per_Failed_Homicide : num 14.7 0 0 0 50 0 0 0 0 0 ...
## $ No_Conv_Offence : num 7805 167 69 99 140 ...
## $ Per_Conv_Offence : num 74.1 78.8 75 81.1 74.9 67.5 80.2 72.6 75.8 82 ...
## $ No_Failed_Conv_Offence : num 2722 45 23 23 47 ...
## $ Per_Failed_Conv_Offence : num 25.9 21.2 25 18.9 25.1 32.5 19.8 27.4 24.2 18 ...
## $ No_Sex_Offence_Conv : num 698 36 5 6 17 11 8 8 11 1 ...
## $ Per_Sex_Offence_Conv : num 72.2 81.8 83.3 66.7 85 73.3 88.9 57.1 73.3 100 ...
## $ No_Failed_Sex_Offence : num 269 8 1 3 3 4 1 6 4 0 ...
## $ Per_Failed_Sex_Offence : num 27.8 18.2 16.7 33.3 15 26.7 11.1 42.9 26.7 0 ...
## $ No_Burg_Conv : num 1470 37 16 8 26 25 12 31 16 18 ...
## $ Per_Burg_Conv : num 86.7 94.9 94.1 100 89.7 71.4 92.3 91.2 94.1 94.7 ...
## $ No_Failed_Burg_Conv : num 226 2 1 0 3 10 1 3 1 1 ...
## $ Per_Failed_Burg_Conv : num 13.3 5.1 5.9 0 10.3 28.6 7.7 8.8 5.9 5.3 ...
## $ No_Rob_Conv : num 517 9 4 6 1 5 1 8 6 3 ...
## $ Per_Rob_Conv : num 81.7 75 100 85.7 100 71.4 100 72.7 100 100 ...
## $ No_Failed_Rob_Conv : num 116 3 0 1 0 2 0 3 0 0 ...
## $ Per_Failed_Rob_Conv : num 18.3 25 0 14.3 0 28.6 0 27.3 0 0 ...
## $ No_TheftAndHandling_Conv : num 10045 266 98 107 206 ...
## $ Per_TheftAndHandling_Conv : num 92.3 92.7 91.6 91.5 98.1 88.8 94.7 93.1 93.8 91.8 ...
## $ No_Failed_TheftAndHandling : num 840 21 9 10 4 32 6 15 10 11 ...
## $ Per_Failed_TheftAndHandling : num 7.7 7.3 8.4 8.5 1.9 11.2 5.3 6.9 6.2 8.2 ...
## $ No_FraudAndForgery_Conv : num 666 11 8 7 16 6 5 11 8 7 ...
## $ Per_FraudAndForgery_Conv : num 86 100 80 100 88.9 75 100 55 100 77.8 ...
## $ No_Failed_FraudAndForgery : num 108 0 2 0 2 2 0 9 0 2 ...
## $ Per_Failed_FraudAndForgery : num 14 0 20 0 11.1 25 0 45 0 22.2 ...
## $ No_CrimeDamage_Conv : num 2259 54 20 21 35 ...
## $ Per_CrimeDamage_Conv : num 85.2 90 76.9 95.5 79.5 80 94.9 85.1 90.3 85.7 ...
## $ No_Failed_CrimeDamage : num 391 6 6 1 9 8 2 7 6 4 ...
## $ Per_Failed_CrimeDamage : num 14.8 10 23.1 4.5 20.5 20 5.1 14.9 9.7 14.3 ...
## $ No_DrugOffences_Conv : num 4536 135 45 40 75 ...
## $ Per_DrugOffences_Conv : num 94.2 98.5 95.7 95.2 88.2 90 95.5 89.3 92.1 93.5 ...
## $ No_Failed_Drug_Offence : num 279 2 2 2 10 7 2 9 6 2 ...
## $ Per_Failed_Drug_Offence : num 5.8 1.5 4.3 4.8 11.8 10 4.5 10.7 7.9 6.5 ...
## $ No_PublicOrderOffences_Conv : num 3549 68 29 45 86 ...
## $ Per_PublicOrderOffences_Conv : num 84.4 86.1 82.9 83.3 92.5 73.3 95.2 92.6 76.5 93.8 ...
## $ No_Failed_PublicOrderOffences : num 654 11 6 9 7 27 2 4 20 3 ...
## $ Per_Failed_PublicOrderOffences: num 15.6 13.9 17.1 16.7 7.5 26.7 4.8 7.4 23.5 6.3 ...
## $ No_Others_Ex_Motoring : num 2640 66 11 6 50 28 64 46 64 25 ...
## $ Per_Others_Ex_Motoring : num 83.7 80.5 64.7 75 89.3 84.8 98.5 75.4 82.1 96.2 ...
## $ No_Failed_Others_Ex_Motoring : num 513 16 6 2 6 5 1 15 14 1 ...
## $ Per_Failed_Others_Ex_Motoring : num 16.3 19.5 35.3 25 10.7 15.2 1.5 24.6 17.9 3.8 ...
## $ No_Motoring_Offences_Conv : num 8283 188 40 79 209 ...
## $ Per_Motoring_Offences_Conv : num 86.3 83.6 88.9 92.9 94.6 87.9 90.5 95.2 91.7 91 ...
## $ No_Failed_Motoring_Offences : num 1314 37 5 6 12 ...
## $ Per_Failed_Motoring_Offences : num 13.7 16.4 11.1 7.1 5.4 12.1 9.5 4.8 8.3 9 ...
## $ No_Failed_AdminFinalised_Conv : num 718 24 16 4 1 10 12 16 15 5 ...
## $ Per_Failed_AdminFinalised_Conv: num 100 100 100 100 100 100 100 100 100 100 ...This confirms that column 2 to column 51 are now Integers and no longer Characters
##Missing Value Check A missing value is one whose value is unknown. Missing values are represented in R by the NA symbol
Missing Value could be detrimental to the analysis if not properly treated, hence it is important to check for missing value.
anyNA(CrimeCases_data)## [1] FALSEThis reveals that there are no missing values in the dataframe.
Completeness Check
This is to confirm that each of the columns has no missing data.
sapply(CrimeCases_data, function(x){sum(is.na(x))} )## Area No_Homicide_Conv
## 0 0
## Per_Homicide_Conv No_Failed_Homicide
## 0 0
## Per_Failed_Homicide No_Conv_Offence
## 0 0
## Per_Conv_Offence No_Failed_Conv_Offence
## 0 0
## Per_Failed_Conv_Offence No_Sex_Offence_Conv
## 0 0
## Per_Sex_Offence_Conv No_Failed_Sex_Offence
## 0 0
## Per_Failed_Sex_Offence No_Burg_Conv
## 0 0
## Per_Burg_Conv No_Failed_Burg_Conv
## 0 0
## Per_Failed_Burg_Conv No_Rob_Conv
## 0 0
## Per_Rob_Conv No_Failed_Rob_Conv
## 0 0
## Per_Failed_Rob_Conv No_TheftAndHandling_Conv
## 0 0
## Per_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 0 0
## Per_Failed_TheftAndHandling No_FraudAndForgery_Conv
## 0 0
## Per_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 0 0
## Per_Failed_FraudAndForgery No_CrimeDamage_Conv
## 0 0
## Per_CrimeDamage_Conv No_Failed_CrimeDamage
## 0 0
## Per_Failed_CrimeDamage No_DrugOffences_Conv
## 0 0
## Per_DrugOffences_Conv No_Failed_Drug_Offence
## 0 0
## Per_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 0 0
## Per_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 0 0
## Per_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 0 0
## Per_Others_Ex_Motoring No_Failed_Others_Ex_Motoring
## 0 0
## Per_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 0 0
## Per_Motoring_Offences_Conv No_Failed_Motoring_Offences
## 0 0
## Per_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 0 0
## Per_Failed_AdminFinalised_Conv
## 0This confirms that there is no missing data in the data frame.
Data Summary/Overview
This is an overview of the data set showing some of the descriptive statistics at a glance prior to further analysis of the data.
summary(CrimeCases_data)## Area No_Homicide_Conv Per_Homicide_Conv No_Failed_Homicide
## Length:2193 Min. : 0.000 Min. : 0.00 Min. : 0.0000
## Class :character 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.0000
## Mode :character Median : 1.000 Median : 75.00 Median : 0.0000
## Mean : 3.798 Mean : 56.85 Mean : 0.9138
## 3rd Qu.: 3.000 3rd Qu.:100.00 3rd Qu.: 1.0000
## Max. :131.000 Max. :100.00 Max. :35.0000
## Per_Failed_Homicide No_Conv_Offence Per_Conv_Offence No_Failed_Conv_Offence
## Min. : 0.00 Min. : 29.0 Min. :55.1 Min. : 5.0
## 1st Qu.: 0.00 1st Qu.: 115.0 1st Qu.:75.6 1st Qu.: 27.0
## Median : 0.00 Median : 179.0 Median :79.2 Median : 46.0
## Mean : 10.82 Mean : 454.9 Mean :79.0 Mean : 135.4
## 3rd Qu.: 10.00 3rd Qu.: 272.0 3rd Qu.:82.5 3rd Qu.: 77.0
## Max. :100.00 Max. :11741.0 Max. :94.2 Max. :3568.0
## Per_Failed_Conv_Offence No_Sex_Offence_Conv Per_Sex_Offence_Conv
## Min. : 5.8 Min. : 0.00 Min. : 0.00
## 1st Qu.:17.5 1st Qu.: 8.00 1st Qu.: 68.40
## Median :20.8 Median : 15.00 Median : 76.00
## Mean :21.0 Mean : 43.78 Mean : 77.13
## 3rd Qu.:24.4 3rd Qu.: 29.00 3rd Qu.: 85.70
## Max. :44.9 Max. :1179.00 Max. :100.00
## No_Failed_Sex_Offence Per_Failed_Sex_Offence No_Burg_Conv Per_Burg_Conv
## Min. : 0.00 Min. : 0.00 Min. : 1.00 Min. : 50.0
## 1st Qu.: 1.00 1st Qu.: 14.30 1st Qu.: 14.00 1st Qu.: 81.8
## Median : 4.00 Median : 24.00 Median : 23.00 Median : 87.5
## Mean : 16.19 Mean : 22.83 Mean : 60.09 Mean : 86.8
## 3rd Qu.: 11.00 3rd Qu.: 31.60 3rd Qu.: 38.00 3rd Qu.: 92.9
## Max. :489.00 Max. :100.00 Max. :1715.00 Max. :100.0
## No_Failed_Burg_Conv Per_Failed_Burg_Conv No_Rob_Conv Per_Rob_Conv
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.00 1st Qu.: 7.1 1st Qu.: 2.00 1st Qu.: 66.70
## Median : 3.00 Median :12.5 Median : 5.00 Median : 83.30
## Mean : 10.14 Mean :13.2 Mean : 19.33 Mean : 76.28
## 3rd Qu.: 6.00 3rd Qu.:18.2 3rd Qu.: 10.00 3rd Qu.:100.00
## Max. :317.00 Max. :50.0 Max. :650.00 Max. :100.00
## No_Failed_Rob_Conv Per_Failed_Rob_Conv No_TheftAndHandling_Conv
## Min. : 0.00 Min. : 0.00 Min. : 13.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 95.0
## Median : 1.00 Median : 14.30 Median : 147.0
## Mean : 5.16 Mean : 18.52 Mean : 373.1
## 3rd Qu.: 3.00 3rd Qu.: 28.60 3rd Qu.: 237.0
## Max. :188.00 Max. :100.00 Max. :11057.0
## Per_TheftAndHandling_Conv No_Failed_TheftAndHandling
## Min. : 72.20 Min. : 0.00
## 1st Qu.: 90.80 1st Qu.: 6.00
## Median : 92.90 Median : 11.00
## Mean : 92.54 Mean : 33.43
## 3rd Qu.: 94.70 3rd Qu.: 19.00
## Max. :100.00 Max. :1025.00
## Per_Failed_TheftAndHandling No_FraudAndForgery_Conv Per_FraudAndForgery_Conv
## Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 5.300 1st Qu.: 8.00 1st Qu.: 81.50
## Median : 7.100 Median : 13.00 Median : 87.50
## Mean : 7.458 Mean : 38.57 Mean : 87.19
## 3rd Qu.: 9.200 3rd Qu.: 21.00 3rd Qu.: 95.70
## Max. :27.800 Max. :1075.00 Max. :100.00
## No_Failed_FraudAndForgery Per_Failed_FraudAndForgery No_CrimeDamage_Conv
## Min. : 0.000 Min. : 0.00 Min. : 3.00
## 1st Qu.: 1.000 1st Qu.: 4.20 1st Qu.: 25.00
## Median : 2.000 Median : 12.50 Median : 40.00
## Mean : 6.232 Mean : 12.77 Mean : 95.82
## 3rd Qu.: 4.000 3rd Qu.: 18.50 3rd Qu.: 59.00
## Max. :180.000 Max. :100.00 Max. :2693.00
## Per_CrimeDamage_Conv No_Failed_CrimeDamage Per_Failed_CrimeDamage
## Min. : 44.40 Min. : 0.00 Min. : 0.00
## 1st Qu.: 82.10 1st Qu.: 3.00 1st Qu.: 9.50
## Median : 86.40 Median : 6.00 Median :13.60
## Mean : 86.04 Mean : 16.43 Mean :13.96
## 3rd Qu.: 90.50 3rd Qu.: 10.00 3rd Qu.:17.90
## Max. :100.00 Max. :491.00 Max. :55.60
## No_DrugOffences_Conv Per_DrugOffences_Conv No_Failed_Drug_Offence
## Min. : 4.0 Min. : 75.00 Min. : 0.00
## 1st Qu.: 38.0 1st Qu.: 92.20 1st Qu.: 2.00
## Median : 63.0 Median : 94.50 Median : 4.00
## Mean : 186.6 Mean : 94.35 Mean : 12.57
## 3rd Qu.: 100.0 3rd Qu.: 96.90 3rd Qu.: 7.00
## Max. :4988.0 Max. :100.00 Max. :346.00
## Per_Failed_Drug_Offence No_PublicOrderOffences_Conv
## Min. : 0.000 Min. : 2.0
## 1st Qu.: 3.100 1st Qu.: 39.0
## Median : 5.500 Median : 63.0
## Mean : 5.653 Mean : 162.4
## 3rd Qu.: 7.800 3rd Qu.: 100.0
## Max. :25.000 Max. :4752.0
## Per_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## Min. : 40.00 Min. : 0.00
## 1st Qu.: 82.60 1st Qu.: 5.00
## Median : 86.80 Median : 9.00
## Mean : 86.23 Mean : 28.45
## 3rd Qu.: 90.50 3rd Qu.: 16.00
## Max. :100.00 Max. :801.00
## Per_Failed_PublicOrderOffences No_Others_Ex_Motoring Per_Others_Ex_Motoring
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 9.50 1st Qu.: 9.00 1st Qu.: 80.00
## Median :13.20 Median : 16.00 Median : 86.20
## Mean :13.77 Mean : 64.34 Mean : 85.44
## 3rd Qu.:17.40 3rd Qu.: 35.00 3rd Qu.: 93.30
## Max. :60.00 Max. :3291.00 Max. :100.00
## No_Failed_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.00 1st Qu.: 6.70
## Median : 3.00 Median : 13.80
## Mean : 11.91 Mean : 14.52
## 3rd Qu.: 7.00 3rd Qu.: 20.00
## Max. :603.00 Max. :100.00
## No_Motoring_Offences_Conv Per_Motoring_Offences_Conv
## Min. : 1.0 Min. : 61.5
## 1st Qu.: 95.0 1st Qu.: 84.3
## Median : 143.0 Median : 87.9
## Mean : 365.5 Mean : 87.3
## 3rd Qu.: 216.0 3rd Qu.: 91.0
## Max. :12945.0 Max. :100.0
## No_Failed_Motoring_Offences Per_Failed_Motoring_Offences
## Min. : 0.00 Min. : 0.0
## 1st Qu.: 11.00 1st Qu.: 9.0
## Median : 20.00 Median :12.1
## Mean : 60.95 Mean :12.7
## 3rd Qu.: 34.00 3rd Qu.:15.7
## Max. :1725.00 Max. :38.5
## No_Failed_AdminFinalised_Conv Per_Failed_AdminFinalised_Conv
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 7.00 1st Qu.:100.00
## Median : 12.00 Median :100.00
## Mean : 38.82 Mean : 99.27
## 3rd Qu.: 21.00 3rd Qu.:100.00
## Max. :1051.00 Max. :100.00From the descriptive statistics above, the following observations were made:
Homicide:Between 2014 to 2018, Successful homicide had a median of 1, mean of 3.79 and a maximum case of 131. While the maximum unsuccessful homicide was 35 in same period. The shows to be the lowest case recorded
Offences against the person: Between 2014 to 2018, Offences against the person had a median of 179, mean oF 454 and a maximum case of 131. While the maximum unsuccessful homicide was 35 in same period. The shows to be the lowest case recorded.
Sexual offences:
Burglary offences:
Robbery:
Theft and handling:
Fraud and forgery:
Criminal damage:
Drugs offences:
Public order:
All other offences excluding motoring:
Motoring offences:
Administrative finalisations:
UNIVARIATE ANALYSIS
Successful Homicide Conviction
Fig 1. Histogram plot
# See this Again
# Histogram and Boxplot Plotting using ggplot
plot_histogram_n_boxplot = function(variable, variableNameString, binw){
h = ggplot(data = CrimeCases_data, aes(x= variable))+
labs(x = variableNameString,y ='count')+
geom_histogram(fill = 'Purple',col = 'Grey',binwidth = binw)+
geom_vline(aes(xintercept=mean(variable)),
color="blue", linetype="dashed", size=0.5)
b = ggplot(data = CrimeCases_data, aes('',variable))+
geom_boxplot(outlier.colour = 'red',col = 'red',outlier.shape = 19)+
labs(x = '',y = variableNameString)+ coord_flip()
grid.arrange(h,b,ncol = 2)
}
plot_histogram_n_boxplot(CrimeCases_data$No_Homicide_Conv, 'Successful Homicide Conviction', 5)
This Histogram reveals that the successful Homicide conviction is
rightly skewed, and the boxplot reveals the existence of outliers. This
will required further analysis to ascertain the case of the outlier. The
median successful homicide outcome is 1 as earlier revealed.
Unsuccessful Homicide Conviction
Fig 2. Histogram plot
Successful Offences against the person
Fig 3. Histogram plot
#save the cleaned data set to the file folder for exploration
write.csv(CrimeCases_data, './Clean_CrimeCases_2014_to_2018.csv', row.names= FALSE)Further Pre-Data Processing
Subset: Number of Succesful and Unsuccesful Convictions Only
No_All_Convictions = as.data.frame(subset(CrimeCases_data,
select = c(
Area,
No_Homicide_Conv,
No_Failed_Homicide,
No_Conv_Offence,
No_Failed_Conv_Offence,
No_Sex_Offence_Conv,
No_Failed_Sex_Offence,
No_Burg_Conv,
No_Failed_Burg_Conv,
No_Rob_Conv,
No_Failed_Rob_Conv,
No_TheftAndHandling_Conv,
No_Failed_TheftAndHandling,
No_FraudAndForgery_Conv,
No_Failed_FraudAndForgery,
No_CrimeDamage_Conv,
No_Failed_CrimeDamage,
No_DrugOffences_Conv,
No_Failed_Drug_Offence,
No_PublicOrderOffences_Conv,
No_Failed_PublicOrderOffences,
No_Others_Ex_Motoring,
No_Failed_Others_Ex_Motoring,
No_Motoring_Offences_Conv,
No_Failed_Motoring_Offences,
No_Failed_AdminFinalised_Conv
)))
print(head((No_All_Convictions)))## Area No_Homicide_Conv No_Failed_Homicide No_Conv_Offence
## 1 National 81 14 7805
## 2 Avon and Somerset 1 0 167
## 3 Bedfordshire 0 0 69
## 4 Cambridgeshire 0 0 99
## 5 Cheshire 1 1 140
## 6 Cleveland 0 0 85
## No_Failed_Conv_Offence No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv
## 1 2722 698 269 1470
## 2 45 36 8 37
## 3 23 5 1 16
## 4 23 6 3 8
## 5 47 17 3 26
## 6 41 11 4 25
## No_Failed_Burg_Conv No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 226 517 116 10045
## 2 2 9 3 266
## 3 1 4 0 98
## 4 0 6 1 107
## 5 3 1 0 206
## 6 10 5 2 254
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 1 840 666 108
## 2 21 11 0
## 3 9 8 2
## 4 10 7 0
## 5 4 16 2
## 6 32 6 2
## No_CrimeDamage_Conv No_Failed_CrimeDamage No_DrugOffences_Conv
## 1 2259 391 4536
## 2 54 6 135
## 3 20 6 45
## 4 21 1 40
## 5 35 9 75
## 6 32 8 63
## No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 279 3549
## 2 2 68
## 3 2 29
## 4 2 45
## 5 10 86
## 6 7 74
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 1 654 2640
## 2 11 66
## 3 6 11
## 4 9 6
## 5 7 50
## 6 27 28
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 1 513 8283
## 2 16 188
## 3 6 40
## 4 2 79
## 5 6 209
## 6 5 124
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 1 1314 718
## 2 37 24
## 3 5 16
## 4 6 4
## 5 12 1
## 6 17 10print(tail((No_All_Convictions)))## Area No_Homicide_Conv No_Failed_Homicide No_Conv_Offence
## 2188 Thames Valley 4 1 333
## 2189 Warwickshire 0 0 65
## 2190 West Mercia 6 0 220
## 2191 West Midlands 11 1 609
## 2192 West Yorkshire 5 2 446
## 2193 Wiltshire 0 0 85
## No_Failed_Conv_Offence No_Sex_Offence_Conv No_Failed_Sex_Offence
## 2188 103 46 15
## 2189 16 9 4
## 2190 60 20 9
## 2191 172 66 23
## 2192 73 71 33
## 2193 13 12 0
## No_Burg_Conv No_Failed_Burg_Conv No_Rob_Conv No_Failed_Rob_Conv
## 2188 58 2 7 3
## 2189 13 1 3 0
## 2190 25 5 4 2
## 2191 63 13 30 9
## 2192 84 5 15 1
## 2193 7 1 0 0
## No_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 2188 233 22
## 2189 42 9
## 2190 112 8
## 2191 446 26
## 2192 243 15
## 2193 58 1
## No_FraudAndForgery_Conv No_Failed_FraudAndForgery No_CrimeDamage_Conv
## 2188 36 5 73
## 2189 10 1 9
## 2190 16 4 41
## 2191 59 11 89
## 2192 34 6 70
## 2193 8 0 13
## No_Failed_CrimeDamage No_DrugOffences_Conv No_Failed_Drug_Offence
## 2188 9 98 7
## 2189 4 21 2
## 2190 5 56 2
## 2191 16 211 13
## 2192 13 137 5
## 2193 1 25 0
## No_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 2188 81 10
## 2189 19 4
## 2190 75 6
## 2191 253 38
## 2192 154 15
## 2193 21 3
## No_Others_Ex_Motoring No_Failed_Others_Ex_Motoring
## 2188 32 1
## 2189 4 0
## 2190 11 2
## 2191 69 15
## 2192 24 8
## 2193 7 1
## No_Motoring_Offences_Conv No_Failed_Motoring_Offences
## 2188 318 40
## 2189 78 21
## 2190 190 27
## 2191 280 68
## 2192 236 21
## 2193 64 2
## No_Failed_AdminFinalised_Conv
## 2188 30
## 2189 11
## 2190 12
## 2191 92
## 2192 51
## 2193 4Viewing the first and last 6 rows, using the head() and tail () functions confirms that a subset comprising only of the Number of Successful and Unsuccessful Convictions have been created.
