Crimes in Boston - Quick EDA and Naive Bayes classifier with R
Siyi Lin, Drexel University, sl3633@drexel.edu
This is my coursework. This project is to discover the crime in Boston area. We use the dataset from Kaggle, Crime in Boston
- What’s the dataset about?
This is a dataset containing records from the new crime incident report system in Boston area, which includes a reduced set of fields focused on capturing the type of incident as well as when and where it occurred.
- Which party collected the dataset?
Crime incident reports are provided by Boston Police Department (BPD) to document the initial details surrounding an incident to which BPD officers respond.
- Which period of time is covered?
June 14, 2015 to September 3, 2018
- Other EDAs by using Tableau
I also create an interactive dashboard by tableau. Feel free to check the link and click them.
The link: Crime in Boston Dashboard
Load Library
library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## datelibrary(DataExplorer)Load Data
Boston_Crime <- read.table("Boston_Crime.csv", header = TRUE, sep=",")Inspecting data frames
Use head() function to view the first few rows.
head(Boston_Crime,5)## INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP
## 1 I182080058 2403 Disorderly Conduct
## 2 I182080053 3201 Property Lost
## 3 I182080052 2647 Other
## 4 I182080051 413 Aggravated Assault
## 5 I182080050 3122 Aircraft
## OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING
## 1 DISTURBING THE PEACE E18 495
## 2 PROPERTY - LOST D14 795
## 3 THREATS TO DO BODILY HARM B2 329
## 4 ASSAULT - AGGRAVATED - BATTERY A1 92
## 5 AIRCRAFT INCIDENTS A7 36
## OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART STREET
## 1 2018-10-03 20:13 2018 10 Wednesday 20 Part Two ARLINGTON ST
## 2 2018-08-30 20:00 2018 8 Thursday 20 Part Three ALLSTON ST
## 3 2018-10-03 19:20 2018 10 Wednesday 19 Part Two DEVON ST
## 4 2018-10-03 20:00 2018 10 Wednesday 20 Part One CAMBRIDGE ST
## 5 2018-10-03 20:49 2018 10 Wednesday 20 Part Three PRESCOTT ST
## Lat Long Location
## 1 42.26261 -71.12119 (42.26260773, -71.12118637)
## 2 42.35211 -71.13531 (42.35211146, -71.13531147)
## 3 42.30813 -71.07693 (42.30812619, -71.07692974)
## 4 42.35945 -71.05965 (42.35945371, -71.05964817)
## 5 42.37526 -71.02466 (42.37525782, -71.02466343)Use str() function to check the structure of the data
str(Boston_Crime)## 'data.frame': 151764 obs. of 17 variables:
## $ INCIDENT_NUMBER : Factor w/ 134389 levels "142052550","I010370257-00",..: 134389 134388 134387 134386 134385 134384 134383 134382 134381 134380 ...
## $ OFFENSE_CODE : int 2403 3201 2647 413 3122 1402 3803 3301 802 3410 ...
## $ OFFENSE_CODE_GROUP : Factor w/ 66 levels "Aggravated Assault",..: 14 52 46 1 2 63 43 64 61 62 ...
## $ OFFENSE_DESCRIPTION: Factor w/ 242 levels "A&B HANDS, FEET, ETC. - MED. ATTENTION REQ.",..: 62 185 220 13 5 229 160 230 21 221 ...
## $ DISTRICT : Factor w/ 13 levels "","A1","A15",..: 12 9 5 2 4 7 1 5 12 10 ...
## $ REPORTING_AREA : int 495 795 329 92 36 351 NA 603 543 621 ...
## $ SHOOTING : Factor w/ 2 levels "","Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OCCURRED_ON_DATE : Factor w/ 114576 levels "2015-06-15 0:00",..: 114572 112099 114564 114571 114574 114524 114573 114567 114565 114571 ...