Subset: Percentage of Successful and Unsuccessful Convictions Only
Per_All_Convictions = as.data.frame(subset(CrimeCases_data,
select = c(
Area,
Per_Homicide_Conv,
Per_Failed_Homicide,
Per_Conv_Offence,
Per_Failed_Conv_Offence,
Per_Sex_Offence_Conv,
Per_Failed_Sex_Offence,
Per_Burg_Conv,
Per_Failed_Burg_Conv,
Per_Rob_Conv,
Per_Failed_Rob_Conv,
Per_TheftAndHandling_Conv,
Per_Failed_TheftAndHandling,
Per_FraudAndForgery_Conv,
Per_Failed_FraudAndForgery,
Per_CrimeDamage_Conv,
Per_Failed_CrimeDamage,
Per_DrugOffences_Conv,
Per_Failed_Drug_Offence,
Per_PublicOrderOffences_Conv,
Per_Failed_PublicOrderOffences,
Per_Others_Ex_Motoring,
Per_Failed_Others_Ex_Motoring,
Per_Motoring_Offences_Conv,
Per_Failed_Motoring_Offences,
Per_Failed_AdminFinalised_Conv
)))
print(head(Per_All_Convictions))## Area Per_Homicide_Conv Per_Failed_Homicide Per_Conv_Offence
## 1 National 85.3 14.7 74.1
## 2 Avon and Somerset 100.0 0.0 78.8
## 3 Bedfordshire 0.0 0.0 75.0
## 4 Cambridgeshire 0.0 0.0 81.1
## 5 Cheshire 50.0 50.0 74.9
## 6 Cleveland 0.0 0.0 67.5
## Per_Failed_Conv_Offence Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## 1 25.9 72.2 27.8
## 2 21.2 81.8 18.2
## 3 25.0 83.3 16.7
## 4 18.9 66.7 33.3
## 5 25.1 85.0 15.0
## 6 32.5 73.3 26.7
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv Per_Failed_Rob_Conv
## 1 86.7 13.3 81.7 18.3
## 2 94.9 5.1 75.0 25.0
## 3 94.1 5.9 100.0 0.0
## 4 100.0 0.0 85.7 14.3
## 5 89.7 10.3 100.0 0.0
## 6 71.4 28.6 71.4 28.6
## Per_TheftAndHandling_Conv Per_Failed_TheftAndHandling
## 1 92.3 7.7
## 2 92.7 7.3
## 3 91.6 8.4
## 4 91.5 8.5
## 5 98.1 1.9
## 6 88.8 11.2
## Per_FraudAndForgery_Conv Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## 1 86.0 14.0 85.2
## 2 100.0 0.0 90.0
## 3 80.0 20.0 76.9
## 4 100.0 0.0 95.5
## 5 88.9 11.1 79.5
## 6 75.0 25.0 80.0
## Per_Failed_CrimeDamage Per_DrugOffences_Conv Per_Failed_Drug_Offence
## 1 14.8 94.2 5.8
## 2 10.0 98.5 1.5
## 3 23.1 95.7 4.3
## 4 4.5 95.2 4.8
## 5 20.5 88.2 11.8
## 6 20.0 90.0 10.0
## Per_PublicOrderOffences_Conv Per_Failed_PublicOrderOffences
## 1 84.4 15.6
## 2 86.1 13.9
## 3 82.9 17.1
## 4 83.3 16.7
## 5 92.5 7.5
## 6 73.3 26.7
## Per_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 1 83.7 16.3
## 2 80.5 19.5
## 3 64.7 35.3
## 4 75.0 25.0
## 5 89.3 10.7
## 6 84.8 15.2
## Per_Motoring_Offences_Conv Per_Failed_Motoring_Offences
## 1 86.3 13.7
## 2 83.6 16.4
## 3 88.9 11.1
## 4 92.9 7.1
## 5 94.6 5.4
## 6 87.9 12.1
## Per_Failed_AdminFinalised_Conv
## 1 100
## 2 100
## 3 100
## 4 100
## 5 100
## 6 100print(tail(Per_All_Convictions))## Area Per_Homicide_Conv Per_Failed_Homicide Per_Conv_Offence
## 2188 Thames Valley 80.0 20.0 76.4
## 2189 Warwickshire 0.0 0.0 80.2
## 2190 West Mercia 100.0 0.0 78.6
## 2191 West Midlands 91.7 8.3 78.0
## 2192 West Yorkshire 71.4 28.6 85.9
## 2193 Wiltshire 0.0 0.0 86.7
## Per_Failed_Conv_Offence Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## 2188 23.6 75.4 24.6
## 2189 19.8 69.2 30.8
## 2190 21.4 69.0 31.0
## 2191 22.0 74.2 25.8
## 2192 14.1 68.3 31.7
## 2193 13.3 100.0 0.0
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv Per_Failed_Rob_Conv
## 2188 96.7 3.3 70.0 30.0
## 2189 92.9 7.1 100.0 0.0
## 2190 83.3 16.7 66.7 33.3
## 2191 82.9 17.1 76.9 23.1
## 2192 94.4 5.6 93.8 6.3
## 2193 87.5 12.5 0.0 0.0
## Per_TheftAndHandling_Conv Per_Failed_TheftAndHandling
## 2188 91.4 8.6
## 2189 82.4 17.6
## 2190 93.3 6.7
## 2191 94.5 5.5
## 2192 94.2 5.8
## 2193 98.3 1.7
## Per_FraudAndForgery_Conv Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## 2188 87.8 12.2 89.0
## 2189 90.9 9.1 69.2
## 2190 80.0 20.0 89.1
## 2191 84.3 15.7 84.8
## 2192 85.0 15.0 84.3
## 2193 100.0 0.0 92.9
## Per_Failed_CrimeDamage Per_DrugOffences_Conv Per_Failed_Drug_Offence
## 2188 11.0 93.3 6.7
## 2189 30.8 91.3 8.7
## 2190 10.9 96.6 3.4
## 2191 15.2 94.2 5.8
## 2192 15.7 96.5 3.5
## 2193 7.1 100.0 0.0
## Per_PublicOrderOffences_Conv Per_Failed_PublicOrderOffences
## 2188 89.0 11.0
## 2189 82.6 17.4
## 2190 92.6 7.4
## 2191 86.9 13.1
## 2192 91.1 8.9
## 2193 87.5 12.5
## Per_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 2188 97.0 3.0
## 2189 100.0 0.0
## 2190 84.6 15.4
## 2191 82.1 17.9
## 2192 75.0 25.0
## 2193 87.5 12.5
## Per_Motoring_Offences_Conv Per_Failed_Motoring_Offences
## 2188 88.8 11.2
## 2189 78.8 21.2
## 2190 87.6 12.4
## 2191 80.5 19.5
## 2192 91.8 8.2
## 2193 97.0 3.0
## Per_Failed_AdminFinalised_Conv
## 2188 100
## 2189 100
## 2190 100
## 2191 100
## 2192 100
## 2193 100Viewing the first and last 6 rows, using the head () and tail () functions confirms that a subset comprising only of the Percentage of Successful and Unsuccessful Convictions have been created.
Sampling:
Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population Here, 500 sample were ramdomly selected from the Total Number of Successful and Unsuccesful Convictions
Sample_No_All_Convictions = No_All_Convictions[sample(nrow(No_All_Convictions), 500), ]
print(head(Sample_No_All_Convictions))## Area No_Homicide_Conv No_Failed_Homicide No_Conv_Offence
## 1828 Leicestershire 2 1 205
## 1387 Durham 1 0 107
## 940 Sussex 0 0 308
## 1851 Avon and Somerset 0 0 203
## 304 Bedfordshire 2 0 102
## 1253 Cleveland 0 0 127
## No_Failed_Conv_Offence No_Sex_Offence_Conv No_Failed_Sex_Offence
## 1828 36 18 9
## 1387 33 5 0
## 940 85 31 16
## 1851 39 48 12
## 304 37 4 0
## 1253 28 4 0
## No_Burg_Conv No_Failed_Burg_Conv No_Rob_Conv No_Failed_Rob_Conv
## 1828 18 5 10 0
## 1387 12 0 3 0
## 940 35 3 8 0
## 1851 26 5 7 1
## 304 26 4 11 0
## 1253 39 2 4 0
## No_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 1828 70 6
## 1387 82 5
## 940 179 13
## 1851 126 7
## 304 147 21
## 1253 203 22
## No_FraudAndForgery_Conv No_Failed_FraudAndForgery No_CrimeDamage_Conv
## 1828 17 4 31
## 1387 2 0 19
## 940 20 2 61
## 1851 19 0 38
## 304 9 1 28
## 1253 10 3 23
## No_Failed_CrimeDamage No_DrugOffences_Conv No_Failed_Drug_Offence
## 1828 6 31 3
## 1387 2 15 0
## 940 6 91 11
## 1851 3 61 3
## 304 4 61 7
## 1253 9 46 7
## No_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 1828 53 7
## 1387 31 6
## 940 65 13
## 1851 64 13
## 304 20 8
## 1253 46 10
## No_Others_Ex_Motoring No_Failed_Others_Ex_Motoring
## 1828 9 1
## 1387 8 0
## 940 9 0
## 1851 10 3
## 304 20 2
## 1253 6 0
## No_Motoring_Offences_Conv No_Failed_Motoring_Offences
## 1828 100 18
## 1387 95 9
## 940 233 26
## 1851 137 8
## 304 89 15
## 1253 65 13
## No_Failed_AdminFinalised_Conv
## 1828 12
## 1387 5
## 940 22
## 1851 36
## 304 8
## 1253 11print(tail(Sample_No_All_Convictions))## Area No_Homicide_Conv No_Failed_Homicide No_Conv_Offence
## 570 Durham 0 0 100
## 1582 Staffordshire 1 0 201
## 243 Northumbria 1 0 293
## 1654 Kent 7 1 317
## 864 Cambridgeshire 1 0 105
## 204 South Wales 0 0 362
## No_Failed_Conv_Offence No_Sex_Offence_Conv No_Failed_Sex_Offence
## 570 20 10 2
## 1582 60 37 7
## 243 128 34 7
## 1654 107 29 6
## 864 24 6 3
## 204 111 7 7
## No_Burg_Conv No_Failed_Burg_Conv No_Rob_Conv No_Failed_Rob_Conv
## 570 18 2 0 0
## 1582 24 5 1 0
## 243 70 10 17 5
## 1654 27 2 5 1
## 864 11 1 11 4
## 204 49 8 4 0
## No_TheftAndHandling_Conv No_Failed_TheftAndHandling
## 570 138 10
## 1582 160 13
## 243 473 59
## 1654 196 13
## 864 105 5
## 204 359 10
## No_FraudAndForgery_Conv No_Failed_FraudAndForgery No_CrimeDamage_Conv
## 570 7 2 31
## 1582 13 8 40
## 243 29 6 106
## 1654 29 2 62
## 864 8 0 27
## 204 19 3 124
## No_Failed_CrimeDamage No_DrugOffences_Conv No_Failed_Drug_Offence
## 570 6 30 0
## 1582 5 77 6
## 243 35 125 19
## 1654 6 92 5
## 864 2 35 3
## 204 12 161 6
## No_PublicOrderOffences_Conv No_Failed_PublicOrderOffences
## 570 58 8
## 1582 69 12
## 243 159 54
## 1654 69 11
## 864 41 6
## 204 243 41
## No_Others_Ex_Motoring No_Failed_Others_Ex_Motoring
## 570 8 6
## 1582 18 0
## 243 147 24
## 1654 33 2
## 864 7 1
## 204 70 12
## No_Motoring_Offences_Conv No_Failed_Motoring_Offences
## 570 102 11
## 1582 166 34
## 243 235 40
## 1654 189 13
## 864 99 14
## 204 735 94
## No_Failed_AdminFinalised_Conv
## 570 5
## 1582 13
## 243 17
## 1654 22
## 864 15
## 204 17This shows the 500 observations representing 2,193 total observation of the number of Successful and Unsuccessful convictions randomly selected for analysis.
Sampling:
Random Selection of 500 sample from the Total Percentage of Successful and Unsuccesful Convictions
Sample_Per_All_Convictions = Per_All_Convictions[sample(nrow(Per_All_Convictions), 500), ]
print(head(Per_All_Convictions))## Area Per_Homicide_Conv Per_Failed_Homicide Per_Conv_Offence
## 1 National 85.3 14.7 74.1
## 2 Avon and Somerset 100.0 0.0 78.8
## 3 Bedfordshire 0.0 0.0 75.0
## 4 Cambridgeshire 0.0 0.0 81.1
## 5 Cheshire 50.0 50.0 74.9
## 6 Cleveland 0.0 0.0 67.5
## Per_Failed_Conv_Offence Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## 1 25.9 72.2 27.8
## 2 21.2 81.8 18.2
## 3 25.0 83.3 16.7
## 4 18.9 66.7 33.3
## 5 25.1 85.0 15.0
## 6 32.5 73.3 26.7
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv Per_Failed_Rob_Conv
## 1 86.7 13.3 81.7 18.3
## 2 94.9 5.1 75.0 25.0
## 3 94.1 5.9 100.0 0.0
## 4 100.0 0.0 85.7 14.3
## 5 89.7 10.3 100.0 0.0
## 6 71.4 28.6 71.4 28.6
## Per_TheftAndHandling_Conv Per_Failed_TheftAndHandling
## 1 92.3 7.7
## 2 92.7 7.3
## 3 91.6 8.4
## 4 91.5 8.5
## 5 98.1 1.9
## 6 88.8 11.2
## Per_FraudAndForgery_Conv Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## 1 86.0 14.0 85.2
## 2 100.0 0.0 90.0
## 3 80.0 20.0 76.9
## 4 100.0 0.0 95.5
## 5 88.9 11.1 79.5
## 6 75.0 25.0 80.0
## Per_Failed_CrimeDamage Per_DrugOffences_Conv Per_Failed_Drug_Offence
## 1 14.8 94.2 5.8
## 2 10.0 98.5 1.5
## 3 23.1 95.7 4.3
## 4 4.5 95.2 4.8
## 5 20.5 88.2 11.8
## 6 20.0 90.0 10.0
## Per_PublicOrderOffences_Conv Per_Failed_PublicOrderOffences
## 1 84.4 15.6
## 2 86.1 13.9
## 3 82.9 17.1
## 4 83.3 16.7
## 5 92.5 7.5
## 6 73.3 26.7
## Per_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 1 83.7 16.3
## 2 80.5 19.5
## 3 64.7 35.3
## 4 75.0 25.0
## 5 89.3 10.7
## 6 84.8 15.2
## Per_Motoring_Offences_Conv Per_Failed_Motoring_Offences
## 1 86.3 13.7
## 2 83.6 16.4
## 3 88.9 11.1
## 4 92.9 7.1
## 5 94.6 5.4
## 6 87.9 12.1
## Per_Failed_AdminFinalised_Conv
## 1 100
## 2 100
## 3 100
## 4 100
## 5 100
## 6 100print(tail(Per_All_Convictions))## Area Per_Homicide_Conv Per_Failed_Homicide Per_Conv_Offence
## 2188 Thames Valley 80.0 20.0 76.4
## 2189 Warwickshire 0.0 0.0 80.2
## 2190 West Mercia 100.0 0.0 78.6
## 2191 West Midlands 91.7 8.3 78.0
## 2192 West Yorkshire 71.4 28.6 85.9
## 2193 Wiltshire 0.0 0.0 86.7
## Per_Failed_Conv_Offence Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## 2188 23.6 75.4 24.6
## 2189 19.8 69.2 30.8
## 2190 21.4 69.0 31.0
## 2191 22.0 74.2 25.8
## 2192 14.1 68.3 31.7
## 2193 13.3 100.0 0.0
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv Per_Failed_Rob_Conv
## 2188 96.7 3.3 70.0 30.0
## 2189 92.9 7.1 100.0 0.0
## 2190 83.3 16.7 66.7 33.3
## 2191 82.9 17.1 76.9 23.1
## 2192 94.4 5.6 93.8 6.3
## 2193 87.5 12.5 0.0 0.0
## Per_TheftAndHandling_Conv Per_Failed_TheftAndHandling
## 2188 91.4 8.6
## 2189 82.4 17.6
## 2190 93.3 6.7
## 2191 94.5 5.5
## 2192 94.2 5.8
## 2193 98.3 1.7
## Per_FraudAndForgery_Conv Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## 2188 87.8 12.2 89.0
## 2189 90.9 9.1 69.2
## 2190 80.0 20.0 89.1
## 2191 84.3 15.7 84.8
## 2192 85.0 15.0 84.3
## 2193 100.0 0.0 92.9
## Per_Failed_CrimeDamage Per_DrugOffences_Conv Per_Failed_Drug_Offence
## 2188 11.0 93.3 6.7
## 2189 30.8 91.3 8.7
## 2190 10.9 96.6 3.4
## 2191 15.2 94.2 5.8
## 2192 15.7 96.5 3.5
## 2193 7.1 100.0 0.0
## Per_PublicOrderOffences_Conv Per_Failed_PublicOrderOffences
## 2188 89.0 11.0
## 2189 82.6 17.4
## 2190 92.6 7.4
## 2191 86.9 13.1
## 2192 91.1 8.9
## 2193 87.5 12.5
## Per_Others_Ex_Motoring Per_Failed_Others_Ex_Motoring
## 2188 97.0 3.0
## 2189 100.0 0.0
## 2190 84.6 15.4
## 2191 82.1 17.9
## 2192 75.0 25.0
## 2193 87.5 12.5
## Per_Motoring_Offences_Conv Per_Failed_Motoring_Offences
## 2188 88.8 11.2
## 2189 78.8 21.2
## 2190 87.6 12.4
## 2191 80.5 19.5
## 2192 91.8 8.2
## 2193 97.0 3.0
## Per_Failed_AdminFinalised_Conv
## 2188 100
## 2189 100
## 2190 100
## 2191 100
## 2192 100
## 2193 100This shows the 500 observations representing 2,193 total observation of the percentage of Successful and Unsuccessful convictions randomly selected for analysis.
Outlier Detection:
Figure 1: Boxplot showing Successful Convicted Offences Across Areas
# Visualize the data frames for skewed data
bPlot_No_All_Convictions = plot_ly(type='box') %>% add_boxplot(Sample_No_All_Convictions,
x=Sample_No_All_Convictions$No_Conv_Offence,
y=Sample_No_All_Convictions$Area,
color=Sample_No_All_Convictions$Area
) %>% layout(title='plot of succesful convicted offences across areas'
)
bPlot_No_All_ConvictionsFigure 2: Histogram showing Successful Convicted Offences Across Areas
hPlot_No_All_Convictions = ggplot(
Sample_No_All_Convictions,
aes(x=No_Conv_Offence, fill=Area)
) + geom_histogram(position='identity', binwidth=500) + labs(
title = 'Successful Convicted offences By Area',
x='Succesful convicted offences'
)
ggplotly(hPlot_No_All_Convictions)From both visualization, “National” was revealed to be an outlier, hence would be removed from the data frame in order to fit.
Outlier Treatment:
“National” removal from Successful Convicted Offences Across Areas
No_All_Convictions = as.data.frame(subset(CrimeCases_data,
Area != 'National',
select = c(
Area,
No_Homicide_Conv,
No_Failed_Homicide,
No_Conv_Offence,
No_Failed_Conv_Offence,
No_Sex_Offence_Conv,
No_Failed_Sex_Offence,
No_Burg_Conv,
No_Failed_Burg_Conv,
No_Rob_Conv,
No_Failed_Rob_Conv,
No_TheftAndHandling_Conv,
No_Failed_TheftAndHandling,
No_FraudAndForgery_Conv,
No_Failed_FraudAndForgery,
No_CrimeDamage_Conv,
No_Failed_CrimeDamage,
No_DrugOffences_Conv,
No_Failed_Drug_Offence,
No_PublicOrderOffences_Conv,
No_Failed_PublicOrderOffences,
No_Others_Ex_Motoring,
No_Failed_Others_Ex_Motoring,
No_Motoring_Offences_Conv,
No_Failed_Motoring_Offences,
No_Failed_AdminFinalised_Conv
)))
print(No_All_Convictions$Area["National"])## [1] NAThis confirms that the outlier Area “National” has been removed from within the number “Successful Convicted Offences Across Counties” sample.
“National” removal from Unsuccessful Convicted Offences Across Areas
Per_All_Convictions = as.data.frame(subset(CrimeCases_data,
Area != 'National',
select = c(
Area,
Per_Homicide_Conv,
Per_Failed_Homicide,
Per_Conv_Offence,
Per_Failed_Conv_Offence,
Per_Sex_Offence_Conv,
Per_Failed_Sex_Offence,
Per_Burg_Conv,
Per_Failed_Burg_Conv,
Per_Rob_Conv,
Per_Failed_Rob_Conv,
Per_TheftAndHandling_Conv,
Per_Failed_TheftAndHandling,
Per_FraudAndForgery_Conv,
Per_Failed_FraudAndForgery,
Per_CrimeDamage_Conv,
Per_Failed_CrimeDamage,
Per_DrugOffences_Conv,
Per_Failed_Drug_Offence,
Per_PublicOrderOffences_Conv,
Per_Failed_PublicOrderOffences,
Per_Others_Ex_Motoring,
Per_Failed_Others_Ex_Motoring,
Per_Motoring_Offences_Conv,
Per_Failed_Motoring_Offences,
Per_Failed_AdminFinalised_Conv
)))
print(Per_All_Convictions$Area["National"])## [1] NAThis confirms that the outlier Area “National” have also been removed from within the percentage of “Successful Convicted Offences Across Counties” sample.
Data Visualization: Number Of Successful Convictions
#Setting the values for Graphical representation of the Number Of Successful Convictions
xRow = with(No_All_Convictions, Area)
yRow = with(No_All_Convictions,
No_Homicide_Conv,
No_Sex_Offence_Conv,
No_Burg_Conv,
No_Rob_Conv,
No_TheftAndHandling_Conv,
No_FraudAndForgery_Conv,
No_CrimeDamage_Conv,
No_DrugOffences_Conv,
No_PublicOrderOffences_Conv,
No_Others_Ex_Motoring,
No_Motoring_Offences_Conv
)
traceVal = with(No_All_Convictions, No_Conv_Offence)
Bar_No_Sucess_Conv = plot_ly(No_All_Convictions, x = ~xRow, y = ~yRow, type = 'bar', name = 'other succesful convictions',
marker = list(color = 'rgb(55, 83, 109)'))
Bar_No_Sucess_Conv = Bar_No_Sucess_Conv %>% add_trace(y = ~traceVal, name = 'successful convicted offence', marker = list(color = 'rgb(26, 118, 255)'))
Bar_No_Sucess_Conv = Bar_No_Sucess_Conv %>% layout(title = 'Successful Offence Convictions',
xaxis = list(
title = "Area",
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
yaxis = list(
title = 'successful convictions',
titlefont = list(
size = 16,
color = 'rgb(107, 107, 107)'),
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
legend = list(x = 0, y = 1, bgcolor = 'rgba(255, 255, 255, 0)', bordercolor = 'rgba(255, 255, 255, 0)'),
barmode = 'group', bargap = 0.15, bargroupgap = 0.1)
Bar_No_Sucess_ConvThe Bar Chart Shows that between year 2014 to 2018, Metropolitan and City Area has the highest successful crime conviction. This is followed by West Midland.
Data Visualization: Number Of Unsuccessful Convictions
#Setting the values for Graphical representation of the Number Of Unsuccessful Convictions
xUnsRow = with(No_All_Convictions, Area)
yUnsRow = with(No_All_Convictions,
No_Failed_Homicide,
No_Failed_Sex_Offence,
No_Failed_Burg_Conv,
No_Failed_Rob_Conv,
No_TheftAndHandling_Conv,
No_Failed_FraudAndForgery,
No_Failed_CrimeDamage,
No_Failed_Drug_Offence,
No_Failed_PublicOrderOffences,
No_Failed_Others_Ex_Motoring,
No_Failed_Motoring_Offences,
No_Failed_AdminFinalised_Conv
)
traceValUns = with(No_All_Convictions, No_Failed_Conv_Offence)
Bar_Unsucess_Conv = plot_ly(No_All_Convictions, x = ~xUnsRow, y = ~yUnsRow, type = 'bar', name = 'other unsuccesful convictions',
marker = list(color = 'rgb(55, 83, 109)'))
Bar_Unsucess_Conv = Bar_Unsucess_Conv %>% add_trace(y = ~traceValUns, name = 'unsuccesful convicted offence', marker = list(color = 'rgb(177, 156, 217)'))
Bar_Unsucess_Conv = Bar_Unsucess_Conv %>% layout(title = 'Unsuccesful offence Convictions',
xaxis = list(
title = "Area",
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
yaxis = list(
title = 'unsuccesful convictions',
titlefont = list(
size = 16,
color = 'rgb(107, 107, 107)'),
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
legend = list(x = 0, y = 1, bgcolor = 'rgba(200, 200, 200, 0)', bordercolor = 'rgba(255, 255, 255, 0)'),
barmode = 'group', bargap = 0.15, bargroupgap = 0.1)
Bar_Unsucess_ConvThe Bar Chart Shows that between year 2014 to 2018, Metropolitan and City Area has the highest unsuccessful crime conviction. This is followed by West Midland.
Data Visualization: Percentage Of Successful Convictions
#Setting the values for Graphical representation of the Percentage Of Successful Convictions
xPerRow = with(Per_All_Convictions, Area)
yPerRow = with(Per_All_Convictions,
Per_Homicide_Conv,
Per_Sex_Offence_Conv,
Per_Burg_Conv,
Per_Rob_Conv,
Per_TheftAndHandling_Conv,
Per_FraudAndForgery_Conv,
Per_CrimeDamage_Conv,
Per_DrugOffences_Conv,
Per_PublicOrderOffences_Conv,
Per_Others_Ex_Motoring,
Per_Motoring_Offences_Conv
)
perTraceVal = with(Per_All_Convictions, Per_Conv_Offence)
Bar_Per_Sucess_Conv = plot_ly(Per_All_Convictions, x = ~xPerRow, y = ~yPerRow, type = 'bar', name = 'other % of succesful convictions',
marker = list(color = 'rgb(55, 83, 109)'))
Bar_Per_Sucess_Conv = Bar_Per_Sucess_Conv %>% add_trace(y = ~perTraceVal, name = '% of succesful convicted offence', marker = list(color = 'rgb(26, 118, 255)'))
Bar_Per_Sucess_Conv = Bar_Per_Sucess_Conv %>% layout(title = 'Percentage of Succesful offence Convictions',
xaxis = list(
title = "Area",
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
yaxis = list(
title = 'per succesful convictions',
titlefont = list(
size = 16,
color = 'rgb(107, 107, 107)'),
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
legend = list(x = 0, y = 1, bgcolor = 'rgba(255, 255, 255, 0)', bordercolor = 'rgba(255, 255, 255, 0)'),
barmode = 'group', bargap = 0.15, bargroupgap = 0.1)
Bar_Per_Sucess_ConvData Visualization: Percentage Of Unsuccessful Convictions
#Setting the values for Graphical representation of the Percentage Of Unsuccessful Convictions
xUnPerRow = with(Per_All_Convictions, Area)
yUnPerRow = with(Per_All_Convictions,
Per_Failed_Homicide,
Per_Failed_Sex_Offence,
Per_Failed_Burg_Conv,
Per_Failed_Rob_Conv,
Per_Failed_TheftAndHandling,
Per_Failed_FraudAndForgery,
Per_Failed_CrimeDamage,
Per_Failed_Drug_Offence,
Per_Failed_PublicOrderOffences,
Per_Failed_Others_Ex_Motoring,
Per_Failed_Motoring_Offences,
Per_Failed_AdminFinalised_Conv
)
perUnTraceVal = with(Per_All_Convictions, Per_Failed_Conv_Offence)
Bar_Per_Unucess_Conv = plot_ly(Per_All_Convictions, x = ~xUnPerRow, y = ~yUnPerRow, type = 'bar', name = '% of other unsuccesful convictions',
marker = list(color = 'rgb(55, 83, 109)'))
Bar_Per_Unucess_Conv = Bar_Per_Unucess_Conv %>% add_trace(y = ~perUnTraceVal, name = '% of unsuccesful convicted offence', marker = list(color = 'rgb(26, 118, 255)'))
Bar_Per_Unucess_Conv = Bar_Per_Unucess_Conv %>% layout(title = 'Percentage of Unsuccesful offence Convictions',
xaxis = list(
title = "Area",
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
yaxis = list(
title = 'unsuccesful convictions',
titlefont = list(
size = 16,
color = 'rgb(107, 107, 107)'),
tickfont = list(
size = 14,
color = 'rgb(107, 107, 107)')),
legend = list(x = 0, y = 1, bgcolor = 'rgba(255, 255, 255, 0)', bordercolor = 'rgba(255, 255, 255, 0)'),
barmode = 'group', bargap = 0.15, bargroupgap = 0.1)
Bar_Per_Unucess_ConvPREDICTIVE ANALYTICS
DATA CLUSTERING
Clus_No_All_Convictions = as.data.frame(subset(CrimeCases_data,
Area != 'National',
select = c(
Area,
No_Homicide_Conv,
No_Failed_Homicide,
No_Conv_Offence,
No_Failed_Conv_Offence,
No_Sex_Offence_Conv,
No_Failed_Sex_Offence,
No_Burg_Conv,
No_Failed_Burg_Conv,
No_Rob_Conv,
No_Failed_Rob_Conv,
No_TheftAndHandling_Conv,
No_Failed_TheftAndHandling,
No_FraudAndForgery_Conv,
No_Failed_FraudAndForgery,
No_CrimeDamage_Conv,
No_Failed_CrimeDamage,
No_DrugOffences_Conv,
No_Failed_Drug_Offence,
No_PublicOrderOffences_Conv,
No_Failed_PublicOrderOffences,
No_Others_Ex_Motoring,
No_Failed_Others_Ex_Motoring,
No_Motoring_Offences_Conv,
No_Failed_Motoring_Offences,
No_Failed_AdminFinalised_Conv
)))
str(Clus_No_All_Convictions)## 'data.frame': 2142 obs. of 26 variables:
## $ Area : chr "Avon and Somerset" "Bedfordshire" "Cambridgeshire" "Cheshire" ...
## $ No_Homicide_Conv : num 1 0 0 1 0 0 0 1 0 2 ...
## $ No_Failed_Homicide : num 0 0 0 1 0 0 0 0 0 0 ...
## $ No_Conv_Offence : num 167 69 99 140 85 77 151 157 73 75 ...
## $ No_Failed_Conv_Offence : num 45 23 23 47 41 19 57 50 16 26 ...
## $ No_Sex_Offence_Conv : num 36 5 6 17 11 8 8 11 1 11 ...
## $ No_Failed_Sex_Offence : num 8 1 3 3 4 1 6 4 0 4 ...
## $ No_Burg_Conv : num 37 16 8 26 25 12 31 16 18 30 ...
## $ No_Failed_Burg_Conv : num 2 1 0 3 10 1 3 1 1 0 ...
## $ No_Rob_Conv : num 9 4 6 1 5 1 8 6 3 0 ...
## $ No_Failed_Rob_Conv : num 3 0 1 0 2 0 3 0 0 1 ...
## $ No_TheftAndHandling_Conv : num 266 98 107 206 254 108 203 151 123 144 ...
## $ No_Failed_TheftAndHandling : num 21 9 10 4 32 6 15 10 11 19 ...
## $ No_FraudAndForgery_Conv : num 11 8 7 16 6 5 11 8 7 4 ...
## $ No_Failed_FraudAndForgery : num 0 2 0 2 2 0 9 0 2 5 ...
## $ No_CrimeDamage_Conv : num 54 20 21 35 32 37 40 56 24 43 ...
## $ No_Failed_CrimeDamage : num 6 6 1 9 8 2 7 6 4 7 ...
## $ No_DrugOffences_Conv : num 135 45 40 75 63 42 75 70 29 19 ...
## $ No_Failed_Drug_Offence : num 2 2 2 10 7 2 9 6 2 2 ...
## $ No_PublicOrderOffences_Conv : num 68 29 45 86 74 40 50 65 45 58 ...
## $ No_Failed_PublicOrderOffences: num 11 6 9 7 27 2 4 20 3 13 ...
## $ No_Others_Ex_Motoring : num 66 11 6 50 28 64 46 64 25 12 ...
## $ No_Failed_Others_Ex_Motoring : num 16 6 2 6 5 1 15 14 1 3 ...
## $ No_Motoring_Offences_Conv : num 188 40 79 209 124 95 258 189 71 66 ...
## $ No_Failed_Motoring_Offences : num 37 5 6 12 17 10 13 17 7 3 ...
## $ No_Failed_AdminFinalised_Conv: num 24 16 4 1 10 12 16 15 5 0 ...Convert Cluster into Matrix
Cluster_Convictions = as.matrix(Clus_No_All_Convictions[,-1])Cluster Missing Value Treatment
This is confirm that there is no missing values (NA) in the matrix
Cluster_Convictions = na.omit(Cluster_Convictions)
sapply(Cluster_Convictions, function(x){sum(is.na(x))} )## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [73] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8605] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8677] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8713] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8821] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8857] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8893] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8965] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17605] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17677] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17713] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17821] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17857] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17893] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17965] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26605] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26677] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26713] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26821] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26857] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26893] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26965] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [34993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35605] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35677] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35713] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35821] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35857] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35893] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [35965] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [40969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [41977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [42985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [43993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44605] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44677] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44713] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44821] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44857] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44893] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [44965] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45469] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45685] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [45973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46045] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46189] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46405] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46549] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46657] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46693] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46729] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46765] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46909] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [46981] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47053] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47125] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47197] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47377] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47413] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47521] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47557] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47701] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47773] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47809] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47845] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47917] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [47989] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48061] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48349] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48421] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48493] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48565] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48637] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48781] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48853] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [48997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49213] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49285] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49393] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49501] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49537] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49645] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49717] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49789] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49861] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49897] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49933] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [49969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50005] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50077] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50365] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50437] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50509] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50581] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50653] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50725] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50797] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50869] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50941] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [50977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51013] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51049] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51085] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51229] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51373] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51589] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51733] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51805] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51877] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51913] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51949] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52165] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52381] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52453] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52489] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52525] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52669] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52705] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52957] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [52993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53173] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53245] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53317] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53389] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53461] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [53533] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0This confirms that there are no missing values in the matrix
Data Standardization
According to https://www.egnyte.com/, data standardization is an important function, because it provides a structure for creating and maintaining data quality by:
- Defining how data should be formatted
- Eliminating extraneous data
- Identifying data errors
# To Standardize the data frame
Cluster_Convictions = scale(Cluster_Convictions)
print(head(Cluster_Convictions, 4))## No_Homicide_Conv No_Failed_Homicide No_Conv_Offence No_Failed_Conv_Offence
## 2 -0.2924729 -0.3586002 -0.2854342 -0.2426991
## 3 -0.6023033 -0.3586002 -0.7099756 -0.4621678
## 4 -0.6023033 -0.3586002 -0.5800139 -0.4621678
## 5 -0.2924729 0.4079882 -0.4023997 -0.2227474
## No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv No_Failed_Burg_Conv
## 2 0.5499727 -0.02362809 0.1891300 -0.3851009
## 3 -0.7048131 -0.60346375 -0.4473215 -0.5057511
## 4 -0.6643361 -0.43779642 -0.6897792 -0.6264014
## 5 -0.2190896 -0.43779642 -0.1442493 -0.2644506
## No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 2 -0.04835828 0.05931916 0.43326907
## 3 -0.31909389 -0.43701534 -0.53694565
## 4 -0.21079965 -0.27157051 -0.48496986
## 5 -0.48153525 -0.43701534 0.08676382
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 2 0.1571143 -0.2626874 -0.4984381
## 3 -0.3279867 -0.3527877 -0.1859386
## 4 -0.2875616 -0.3828211 -0.4984381
## 5 -0.5301121 -0.1125202 -0.1859386
## No_CrimeDamage_Conv No_Failed_CrimeDamage No_DrugOffences_Conv
## 2 0.1132726 -0.24323767 0.2602186
## 3 -0.6649006 -0.24323767 -0.3333721
## 4 -0.6420132 -0.74721820 -0.3663493
## 5 -0.3215889 0.05915066 -0.1355085
## No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 2 -0.3452683 -0.1768218
## 3 -0.3452683 -0.6321510
## 4 -0.3452683 -0.4453493
## 5 0.2773919 0.0333302
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 2 -0.1592045 0.5764698
## 3 -0.3827337 -0.3824411
## 4 -0.2486162 -0.4696148
## 5 -0.3380279 0.2975139
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 2 0.799569899 0.004943085
## 3 -0.007992683 -0.786889616
## 4 -0.331017716 -0.578230999
## 5 -0.007992683 0.117297725
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 2 0.1137421 0.1216016
## 3 -0.5140330 -0.1139586
## 4 -0.4944151 -0.4672989
## 5 -0.3767072 -0.5556339print(tail(Cluster_Convictions, 4))## No_Homicide_Conv No_Failed_Homicide No_Conv_Offence No_Failed_Conv_Offence
## 2190 1.2566789 -0.3586002 -0.05583537 -0.09306136
## 2191 2.8058308 0.4079882 1.62933378 1.02423378
## 2192 0.9468486 1.1745766 0.92320892 0.03662469
## 2193 -0.6023033 -0.3586002 -0.64066271 -0.56192628
## No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv No_Failed_Burg_Conv
## 2190 -0.0976587 0.05920557 -0.1745565 -0.02314999
## 2191 1.7642815 1.21887689 0.9771176 0.94205232
## 2192 1.9666663 2.04721354 1.6135691 -0.02314999
## 2193 -0.4214744 -0.68629741 -0.7200864 -0.50575114
## No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 2190 -0.3190939 -0.1061257 -0.4560944
## 2191 1.0887313 1.0519882 1.4727848
## 2192 0.2765244 -0.2715705 0.3004421
## 2193 -0.5356824 -0.4370153 -0.7679492
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv
## 2190 -0.3684118 -0.1125202
## 2191 0.3592397 1.1789174
## 2192 -0.0854362 0.4280816
## 2193 -0.6513873 -0.3527877
## No_Failed_FraudAndForgery No_CrimeDamage_Conv No_Failed_CrimeDamage
## 2190 0.1265608 -0.1842642 -0.3440338
## 2191 1.2203089 0.9143332 0.7647234
## 2192 0.4390603 0.4794717 0.4623351
## 2193 -0.4984381 -0.8251127 -0.7472182
## No_DrugOffences_Conv No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 2190 -0.2608221 -0.3452683 -0.0950960
## 2191 0.7614730 0.5108895 1.9830734
## 2192 0.2734095 -0.1117707 0.8272376
## 2193 -0.4652811 -0.5009333 -0.7255519
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 2190 -0.38273373 -0.3824411
## 2191 1.04785307 0.6287740
## 2192 0.01961881 -0.1557895
## 2193 -0.51685124 -0.4521801
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 2190 -0.3310177 0.01564353
## 2191 0.7188136 0.49716341
## 2192 0.1535198 0.26175369
## 2193 -0.4117740 -0.65848431
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 2190 -0.08243762 -0.2317387
## 2191 0.72189928 2.1238633
## 2192 -0.20014546 0.9166173
## 2193 -0.57288695 -0.4672989This confirms that the matrix is standardized and set for analysis.
K-means Clustering
The idea of k-means clustering consists of defining clusters so that the total intra-cluster variation (known as total within-cluster variation) is minimized
set.seed(1234) # This is set to ensure same output
# To determine the K value
withinSum_Conviction = numeric(15)
for (k in 1:15) {
withinSum_Conviction[k] = sum(kmeans(Cluster_Convictions, centers=k, nstart=25, iter.max=30)$withinss)
}# Graphical presentation to determine the elbow of the WSS curve
fviz_nbclust(Cluster_Convictions, kmeans, method = 'wss') +
geom_vline(xintercept = withinSum_Conviction, linetype = 2)
This plot shows the variance within the clusters. It tends to decrease
as k increases. It has a bend at k= 3, however in order to achieve a
lower total witin sum of squares, the cluster is increased to 6
To determine the clusters of the data
kms_Convictions = kmeans(Cluster_Convictions, centers = 6, nstart = 25, iter.max = 30)
print (kms_Convictions)## K-means clustering with 6 clusters of sizes 80, 149, 173, 51, 682, 1007
##
## Cluster means:
## No_Homicide_Conv No_Failed_Homicide No_Conv_Offence No_Failed_Conv_Offence
## 1 0.79967914 0.5517235 1.49834328 0.99929416
## 2 -0.14483564 -0.1991086 0.22862651 0.29813924
## 3 0.85730222 0.5453537 0.61297874 0.35371831
## 4 4.49471011 4.4964594 5.33009739 5.81518436
## 5 -0.09303666 -0.1315461 -0.03458162 -0.08658144
## 6 -0.35400826 -0.2466950 -0.50469557 -0.42014439
## No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv No_Failed_Burg_Conv
## 1 1.245670437 1.18677884 1.45180438 1.0838164
## 2 0.165577375 0.19707637 0.57234342 0.3558053
## 3 1.160168706 0.98186727 0.48361806 0.2676660
## 4 4.658781025 4.96425790 5.21359088 5.3824561
## 5 0.001871892 -0.07694771 -0.07341399 -0.1154952
## 6 -0.559988394 -0.49142854 -0.49743221 -0.3791103
## No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 1.5950068 1.26706643 1.87595815
## 2 0.1616889 0.06598136 0.83399814
## 3 0.2383398 0.09757230 0.33442494
## 4 5.2909725 5.27245339 4.75065664
## 5 -0.1470458 -0.12407717 -0.02180275
## 6 -0.3599596 -0.31017978 -0.55572185
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 1 1.01564195 0.797492833 0.7398410
## 2 0.57194616 0.003380617 0.0248412
## 3 0.10313629 0.198230342 0.1961055
## 4 5.55346630 5.947755658 5.7086589
## 5 -0.09853582 -0.089004304 -0.1078138
## 6 -0.39755637 -0.338859574 -0.3122418
## No_CrimeDamage_Conv No_Failed_CrimeDamage No_DrugOffences_Conv
## 1 1.59466249 0.96001587 0.72676440
## 2 0.75273858 0.74713483 0.28177556
## 3 0.45261532 0.30793642 0.14443602
## 4 4.92053374 5.20765771 6.00446947
## 5 -0.03217347 -0.06514484 -0.07992105
## 6 -0.54323576 -0.45934372 -0.37421542
## No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 0.6568254 1.49257287
## 2 0.1979923 0.62155196
## 3 0.1532198 0.39431377
## 4 5.9362739 5.13031646
## 5 -0.1025267 -0.06251868
## 6 -0.3390076 -0.49577136
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 1 0.9461473 1.10256322
## 2 0.4903803 0.95617280
## 3 0.2064530 -0.06105732
## 4 5.6779753 4.53518233
## 5 -0.1097790 -0.13285842
## 6 -0.3964074 -0.35828873
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 1 0.89647741 0.96972168
## 2 0.88303610 0.65048854
## 3 -0.06260819 0.37562371
## 4 4.37501364 5.15384886
## 5 -0.13694219 -0.03268105
## 6 -0.31995082 -0.47670431
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 1 0.5926659 0.87134555
## 2 0.2522529 0.03998555
## 3 0.2324705 0.22355290
## 4 5.8406516 5.80218188
## 5 -0.0989777 -0.12501124
## 6 -0.3531150 -0.32273461
##
## Clustering vector:
## 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 5 6 6 5 5 6 5 5 6 6 6 5 6 1 6 2
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## 5 5 3 2 5 6 2 4 6 6 2 6 6 5 5 5
## 34 35 36 37 38 39 40 41 42 43 45 46 47 48 49 50
## 5 6 6 5 3 6 5 1 2 6 5 6 6 6 5 6
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
## 6 5 6 6 6 5 6 1 6 2 5 5 2 2 6 6
## 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
## 2 4 6 6 2 6 6 5 2 5 5 6 5 5 2 6
## 83 84 85 86 88 89 90 91 92 93 94 95 96 97 98 99
## 5 1 1 6 5 6 6 5 5 6 5 5 6 6 6 5
## 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115
## 6 1 6 5 5 5 2 2 6 6 2 4 6 6 2 6
## 116 117 118 119 120 121 122 123 124 125 126 127 128 129 131 132
## 6 5 2 5 5 6 6 5 3 6 5 1 1 6 2 6
## 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148
## 6 5 5 6 5 5 6 6 6 5 6 1 5 2 6 5
## 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164
## 2 2 5 6 2 4 6 6 2 6 6 5 2 5 5 6
## 165 166 167 168 169 170 171 172 174 175 176 177 178 179 180 181
## 6 2 2 6 5 1 2 6 2 6 6 5 5 6 5 5
## 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
## 6 6 6 2 6 1 6 2 5 5 2 2 5 6 2 4
## 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213
## 5 6 2 5 6 5 2 2 5 6 5 2 2 6 5 1
## 214 215 217 218 219 220 221 222 223 224 225 226 227 228 229 230
## 1 6 2 6 6 5 5 6 5 5 6 6 6 5 6 1
## 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246
## 6 2 5 5 2 2 6 5 2 4 5 6 2 6 6 3
## 247 248 249 250 251 252 253 254 255 256 257 258 260 261 262 263
## 2 2 5 6 5 2 2 6 5 1 1 6 3 6 6 5
## 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279
## 5 6 6 5 6 6 6 5 6 1 6 2 5 5 2 2
## 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295
## 6 6 2 4 5 6 2 5 6 5 2 5 5 6 5 5
## 296 297 298 299 300 301 303 304 305 306 307 308 309 310 311 312
## 3 6 6 1 2 6 2 6 6 5 5 6 6 5 6 6
## 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328
## 6 5 6 1 6 2 5 5 2 2 5 6 2 4 5 6
## 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344
## 2 6 6 5 2 5 5 6 2 5 3 6 5 1 2 6
## 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361
## 2 6 6 5 5 6 5 5 6 6 6 5 6 1 6 2
## 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
## 5 5 3 2 5 6 2 4 6 6 2 6 6 3 2 5
## 378 379 380 381 382 383 384 385 386 387 389 390 391 392 393 394
## 5 6 6 5 2 6 5 1 2 6 5 6 6 5 5 6
## 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410
## 5 5 6 6 6 5 6 1 6 2 5 5 2 2 6 6
## 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426
## 2 4 6 6 2 6 6 5 2 5 5 6 6 5 3 6
## 427 428 429 430 432 433 434 435 436 437 438 439 440 441 442 443
## 6 1 3 6 2 6 6 5 5 6 5 5 6 6 6 5
## 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459
## 6 1 5 2 5 5 2 2 5 6 2 4 5 6 2 5
## 460 461 462 463 464 465 466 467 468 469 470 471 472 473 475 476
## 6 2 2 5 5 6 5 2 3 6 5 1 3 6 2 6
## 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492
## 6 5 5 6 5 5 6 6 6 5 6 1 6 2 5 5
## 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508
## 2 2 6 6 2 4 5 6 2 6 5 2 2 2 5 6
## 509 510 511 512 513 514 515 516 518 519 520 521 522 523 524 525
## 5 2 3 6 5 1 1 6 3 6 6 6 5 6 6 6
## 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541
## 6 6 6 5 6 1 6 5 6 5 5 2 6 6 5 4
## 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557
## 6 6 2 6 6 5 2 5 5 6 6 5 3 6 5 1
## 558 559 561 562 563 564 565 566 567 568 569 570 571 572 573 574
## 2 6 3 6 6 5 5 6 5 5 6 6 6 5 6 2
## 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590
## 6 3 6 5 5 2 5 6 5 4 6 5 2 6 6 5
## 591 592 593 594 595 596 597 598 599 600 601 602 604 605 606 607
## 2 5 5 6 6 5 3 6 6 1 3 6 5 6 6 5
## 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
## 6 6 5 6 6 6 6 5 6 3 6 5 6 5 5 5
## 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639
## 6 6 3 4 6 6 3 6 6 5 3 5 5 6 6 5
## 640 641 642 643 644 645 647 648 649 650 651 652 653 654 655 656
## 3 6 5 1 3 6 2 6 6 5 5 6 5 5 6 6
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 6 5 6 1 6 3 6 5 2 2 6 6 2 4 5 6
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 2 6 6 5 2 5 5 6 6 5 2 6 5 1 2 6
## 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705
## 2 6 6 5 5 6 5 5 6 6 6 5 6 1 6 5
## 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
## 5 5 2 2 5 6 2 4 5 6 2 6 6 5 2 5
## 722 723 724 725 726 727 728 729 730 731 733 734 735 736 737 738
## 5 6 6 5 2 6 5 1 1 6 3 6 6 5 5 6
## 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754
## 5 5 6 6 6 3 6 1 6 5 5 5 2 2 6 6
## 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770
## 5 4 6 6 2 5 6 2 2 5 5 6 6 5 3 6
## 771 772 773 774 776 777 778 779 780 781 782 783 784 785 786 787
## 5 1 1 6 3 6 6 5 5 6 5 5 6 6 6 5
## 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803
## 6 1 6 5 5 5 2 2 6 6 5 4 5 6 2 6
## 804 805 806 807 808 809 810 811 812 813 814 815 816 817 819 820
## 6 5 2 5 5 6 6 5 2 6 5 1 1 6 2 6
## 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
## 6 5 5 6 5 5 6 5 6 5 6 1 6 3 5 5
## 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852
## 2 2 5 6 2 4 6 6 2 6 6 5 2 5 5 6
## 853 854 855 856 857 858 859 860 862 863 864 865 866 867 868 869
## 6 5 3 6 5 1 1 6 5 6 6 5 5 6 5 5
## 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885
## 6 6 6 5 6 1 6 5 6 5 5 2 6 6 2 4
## 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901
## 6 6 2 6 6 5 2 5 5 6 6 5 3 6 5 1
## 902 903 905 906 907 908 909 910 911 912 913 914 915 916 917 918
## 2 6 3 6 6 5 5 6 5 5 6 6 6 5 6 3
## 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934
## 6 5 6 5 5 5 5 6 5 4 5 6 2 6 6 3
## 935 936 937 938 939 940 941 942 943 944 945 946 948 949 950 951
## 5 5 5 6 6 5 3 6 5 1 3 6 2 6 6 5
## 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967
## 5 6 5 5 6 6 6 3 6 1 6 5 5 5 3 2
## 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983
## 6 6 2 4 6 6 2 6 6 3 3 5 5 6 6 5
## 984 985 986 987 988 989 991 992 993 994 995 996 997 998 999 1000
## 3 6 5 1 1 6 5 6 6 5 6 6 5 5 6 6
## 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016
## 6 3 6 3 6 5 5 5 3 3 5 6 5 4 6 6
## 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032
## 3 6 6 5 3 5 5 6 6 5 3 6 5 1 3 6
## 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
## 5 6 6 5 6 6 6 5 6 6 6 5 6 3 6 5
## 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065
## 6 5 5 5 6 6 5 4 6 6 3 6 6 5 3 5
## 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1077 1078 1079 1080 1081 1082
## 5 6 6 5 3 6 5 1 3 6 3 6 6 6 6 6
## 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098
## 6 6 6 6 6 5 6 3 6 5 6 5 5 5 6 6
## 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114
## 5 4 6 6 3 6 6 5 5 5 5 6 6 6 3 6
## 1115 1116 1117 1118 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131
## 5 1 3 6 3 6 6 5 5 6 5 5 6 6 6 3
## 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
## 6 1 6 5 5 5 5 5 6 6 5 4 6 6 2 6
## 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1163 1164
## 6 5 5 5 5 6 6 5 3 6 5 1 3 6 3 6
## 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180
## 6 5 5 6 6 6 6 6 6 3 6 3 6 5 6 5
## 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196
## 5 5 6 6 5 4 6 6 3 6 6 5 5 5 5 6
## 1197 1198 1199 1200 1201 1202 1203 1204 1206 1207 1208 1209 1210 1211 1212 1213
## 6 5 5 6 5 1 3 6 3 6 6 5 5 6 5 5
## 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229
## 6 6 6 5 6 3 6 5 6 5 5 5 6 6 5 4
## 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245
## 6 6 2 6 6 5 5 5 5 6 6 5 3 6 5 1
## 1246 1247 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262
## 3 6 5 6 6 5 6 6 5 5 6 6 6 3 6 3
## 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278
## 6 5 6 5 5 3 5 6 5 4 6 6 2 6 6 5
## 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1292 1293 1294 1295
## 5 5 5 6 6 5 3 6 5 1 3 6 5 6 6 5
## 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311
## 6 6 6 6 6 6 6 5 6 3 6 5 5 5 5 5
## 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
## 6 6 3 4 6 6 3 6 6 3 3 5 5 6 6 5
## 1328 1329 1330 1331 1332 1333 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 3 6 5 1 3 6 3 6 6 5 6 6 6 6 6 6
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
## 6 3 6 3 6 5 5 5 5 5 5 6 5 4 6 6
## 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 3 6 6 5 5 5 5 6 6 5 3 6 5 1 3 6
## 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393
## 5 6 6 5 5 6 6 6 6 6 6 5 6 3 6 5
## 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409
## 6 5 3 5 5 6 5 4 6 6 3 6 6 5 3 5
## 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1421 1422 1423 1424 1425 1426
## 5 6 6 5 3 6 5 1 3 6 5 6 6 5 6 6
## 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442
## 6 6 6 6 6 5 6 3 6 5 6 6 5 5 6 6
## 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458
## 5 4 6 6 3 6 6 5 5 5 5 6 6 5 5 6
## 1459 1460 1461 1462 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475
## 5 1 3 6 5 6 6 5 6 6 6 6 6 6 6 5
## 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491
## 6 3 6 5 6 6 5 5 6 6 5 4 6 6 5 6
## 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1507 1508
## 6 5 5 5 6 6 6 6 5 6 6 3 3 6 5 6
## 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524
## 6 5 6 6 6 6 6 6 6 5 6 3 6 5 5 5
## 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540
## 5 5 6 6 5 4 6 6 3 6 6 5 3 5 5 6
## 1541 1542 1543 1544 1545 1546 1547 1548 1550 1551 1552 1553 1554 1555 1556 1557
## 6 5 3 6 5 1 3 6 3 6 6 5 6 6 5 5
## 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573
## 6 6 6 3 6 3 6 5 6 5 5 5 6 6 5 4
## 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589
## 6 6 5 6 6 5 5 5 5 6 6 5 3 6 5 1
## 1590 1591 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606
## 3 6 3 6 6 5 6 6 6 6 6 6 6 5 6 3
## 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622
## 6 5 5 6 5 5 6 6 5 4 6 6 3 6 6 5
## 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1636 1637 1638 1639
## 3 5 5 6 6 6 5 6 5 1 3 6 5 6 6 5
## 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655
## 6 6 6 5 6 6 6 3 6 3 6 5 5 5 5 5
## 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671
## 5 6 3 4 6 6 3 6 6 5 3 5 5 6 6 5
## 1672 1673 1674 1675 1676 1677 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688
## 3 6 5 1 3 6 5 6 6 5 6 6 6 6 6 6
## 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704
## 6 5 6 3 6 5 6 6 3 5 5 6 5 4 6 6
## 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720
## 3 6 6 5 5 5 5 6 6 5 3 6 5 1 3 6
## 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737
## 5 6 6 5 6 6 6 5 6 6 6 5 6 3 6 5
## 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753
## 6 6 5 5 6 6 5 4 6 6 3 6 6 5 5 5
## 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1765 1766 1767 1768 1769 1770
## 5 6 6 5 3 6 6 1 3 6 5 6 6 5 6 6
## 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786
## 6 6 6 6 6 5 6 3 6 5 6 6 5 5 6 6
## 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802
## 5 4 6 6 3 6 6 5 5 5 5 6 6 5 3 6
## 1803 1804 1805 1806 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
## 5 1 3 6 5 6 6 5 6 6 6 6 6 6 6 5
## 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835
## 6 3 6 5 6 6 5 5 6 6 5 4 6 6 3 6
## 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1851 1852
## 6 5 5 5 5 6 6 5 5 6 5 1 3 6 5 6
## 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868
## 6 5 6 6 6 6 6 6 6 5 6 3 6 5 6 6
## 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884
## 5 5 6 6 5 4 6 6 5 6 6 5 5 5 6 6
## 1885 1886 1887 1888 1889 1890 1891 1892 1894 1895 1896 1897 1898 1899 1900 1901
## 6 6 5 6 6 3 3 6 5 6 6 5 6 6 6 6
## 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
## 6 6 6 5 6 3 6 5 6 6 5 5 5 6 5 4
## 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933
## 6 6 5 6 6 5 5 5 6 6 6 5 5 6 6 1
## 1934 1935 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950
## 3 6 5 6 6 5 6 6 5 6 6 6 6 5 6 3
## 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
## 6 5 6 6 5 5 5 6 5 4 6 6 3 6 6 5
## 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1980 1981 1982 1983
## 5 5 5 6 6 6 3 6 5 1 3 6 3 6 6 5
## 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## 6 6 6 6 6 6 6 5 6 3 6 5 5 6 5 5
## 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 6 6 5 4 6 6 3 6 6 5 3 5 5 6 6 6
## 2016 2017 2018 2019 2020 2021 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
## 5 6 5 1 3 6 5 6 6 5 6 6 6 6 6 6
## 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048
## 6 5 6 3 6 5 6 6 3 5 5 6 5 4 6 6
## 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064
## 5 6 6 5 5 5 6 6 6 6 5 6 5 1 3 6
## 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081
## 5 6 6 5 6 6 6 6 6 6 6 5 6 3 6 5
## 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097
## 6 6 3 5 5 6 5 4 6 6 3 6 6 5 5 5
## 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2109 2110 2111 2112 2113 2114
## 5 6 6 5 3 6 5 1 3 6 5 6 6 5 6 6
## 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130
## 6 5 6 6 6 5 6 3 6 5 6 6 5 5 6 6
## 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146
## 5 4 6 6 3 6 6 5 5 5 5 6 6 5 3 6
## 2147 2148 2149 2150 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163
## 6 1 3 6 5 6 6 5 6 6 6 6 6 6 6 5
## 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179
## 6 3 6 5 6 6 5 5 6 6 5 4 6 6 3 6
## 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193
## 6 5 5 5 5 6 6 5 3 6 5 1 3 6
##
## Within cluster sum of squares by cluster:
## [1] 908.4652 1007.2810 1117.3301 3263.4504 1778.1204 1046.4948
## (between_SS / total_SS = 83.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"From the output, it reveals that: 984, 698, 30, 313, 21, 96 * 984 Areas were allocated to the 1st cluster * 698 Areas were allocated to the 2nd cluster * 30 Areas were allocated to the 3rd cluster * 313 Areas were allocated to the 4th cluster * 21 Areas were allocated to the 5th cluster * 96 Areas were allocated to the 6th cluster
Graphical Presentation of the cluster
fviz_cluster(kms_Convictions, data = Cluster_Convictions, ggtheme = theme_minimal(),
pallete = c('#2E9FDF', '00AFBB', '#E7B800', 'FC4E07'),
repel = T)
kms_Convictions$centers## No_Homicide_Conv No_Failed_Homicide No_Conv_Offence No_Failed_Conv_Offence
## 1 0.79967914 0.5517235 1.49834328 0.99929416
## 2 -0.14483564 -0.1991086 0.22862651 0.29813924
## 3 0.85730222 0.5453537 0.61297874 0.35371831
## 4 4.49471011 4.4964594 5.33009739 5.81518436
## 5 -0.09303666 -0.1315461 -0.03458162 -0.08658144
## 6 -0.35400826 -0.2466950 -0.50469557 -0.42014439
## No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv No_Failed_Burg_Conv
## 1 1.245670437 1.18677884 1.45180438 1.0838164
## 2 0.165577375 0.19707637 0.57234342 0.3558053
## 3 1.160168706 0.98186727 0.48361806 0.2676660
## 4 4.658781025 4.96425790 5.21359088 5.3824561
## 5 0.001871892 -0.07694771 -0.07341399 -0.1154952
## 6 -0.559988394 -0.49142854 -0.49743221 -0.3791103
## No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 1 1.5950068 1.26706643 1.87595815
## 2 0.1616889 0.06598136 0.83399814
## 3 0.2383398 0.09757230 0.33442494
## 4 5.2909725 5.27245339 4.75065664
## 5 -0.1470458 -0.12407717 -0.02180275
## 6 -0.3599596 -0.31017978 -0.55572185
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 1 1.01564195 0.797492833 0.7398410
## 2 0.57194616 0.003380617 0.0248412
## 3 0.10313629 0.198230342 0.1961055
## 4 5.55346630 5.947755658 5.7086589
## 5 -0.09853582 -0.089004304 -0.1078138
## 6 -0.39755637 -0.338859574 -0.3122418
## No_CrimeDamage_Conv No_Failed_CrimeDamage No_DrugOffences_Conv
## 1 1.59466249 0.96001587 0.72676440
## 2 0.75273858 0.74713483 0.28177556
## 3 0.45261532 0.30793642 0.14443602
## 4 4.92053374 5.20765771 6.00446947
## 5 -0.03217347 -0.06514484 -0.07992105
## 6 -0.54323576 -0.45934372 -0.37421542
## No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 1 0.6568254 1.49257287
## 2 0.1979923 0.62155196
## 3 0.1532198 0.39431377
## 4 5.9362739 5.13031646
## 5 -0.1025267 -0.06251868
## 6 -0.3390076 -0.49577136
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 1 0.9461473 1.10256322
## 2 0.4903803 0.95617280
## 3 0.2064530 -0.06105732
## 4 5.6779753 4.53518233
## 5 -0.1097790 -0.13285842
## 6 -0.3964074 -0.35828873
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 1 0.89647741 0.96972168
## 2 0.88303610 0.65048854
## 3 -0.06260819 0.37562371
## 4 4.37501364 5.15384886
## 5 -0.13694219 -0.03268105
## 6 -0.31995082 -0.47670431
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv
## 1 0.5926659 0.87134555
## 2 0.2522529 0.03998555
## 3 0.2324705 0.22355290
## 4 5.8406516 5.80218188
## 5 -0.0989777 -0.12501124
## 6 -0.3531150 -0.32273461This shows how the various Areas are arranged into a cluster as well as the mean of each of the principal offence categories by cluster
To include point classifications to the data
No_All_Convictions = cbind(No_All_Convictions, cluster = kms_Convictions$cluster)
head(No_All_Convictions, 6)## Area No_Homicide_Conv No_Failed_Homicide No_Conv_Offence
## 2 Avon and Somerset 1 0 167
## 3 Bedfordshire 0 0 69
## 4 Cambridgeshire 0 0 99
## 5 Cheshire 1 1 140
## 6 Cleveland 0 0 85
## 7 Cumbria 0 0 77
## No_Failed_Conv_Offence No_Sex_Offence_Conv No_Failed_Sex_Offence No_Burg_Conv
## 2 45 36 8 37
## 3 23 5 1 16
## 4 23 6 3 8
## 5 47 17 3 26
## 6 41 11 4 25
## 7 19 8 1 12
## No_Failed_Burg_Conv No_Rob_Conv No_Failed_Rob_Conv No_TheftAndHandling_Conv
## 2 2 9 3 266
## 3 1 4 0 98
## 4 0 6 1 107
## 5 3 1 0 206
## 6 10 5 2 254
## 7 1 1 0 108
## No_Failed_TheftAndHandling No_FraudAndForgery_Conv No_Failed_FraudAndForgery
## 2 21 11 0
## 3 9 8 2
## 4 10 7 0
## 5 4 16 2
## 6 32 6 2
## 7 6 5 0
## No_CrimeDamage_Conv No_Failed_CrimeDamage No_DrugOffences_Conv
## 2 54 6 135
## 3 20 6 45
## 4 21 1 40
## 5 35 9 75
## 6 32 8 63
## 7 37 2 42
## No_Failed_Drug_Offence No_PublicOrderOffences_Conv
## 2 2 68
## 3 2 29
## 4 2 45
## 5 10 86
## 6 7 74
## 7 2 40
## No_Failed_PublicOrderOffences No_Others_Ex_Motoring
## 2 11 66
## 3 6 11
## 4 9 6
## 5 7 50
## 6 27 28
## 7 2 64
## No_Failed_Others_Ex_Motoring No_Motoring_Offences_Conv
## 2 16 188
## 3 6 40
## 4 2 79
## 5 6 209
## 6 5 124
## 7 1 95
## No_Failed_Motoring_Offences No_Failed_AdminFinalised_Conv cluster
## 2 37 24 5
## 3 5 16 6
## 4 6 4 6
## 5 12 1 5
## 6 17 10 5
## 7 10 12 6This confirms that each of the Areas from the original data frame have been assigned into one of the six clusters.
To reveal the cluster in which the various Area belongs
Area_Cluster = data.frame(No_All_Convictions$Area, No_All_Convictions$cluster)
print(head(Area_Cluster))## No_All_Convictions.Area No_All_Convictions.cluster
## 1 Avon and Somerset 5
## 2 Bedfordshire 6
## 3 Cambridgeshire 6
## 4 Cheshire 5
## 5 Cleveland 5
## 6 Cumbria 6print(tail(Area_Cluster))## No_All_Convictions.Area No_All_Convictions.cluster
## 2137 Thames Valley 3
## 2138 Warwickshire 6
## 2139 West Mercia 5
## 2140 West Midlands 1
## 2141 West Yorkshire 3
## 2142 Wiltshire 6This confirms the cluster in which the various Area belongs
DATA CLASSIFICATION:
Decision Tree Analysis
Creating a new data frame by assigning the Percentage of Conviction data frame (Per_All_Convictions) for the decision tree analysis
DTA_Per_Convictions = Per_All_Convictions
str(DTA_Per_Convictions)## 'data.frame': 2142 obs. of 26 variables:
## $ Area : chr "Avon and Somerset" "Bedfordshire" "Cambridgeshire" "Cheshire" ...
## $ Per_Homicide_Conv : num 100 0 0 50 0 0 0 100 0 100 ...
## $ Per_Failed_Homicide : num 0 0 0 50 0 0 0 0 0 0 ...
## $ Per_Conv_Offence : num 78.8 75 81.1 74.9 67.5 80.2 72.6 75.8 82 74.3 ...
## $ Per_Failed_Conv_Offence : num 21.2 25 18.9 25.1 32.5 19.8 27.4 24.2 18 25.7 ...
## $ Per_Sex_Offence_Conv : num 81.8 83.3 66.7 85 73.3 88.9 57.1 73.3 100 73.3 ...
## $ Per_Failed_Sex_Offence : num 18.2 16.7 33.3 15 26.7 11.1 42.9 26.7 0 26.7 ...
## $ Per_Burg_Conv : num 94.9 94.1 100 89.7 71.4 92.3 91.2 94.1 94.7 100 ...
## $ Per_Failed_Burg_Conv : num 5.1 5.9 0 10.3 28.6 7.7 8.8 5.9 5.3 0 ...
## $ Per_Rob_Conv : num 75 100 85.7 100 71.4 100 72.7 100 100 0 ...
## $ Per_Failed_Rob_Conv : num 25 0 14.3 0 28.6 0 27.3 0 0 100 ...
## $ Per_TheftAndHandling_Conv : num 92.7 91.6 91.5 98.1 88.8 94.7 93.1 93.8 91.8 88.3 ...
## $ Per_Failed_TheftAndHandling : num 7.3 8.4 8.5 1.9 11.2 5.3 6.9 6.2 8.2 11.7 ...
## $ Per_FraudAndForgery_Conv : num 100 80 100 88.9 75 100 55 100 77.8 44.4 ...
## $ Per_Failed_FraudAndForgery : num 0 20 0 11.1 25 0 45 0 22.2 55.6 ...
## $ Per_CrimeDamage_Conv : num 90 76.9 95.5 79.5 80 94.9 85.1 90.3 85.7 86 ...
## $ Per_Failed_CrimeDamage : num 10 23.1 4.5 20.5 20 5.1 14.9 9.7 14.3 14 ...
## $ Per_DrugOffences_Conv : num 98.5 95.7 95.2 88.2 90 95.5 89.3 92.1 93.5 90.5 ...
## $ Per_Failed_Drug_Offence : num 1.5 4.3 4.8 11.8 10 4.5 10.7 7.9 6.5 9.5 ...
## $ Per_PublicOrderOffences_Conv : num 86.1 82.9 83.3 92.5 73.3 95.2 92.6 76.5 93.8 81.7 ...
## $ Per_Failed_PublicOrderOffences: num 13.9 17.1 16.7 7.5 26.7 4.8 7.4 23.5 6.3 18.3 ...
## $ Per_Others_Ex_Motoring : num 80.5 64.7 75 89.3 84.8 98.5 75.4 82.1 96.2 80 ...
## $ Per_Failed_Others_Ex_Motoring : num 19.5 35.3 25 10.7 15.2 1.5 24.6 17.9 3.8 20 ...
## $ Per_Motoring_Offences_Conv : num 83.6 88.9 92.9 94.6 87.9 90.5 95.2 91.7 91 95.7 ...
## $ Per_Failed_Motoring_Offences : num 16.4 11.1 7.1 5.4 12.1 9.5 4.8 8.3 9 4.3 ...
## $ Per_Failed_AdminFinalised_Conv: num 100 100 100 100 100 100 100 100 100 0 ...This confirms the assignment hence creating a new data frame for the Decision Tree analysis.
Setting values for the decision tree analysis and Data Split for analysis
For the purpose of the decision tree analysis, the selected data frame is splited in a proportion of 70/30 for the train and test data respectively.
set.seed(1234) #This is set to ensure same result
# In order to generate a list of random number from the total observations in the data frame
DTARand_Per_Convictions = DTA_Per_Convictions[order(runif(nrow(DTA_Per_Convictions))),]
# To split the selected data into 70% training and 30% test data
Train_Per_Convictions = DTARand_Per_Convictions[1: round(0.7 * nrow(DTA_Per_Convictions)),]
Test_Per_Convictions = DTARand_Per_Convictions[(round(0.7 * nrow(DTA_Per_Convictions)) + 1): nrow(DTA_Per_Convictions),]
print(dim(Train_Per_Convictions))## [1] 1499 26print(dim(Test_Per_Convictions))## [1] 643 26This confirms that the data frame is now splitted into Train and Test data. The Train data set (Train_Per_Convictions) consist of 1,499 rows (observations) and 26 columns (variables) while the test data set (Test_Per_Convictions) consist of 643 rows (observations) and 26 columns (variables)
Fit the Train Data
fit_Per_Convictions = rpart(
Per_Conv_Offence ~ Per_Sex_Offence_Conv +
Per_Burg_Conv +
Per_TheftAndHandling_Conv +
Per_CrimeDamage_Conv +
Per_PublicOrderOffences_Conv +
Per_Others_Ex_Motoring +
Per_Motoring_Offences_Conv,
data=Train_Per_Convictions,
control=rpart.control(minsplit = 0.1*length(Train_Per_Convictions)),
parms=list(split='information')
)
fit_Per_Convictions## n= 1499
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1499 42215.98000 79.07518
## 2) Per_PublicOrderOffences_Conv< 87.85 841 23348.05000 77.54067
## 4) Per_TheftAndHandling_Conv< 91.65 358 10696.99000 75.63101
## 8) Per_TheftAndHandling_Conv< 86.85 60 1578.26700 71.75667
## 16) Per_CrimeDamage_Conv< 85.7 46 465.75410 70.04565 *
## 17) Per_CrimeDamage_Conv>=85.7 14 535.36360 77.37857 *
## 9) Per_TheftAndHandling_Conv>=86.85 298 8036.75300 76.41107
## 18) Per_Sex_Offence_Conv< 85.15 230 5569.77500 75.46609
## 36) Per_Motoring_Offences_Conv< 85.45 97 2632.35400 73.51649
## 72) Per_Others_Ex_Motoring< 90.25 70 1713.52900 72.17571 *
## 73) Per_Others_Ex_Motoring>=90.25 27 466.73850 76.99259 *
## 37) Per_Motoring_Offences_Conv>=85.45 133 2299.84100 76.88797 *
## 19) Per_Sex_Offence_Conv>=85.15 68 1566.88600 79.60735 *
## 5) Per_TheftAndHandling_Conv>=91.65 483 10377.83000 78.95611
## 10) Per_CrimeDamage_Conv< 92.25 402 8157.72600 78.39677
## 20) Per_Others_Ex_Motoring< 96.85 338 6507.76600 77.92456 *
## 21) Per_Others_Ex_Motoring>=96.85 64 1176.55400 80.89062 *
## 11) Per_CrimeDamage_Conv>=92.25 81 1470.13700 81.73210 *
## 3) Per_PublicOrderOffences_Conv>=87.85 658 14356.48000 81.03647
## 6) Per_Motoring_Offences_Conv< 89.55 391 7566.41900 80.08977
## 12) Per_PublicOrderOffences_Conv< 94.5 341 6245.31100 79.65191 *
## 13) Per_PublicOrderOffences_Conv>=94.5 50 809.85120 83.07600 *
## 7) Per_Motoring_Offences_Conv>=89.55 267 5926.45100 82.42285
## 14) Per_TheftAndHandling_Conv< 87.55 7 82.91429 73.57143 *
## 15) Per_TheftAndHandling_Conv>=87.55 260 5280.33800 82.66115 *This shows the data values within the train data.
To Plot the decision tree for successful conviction cases
rpart.plot(fit_Per_Convictions, type=4,fallen.leaves = TRUE, extra=0, digits = 2, clip.right.labs = TRUE, varlen = -5, faclen = 0)
Predict_Per_Convictions = predict(fit_Per_Convictions, Test_Per_Convictions)Prediction Outcome Comparison
summary(Predict_Per_Convictions) # Prediction result## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 70.05 77.92 79.65 78.79 80.89 83.08summary(Test_Per_Convictions$Per_Conv_Offence) #Test Result## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 55.10 75.60 79.20 78.98 82.50 94.20cor(Predict_Per_Convictions, Test_Per_Convictions$Per_Conv_Offence) #Cor## [1] 0.4448331MAE = function(actual, predictd) {
mean(abs(actual - predictd))
}
MAE(Test_Per_Convictions$Per_Conv_Offence, Predict_Per_Convictions) #MAE## [1] 3.913964mean(Train_Per_Convictions$Per_Conv_Offence) # Mean## [1] 79.07518mae(round(mean(Train_Per_Convictions$Per_Conv_Offence), 2), Test_Per_Convictions$Per_Conv_Offence) #MAE & Test## [1] 4.23042The prediction result for the train and test shows a mean of 78% and a median of 79% accuracy.
Applying the M5P algorithm.
In the M5P algorithm, all selected attributes are converted into binary variables before the tree construction. This algorithm also treat for any missing values and the selected attributes.
Rweka_Predict_Per_Convictions = M5P(
Per_Conv_Offence ~ Per_Sex_Offence_Conv +
Per_Burg_Conv +
Per_TheftAndHandling_Conv +
Per_CrimeDamage_Conv +
Per_DrugOffences_Conv +
Per_PublicOrderOffences_Conv +
Per_Others_Ex_Motoring +
Per_Motoring_Offences_Conv,
data=Train_Per_Convictions
)
Rweka_Predict_Per_Convictions## M5 pruned model tree:
## (using smoothed linear models)
##
## Per_PublicOrderOffences_Conv <= 85.45 :
## | Per_TheftAndHandling_Conv <= 92.85 :
## | | Per_CrimeDamage_Conv <= 87.35 :
## | | | Per_Sex_Offence_Conv <= 82.85 :
## | | | | Per_TheftAndHandling_Conv <= 87.15 :
## | | | | | Per_Others_Ex_Motoring <= 85.15 : LM1 (29/42.904%)
## | | | | | Per_Others_Ex_Motoring > 85.15 : LM2 (16/68.917%)
## | | | | Per_TheftAndHandling_Conv > 87.15 :
## | | | | | Per_DrugOffences_Conv <= 95.75 :
## | | | | | | Per_Motoring_Offences_Conv <= 80.15 :
## | | | | | | | Per_Motoring_Offences_Conv <= 77.25 :
## | | | | | | | | Per_Sex_Offence_Conv <= 64.85 : LM3 (5/38.386%)
## | | | | | | | | Per_Sex_Offence_Conv > 64.85 : LM4 (7/30.926%)
## | | | | | | | Per_Motoring_Offences_Conv > 77.25 :
## | | | | | | | | Per_Others_Ex_Motoring <= 87.55 : LM5 (17/25.271%)
## | | | | | | | | Per_Others_Ex_Motoring > 87.55 : LM6 (5/14.371%)
## | | | | | | Per_Motoring_Offences_Conv > 80.15 :
## | | | | | | | Per_Others_Ex_Motoring <= 87.15 : LM7 (72/84.166%)
## | | | | | | | Per_Others_Ex_Motoring > 87.15 : LM8 (24/80.354%)
## | | | | | Per_DrugOffences_Conv > 95.75 : LM9 (42/59.551%)
## | | | Per_Sex_Offence_Conv > 82.85 :
## | | | | Per_Others_Ex_Motoring <= 82.9 : LM10 (25/87.307%)
## | | | | Per_Others_Ex_Motoring > 82.9 : LM11 (31/83.035%)
## | | Per_CrimeDamage_Conv > 87.35 :
## | | | Per_Burg_Conv <= 91.1 :
## | | | | Per_PublicOrderOffences_Conv <= 81.9 :
## | | | | | Per_TheftAndHandling_Conv <= 91 :
## | | | | | | Per_Others_Ex_Motoring <= 88.55 :
## | | | | | | | Per_Others_Ex_Motoring <= 66.2 : LM12 (3/13.324%)
## | | | | | | | Per_Others_Ex_Motoring > 66.2 :
## | | | | | | | | Per_Burg_Conv <= 85.7 : LM13 (4/32.38%)
## | | | | | | | | Per_Burg_Conv > 85.7 : LM14 (4/7.359%)
## | | | | | | Per_Others_Ex_Motoring > 88.55 :
## | | | | | | | Per_DrugOffences_Conv <= 95.25 : LM15 (2/17.901%)
## | | | | | | | Per_DrugOffences_Conv > 95.25 : LM16 (3/20.431%)
## | | | | | Per_TheftAndHandling_Conv > 91 : LM17 (16/34.089%)
## | | | | Per_PublicOrderOffences_Conv > 81.9 :
## | | | | | Per_Others_Ex_Motoring <= 81.55 :
## | | | | | | Per_Others_Ex_Motoring <= 72.9 : LM18 (4/55.834%)
## | | | | | | Per_Others_Ex_Motoring > 72.9 : LM19 (12/29.31%)
## | | | | | Per_Others_Ex_Motoring > 81.55 : LM20 (25/53.979%)
## | | | Per_Burg_Conv > 91.1 : LM21 (39/65.181%)
## | Per_TheftAndHandling_Conv > 92.85 :
## | | Per_Burg_Conv <= 92.15 :
## | | | Per_Motoring_Offences_Conv <= 91.55 : LM22 (120/65.522%)
## | | | Per_Motoring_Offences_Conv > 91.55 : LM23 (32/81.071%)
## | | Per_Burg_Conv > 92.15 :
## | | | Per_DrugOffences_Conv <= 97 : LM24 (50/62.924%)
## | | | Per_DrugOffences_Conv > 97 : LM25 (20/56.682%)
## Per_PublicOrderOffences_Conv > 85.45 :
## | Per_TheftAndHandling_Conv <= 94.45 :
## | | Per_PublicOrderOffences_Conv <= 88.8 : LM26 (225/74.191%)
## | | Per_PublicOrderOffences_Conv > 88.8 :
## | | | Per_CrimeDamage_Conv <= 89.45 :
## | | | | Per_Motoring_Offences_Conv <= 90.7 :
## | | | | | Per_PublicOrderOffences_Conv <= 93.25 :
## | | | | | | Per_Others_Ex_Motoring <= 93 : LM27 (91/83.042%)
## | | | | | | Per_Others_Ex_Motoring > 93 :
## | | | | | | | Per_Sex_Offence_Conv <= 71.55 : LM28 (5/21.669%)
## | | | | | | | Per_Sex_Offence_Conv > 71.55 : LM29 (19/51.143%)
## | | | | | Per_PublicOrderOffences_Conv > 93.25 :
## | | | | | | Per_PublicOrderOffences_Conv <= 94.5 : LM30 (15/67.063%)
## | | | | | | Per_PublicOrderOffences_Conv > 94.5 : LM31 (17/63.115%)
## | | | | Per_Motoring_Offences_Conv > 90.7 :
## | | | | | Per_CrimeDamage_Conv <= 81.9 : LM32 (21/77.226%)
## | | | | | Per_CrimeDamage_Conv > 81.9 : LM33 (39/75.115%)
## | | | Per_CrimeDamage_Conv > 89.45 :
## | | | | Per_Motoring_Offences_Conv <= 92.25 :
## | | | | | Per_DrugOffences_Conv <= 94.9 : LM34 (43/73.902%)
## | | | | | Per_DrugOffences_Conv > 94.9 :
## | | | | | | Per_DrugOffences_Conv <= 97.25 : LM35 (33/36.381%)
## | | | | | | Per_DrugOffences_Conv > 97.25 : LM36 (20/68.857%)
## | | | | Per_Motoring_Offences_Conv > 92.25 : LM37 (25/70.652%)
## | Per_TheftAndHandling_Conv > 94.45 :
## | | Per_Motoring_Offences_Conv <= 87.75 :
## | | | Per_DrugOffences_Conv <= 94.35 : LM38 (46/43.434%)
## | | | Per_DrugOffences_Conv > 94.35 :
## | | | | Per_TheftAndHandling_Conv <= 95.75 :
## | | | | | Per_PublicOrderOffences_Conv <= 89 : LM39 (12/41.648%)
## | | | | | Per_PublicOrderOffences_Conv > 89 :
## | | | | | | Per_DrugOffences_Conv <= 98.35 : LM40 (16/22.008%)
## | | | | | | Per_DrugOffences_Conv > 98.35 : LM41 (5/11.918%)
## | | | | Per_TheftAndHandling_Conv > 95.75 : LM42 (36/108.942%)
## | | Per_Motoring_Offences_Conv > 87.75 :
## | | | Per_CrimeDamage_Conv <= 89.6 :
## | | | | Per_DrugOffences_Conv <= 93.65 :
## | | | | | Per_TheftAndHandling_Conv <= 95.6 : LM43 (10/95.384%)
## | | | | | Per_TheftAndHandling_Conv > 95.6 :
## | | | | | | Per_PublicOrderOffences_Conv <= 87.5 : LM44 (5/17.491%)
## | | | | | | Per_PublicOrderOffences_Conv > 87.5 :
## | | | | | | | Per_DrugOffences_Conv <= 89.15 : LM45 (3/7.995%)
## | | | | | | | Per_DrugOffences_Conv > 89.15 : LM46 (7/12.084%)
## | | | | Per_DrugOffences_Conv > 93.65 :
## | | | | | Per_TheftAndHandling_Conv <= 96.75 :
## | | | | | | Per_PublicOrderOffences_Conv <= 93.05 : LM47 (46/82.185%)
## | | | | | | Per_PublicOrderOffences_Conv > 93.05 :
## | | | | | | | Per_DrugOffences_Conv <= 98.5 :
## | | | | | | | | Per_CrimeDamage_Conv <= 79 : LM48 (3/9.771%)
## | | | | | | | | Per_CrimeDamage_Conv > 79 :
## | | | | | | | | | Per_Burg_Conv <= 81.95 : LM49 (2/3.769%)
## | | | | | | | | | Per_Burg_Conv > 81.95 : LM50 (6/6.474%)
## | | | | | | | Per_DrugOffences_Conv > 98.5 : LM51 (4/20.787%)
## | | | | | Per_TheftAndHandling_Conv > 96.75 : LM52 (27/61.477%)
## | | | Per_CrimeDamage_Conv > 89.6 :
## | | | | Per_Sex_Offence_Conv <= 71.65 : LM53 (26/63.88%)
## | | | | Per_Sex_Offence_Conv > 71.65 :
## | | | | | Per_Sex_Offence_Conv <= 98.1 : LM54 (62/54.015%)
## | | | | | Per_Sex_Offence_Conv > 98.1 : LM55 (23/73.365%)
##
## LM num: 1
## Per_Conv_Offence =
## 0.012 * Per_Burg_Conv
## + 0.2583 * Per_TheftAndHandling_Conv
## + 0.1857 * Per_CrimeDamage_Conv
## - 0.0004 * Per_DrugOffences_Conv
## + 0.043 * Per_PublicOrderOffences_Conv
## + 0.0427 * Per_Others_Ex_Motoring
## + 0.078 * Per_Motoring_Offences_Conv
## + 13.0694
##
## LM num: 2
## Per_Conv_Offence =
## 0.1327 * Per_Sex_Offence_Conv
## + 0.012 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1857 * Per_CrimeDamage_Conv
## - 0.0004 * Per_DrugOffences_Conv
## + 0.043 * Per_PublicOrderOffences_Conv
## + 0.0591 * Per_Others_Ex_Motoring
## + 0.0969 * Per_Motoring_Offences_Conv
## + 22.4726
##
## LM num: 3
## Per_Conv_Offence =
## 0.142 * Per_Sex_Offence_Conv
## - 0.0112 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.0388 * Per_PublicOrderOffences_Conv
## + 0.0388 * Per_Others_Ex_Motoring
## - 0.1131 * Per_Motoring_Offences_Conv
## + 46.4167
##
## LM num: 4
## Per_Conv_Offence =
## 0.1346 * Per_Sex_Offence_Conv
## + 0.012 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.0388 * Per_PublicOrderOffences_Conv
## + 0.0388 * Per_Others_Ex_Motoring
## - 0.0676 * Per_Motoring_Offences_Conv
## + 42.1046
##
## LM num: 5
## Per_Conv_Offence =
## 0.0435 * Per_Sex_Offence_Conv
## - 0.006 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.0388 * Per_PublicOrderOffences_Conv
## + 0.0644 * Per_Others_Ex_Motoring
## - 0.0176 * Per_Motoring_Offences_Conv
## + 42.6654
##
## LM num: 6
## Per_Conv_Offence =
## 0.0435 * Per_Sex_Offence_Conv
## - 0.0263 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.0388 * Per_PublicOrderOffences_Conv
## + 0.0854 * Per_Others_Ex_Motoring
## - 0.0176 * Per_Motoring_Offences_Conv
## + 42.9827
##
## LM num: 7
## Per_Conv_Offence =
## 0.012 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.0388 * Per_PublicOrderOffences_Conv
## + 0.0035 * Per_Others_Ex_Motoring
## + 0.0517 * Per_Motoring_Offences_Conv
## + 48.1914
##
## LM num: 8
## Per_Conv_Offence =
## -0.1459 * Per_Sex_Offence_Conv
## + 0.012 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.1119 * Per_CrimeDamage_Conv
## - 0.0091 * Per_DrugOffences_Conv
## + 0.3377 * Per_PublicOrderOffences_Conv
## + 0.0035 * Per_Others_Ex_Motoring
## + 0.0685 * Per_Motoring_Offences_Conv
## + 34.1567
##
## LM num: 9
## Per_Conv_Offence =
## 0.012 * Per_Burg_Conv
## + 0.0175 * Per_TheftAndHandling_Conv
## + 0.0216 * Per_CrimeDamage_Conv
## + 0.3848 * Per_DrugOffences_Conv
## + 0.0584 * Per_PublicOrderOffences_Conv
## + 0.0035 * Per_Others_Ex_Motoring
## + 0.0397 * Per_Motoring_Offences_Conv
## + 17.5309
##
## LM num: 10
## Per_Conv_Offence =
## 0.1484 * Per_Burg_Conv
## + 0.078 * Per_TheftAndHandling_Conv
## + 0.1458 * Per_CrimeDamage_Conv
## - 0.0184 * Per_DrugOffences_Conv
## + 0.0344 * Per_PublicOrderOffences_Conv
## + 0.0035 * Per_Others_Ex_Motoring
## + 0.0414 * Per_Motoring_Offences_Conv
## + 35.0135
##
## LM num: 11
## Per_Conv_Offence =
## 0.1072 * Per_Sex_Offence_Conv
## + 0.1638 * Per_Burg_Conv
## + 0.0715 * Per_TheftAndHandling_Conv
## + 0.1458 * Per_CrimeDamage_Conv
## - 0.0184 * Per_DrugOffences_Conv
## + 0.0344 * Per_PublicOrderOffences_Conv
## + 0.0035 * Per_Others_Ex_Motoring
## + 0.0386 * Per_Motoring_Offences_Conv
## + 26.7555
##
## LM num: 12
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## + 0.0003 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.1231 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0321 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## - 1.7917
##
## LM num: 13
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## - 0.0001 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.1231 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0321 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## - 1.9709
##
## LM num: 14
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## + 0.0091 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.1231 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0321 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## - 2.6818
##
## LM num: 15
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## + 0.0409 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.0839 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0173 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## - 1.0608
##
## LM num: 16
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## + 0.0409 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.0861 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0173 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## - 1.3281
##
## LM num: 17
## Per_Conv_Offence =
## 0.041 * Per_Sex_Offence_Conv
## + 0.0946 * Per_Burg_Conv
## + 0.383 * Per_TheftAndHandling_Conv
## + 0.2133 * Per_CrimeDamage_Conv
## + 0.1231 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.1028 * Per_Others_Ex_Motoring
## + 0.0531 * Per_Motoring_Offences_Conv
## - 18.6034
##
## LM num: 18
## Per_Conv_Offence =
## 0.0228 * Per_Sex_Offence_Conv
## + 0.038 * Per_Burg_Conv
## - 0.0448 * Per_TheftAndHandling_Conv
## + 0.0864 * Per_CrimeDamage_Conv
## + 0.2207 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## - 0.1943 * Per_Others_Ex_Motoring
## + 0.1233 * Per_Motoring_Offences_Conv
## + 49.7279
##
## LM num: 19
## Per_Conv_Offence =
## 0.038 * Per_Burg_Conv
## + 0.0002 * Per_TheftAndHandling_Conv
## + 0.1198 * Per_CrimeDamage_Conv
## + 0.2516 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## - 0.2089 * Per_Others_Ex_Motoring
## + 0.1029 * Per_Motoring_Offences_Conv
## + 44.5106
##
## LM num: 20
## Per_Conv_Offence =
## 0.0985 * Per_Burg_Conv
## + 0.094 * Per_TheftAndHandling_Conv
## + 0.3897 * Per_CrimeDamage_Conv
## + 0.1963 * Per_DrugOffences_Conv
## - 0.2939 * Per_PublicOrderOffences_Conv
## - 0.1657 * Per_Others_Ex_Motoring
## + 0.0441 * Per_Motoring_Offences_Conv
## + 39.7819
##
## LM num: 21
## Per_Conv_Offence =
## 0.0294 * Per_Burg_Conv
## + 0.0667 * Per_TheftAndHandling_Conv
## + 0.1588 * Per_CrimeDamage_Conv
## + 0.0822 * Per_DrugOffences_Conv
## + 0.0179 * Per_PublicOrderOffences_Conv
## + 0.0634 * Per_Others_Ex_Motoring
## + 0.0085 * Per_Motoring_Offences_Conv
## + 40.6104
##
## LM num: 22
## Per_Conv_Offence =
## -0.0397 * Per_Sex_Offence_Conv
## + 0.0628 * Per_Burg_Conv
## + 0.3869 * Per_TheftAndHandling_Conv
## + 0.1485 * Per_CrimeDamage_Conv
## + 0.0055 * Per_DrugOffences_Conv
## + 0.0081 * Per_PublicOrderOffences_Conv
## - 0.0164 * Per_Others_Ex_Motoring
## + 0.0103 * Per_Motoring_Offences_Conv
## + 20.8022
##
## LM num: 23
## Per_Conv_Offence =
## -0.0088 * Per_Burg_Conv
## + 0.5059 * Per_TheftAndHandling_Conv
## + 0.2593 * Per_CrimeDamage_Conv
## + 0.0055 * Per_DrugOffences_Conv
## + 0.0081 * Per_PublicOrderOffences_Conv
## + 0.0214 * Per_Others_Ex_Motoring
## + 0.0172 * Per_Motoring_Offences_Conv
## - 2.7224
##
## LM num: 24
## Per_Conv_Offence =
## 0.0398 * Per_Sex_Offence_Conv
## - 0.0011 * Per_Burg_Conv
## + 0.0284 * Per_TheftAndHandling_Conv
## + 0.2188 * Per_CrimeDamage_Conv
## - 0.2065 * Per_DrugOffences_Conv
## + 0.0422 * Per_PublicOrderOffences_Conv
## - 0.0157 * Per_Others_Ex_Motoring
## + 0.0096 * Per_Motoring_Offences_Conv
## + 62.8438
##
## LM num: 25
## Per_Conv_Offence =
## -0.0011 * Per_Burg_Conv
## + 0.2957 * Per_TheftAndHandling_Conv
## + 0.2872 * Per_CrimeDamage_Conv
## + 0.0055 * Per_DrugOffences_Conv
## - 0.1107 * Per_PublicOrderOffences_Conv
## - 0.0157 * Per_Others_Ex_Motoring
## + 0.0096 * Per_Motoring_Offences_Conv
## + 25.7282
##
## LM num: 26
## Per_Conv_Offence =
## 0.0015 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0121 * Per_CrimeDamage_Conv
## + 0.074 * Per_DrugOffences_Conv
## + 0.1205 * Per_PublicOrderOffences_Conv
## + 0.024 * Per_Others_Ex_Motoring
## + 0.08 * Per_Motoring_Offences_Conv
## + 49.5982
##
## LM num: 27
## Per_Conv_Offence =
## 0.0044 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.106 * Per_CrimeDamage_Conv
## + 0.0071 * Per_DrugOffences_Conv
## - 0.0137 * Per_PublicOrderOffences_Conv
## + 0.0766 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## + 58.9478
##
## LM num: 28
## Per_Conv_Offence =
## -0.0259 * Per_Sex_Offence_Conv
## + 0.0306 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.1479 * Per_CrimeDamage_Conv
## - 0.0149 * Per_DrugOffences_Conv
## + 0.2589 * Per_PublicOrderOffences_Conv
## + 0.0766 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## + 32.7487
##
## LM num: 29
## Per_Conv_Offence =
## -0.0152 * Per_Sex_Offence_Conv
## + 0.0198 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.1479 * Per_CrimeDamage_Conv
## - 0.0149 * Per_DrugOffences_Conv
## + 0.1467 * Per_PublicOrderOffences_Conv
## + 0.0766 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## + 42.4444
##
## LM num: 30
## Per_Conv_Offence =
## 0.0898 * Per_Sex_Offence_Conv
## + 0.0044 * Per_Burg_Conv
## + 0.8072 * Per_TheftAndHandling_Conv
## + 0.1277 * Per_CrimeDamage_Conv
## + 0.0199 * Per_DrugOffences_Conv
## + 0.432 * Per_PublicOrderOffences_Conv
## + 0.1439 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## - 72.1344
##
## LM num: 31
## Per_Conv_Offence =
## 0.0339 * Per_Sex_Offence_Conv
## + 0.0044 * Per_Burg_Conv
## + 0.2748 * Per_TheftAndHandling_Conv
## + 0.1277 * Per_CrimeDamage_Conv
## + 0.0199 * Per_DrugOffences_Conv
## + 0.4025 * Per_PublicOrderOffences_Conv
## + 0.1439 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## - 15.2154
##
## LM num: 32
## Per_Conv_Offence =
## 0.0415 * Per_Sex_Offence_Conv
## + 0.0078 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0912 * Per_CrimeDamage_Conv
## + 0.1237 * Per_DrugOffences_Conv
## + 0.0012 * Per_PublicOrderOffences_Conv
## + 0.0604 * Per_Others_Ex_Motoring
## + 0.0205 * Per_Motoring_Offences_Conv
## + 62.9273
##
## LM num: 33
## Per_Conv_Offence =
## 0.0078 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0912 * Per_CrimeDamage_Conv
## + 0.0891 * Per_DrugOffences_Conv
## + 0.0012 * Per_PublicOrderOffences_Conv
## + 0.0604 * Per_Others_Ex_Motoring
## + 0.0144 * Per_Motoring_Offences_Conv
## + 65.8063
##
## LM num: 34
## Per_Conv_Offence =
## -0.0413 * Per_Sex_Offence_Conv
## + 0.0015 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.3919 * Per_CrimeDamage_Conv
## + 0.3496 * Per_DrugOffences_Conv
## + 0.0012 * Per_PublicOrderOffences_Conv
## + 0.0196 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## + 12.0039
##
## LM num: 35
## Per_Conv_Offence =
## 0.0015 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0324 * Per_CrimeDamage_Conv
## + 0.1088 * Per_DrugOffences_Conv
## + 0.1562 * Per_PublicOrderOffences_Conv
## - 0.0212 * Per_Others_Ex_Motoring
## + 0.1166 * Per_Motoring_Offences_Conv
## + 37.5922
##
## LM num: 36
## Per_Conv_Offence =
## 0.0015 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0324 * Per_CrimeDamage_Conv
## + 0.1088 * Per_DrugOffences_Conv
## + 0.2138 * Per_PublicOrderOffences_Conv
## + 0.0196 * Per_Others_Ex_Motoring
## - 0.0171 * Per_Motoring_Offences_Conv
## + 39.6901
##
## LM num: 37
## Per_Conv_Offence =
## 0.0015 * Per_Burg_Conv
## + 0.0018 * Per_TheftAndHandling_Conv
## + 0.0324 * Per_CrimeDamage_Conv
## + 0.1206 * Per_DrugOffences_Conv
## + 0.549 * Per_PublicOrderOffences_Conv
## + 0.0196 * Per_Others_Ex_Motoring
## + 0.0021 * Per_Motoring_Offences_Conv
## + 13.4894
##
## LM num: 38
## Per_Conv_Offence =
## 0.0068 * Per_Burg_Conv
## + 0.0063 * Per_TheftAndHandling_Conv
## + 0.0159 * Per_CrimeDamage_Conv
## + 0.0174 * Per_DrugOffences_Conv
## + 0.021 * Per_PublicOrderOffences_Conv
## + 0.0433 * Per_Others_Ex_Motoring
## - 0.0093 * Per_Motoring_Offences_Conv
## + 64.1654
##
## LM num: 39
## Per_Conv_Offence =
## -0.0354 * Per_Burg_Conv
## - 0.0215 * Per_TheftAndHandling_Conv
## - 0.153 * Per_CrimeDamage_Conv
## + 0.0174 * Per_DrugOffences_Conv
## - 0.0966 * Per_PublicOrderOffences_Conv
## + 0.1595 * Per_Others_Ex_Motoring
## - 0.0067 * Per_Motoring_Offences_Conv
## + 81.1607
##
## LM num: 40
## Per_Conv_Offence =
## 0.0068 * Per_Burg_Conv
## + 1.0329 * Per_TheftAndHandling_Conv
## - 0.153 * Per_CrimeDamage_Conv
## + 0.0174 * Per_DrugOffences_Conv
## - 0.0672 * Per_PublicOrderOffences_Conv
## + 0.1554 * Per_Others_Ex_Motoring
## + 0.219 * Per_Motoring_Offences_Conv
## - 41.3331
##
## LM num: 41
## Per_Conv_Offence =
## 0.0068 * Per_Burg_Conv
## + 0.6367 * Per_TheftAndHandling_Conv
## - 0.153 * Per_CrimeDamage_Conv
## + 0.0174 * Per_DrugOffences_Conv
## - 0.0672 * Per_PublicOrderOffences_Conv
## + 0.1454 * Per_Others_Ex_Motoring
## + 0.1309 * Per_Motoring_Offences_Conv
## + 4.4146
##
## LM num: 42
## Per_Conv_Offence =
## 0.0068 * Per_Burg_Conv
## + 0.0063 * Per_TheftAndHandling_Conv
## - 0.143 * Per_CrimeDamage_Conv
## + 0.0174 * Per_DrugOffences_Conv
## + 0.021 * Per_PublicOrderOffences_Conv
## + 0.0433 * Per_Others_Ex_Motoring
## - 0.0067 * Per_Motoring_Offences_Conv
## + 80.2182
##
## LM num: 43
## Per_Conv_Offence =
## 0.0104 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## - 0.5936 * Per_CrimeDamage_Conv
## + 0.0297 * Per_DrugOffences_Conv
## + 0.1583 * Per_PublicOrderOffences_Conv
## + 0.2953 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 89.8764
##
## LM num: 44
## Per_Conv_Offence =
## 0.0104 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## - 0.4631 * Per_CrimeDamage_Conv
## + 0.0889 * Per_DrugOffences_Conv
## + 0.2203 * Per_PublicOrderOffences_Conv
## + 0.2732 * Per_Others_Ex_Motoring
## - 0.0671 * Per_Motoring_Offences_Conv
## + 74.1137
##
## LM num: 45
## Per_Conv_Offence =
## 0.0104 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## - 0.4689 * Per_CrimeDamage_Conv
## + 0.1192 * Per_DrugOffences_Conv
## + 0.2079 * Per_PublicOrderOffences_Conv
## + 0.2732 * Per_Others_Ex_Motoring
## - 0.0535 * Per_Motoring_Offences_Conv
## + 71.9276
##
## LM num: 46
## Per_Conv_Offence =
## 0.0104 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## - 0.4717 * Per_CrimeDamage_Conv
## + 0.1115 * Per_DrugOffences_Conv
## + 0.2079 * Per_PublicOrderOffences_Conv
## + 0.2732 * Per_Others_Ex_Motoring
## - 0.0535 * Per_Motoring_Offences_Conv
## + 72.9085
##
## LM num: 47
## Per_Conv_Offence =
## 0.0191 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## + 0.0883 * Per_CrimeDamage_Conv
## + 0.0297 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.2023 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 39.9463
##
## LM num: 48
## Per_Conv_Offence =
## 0.0524 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## + 0.0883 * Per_CrimeDamage_Conv
## - 0.106 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.2635 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 46.1332
##
## LM num: 49
## Per_Conv_Offence =
## 0.0577 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## + 0.0883 * Per_CrimeDamage_Conv
## - 0.106 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.2635 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 45.75
##
## LM num: 50
## Per_Conv_Offence =
## 0.0567 * Per_Burg_Conv
## - 0.0095 * Per_TheftAndHandling_Conv
## + 0.0883 * Per_CrimeDamage_Conv
## - 0.106 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.2635 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 47.1909
##
## LM num: 51
## Per_Conv_Offence =
## 0.0646 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## + 0.0883 * Per_CrimeDamage_Conv
## - 0.156 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.2635 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 49.5619
##
## LM num: 52
## Per_Conv_Offence =
## 0.0591 * Per_Sex_Offence_Conv
## + 0.0262 * Per_Burg_Conv
## + 0.0044 * Per_TheftAndHandling_Conv
## + 0.147 * Per_CrimeDamage_Conv
## + 0.0297 * Per_DrugOffences_Conv
## + 0.0852 * Per_PublicOrderOffences_Conv
## + 0.1799 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 33.1422
##
## LM num: 53
## Per_Conv_Offence =
## 0.0496 * Per_Sex_Offence_Conv
## + 0.1717 * Per_Burg_Conv
## + 0.0356 * Per_TheftAndHandling_Conv
## + 0.4304 * Per_CrimeDamage_Conv
## + 0.0747 * Per_DrugOffences_Conv
## + 0.0392 * Per_PublicOrderOffences_Conv
## + 0.0569 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 3.4288
##
## LM num: 54
## Per_Conv_Offence =
## 0.0319 * Per_Burg_Conv
## + 0.0267 * Per_TheftAndHandling_Conv
## + 0.0839 * Per_CrimeDamage_Conv
## + 0.0483 * Per_DrugOffences_Conv
## + 0.0392 * Per_PublicOrderOffences_Conv
## + 0.0762 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 53.8806
##
## LM num: 55
## Per_Conv_Offence =
## 0.0999 * Per_Burg_Conv
## + 0.0365 * Per_TheftAndHandling_Conv
## + 0.1537 * Per_CrimeDamage_Conv
## + 0.0483 * Per_DrugOffences_Conv
## + 0.0392 * Per_PublicOrderOffences_Conv
## + 0.0959 * Per_Others_Ex_Motoring
## + 0.0005 * Per_Motoring_Offences_Conv
## + 39.5826
##
## Number of Rules : 55Rweka_Pre_Per_Convictions = predict(Rweka_Predict_Per_Convictions, Test_Per_Convictions)
summary(Rweka_Pre_Per_Convictions)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 58.55 71.94 74.89 75.04 78.03 98.95summary(Predict_Per_Convictions)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 70.05 77.92 79.65 78.79 80.89 83.08The M5P algorithm reveals a mean value of 75% and 78.79% for the train and test respectively
Linear Regression
Creating a new data frame by assigning the Percentage of Conviction data frame (Per_All_Convictions) for the Linear Regression.
LN_Per_Convictions = Per_All_Convictions
str(LN_Per_Convictions)## 'data.frame': 2142 obs. of 26 variables:
## $ Area : chr "Avon and Somerset" "Bedfordshire" "Cambridgeshire" "Cheshire" ...
## $ Per_Homicide_Conv : num 100 0 0 50 0 0 0 100 0 100 ...
## $ Per_Failed_Homicide : num 0 0 0 50 0 0 0 0 0 0 ...
## $ Per_Conv_Offence : num 78.8 75 81.1 74.9 67.5 80.2 72.6 75.8 82 74.3 ...
## $ Per_Failed_Conv_Offence : num 21.2 25 18.9 25.1 32.5 19.8 27.4 24.2 18 25.7 ...
## $ Per_Sex_Offence_Conv : num 81.8 83.3 66.7 85 73.3 88.9 57.1 73.3 100 73.3 ...
## $ Per_Failed_Sex_Offence : num 18.2 16.7 33.3 15 26.7 11.1 42.9 26.7 0 26.7 ...
## $ Per_Burg_Conv : num 94.9 94.1 100 89.7 71.4 92.3 91.2 94.1 94.7 100 ...
## $ Per_Failed_Burg_Conv : num 5.1 5.9 0 10.3 28.6 7.7 8.8 5.9 5.3 0 ...
## $ Per_Rob_Conv : num 75 100 85.7 100 71.4 100 72.7 100 100 0 ...
## $ Per_Failed_Rob_Conv : num 25 0 14.3 0 28.6 0 27.3 0 0 100 ...
## $ Per_TheftAndHandling_Conv : num 92.7 91.6 91.5 98.1 88.8 94.7 93.1 93.8 91.8 88.3 ...
## $ Per_Failed_TheftAndHandling : num 7.3 8.4 8.5 1.9 11.2 5.3 6.9 6.2 8.2 11.7 ...
## $ Per_FraudAndForgery_Conv : num 100 80 100 88.9 75 100 55 100 77.8 44.4 ...
## $ Per_Failed_FraudAndForgery : num 0 20 0 11.1 25 0 45 0 22.2 55.6 ...
## $ Per_CrimeDamage_Conv : num 90 76.9 95.5 79.5 80 94.9 85.1 90.3 85.7 86 ...
## $ Per_Failed_CrimeDamage : num 10 23.1 4.5 20.5 20 5.1 14.9 9.7 14.3 14 ...
## $ Per_DrugOffences_Conv : num 98.5 95.7 95.2 88.2 90 95.5 89.3 92.1 93.5 90.5 ...
## $ Per_Failed_Drug_Offence : num 1.5 4.3 4.8 11.8 10 4.5 10.7 7.9 6.5 9.5 ...
## $ Per_PublicOrderOffences_Conv : num 86.1 82.9 83.3 92.5 73.3 95.2 92.6 76.5 93.8 81.7 ...
## $ Per_Failed_PublicOrderOffences: num 13.9 17.1 16.7 7.5 26.7 4.8 7.4 23.5 6.3 18.3 ...
## $ Per_Others_Ex_Motoring : num 80.5 64.7 75 89.3 84.8 98.5 75.4 82.1 96.2 80 ...
## $ Per_Failed_Others_Ex_Motoring : num 19.5 35.3 25 10.7 15.2 1.5 24.6 17.9 3.8 20 ...
## $ Per_Motoring_Offences_Conv : num 83.6 88.9 92.9 94.6 87.9 90.5 95.2 91.7 91 95.7 ...
## $ Per_Failed_Motoring_Offences : num 16.4 11.1 7.1 5.4 12.1 9.5 4.8 8.3 9 4.3 ...
## $ Per_Failed_AdminFinalised_Conv: num 100 100 100 100 100 100 100 100 100 0 ...This confirms the assignment hence creating a new data frame for the Linear Regression analysis.
Overview Of Correlation
Correlations of All Percentage of Convictions
Using the rcorr() to determine the relationship between the variables.
Corr_Per_Convictions = rcorr(as.matrix(LN_Per_Convictions[, -1]))
Corr_Per_Convictions## Per_Homicide_Conv Per_Failed_Homicide
## Per_Homicide_Conv 1.00 -0.17
## Per_Failed_Homicide -0.17 1.00
## Per_Conv_Offence -0.06 -0.07
## Per_Failed_Conv_Offence 0.06 0.07
## Per_Sex_Offence_Conv -0.02 -0.04
## Per_Failed_Sex_Offence 0.02 0.03
## Per_Burg_Conv 0.01 -0.01
## Per_Failed_Burg_Conv -0.01 0.01
## Per_Rob_Conv 0.01 0.00
## Per_Failed_Rob_Conv 0.06 0.05
## Per_TheftAndHandling_Conv 0.00 -0.02
## Per_Failed_TheftAndHandling 0.00 0.02
## Per_FraudAndForgery_Conv -0.03 0.00
## Per_Failed_FraudAndForgery 0.03 0.01
## Per_CrimeDamage_Conv -0.05 -0.01
## Per_Failed_CrimeDamage 0.05 0.01
## Per_DrugOffences_Conv -0.07 -0.07
## Per_Failed_Drug_Offence 0.07 0.07
## Per_PublicOrderOffences_Conv 0.01 -0.05
## Per_Failed_PublicOrderOffences -0.01 0.05
## Per_Others_Ex_Motoring 0.00 0.00
## Per_Failed_Others_Ex_Motoring 0.01 0.00
## Per_Motoring_Offences_Conv -0.12 -0.07
## Per_Failed_Motoring_Offences 0.12 0.07
## Per_Failed_AdminFinalised_Conv 0.04 -0.01
## Per_Conv_Offence Per_Failed_Conv_Offence
## Per_Homicide_Conv -0.06 0.06
## Per_Failed_Homicide -0.07 0.07
## Per_Conv_Offence 1.00 -1.00
## Per_Failed_Conv_Offence -1.00 1.00
## Per_Sex_Offence_Conv 0.18 -0.18
## Per_Failed_Sex_Offence -0.18 0.18
## Per_Burg_Conv 0.20 -0.20
## Per_Failed_Burg_Conv -0.20 0.20
## Per_Rob_Conv -0.04 0.04
## Per_Failed_Rob_Conv -0.08 0.08
## Per_TheftAndHandling_Conv 0.39 -0.39
## Per_Failed_TheftAndHandling -0.39 0.39
## Per_FraudAndForgery_Conv 0.10 -0.10
## Per_Failed_FraudAndForgery -0.11 0.11
## Per_CrimeDamage_Conv 0.27 -0.27
## Per_Failed_CrimeDamage -0.27 0.27
## Per_DrugOffences_Conv 0.20 -0.20
## Per_Failed_Drug_Offence -0.20 0.20
## Per_PublicOrderOffences_Conv 0.35 -0.35
## Per_Failed_PublicOrderOffences -0.35 0.35
## Per_Others_Ex_Motoring 0.14 -0.14
## Per_Failed_Others_Ex_Motoring -0.14 0.14
## Per_Motoring_Offences_Conv 0.28 -0.28
## Per_Failed_Motoring_Offences -0.28 0.28
## Per_Failed_AdminFinalised_Conv 0.01 -0.01
## Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## Per_Homicide_Conv -0.02 0.02
## Per_Failed_Homicide -0.04 0.03
## Per_Conv_Offence 0.18 -0.18
## Per_Failed_Conv_Offence -0.18 0.18
## Per_Sex_Offence_Conv 1.00 -0.99
## Per_Failed_Sex_Offence -0.99 1.00
## Per_Burg_Conv 0.04 -0.04
## Per_Failed_Burg_Conv -0.04 0.04
## Per_Rob_Conv -0.04 0.04
## Per_Failed_Rob_Conv 0.01 -0.01
## Per_TheftAndHandling_Conv 0.07 -0.06
## Per_Failed_TheftAndHandling -0.07 0.06
## Per_FraudAndForgery_Conv 0.06 -0.07
## Per_Failed_FraudAndForgery -0.06 0.06
## Per_CrimeDamage_Conv 0.10 -0.10
## Per_Failed_CrimeDamage -0.10 0.10
## Per_DrugOffences_Conv 0.07 -0.06
## Per_Failed_Drug_Offence -0.07 0.06
## Per_PublicOrderOffences_Conv 0.08 -0.08
## Per_Failed_PublicOrderOffences -0.08 0.08
## Per_Others_Ex_Motoring 0.07 -0.07
## Per_Failed_Others_Ex_Motoring -0.08 0.08
## Per_Motoring_Offences_Conv 0.12 -0.12
## Per_Failed_Motoring_Offences -0.12 0.12
## Per_Failed_AdminFinalised_Conv 0.03 -0.03
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv 0.01 -0.01 0.01
## Per_Failed_Homicide -0.01 0.01 0.00
## Per_Conv_Offence 0.20 -0.20 -0.04
## Per_Failed_Conv_Offence -0.20 0.20 0.04
## Per_Sex_Offence_Conv 0.04 -0.04 -0.04
## Per_Failed_Sex_Offence -0.04 0.04 0.04
## Per_Burg_Conv 1.00 -1.00 0.06
## Per_Failed_Burg_Conv -1.00 1.00 -0.06
## Per_Rob_Conv 0.06 -0.06 1.00
## Per_Failed_Rob_Conv -0.08 0.08 -0.60
## Per_TheftAndHandling_Conv 0.13 -0.13 -0.06
## Per_Failed_TheftAndHandling -0.13 0.13 0.06
## Per_FraudAndForgery_Conv 0.03 -0.03 0.02
## Per_Failed_FraudAndForgery -0.01 0.01 -0.01
## Per_CrimeDamage_Conv 0.14 -0.14 -0.02
## Per_Failed_CrimeDamage -0.14 0.14 0.02
## Per_DrugOffences_Conv 0.11 -0.11 -0.02
## Per_Failed_Drug_Offence -0.11 0.11 0.02
## Per_PublicOrderOffences_Conv 0.14 -0.14 0.00
## Per_Failed_PublicOrderOffences -0.14 0.14 0.00
## Per_Others_Ex_Motoring 0.05 -0.05 0.02
## Per_Failed_Others_Ex_Motoring -0.05 0.05 -0.02
## Per_Motoring_Offences_Conv 0.09 -0.09 -0.02
## Per_Failed_Motoring_Offences -0.09 0.09 0.02
## Per_Failed_AdminFinalised_Conv -0.05 0.05 -0.01
## Per_Failed_Rob_Conv Per_TheftAndHandling_Conv
## Per_Homicide_Conv 0.06 0.00
## Per_Failed_Homicide 0.05 -0.02
## Per_Conv_Offence -0.08 0.39
## Per_Failed_Conv_Offence 0.08 -0.39
## Per_Sex_Offence_Conv 0.01 0.07
## Per_Failed_Sex_Offence -0.01 -0.06
## Per_Burg_Conv -0.08 0.13
## Per_Failed_Burg_Conv 0.08 -0.13
## Per_Rob_Conv -0.60 -0.06
## Per_Failed_Rob_Conv 1.00 -0.01
## Per_TheftAndHandling_Conv -0.01 1.00
## Per_Failed_TheftAndHandling 0.01 -1.00
## Per_FraudAndForgery_Conv -0.06 0.09
## Per_Failed_FraudAndForgery 0.07 -0.10
## Per_CrimeDamage_Conv -0.03 0.27
## Per_Failed_CrimeDamage 0.03 -0.27
## Per_DrugOffences_Conv -0.04 0.19
## Per_Failed_Drug_Offence 0.04 -0.19
## Per_PublicOrderOffences_Conv -0.04 0.33
## Per_Failed_PublicOrderOffences 0.04 -0.33
## Per_Others_Ex_Motoring -0.06 0.13
## Per_Failed_Others_Ex_Motoring 0.06 -0.13
## Per_Motoring_Offences_Conv -0.03 0.23
## Per_Failed_Motoring_Offences 0.03 -0.23
## Per_Failed_AdminFinalised_Conv 0.01 0.00
## Per_Failed_TheftAndHandling
## Per_Homicide_Conv 0.00
## Per_Failed_Homicide 0.02
## Per_Conv_Offence -0.39
## Per_Failed_Conv_Offence 0.39
## Per_Sex_Offence_Conv -0.07
## Per_Failed_Sex_Offence 0.06
## Per_Burg_Conv -0.13
## Per_Failed_Burg_Conv 0.13
## Per_Rob_Conv 0.06
## Per_Failed_Rob_Conv 0.01
## Per_TheftAndHandling_Conv -1.00
## Per_Failed_TheftAndHandling 1.00
## Per_FraudAndForgery_Conv -0.09
## Per_Failed_FraudAndForgery 0.10
## Per_CrimeDamage_Conv -0.27
## Per_Failed_CrimeDamage 0.27
## Per_DrugOffences_Conv -0.19
## Per_Failed_Drug_Offence 0.19
## Per_PublicOrderOffences_Conv -0.33
## Per_Failed_PublicOrderOffences 0.33
## Per_Others_Ex_Motoring -0.13
## Per_Failed_Others_Ex_Motoring 0.13
## Per_Motoring_Offences_Conv -0.23
## Per_Failed_Motoring_Offences 0.23
## Per_Failed_AdminFinalised_Conv 0.00
## Per_FraudAndForgery_Conv
## Per_Homicide_Conv -0.03
## Per_Failed_Homicide 0.00
## Per_Conv_Offence 0.10
## Per_Failed_Conv_Offence -0.10
## Per_Sex_Offence_Conv 0.06
## Per_Failed_Sex_Offence -0.07
## Per_Burg_Conv 0.03
## Per_Failed_Burg_Conv -0.03
## Per_Rob_Conv 0.02
## Per_Failed_Rob_Conv -0.06
## Per_TheftAndHandling_Conv 0.09
## Per_Failed_TheftAndHandling -0.09
## Per_FraudAndForgery_Conv 1.00
## Per_Failed_FraudAndForgery -0.98
## Per_CrimeDamage_Conv 0.06
## Per_Failed_CrimeDamage -0.06
## Per_DrugOffences_Conv 0.05
## Per_Failed_Drug_Offence -0.05
## Per_PublicOrderOffences_Conv 0.07
## Per_Failed_PublicOrderOffences -0.07
## Per_Others_Ex_Motoring 0.02
## Per_Failed_Others_Ex_Motoring -0.02
## Per_Motoring_Offences_Conv 0.05
## Per_Failed_Motoring_Offences -0.05
## Per_Failed_AdminFinalised_Conv 0.00
## Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## Per_Homicide_Conv 0.03 -0.05
## Per_Failed_Homicide 0.01 -0.01
## Per_Conv_Offence -0.11 0.27
## Per_Failed_Conv_Offence 0.11 -0.27
## Per_Sex_Offence_Conv -0.06 0.10
## Per_Failed_Sex_Offence 0.06 -0.10
## Per_Burg_Conv -0.01 0.14
## Per_Failed_Burg_Conv 0.01 -0.14
## Per_Rob_Conv -0.01 -0.02
## Per_Failed_Rob_Conv 0.07 -0.03
## Per_TheftAndHandling_Conv -0.10 0.27
## Per_Failed_TheftAndHandling 0.10 -0.27
## Per_FraudAndForgery_Conv -0.98 0.06
## Per_Failed_FraudAndForgery 1.00 -0.05
## Per_CrimeDamage_Conv -0.05 1.00
## Per_Failed_CrimeDamage 0.05 -1.00
## Per_DrugOffences_Conv -0.05 0.18
## Per_Failed_Drug_Offence 0.05 -0.18
## Per_PublicOrderOffences_Conv -0.07 0.25
## Per_Failed_PublicOrderOffences 0.07 -0.25
## Per_Others_Ex_Motoring -0.02 0.09
## Per_Failed_Others_Ex_Motoring 0.03 -0.09
## Per_Motoring_Offences_Conv -0.06 0.15
## Per_Failed_Motoring_Offences 0.06 -0.15
## Per_Failed_AdminFinalised_Conv 0.00 0.00
## Per_Failed_CrimeDamage Per_DrugOffences_Conv
## Per_Homicide_Conv 0.05 -0.07
## Per_Failed_Homicide 0.01 -0.07
## Per_Conv_Offence -0.27 0.20
## Per_Failed_Conv_Offence 0.27 -0.20
## Per_Sex_Offence_Conv -0.10 0.07
## Per_Failed_Sex_Offence 0.10 -0.06
## Per_Burg_Conv -0.14 0.11
## Per_Failed_Burg_Conv 0.14 -0.11
## Per_Rob_Conv 0.02 -0.02
## Per_Failed_Rob_Conv 0.03 -0.04
## Per_TheftAndHandling_Conv -0.27 0.19
## Per_Failed_TheftAndHandling 0.27 -0.19
## Per_FraudAndForgery_Conv -0.06 0.05
## Per_Failed_FraudAndForgery 0.05 -0.05
## Per_CrimeDamage_Conv -1.00 0.18
## Per_Failed_CrimeDamage 1.00 -0.18
## Per_DrugOffences_Conv -0.18 1.00
## Per_Failed_Drug_Offence 0.18 -1.00
## Per_PublicOrderOffences_Conv -0.25 0.19
## Per_Failed_PublicOrderOffences 0.25 -0.19
## Per_Others_Ex_Motoring -0.09 0.06
## Per_Failed_Others_Ex_Motoring 0.09 -0.06
## Per_Motoring_Offences_Conv -0.15 0.15
## Per_Failed_Motoring_Offences 0.15 -0.15
## Per_Failed_AdminFinalised_Conv 0.00 0.03
## Per_Failed_Drug_Offence
## Per_Homicide_Conv 0.07
## Per_Failed_Homicide 0.07
## Per_Conv_Offence -0.20
## Per_Failed_Conv_Offence 0.20
## Per_Sex_Offence_Conv -0.07
## Per_Failed_Sex_Offence 0.06
## Per_Burg_Conv -0.11
## Per_Failed_Burg_Conv 0.11
## Per_Rob_Conv 0.02
## Per_Failed_Rob_Conv 0.04
## Per_TheftAndHandling_Conv -0.19
## Per_Failed_TheftAndHandling 0.19
## Per_FraudAndForgery_Conv -0.05
## Per_Failed_FraudAndForgery 0.05
## Per_CrimeDamage_Conv -0.18
## Per_Failed_CrimeDamage 0.18
## Per_DrugOffences_Conv -1.00
## Per_Failed_Drug_Offence 1.00
## Per_PublicOrderOffences_Conv -0.19
## Per_Failed_PublicOrderOffences 0.19
## Per_Others_Ex_Motoring -0.06
## Per_Failed_Others_Ex_Motoring 0.06
## Per_Motoring_Offences_Conv -0.15
## Per_Failed_Motoring_Offences 0.15
## Per_Failed_AdminFinalised_Conv -0.03
## Per_PublicOrderOffences_Conv
## Per_Homicide_Conv 0.01
## Per_Failed_Homicide -0.05
## Per_Conv_Offence 0.35
## Per_Failed_Conv_Offence -0.35
## Per_Sex_Offence_Conv 0.08
## Per_Failed_Sex_Offence -0.08
## Per_Burg_Conv 0.14
## Per_Failed_Burg_Conv -0.14
## Per_Rob_Conv 0.00
## Per_Failed_Rob_Conv -0.04
## Per_TheftAndHandling_Conv 0.33
## Per_Failed_TheftAndHandling -0.33
## Per_FraudAndForgery_Conv 0.07
## Per_Failed_FraudAndForgery -0.07
## Per_CrimeDamage_Conv 0.25
## Per_Failed_CrimeDamage -0.25
## Per_DrugOffences_Conv 0.19
## Per_Failed_Drug_Offence -0.19
## Per_PublicOrderOffences_Conv 1.00
## Per_Failed_PublicOrderOffences -1.00
## Per_Others_Ex_Motoring 0.10
## Per_Failed_Others_Ex_Motoring -0.09
## Per_Motoring_Offences_Conv 0.17
## Per_Failed_Motoring_Offences -0.17
## Per_Failed_AdminFinalised_Conv 0.02
## Per_Failed_PublicOrderOffences
## Per_Homicide_Conv -0.01
## Per_Failed_Homicide 0.05
## Per_Conv_Offence -0.35
## Per_Failed_Conv_Offence 0.35
## Per_Sex_Offence_Conv -0.08
## Per_Failed_Sex_Offence 0.08
## Per_Burg_Conv -0.14
## Per_Failed_Burg_Conv 0.14
## Per_Rob_Conv 0.00
## Per_Failed_Rob_Conv 0.04
## Per_TheftAndHandling_Conv -0.33
## Per_Failed_TheftAndHandling 0.33
## Per_FraudAndForgery_Conv -0.07
## Per_Failed_FraudAndForgery 0.07
## Per_CrimeDamage_Conv -0.25
## Per_Failed_CrimeDamage 0.25
## Per_DrugOffences_Conv -0.19
## Per_Failed_Drug_Offence 0.19
## Per_PublicOrderOffences_Conv -1.00
## Per_Failed_PublicOrderOffences 1.00
## Per_Others_Ex_Motoring -0.10
## Per_Failed_Others_Ex_Motoring 0.09
## Per_Motoring_Offences_Conv -0.17
## Per_Failed_Motoring_Offences 0.17
## Per_Failed_AdminFinalised_Conv -0.02
## Per_Others_Ex_Motoring
## Per_Homicide_Conv 0.00
## Per_Failed_Homicide 0.00
## Per_Conv_Offence 0.14
## Per_Failed_Conv_Offence -0.14
## Per_Sex_Offence_Conv 0.07
## Per_Failed_Sex_Offence -0.07
## Per_Burg_Conv 0.05
## Per_Failed_Burg_Conv -0.05
## Per_Rob_Conv 0.02
## Per_Failed_Rob_Conv -0.06
## Per_TheftAndHandling_Conv 0.13
## Per_Failed_TheftAndHandling -0.13
## Per_FraudAndForgery_Conv 0.02
## Per_Failed_FraudAndForgery -0.02
## Per_CrimeDamage_Conv 0.09
## Per_Failed_CrimeDamage -0.09
## Per_DrugOffences_Conv 0.06
## Per_Failed_Drug_Offence -0.06
## Per_PublicOrderOffences_Conv 0.10
## Per_Failed_PublicOrderOffences -0.10
## Per_Others_Ex_Motoring 1.00
## Per_Failed_Others_Ex_Motoring -0.98
## Per_Motoring_Offences_Conv 0.04
## Per_Failed_Motoring_Offences -0.04
## Per_Failed_AdminFinalised_Conv -0.03
## Per_Failed_Others_Ex_Motoring
## Per_Homicide_Conv 0.01
## Per_Failed_Homicide 0.00
## Per_Conv_Offence -0.14
## Per_Failed_Conv_Offence 0.14
## Per_Sex_Offence_Conv -0.08
## Per_Failed_Sex_Offence 0.08
## Per_Burg_Conv -0.05
## Per_Failed_Burg_Conv 0.05
## Per_Rob_Conv -0.02
## Per_Failed_Rob_Conv 0.06
## Per_TheftAndHandling_Conv -0.13
## Per_Failed_TheftAndHandling 0.13
## Per_FraudAndForgery_Conv -0.02
## Per_Failed_FraudAndForgery 0.03
## Per_CrimeDamage_Conv -0.09
## Per_Failed_CrimeDamage 0.09
## Per_DrugOffences_Conv -0.06
## Per_Failed_Drug_Offence 0.06
## Per_PublicOrderOffences_Conv -0.09
## Per_Failed_PublicOrderOffences 0.09
## Per_Others_Ex_Motoring -0.98
## Per_Failed_Others_Ex_Motoring 1.00
## Per_Motoring_Offences_Conv -0.04
## Per_Failed_Motoring_Offences 0.04
## Per_Failed_AdminFinalised_Conv 0.03
## Per_Motoring_Offences_Conv
## Per_Homicide_Conv -0.12
## Per_Failed_Homicide -0.07
## Per_Conv_Offence 0.28
## Per_Failed_Conv_Offence -0.28
## Per_Sex_Offence_Conv 0.12
## Per_Failed_Sex_Offence -0.12
## Per_Burg_Conv 0.09
## Per_Failed_Burg_Conv -0.09
## Per_Rob_Conv -0.02
## Per_Failed_Rob_Conv -0.03
## Per_TheftAndHandling_Conv 0.23
## Per_Failed_TheftAndHandling -0.23
## Per_FraudAndForgery_Conv 0.05
## Per_Failed_FraudAndForgery -0.06
## Per_CrimeDamage_Conv 0.15
## Per_Failed_CrimeDamage -0.15
## Per_DrugOffences_Conv 0.15
## Per_Failed_Drug_Offence -0.15
## Per_PublicOrderOffences_Conv 0.17
## Per_Failed_PublicOrderOffences -0.17
## Per_Others_Ex_Motoring 0.04
## Per_Failed_Others_Ex_Motoring -0.04
## Per_Motoring_Offences_Conv 1.00
## Per_Failed_Motoring_Offences -1.00
## Per_Failed_AdminFinalised_Conv -0.03
## Per_Failed_Motoring_Offences
## Per_Homicide_Conv 0.12
## Per_Failed_Homicide 0.07
## Per_Conv_Offence -0.28
## Per_Failed_Conv_Offence 0.28
## Per_Sex_Offence_Conv -0.12
## Per_Failed_Sex_Offence 0.12
## Per_Burg_Conv -0.09
## Per_Failed_Burg_Conv 0.09
## Per_Rob_Conv 0.02
## Per_Failed_Rob_Conv 0.03
## Per_TheftAndHandling_Conv -0.23
## Per_Failed_TheftAndHandling 0.23
## Per_FraudAndForgery_Conv -0.05
## Per_Failed_FraudAndForgery 0.06
## Per_CrimeDamage_Conv -0.15
## Per_Failed_CrimeDamage 0.15
## Per_DrugOffences_Conv -0.15
## Per_Failed_Drug_Offence 0.15
## Per_PublicOrderOffences_Conv -0.17
## Per_Failed_PublicOrderOffences 0.17
## Per_Others_Ex_Motoring -0.04
## Per_Failed_Others_Ex_Motoring 0.04
## Per_Motoring_Offences_Conv -1.00
## Per_Failed_Motoring_Offences 1.00
## Per_Failed_AdminFinalised_Conv 0.03
## Per_Failed_AdminFinalised_Conv
## Per_Homicide_Conv 0.04
## Per_Failed_Homicide -0.01
## Per_Conv_Offence 0.01
## Per_Failed_Conv_Offence -0.01
## Per_Sex_Offence_Conv 0.03
## Per_Failed_Sex_Offence -0.03
## Per_Burg_Conv -0.05
## Per_Failed_Burg_Conv 0.05
## Per_Rob_Conv -0.01
## Per_Failed_Rob_Conv 0.01
## Per_TheftAndHandling_Conv 0.00
## Per_Failed_TheftAndHandling 0.00
## Per_FraudAndForgery_Conv 0.00
## Per_Failed_FraudAndForgery 0.00
## Per_CrimeDamage_Conv 0.00
## Per_Failed_CrimeDamage 0.00
## Per_DrugOffences_Conv 0.03
## Per_Failed_Drug_Offence -0.03
## Per_PublicOrderOffences_Conv 0.02
## Per_Failed_PublicOrderOffences -0.02
## Per_Others_Ex_Motoring -0.03
## Per_Failed_Others_Ex_Motoring 0.03
## Per_Motoring_Offences_Conv -0.03
## Per_Failed_Motoring_Offences 0.03
## Per_Failed_AdminFinalised_Conv 1.00
##
## n= 2142
##
##
## P
## Per_Homicide_Conv Per_Failed_Homicide
## Per_Homicide_Conv 0.0000
## Per_Failed_Homicide 0.0000
## Per_Conv_Offence 0.0068 0.0015
## Per_Failed_Conv_Offence 0.0068 0.0015
## Per_Sex_Offence_Conv 0.3480 0.0992
## Per_Failed_Sex_Offence 0.3333 0.1656
## Per_Burg_Conv 0.5370 0.6085
## Per_Failed_Burg_Conv 0.5368 0.6084
## Per_Rob_Conv 0.8072 0.9755
## Per_Failed_Rob_Conv 0.0028 0.0152
## Per_TheftAndHandling_Conv 0.9059 0.4434
## Per_Failed_TheftAndHandling 0.9098 0.4445
## Per_FraudAndForgery_Conv 0.2328 0.8476
## Per_Failed_FraudAndForgery 0.1449 0.7749
## Per_CrimeDamage_Conv 0.0185 0.6942
## Per_Failed_CrimeDamage 0.0185 0.6950
## Per_DrugOffences_Conv 0.0006 0.0015
## Per_Failed_Drug_Offence 0.0006 0.0015
## Per_PublicOrderOffences_Conv 0.7995 0.0146
## Per_Failed_PublicOrderOffences 0.7998 0.0146
## Per_Others_Ex_Motoring 0.9019 0.8218
## Per_Failed_Others_Ex_Motoring 0.7260 0.8842
## Per_Motoring_Offences_Conv 0.0000 0.0013
## Per_Failed_Motoring_Offences 0.0000 0.0013
## Per_Failed_AdminFinalised_Conv 0.0952 0.7423
## Per_Conv_Offence Per_Failed_Conv_Offence
## Per_Homicide_Conv 0.0068 0.0068
## Per_Failed_Homicide 0.0015 0.0015
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0000 0.0000
## Per_Failed_Sex_Offence 0.0000 0.0000
## Per_Burg_Conv 0.0000 0.0000
## Per_Failed_Burg_Conv 0.0000 0.0000
## Per_Rob_Conv 0.0698 0.0701
## Per_Failed_Rob_Conv 0.0002 0.0002
## Per_TheftAndHandling_Conv 0.0000 0.0000
## Per_Failed_TheftAndHandling 0.0000 0.0000
## Per_FraudAndForgery_Conv 0.0000 0.0000
## Per_Failed_FraudAndForgery 0.0000 0.0000
## Per_CrimeDamage_Conv 0.0000 0.0000
## Per_Failed_CrimeDamage 0.0000 0.0000
## Per_DrugOffences_Conv 0.0000 0.0000
## Per_Failed_Drug_Offence 0.0000 0.0000
## Per_PublicOrderOffences_Conv 0.0000 0.0000
## Per_Failed_PublicOrderOffences 0.0000 0.0000
## Per_Others_Ex_Motoring 0.0000 0.0000
## Per_Failed_Others_Ex_Motoring 0.0000 0.0000
## Per_Motoring_Offences_Conv 0.0000 0.0000
## Per_Failed_Motoring_Offences 0.0000 0.0000
## Per_Failed_AdminFinalised_Conv 0.7696 0.7699
## Per_Sex_Offence_Conv Per_Failed_Sex_Offence
## Per_Homicide_Conv 0.3480 0.3333
## Per_Failed_Homicide 0.0992 0.1656
## Per_Conv_Offence 0.0000 0.0000
## Per_Failed_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0000
## Per_Failed_Sex_Offence 0.0000
## Per_Burg_Conv 0.0725 0.0410
## Per_Failed_Burg_Conv 0.0729 0.0413
## Per_Rob_Conv 0.0752 0.0595
## Per_Failed_Rob_Conv 0.7818 0.6596
## Per_TheftAndHandling_Conv 0.0022 0.0028
## Per_Failed_TheftAndHandling 0.0023 0.0029
## Per_FraudAndForgery_Conv 0.0029 0.0024
## Per_Failed_FraudAndForgery 0.0055 0.0046
## Per_CrimeDamage_Conv 0.0000 0.0000
## Per_Failed_CrimeDamage 0.0000 0.0000
## Per_DrugOffences_Conv 0.0022 0.0027
## Per_Failed_Drug_Offence 0.0022 0.0027
## Per_PublicOrderOffences_Conv 0.0002 0.0000
## Per_Failed_PublicOrderOffences 0.0002 0.0000
## Per_Others_Ex_Motoring 0.0007 0.0018
## Per_Failed_Others_Ex_Motoring 0.0002 0.0005
## Per_Motoring_Offences_Conv 0.0000 0.0000
## Per_Failed_Motoring_Offences 0.0000 0.0000
## Per_Failed_AdminFinalised_Conv 0.2080 0.2000
## Per_Burg_Conv Per_Failed_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv 0.5370 0.5368 0.8072
## Per_Failed_Homicide 0.6085 0.6084 0.9755
## Per_Conv_Offence 0.0000 0.0000 0.0698
## Per_Failed_Conv_Offence 0.0000 0.0000 0.0701
## Per_Sex_Offence_Conv 0.0725 0.0729 0.0752
## Per_Failed_Sex_Offence 0.0410 0.0413 0.0595
## Per_Burg_Conv 0.0000 0.0028
## Per_Failed_Burg_Conv 0.0000 0.0028
## Per_Rob_Conv 0.0028 0.0028
## Per_Failed_Rob_Conv 0.0003 0.0003 0.0000
## Per_TheftAndHandling_Conv 0.0000 0.0000 0.0071
## Per_Failed_TheftAndHandling 0.0000 0.0000 0.0072
## Per_FraudAndForgery_Conv 0.2402 0.2408 0.3250
## Per_Failed_FraudAndForgery 0.5470 0.5481 0.6497
## Per_CrimeDamage_Conv 0.0000 0.0000 0.4311
## Per_Failed_CrimeDamage 0.0000 0.0000 0.4324
## Per_DrugOffences_Conv 0.0000 0.0000 0.3760
## Per_Failed_Drug_Offence 0.0000 0.0000 0.3763
## Per_PublicOrderOffences_Conv 0.0000 0.0000 0.8718
## Per_Failed_PublicOrderOffences 0.0000 0.0000 0.8683
## Per_Others_Ex_Motoring 0.0143 0.0144 0.2902
## Per_Failed_Others_Ex_Motoring 0.0133 0.0134 0.3148
## Per_Motoring_Offences_Conv 0.0000 0.0000 0.2899
## Per_Failed_Motoring_Offences 0.0000 0.0000 0.2919
## Per_Failed_AdminFinalised_Conv 0.0126 0.0125 0.7644
## Per_Failed_Rob_Conv Per_TheftAndHandling_Conv
## Per_Homicide_Conv 0.0028 0.9059
## Per_Failed_Homicide 0.0152 0.4434
## Per_Conv_Offence 0.0002 0.0000
## Per_Failed_Conv_Offence 0.0002 0.0000
## Per_Sex_Offence_Conv 0.7818 0.0022
## Per_Failed_Sex_Offence 0.6596 0.0028
## Per_Burg_Conv 0.0003 0.0000
## Per_Failed_Burg_Conv 0.0003 0.0000
## Per_Rob_Conv 0.0000 0.0071
## Per_Failed_Rob_Conv 0.5791
## Per_TheftAndHandling_Conv 0.5791
## Per_Failed_TheftAndHandling 0.5825 0.0000
## Per_FraudAndForgery_Conv 0.0046 0.0000
## Per_Failed_FraudAndForgery 0.0023 0.0000
## Per_CrimeDamage_Conv 0.2379 0.0000
## Per_Failed_CrimeDamage 0.2382 0.0000
## Per_DrugOffences_Conv 0.0506 0.0000
## Per_Failed_Drug_Offence 0.0508 0.0000
## Per_PublicOrderOffences_Conv 0.0664 0.0000
## Per_Failed_PublicOrderOffences 0.0664 0.0000
## Per_Others_Ex_Motoring 0.0077 0.0000
## Per_Failed_Others_Ex_Motoring 0.0102 0.0000
## Per_Motoring_Offences_Conv 0.1204 0.0000
## Per_Failed_Motoring_Offences 0.1200 0.0000
## Per_Failed_AdminFinalised_Conv 0.5727 0.8186
## Per_Failed_TheftAndHandling
## Per_Homicide_Conv 0.9098
## Per_Failed_Homicide 0.4445
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0023
## Per_Failed_Sex_Offence 0.0029
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.0072
## Per_Failed_Rob_Conv 0.5825
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling
## Per_FraudAndForgery_Conv 0.0000
## Per_Failed_FraudAndForgery 0.0000
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0000
## Per_Failed_Others_Ex_Motoring 0.0000
## Per_Motoring_Offences_Conv 0.0000
## Per_Failed_Motoring_Offences 0.0000
## Per_Failed_AdminFinalised_Conv 0.8178
## Per_FraudAndForgery_Conv
## Per_Homicide_Conv 0.2328
## Per_Failed_Homicide 0.8476
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0029
## Per_Failed_Sex_Offence 0.0024
## Per_Burg_Conv 0.2402
## Per_Failed_Burg_Conv 0.2408
## Per_Rob_Conv 0.3250
## Per_Failed_Rob_Conv 0.0046
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv
## Per_Failed_FraudAndForgery 0.0000
## Per_CrimeDamage_Conv 0.0092
## Per_Failed_CrimeDamage 0.0092
## Per_DrugOffences_Conv 0.0330
## Per_Failed_Drug_Offence 0.0329
## Per_PublicOrderOffences_Conv 0.0016
## Per_Failed_PublicOrderOffences 0.0016
## Per_Others_Ex_Motoring 0.4758
## Per_Failed_Others_Ex_Motoring 0.3517
## Per_Motoring_Offences_Conv 0.0117
## Per_Failed_Motoring_Offences 0.0118
## Per_Failed_AdminFinalised_Conv 0.9558
## Per_Failed_FraudAndForgery Per_CrimeDamage_Conv
## Per_Homicide_Conv 0.1449 0.0185
## Per_Failed_Homicide 0.7749 0.6942
## Per_Conv_Offence 0.0000 0.0000
## Per_Failed_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0055 0.0000
## Per_Failed_Sex_Offence 0.0046 0.0000
## Per_Burg_Conv 0.5470 0.0000
## Per_Failed_Burg_Conv 0.5481 0.0000
## Per_Rob_Conv 0.6497 0.4311
## Per_Failed_Rob_Conv 0.0023 0.2379
## Per_TheftAndHandling_Conv 0.0000 0.0000
## Per_Failed_TheftAndHandling 0.0000 0.0000
## Per_FraudAndForgery_Conv 0.0000 0.0092
## Per_Failed_FraudAndForgery 0.0224
## Per_CrimeDamage_Conv 0.0224
## Per_Failed_CrimeDamage 0.0224 0.0000
## Per_DrugOffences_Conv 0.0175 0.0000
## Per_Failed_Drug_Offence 0.0175 0.0000
## Per_PublicOrderOffences_Conv 0.0017 0.0000
## Per_Failed_PublicOrderOffences 0.0017 0.0000
## Per_Others_Ex_Motoring 0.3356 0.0000
## Per_Failed_Others_Ex_Motoring 0.2355 0.0000
## Per_Motoring_Offences_Conv 0.0108 0.0000
## Per_Failed_Motoring_Offences 0.0108 0.0000
## Per_Failed_AdminFinalised_Conv 0.9421 0.9895
## Per_Failed_CrimeDamage Per_DrugOffences_Conv
## Per_Homicide_Conv 0.0185 0.0006
## Per_Failed_Homicide 0.6950 0.0015
## Per_Conv_Offence 0.0000 0.0000
## Per_Failed_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0000 0.0022
## Per_Failed_Sex_Offence 0.0000 0.0027
## Per_Burg_Conv 0.0000 0.0000
## Per_Failed_Burg_Conv 0.0000 0.0000
## Per_Rob_Conv 0.4324 0.3760
## Per_Failed_Rob_Conv 0.2382 0.0506
## Per_TheftAndHandling_Conv 0.0000 0.0000
## Per_Failed_TheftAndHandling 0.0000 0.0000
## Per_FraudAndForgery_Conv 0.0092 0.0330
## Per_Failed_FraudAndForgery 0.0224 0.0175
## Per_CrimeDamage_Conv 0.0000 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000 0.0000
## Per_PublicOrderOffences_Conv 0.0000 0.0000
## Per_Failed_PublicOrderOffences 0.0000 0.0000
## Per_Others_Ex_Motoring 0.0000 0.0039
## Per_Failed_Others_Ex_Motoring 0.0000 0.0031
## Per_Motoring_Offences_Conv 0.0000 0.0000
## Per_Failed_Motoring_Offences 0.0000 0.0000
## Per_Failed_AdminFinalised_Conv 0.9902 0.1115
## Per_Failed_Drug_Offence
## Per_Homicide_Conv 0.0006
## Per_Failed_Homicide 0.0015
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0022
## Per_Failed_Sex_Offence 0.0027
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.3763
## Per_Failed_Rob_Conv 0.0508
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.0329
## Per_Failed_FraudAndForgery 0.0175
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0039
## Per_Failed_Others_Ex_Motoring 0.0030
## Per_Motoring_Offences_Conv 0.0000
## Per_Failed_Motoring_Offences 0.0000
## Per_Failed_AdminFinalised_Conv 0.1118
## Per_PublicOrderOffences_Conv
## Per_Homicide_Conv 0.7995
## Per_Failed_Homicide 0.0146
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0002
## Per_Failed_Sex_Offence 0.0000
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.8718
## Per_Failed_Rob_Conv 0.0664
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.0016
## Per_Failed_FraudAndForgery 0.0017
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000
## Per_PublicOrderOffences_Conv
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0000
## Per_Failed_Others_Ex_Motoring 0.0000
## Per_Motoring_Offences_Conv 0.0000
## Per_Failed_Motoring_Offences 0.0000
## Per_Failed_AdminFinalised_Conv 0.4512
## Per_Failed_PublicOrderOffences
## Per_Homicide_Conv 0.7998
## Per_Failed_Homicide 0.0146
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0002
## Per_Failed_Sex_Offence 0.0000
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.8683
## Per_Failed_Rob_Conv 0.0664
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.0016
## Per_Failed_FraudAndForgery 0.0017
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences
## Per_Others_Ex_Motoring 0.0000
## Per_Failed_Others_Ex_Motoring 0.0000
## Per_Motoring_Offences_Conv 0.0000
## Per_Failed_Motoring_Offences 0.0000
## Per_Failed_AdminFinalised_Conv 0.4517
## Per_Others_Ex_Motoring
## Per_Homicide_Conv 0.9019
## Per_Failed_Homicide 0.8218
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0007
## Per_Failed_Sex_Offence 0.0018
## Per_Burg_Conv 0.0143
## Per_Failed_Burg_Conv 0.0144
## Per_Rob_Conv 0.2902
## Per_Failed_Rob_Conv 0.0077
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.4758
## Per_Failed_FraudAndForgery 0.3356
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0039
## Per_Failed_Drug_Offence 0.0039
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring
## Per_Failed_Others_Ex_Motoring 0.0000
## Per_Motoring_Offences_Conv 0.0486
## Per_Failed_Motoring_Offences 0.0482
## Per_Failed_AdminFinalised_Conv 0.2087
## Per_Failed_Others_Ex_Motoring
## Per_Homicide_Conv 0.7260
## Per_Failed_Homicide 0.8842
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0002
## Per_Failed_Sex_Offence 0.0005
## Per_Burg_Conv 0.0133
## Per_Failed_Burg_Conv 0.0134
## Per_Rob_Conv 0.3148
## Per_Failed_Rob_Conv 0.0102
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.3517
## Per_Failed_FraudAndForgery 0.2355
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0031
## Per_Failed_Drug_Offence 0.0030
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0000
## Per_Failed_Others_Ex_Motoring
## Per_Motoring_Offences_Conv 0.0468
## Per_Failed_Motoring_Offences 0.0464
## Per_Failed_AdminFinalised_Conv 0.2088
## Per_Motoring_Offences_Conv
## Per_Homicide_Conv 0.0000
## Per_Failed_Homicide 0.0013
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0000
## Per_Failed_Sex_Offence 0.0000
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.2899
## Per_Failed_Rob_Conv 0.1204
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.0117
## Per_Failed_FraudAndForgery 0.0108
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0486
## Per_Failed_Others_Ex_Motoring 0.0468
## Per_Motoring_Offences_Conv
## Per_Failed_Motoring_Offences 0.0000
## Per_Failed_AdminFinalised_Conv 0.2004
## Per_Failed_Motoring_Offences
## Per_Homicide_Conv 0.0000
## Per_Failed_Homicide 0.0013
## Per_Conv_Offence 0.0000
## Per_Failed_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0000
## Per_Failed_Sex_Offence 0.0000
## Per_Burg_Conv 0.0000
## Per_Failed_Burg_Conv 0.0000
## Per_Rob_Conv 0.2919
## Per_Failed_Rob_Conv 0.1200
## Per_TheftAndHandling_Conv 0.0000
## Per_Failed_TheftAndHandling 0.0000
## Per_FraudAndForgery_Conv 0.0118
## Per_Failed_FraudAndForgery 0.0108
## Per_CrimeDamage_Conv 0.0000
## Per_Failed_CrimeDamage 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_Failed_Drug_Offence 0.0000
## Per_PublicOrderOffences_Conv 0.0000
## Per_Failed_PublicOrderOffences 0.0000
## Per_Others_Ex_Motoring 0.0482
## Per_Failed_Others_Ex_Motoring 0.0464
## Per_Motoring_Offences_Conv 0.0000
## Per_Failed_Motoring_Offences
## Per_Failed_AdminFinalised_Conv 0.2001
## Per_Failed_AdminFinalised_Conv
## Per_Homicide_Conv 0.0952
## Per_Failed_Homicide 0.7423
## Per_Conv_Offence 0.7696
## Per_Failed_Conv_Offence 0.7699
## Per_Sex_Offence_Conv 0.2080
## Per_Failed_Sex_Offence 0.2000
## Per_Burg_Conv 0.0126
## Per_Failed_Burg_Conv 0.0125
## Per_Rob_Conv 0.7644
## Per_Failed_Rob_Conv 0.5727
## Per_TheftAndHandling_Conv 0.8186
## Per_Failed_TheftAndHandling 0.8178
## Per_FraudAndForgery_Conv 0.9558
## Per_Failed_FraudAndForgery 0.9421
## Per_CrimeDamage_Conv 0.9895
## Per_Failed_CrimeDamage 0.9902
## Per_DrugOffences_Conv 0.1115
## Per_Failed_Drug_Offence 0.1118
## Per_PublicOrderOffences_Conv 0.4512
## Per_Failed_PublicOrderOffences 0.4517
## Per_Others_Ex_Motoring 0.2087
## Per_Failed_Others_Ex_Motoring 0.2088
## Per_Motoring_Offences_Conv 0.2004
## Per_Failed_Motoring_Offences 0.2001
## Per_Failed_AdminFinalised_ConvCorrelations of Successful Convictions
Creating Subset of Successful Convictions
LN_Matrix_Succ_Convic = LN_Per_Convictions[c(
'Per_Homicide_Conv',
'Per_Conv_Offence',
'Per_Sex_Offence_Conv',
'Per_Burg_Conv',
'Per_Rob_Conv',
'Per_TheftAndHandling_Conv',
'Per_FraudAndForgery_Conv',
'Per_CrimeDamage_Conv',
'Per_DrugOffences_Conv',
'Per_PublicOrderOffences_Conv',
'Per_Others_Ex_Motoring',
'Per_Motoring_Offences_Conv'
)]
summary(LN_Matrix_Succ_Convic)## Per_Homicide_Conv Per_Conv_Offence Per_Sex_Offence_Conv Per_Burg_Conv
## Min. : 0.00 Min. :55.10 Min. : 0.00 Min. : 50.00
## 1st Qu.: 0.00 1st Qu.:75.60 1st Qu.: 68.20 1st Qu.: 81.50
## Median : 75.00 Median :79.30 Median : 76.35 Median : 87.50
## Mean : 56.29 Mean :79.05 Mean : 77.23 Mean : 86.83
## 3rd Qu.:100.00 3rd Qu.:82.60 3rd Qu.: 85.70 3rd Qu.: 92.90
## Max. :100.00 Max. :94.20 Max. :100.00 Max. :100.00
## Per_Rob_Conv Per_TheftAndHandling_Conv Per_FraudAndForgery_Conv
## Min. : 0.00 Min. : 72.20 Min. : 0.00
## 1st Qu.: 66.70 1st Qu.: 90.70 1st Qu.: 81.30
## Median : 83.30 Median : 92.90 Median : 87.90
## Mean : 76.22 Mean : 92.56 Mean : 87.22
## 3rd Qu.:100.00 3rd Qu.: 94.80 3rd Qu.: 96.20
## Max. :100.00 Max. :100.00 Max. :100.00
## Per_CrimeDamage_Conv Per_DrugOffences_Conv Per_PublicOrderOffences_Conv
## Min. : 44.40 Min. : 75.00 Min. : 40.00
## 1st Qu.: 81.83 1st Qu.: 92.20 1st Qu.: 82.60
## Median : 86.50 Median : 94.60 Median : 87.00
## Mean : 86.06 Mean : 94.36 Mean : 86.25
## 3rd Qu.: 90.60 3rd Qu.: 97.00 3rd Qu.: 90.60
## Max. :100.00 Max. :100.00 Max. :100.00
## Per_Others_Ex_Motoring Per_Motoring_Offences_Conv
## Min. : 0.00 Min. : 61.50
## 1st Qu.: 80.00 1st Qu.: 84.30
## Median : 86.35 Median : 88.00
## Mean : 85.44 Mean : 87.34
## 3rd Qu.: 93.80 3rd Qu.: 91.10
## Max. :100.00 Max. :100.00To determine the correlation coefficients with p-value for successful convictions
Corr_Coef_Succ_Convic = rcorr(as.matrix(LN_Matrix_Succ_Convic))
Corr_Coef_Succ_Convic## Per_Homicide_Conv Per_Conv_Offence
## Per_Homicide_Conv 1.00 -0.06
## Per_Conv_Offence -0.06 1.00
## Per_Sex_Offence_Conv -0.02 0.18
## Per_Burg_Conv 0.01 0.20
## Per_Rob_Conv 0.01 -0.04
## Per_TheftAndHandling_Conv 0.00 0.39
## Per_FraudAndForgery_Conv -0.03 0.10
## Per_CrimeDamage_Conv -0.05 0.27
## Per_DrugOffences_Conv -0.07 0.20
## Per_PublicOrderOffences_Conv 0.01 0.35
## Per_Others_Ex_Motoring 0.00 0.14
## Per_Motoring_Offences_Conv -0.12 0.28
## Per_Sex_Offence_Conv Per_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv -0.02 0.01 0.01
## Per_Conv_Offence 0.18 0.20 -0.04
## Per_Sex_Offence_Conv 1.00 0.04 -0.04
## Per_Burg_Conv 0.04 1.00 0.06
## Per_Rob_Conv -0.04 0.06 1.00
## Per_TheftAndHandling_Conv 0.07 0.13 -0.06
## Per_FraudAndForgery_Conv 0.06 0.03 0.02
## Per_CrimeDamage_Conv 0.10 0.14 -0.02
## Per_DrugOffences_Conv 0.07 0.11 -0.02
## Per_PublicOrderOffences_Conv 0.08 0.14 0.00
## Per_Others_Ex_Motoring 0.07 0.05 0.02
## Per_Motoring_Offences_Conv 0.12 0.09 -0.02
## Per_TheftAndHandling_Conv Per_FraudAndForgery_Conv
## Per_Homicide_Conv 0.00 -0.03
## Per_Conv_Offence 0.39 0.10
## Per_Sex_Offence_Conv 0.07 0.06
## Per_Burg_Conv 0.13 0.03
## Per_Rob_Conv -0.06 0.02
## Per_TheftAndHandling_Conv 1.00 0.09
## Per_FraudAndForgery_Conv 0.09 1.00
## Per_CrimeDamage_Conv 0.27 0.06
## Per_DrugOffences_Conv 0.19 0.05
## Per_PublicOrderOffences_Conv 0.33 0.07
## Per_Others_Ex_Motoring 0.13 0.02
## Per_Motoring_Offences_Conv 0.23 0.05
## Per_CrimeDamage_Conv Per_DrugOffences_Conv
## Per_Homicide_Conv -0.05 -0.07
## Per_Conv_Offence 0.27 0.20
## Per_Sex_Offence_Conv 0.10 0.07
## Per_Burg_Conv 0.14 0.11
## Per_Rob_Conv -0.02 -0.02
## Per_TheftAndHandling_Conv 0.27 0.19
## Per_FraudAndForgery_Conv 0.06 0.05
## Per_CrimeDamage_Conv 1.00 0.18
## Per_DrugOffences_Conv 0.18 1.00
## Per_PublicOrderOffences_Conv 0.25 0.19
## Per_Others_Ex_Motoring 0.09 0.06
## Per_Motoring_Offences_Conv 0.15 0.15
## Per_PublicOrderOffences_Conv
## Per_Homicide_Conv 0.01
## Per_Conv_Offence 0.35
## Per_Sex_Offence_Conv 0.08
## Per_Burg_Conv 0.14
## Per_Rob_Conv 0.00
## Per_TheftAndHandling_Conv 0.33
## Per_FraudAndForgery_Conv 0.07
## Per_CrimeDamage_Conv 0.25
## Per_DrugOffences_Conv 0.19
## Per_PublicOrderOffences_Conv 1.00
## Per_Others_Ex_Motoring 0.10
## Per_Motoring_Offences_Conv 0.17
## Per_Others_Ex_Motoring Per_Motoring_Offences_Conv
## Per_Homicide_Conv 0.00 -0.12
## Per_Conv_Offence 0.14 0.28
## Per_Sex_Offence_Conv 0.07 0.12
## Per_Burg_Conv 0.05 0.09
## Per_Rob_Conv 0.02 -0.02
## Per_TheftAndHandling_Conv 0.13 0.23
## Per_FraudAndForgery_Conv 0.02 0.05
## Per_CrimeDamage_Conv 0.09 0.15
## Per_DrugOffences_Conv 0.06 0.15
## Per_PublicOrderOffences_Conv 0.10 0.17
## Per_Others_Ex_Motoring 1.00 0.04
## Per_Motoring_Offences_Conv 0.04 1.00
##
## n= 2142
##
##
## P
## Per_Homicide_Conv Per_Conv_Offence
## Per_Homicide_Conv 0.0068
## Per_Conv_Offence 0.0068
## Per_Sex_Offence_Conv 0.3480 0.0000
## Per_Burg_Conv 0.5370 0.0000
## Per_Rob_Conv 0.8072 0.0698
## Per_TheftAndHandling_Conv 0.9059 0.0000
## Per_FraudAndForgery_Conv 0.2328 0.0000
## Per_CrimeDamage_Conv 0.0185 0.0000
## Per_DrugOffences_Conv 0.0006 0.0000
## Per_PublicOrderOffences_Conv 0.7995 0.0000
## Per_Others_Ex_Motoring 0.9019 0.0000
## Per_Motoring_Offences_Conv 0.0000 0.0000
## Per_Sex_Offence_Conv Per_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv 0.3480 0.5370 0.8072
## Per_Conv_Offence 0.0000 0.0000 0.0698
## Per_Sex_Offence_Conv 0.0725 0.0752
## Per_Burg_Conv 0.0725 0.0028
## Per_Rob_Conv 0.0752 0.0028
## Per_TheftAndHandling_Conv 0.0022 0.0000 0.0071
## Per_FraudAndForgery_Conv 0.0029 0.2402 0.3250
## Per_CrimeDamage_Conv 0.0000 0.0000 0.4311
## Per_DrugOffences_Conv 0.0022 0.0000 0.3760
## Per_PublicOrderOffences_Conv 0.0002 0.0000 0.8718
## Per_Others_Ex_Motoring 0.0007 0.0143 0.2902
## Per_Motoring_Offences_Conv 0.0000 0.0000 0.2899
## Per_TheftAndHandling_Conv Per_FraudAndForgery_Conv
## Per_Homicide_Conv 0.9059 0.2328
## Per_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0022 0.0029
## Per_Burg_Conv 0.0000 0.2402
## Per_Rob_Conv 0.0071 0.3250
## Per_TheftAndHandling_Conv 0.0000
## Per_FraudAndForgery_Conv 0.0000
## Per_CrimeDamage_Conv 0.0000 0.0092
## Per_DrugOffences_Conv 0.0000 0.0330
## Per_PublicOrderOffences_Conv 0.0000 0.0016
## Per_Others_Ex_Motoring 0.0000 0.4758
## Per_Motoring_Offences_Conv 0.0000 0.0117
## Per_CrimeDamage_Conv Per_DrugOffences_Conv
## Per_Homicide_Conv 0.0185 0.0006
## Per_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0000 0.0022
## Per_Burg_Conv 0.0000 0.0000
## Per_Rob_Conv 0.4311 0.3760
## Per_TheftAndHandling_Conv 0.0000 0.0000
## Per_FraudAndForgery_Conv 0.0092 0.0330
## Per_CrimeDamage_Conv 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_PublicOrderOffences_Conv 0.0000 0.0000
## Per_Others_Ex_Motoring 0.0000 0.0039
## Per_Motoring_Offences_Conv 0.0000 0.0000
## Per_PublicOrderOffences_Conv
## Per_Homicide_Conv 0.7995
## Per_Conv_Offence 0.0000
## Per_Sex_Offence_Conv 0.0002
## Per_Burg_Conv 0.0000
## Per_Rob_Conv 0.8718
## Per_TheftAndHandling_Conv 0.0000
## Per_FraudAndForgery_Conv 0.0016
## Per_CrimeDamage_Conv 0.0000
## Per_DrugOffences_Conv 0.0000
## Per_PublicOrderOffences_Conv
## Per_Others_Ex_Motoring 0.0000
## Per_Motoring_Offences_Conv 0.0000
## Per_Others_Ex_Motoring Per_Motoring_Offences_Conv
## Per_Homicide_Conv 0.9019 0.0000
## Per_Conv_Offence 0.0000 0.0000
## Per_Sex_Offence_Conv 0.0007 0.0000
## Per_Burg_Conv 0.0143 0.0000
## Per_Rob_Conv 0.2902 0.2899
## Per_TheftAndHandling_Conv 0.0000 0.0000
## Per_FraudAndForgery_Conv 0.4758 0.0117
## Per_CrimeDamage_Conv 0.0000 0.0000
## Per_DrugOffences_Conv 0.0039 0.0000
## Per_PublicOrderOffences_Conv 0.0000 0.0000
## Per_Others_Ex_Motoring 0.0486
## Per_Motoring_Offences_Conv 0.0486Correlation measurement for visualization for successful convictions
CorrRound_Succ_Convic = round(cor(LN_Matrix_Succ_Convic), 1) #This is to round to one decimal place.
head(CorrRound_Succ_Convic)## Per_Homicide_Conv Per_Conv_Offence
## Per_Homicide_Conv 1.0 -0.1
## Per_Conv_Offence -0.1 1.0
## Per_Sex_Offence_Conv 0.0 0.2
## Per_Burg_Conv 0.0 0.2
## Per_Rob_Conv 0.0 0.0
## Per_TheftAndHandling_Conv 0.0 0.4
## Per_Sex_Offence_Conv Per_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv 0.0 0.0 0.0
## Per_Conv_Offence 0.2 0.2 0.0
## Per_Sex_Offence_Conv 1.0 0.0 0.0
## Per_Burg_Conv 0.0 1.0 0.1
## Per_Rob_Conv 0.0 0.1 1.0
## Per_TheftAndHandling_Conv 0.1 0.1 -0.1
## Per_TheftAndHandling_Conv Per_FraudAndForgery_Conv
## Per_Homicide_Conv 0.0 0.0
## Per_Conv_Offence 0.4 0.1
## Per_Sex_Offence_Conv 0.1 0.1
## Per_Burg_Conv 0.1 0.0
## Per_Rob_Conv -0.1 0.0
## Per_TheftAndHandling_Conv 1.0 0.1
## Per_CrimeDamage_Conv Per_DrugOffences_Conv
## Per_Homicide_Conv -0.1 -0.1
## Per_Conv_Offence 0.3 0.2
## Per_Sex_Offence_Conv 0.1 0.1
## Per_Burg_Conv 0.1 0.1
## Per_Rob_Conv 0.0 0.0
## Per_TheftAndHandling_Conv 0.3 0.2
## Per_PublicOrderOffences_Conv Per_Others_Ex_Motoring
## Per_Homicide_Conv 0.0 0.0
## Per_Conv_Offence 0.3 0.1
## Per_Sex_Offence_Conv 0.1 0.1
## Per_Burg_Conv 0.1 0.1
## Per_Rob_Conv 0.0 0.0
## Per_TheftAndHandling_Conv 0.3 0.1
## Per_Motoring_Offences_Conv
## Per_Homicide_Conv -0.1
## Per_Conv_Offence 0.3
## Per_Sex_Offence_Conv 0.1
## Per_Burg_Conv 0.1
## Per_Rob_Conv 0.0
## Per_TheftAndHandling_Conv 0.2This is to confirm that the calculated correlation coefficient has been rounded to one decimal place.
Computation of correlation p-values for successful convictions
PVcorr_Succ_Convic = cor_pmat(LN_Matrix_Succ_Convic)
head(PVcorr_Succ_Convic )## Per_Homicide_Conv Per_Conv_Offence
## Per_Homicide_Conv 0.000000000 6.820633e-03
## Per_Conv_Offence 0.006820633 0.000000e+00
## Per_Sex_Offence_Conv 0.348010790 6.452465e-17
## Per_Burg_Conv 0.537020086 2.746020e-21
## Per_Rob_Conv 0.807207867 6.982532e-02
## Per_TheftAndHandling_Conv 0.905931034 2.379218e-78
## Per_Sex_Offence_Conv Per_Burg_Conv Per_Rob_Conv
## Per_Homicide_Conv 3.480108e-01 5.370201e-01 0.807207867
## Per_Conv_Offence 6.452465e-17 2.746020e-21 0.069825323
## Per_Sex_Offence_Conv 0.000000e+00 7.253813e-02 0.075240152
## Per_Burg_Conv 7.253813e-02 0.000000e+00 0.002774121
## Per_Rob_Conv 7.524015e-02 2.774121e-03 0.000000000
## Per_TheftAndHandling_Conv 2.227998e-03 4.239391e-10 0.007143215
## Per_TheftAndHandling_Conv Per_FraudAndForgery_Conv
## Per_Homicide_Conv 9.059310e-01 2.327797e-01
## Per_Conv_Offence 2.379218e-78 3.106134e-06
## Per_Sex_Offence_Conv 2.227998e-03 2.877458e-03
## Per_Burg_Conv 4.239391e-10 2.401552e-01
## Per_Rob_Conv 7.143215e-03 3.249799e-01
## Per_TheftAndHandling_Conv 0.000000e+00 3.006188e-05
## Per_CrimeDamage_Conv Per_DrugOffences_Conv
## Per_Homicide_Conv 1.851777e-02 5.740209e-04
## Per_Conv_Offence 3.928852e-38 3.410485e-20
## Per_Sex_Offence_Conv 2.652135e-06 2.174705e-03
## Per_Burg_Conv 5.376042e-11 1.466876e-07
## Per_Rob_Conv 4.311071e-01 3.759707e-01
## Per_TheftAndHandling_Conv 6.087503e-37 5.811372e-19
## Per_PublicOrderOffences_Conv Per_Others_Ex_Motoring
## Per_Homicide_Conv 7.994592e-01 9.019275e-01
## Per_Conv_Offence 3.657528e-62 4.512655e-11
## Per_Sex_Offence_Conv 1.778122e-04 6.809998e-04
## Per_Burg_Conv 3.907023e-11 1.432420e-02
## Per_Rob_Conv 8.717874e-01 2.901956e-01
## Per_TheftAndHandling_Conv 8.697534e-56 4.078150e-10
## Per_Motoring_Offences_Conv
## Per_Homicide_Conv 8.746040e-09
## Per_Conv_Offence 6.137220e-41
## Per_Sex_Offence_Conv 1.359617e-08
## Per_Burg_Conv 1.907582e-05
## Per_Rob_Conv 2.899495e-01
## Per_TheftAndHandling_Conv 7.936549e-28This shows the correlation p-values for successful convictions
Graphical presentation of the correlation matrix for successful convictions
CorrRound_Succ_Convic.plot = ggcorrplot(
CorrRound_Succ_Convic, hc.order = TRUE, type='lower', outline.color = 'black', method = 'square',
p.mat = PVcorr_Succ_Convic
)
CorrRound_Succ_Convic.plot
ggplotly(CorrRound_Succ_Convic.plot)This shows the relationships that exist between the principal offences.
Linear Regression of the successful convictions
LNModel_Succ_Convic = lm( formular = Per_All_Convictions ~ Per_Sex_Offence_Conv + Per_Burg_Conv + Per_TheftAndHandling_Conv + Per_CrimeDamage_Conv + Per_DrugOffences_Conv + Per_PublicOrderOffences_Conv + Per_Rob_Conv + Per_Others_Ex_Motoring + Per_Motoring_Offences_Conv, data=LN_Matrix_Succ_Convic)
summary(LNModel_Succ_Convic)##
## Call:
## lm(data = LN_Matrix_Succ_Convic, formular = Per_All_Convictions ~
## Per_Sex_Offence_Conv + Per_Burg_Conv + Per_TheftAndHandling_Conv +
## Per_CrimeDamage_Conv + Per_DrugOffences_Conv + Per_PublicOrderOffences_Conv +
## Per_Rob_Conv + Per_Others_Ex_Motoring + Per_Motoring_Offences_Conv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.40 -51.02 15.76 41.88 64.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 173.822908 35.298275 4.924 9.11e-07 ***
## Per_Conv_Offence -0.344672 0.214704 -1.605 0.1086
## Per_Sex_Offence_Conv 0.008437 0.072688 0.116 0.9076
## Per_Burg_Conv 0.166385 0.111890 1.487 0.1371
## Per_Rob_Conv -0.000104 0.035362 -0.003 0.9977
## Per_TheftAndHandling_Conv 0.612925 0.331547 1.849 0.0646 .
## Per_FraudAndForgery_Conv -0.074310 0.087533 -0.849 0.3960
## Per_CrimeDamage_Conv -0.254201 0.153509 -1.656 0.0979 .
## Per_DrugOffences_Conv -0.753211 0.273830 -2.751 0.0060 **
## Per_PublicOrderOffences_Conv 0.303841 0.171515 1.772 0.0766 .
## Per_Others_Ex_Motoring 0.013167 0.082075 0.160 0.8726
## Per_Motoring_Offences_Conv -1.030608 0.199348 -5.170 2.56e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 44.78 on 2130 degrees of freedom
## Multiple R-squared: 0.02442, Adjusted R-squared: 0.01938
## F-statistic: 4.847 on 11 and 2130 DF, p-value: 1.979e-07The p-value is shown to be above 0.05 which imply that there could be a relationship between the variables.
To improve the model, dropping robbery conviction
LNModel_Succ_Convic = lm( formular = Per_All_Convictions ~ Per_Sex_Offence_Conv + Per_Burg_Conv+ Per_TheftAndHandling_Conv + Per_CrimeDamage_Conv + Per_DrugOffences_Conv + Per_PublicOrderOffences_Conv + Per_Others_Ex_Motoring + Per_Motoring_Offences_Conv, data=LN_Matrix_Succ_Convic)
summary(LNModel_Succ_Convic)##
## Call:
## lm(data = LN_Matrix_Succ_Convic, formular = Per_All_Convictions ~
## Per_Sex_Offence_Conv + Per_Burg_Conv + Per_TheftAndHandling_Conv +
## Per_CrimeDamage_Conv + Per_DrugOffences_Conv + Per_PublicOrderOffences_Conv +
## Per_Others_Ex_Motoring + Per_Motoring_Offences_Conv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.40 -51.02 15.76 41.88 64.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 173.822908 35.298275 4.924 9.11e-07 ***
## Per_Conv_Offence -0.344672 0.214704 -1.605 0.1086
## Per_Sex_Offence_Conv 0.008437 0.072688 0.116 0.9076
## Per_Burg_Conv 0.166385 0.111890 1.487 0.1371
## Per_Rob_Conv -0.000104 0.035362 -0.003 0.9977
## Per_TheftAndHandling_Conv 0.612925 0.331547 1.849 0.0646 .
## Per_FraudAndForgery_Conv -0.074310 0.087533 -0.849 0.3960
## Per_CrimeDamage_Conv -0.254201 0.153509 -1.656 0.0979 .
## Per_DrugOffences_Conv -0.753211 0.273830 -2.751 0.0060 **
## Per_PublicOrderOffences_Conv 0.303841 0.171515 1.772 0.0766 .
## Per_Others_Ex_Motoring 0.013167 0.082075 0.160 0.8726
## Per_Motoring_Offences_Conv -1.030608 0.199348 -5.170 2.56e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 44.78 on 2130 degrees of freedom
## Multiple R-squared: 0.02442, Adjusted R-squared: 0.01938
## F-statistic: 4.847 on 11 and 2130 DF, p-value: 1.979e-07Despite dropping robbery conviction, there still doesn’t seem to be a significant impact on the model.
#Predict the model by assigning abitrary values
Arbitrary_values = data.frame(
Per_Conv_Offence = 0,
Per_Rob_Conv = 0,
Per_FraudAndForgery_Conv = 0,
Per_Sex_Offence_Conv = 80.5,
Per_Burg_Conv = 90.5,
Per_TheftAndHandling_Conv = 86.5,
Per_CrimeDamage_Conv = 60,
Per_DrugOffences_Conv = 95.7,
Per_PublicOrderOffences_Conv = 77.68,
Per_Others_Ex_Motoring = 99.9,
Per_Motoring_Offences_Conv = 92.0
)To Predict the assigned values
predictSucConvic = predict(LNModel_Succ_Convic, Arbitrary_values, level=.95, interval='prediction')
print(predictSucConvic)## fit lwr upr
## 1 85.34545 -9.521973 180.2129The lower limit shows -9.52, while the upper limit shows 180.21 with a fit of 85.34%. This shows a great fit for the model.
References
About CPS https://www.cps.gov.uk/about-cps
Define Libraries: https://hbctraining.github.io/Intro-to-R-flipped/lessons/04_introR_packages.html
Defining working directory https://r-coder.com/working-directory-r/#:~:text=The%20working%20directory%20in%20R,R%20objects%20will%20be%20saved.
Data Frame defined https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/data.frame
Data frame explained http://uc-r.github.io/dataframes
EDA DEFINED https://www.jmp.com/en_no/statistics-knowledge-portal/exploratory-data-analysis.html
Data Cleaning https://www.tableau.com/learn/articles/what-is-data-cleaning#:~:text=Data%20cleaning%20is%20the%20process,to%20be%20duplicated%20or%20mislabeled.
Data Structure https://www.geeksforgeeks.org/data-structures/
random sampling https://www.scribbr.co.uk/research-methods/simple-random-sampling-method/
k-means Clustering https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/
Removing columnhttps://www.listendata.com/2015/06/r-keep-drop-columns-from-data-frame.html
Missing valueshttps://www.geeksforgeeks.org/how-to-find-and-count-missing-values-in-r-dataframe/
Removing NAshttps://sparkbyexamples.com/r-programming/remove-rows-with-na-in-r/#:~:text=By%20using%20na.,values)%20from%20R%20data%20frame.
Random Samplinghttps://www.programmingr.com/examples/neat-tricks/sample-r-function/
Box Plothttps://www.simplypsychology.org/boxplots.html#:~:text=When%20the%20median%20is%20in,positively%20skewed%20(skewed%20right).
Outlier detection and treatmenthttp://r-statistics.co/Outlier-Treatment-With-R.html
Data Standardizationhttps://www.r-bloggers.com/2022/07/how-to-standardize-data-in-r/#:~:text=How%20to%20Standardize%20Data%20in%20R%3F%2C%20A%20dataset%20must%20be,used%20method%20for%20doing%20this.
https://www.egnyte.com/guides/life-sciences/data-standardization#:~:text=Data%20standardization%20is%20an%20important,Identifying%20data%20errors
k-mean clusteringhttps://www.geeksforgeeks.org/k-means-clustering-in-r-programming/
Correlation https://www.displayr.com/how-to-create-a-correlation-matrix-in-r/
Linear Regression https://www.scribbr.com/statistics/linear-regression-in-r/