## $ YEAR : int 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
## $ MONTH : int 10 8 10 10 10 10 10 10 10 10 ...
## $ DAY_OF_WEEK : Factor w/ 7 levels "Friday","Monday",..: 7 5 7 7 7 6 7 7 7 7 ...
## $ HOUR : int 20 20 19 20 20 20 20 19 19 20 ...
## $ UCR_PART : Factor w/ 5 levels "","Other","Part One",..: 5 4 5 3 4 5 4 4 5 4 ...
## $ STREET : Factor w/ 4241 levels ""," ALBANY ST ",..: 180 100 1059 615 3129 1093 1 3834 241 856 ...
## $ Lat : num 42.3 42.4 42.3 42.4 42.4 ...
## $ Long : num -71.1 -71.1 -71.1 -71.1 -71 ...
## $ Location : Factor w/ 15433 levels "(-1.00000000, -1.00000000)",..: 729 12456 5788 13499 14566 4885 7672 9497 477 11790 ...summary(Boston_Crime)## INCIDENT_NUMBER OFFENSE_CODE
## I152080623 : 11 Min. : 111
## I172096394 : 10 1st Qu.:1001
## I182065208 : 10 Median :2914
## I162071327 : 9 Mean :2318
## I172056883 : 9 3rd Qu.:3201
## I130041200-00: 8 Max. :3831
## (Other) :151707
## OFFENSE_CODE_GROUP
## Motor Vehicle Accident Response:17733
## Larceny :12268
## Medical Assistance :11193
## Investigate Person : 8883
## Other : 8502
## Drug Violation : 7702
## (Other) :85483
## OFFENSE_DESCRIPTION DISTRICT
## SICK/INJURED/MEDICAL - PERSON : 8908 B2 :23667
## INVESTIGATE PERSON : 8887 C11 :20340
## M/V - LEAVING SCENE - PROPERTY DAMAGE: 7790 D4 :19929
## VANDALISM : 7351 A1 :17074
## ASSAULT SIMPLE - BATTERY : 7076 B3 :16875
## VERBAL DISPUTE : 6190 C6 :11204
## (Other) :105562 (Other):42675
## REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR
## Min. : 0.0 :151229 2015-06-18 5:00 : 22 Min. :2015
## 1st Qu.:177.0 Y: 535 2015-07-01 0:00 : 16 1st Qu.:2016
## Median :344.0 2017-06-01 0:00 : 16 Median :2016
## Mean :383.7 2015-12-07 11:38: 14 Mean :2017
## 3rd Qu.:546.0 2015-10-02 21:00: 13 3rd Qu.:2017
## Max. :962.0 2016-04-01 0:00 : 13 Max. :2018
## NA's :9664 (Other) :151670
## MONTH DAY_OF_WEEK HOUR UCR_PART
## Min. : 1.000 Friday :23242 Min. : 0.00 : 42
## 1st Qu.: 4.000 Monday :21896 1st Qu.: 9.00 Other : 578
## Median : 7.000 Saturday :22236 Median :14.00 Part One :29375
## Mean : 6.733 Sunday :19905 Mean :13.08 Part Three:75654
## 3rd Qu.: 9.000 Thursday :21212 3rd Qu.:18.00 Part Two :46115
## Max. :12.000 Tuesday :22050 Max. :23.00
## Wednesday:21223
## STREET Lat Long
## WASHINGTON ST : 6596 Min. :-1.00 Min. :-71.18
## : 5303 1st Qu.:42.30 1st Qu.:-71.10
## BLUE HILL AVE : 3703 Median :42.33 Median :-71.08
## BOYLSTON ST : 3472 Mean :42.21 Mean :-70.89
## DORCHESTER AVE: 2457 3rd Qu.:42.35 3rd Qu.:-71.06
## TREMONT ST : 2288 Max. :42.40 Max. : -1.00
## (Other) :127945 NA's :9460 NA's :9460
## Location
## (0.00000000, 0.00000000) : 9460
## (42.34862382, -71.08277637): 572
## (42.36183857, -71.05976489): 548
## (42.28482577, -71.09137369): 542
## (42.32866284, -71.08563401): 461
## (42.25621592, -71.12401947): 395
## (Other) :139786Anthor way: to use glimpse() function to see every column in a data frame
glimpse(Boston_Crime)## Observations: 151,764
## Variables: 17
## $ INCIDENT_NUMBER <fct> I182080058, I182080053, I182080052, I18208...
## $ OFFENSE_CODE <int> 2403, 3201, 2647, 413, 3122, 1402, 3803, 3...
## $ OFFENSE_CODE_GROUP <fct> Disorderly Conduct, Property Lost, Other, ...
## $ OFFENSE_DESCRIPTION <fct> "DISTURBING THE PEACE", "PROPERTY - LOST",...
## $ DISTRICT <fct> E18, D14, B2, A1, A7, C11, , B2, E18, D4, ...
## $ REPORTING_AREA <int> 495, 795, 329, 92, 36, 351, NA, 603, 543, ...
## $ SHOOTING <fct> , , , , , , , , , , , , , , , , , , , , , ...
## $ OCCURRED_ON_DATE <fct> 2018-10-03 20:13, 2018-08-30 20:00, 2018-1...
## $ YEAR <int> 2018, 2018, 2018, 2018, 2018, 2018, 2018, ...
## $ MONTH <int> 10, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10,...
## $ DAY_OF_WEEK <fct> Wednesday, Thursday, Wednesday, Wednesday,...
## $ HOUR <int> 20, 20, 19, 20, 20, 20, 20, 19, 19, 20, 19...
## $ UCR_PART <fct> Part Two, Part Three, Part Two, Part One, ...
## $ STREET <fct> ARLINGTON ST, ALLSTON ST, DEVON ST, CAMBRI...
## $ Lat <dbl> 42.26261, 42.35211, 42.30813, 42.35945, 42...
## $ Long <dbl> -71.12119, -71.13531, -71.07693, -71.05965...
## $ Location <fct> "(42.26260773, -71.12118637)", "(42.352111...Let’s check the completeness of the data.
plot_intro(Boston_Crime)
Let’s start the visualization and Exploratory Data Analysis (EDA)
plot_correlation(Boston_Crime)## 6 features with more than 20 categories ignored!
## INCIDENT_NUMBER: 134389 categories
## OFFENSE_CODE_GROUP: 66 categories
## OFFENSE_DESCRIPTION: 242 categories
## OCCURRED_ON_DATE: 114576 categories
## STREET: 4241 categories
## Location: 15433 categories
plot_histogram(Boston_Crime)
plot_bar(Boston_Crime)## 6 columns ignored with more than 50 categories.
## INCIDENT_NUMBER: 134389 categories
## OFFENSE_CODE_GROUP: 66 categories
## OFFENSE_DESCRIPTION: 242 categories
## OCCURRED_ON_DATE: 114576 categories
## STREET: 4241 categories
## Location: 15433 categories
Boston_Crime %>% separate(OCCURRED_ON_DATE, c("Date", "Time"), sep = " ") %>% mutate(Date = ymd(Date)) %>%
ggplot(aes(Date))+
geom_freqpoly() +
ylab("Number of Crimes")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(30) %>%
ggplot(aes(reorder(OFFENSE_CODE_GROUP,n), n))+
geom_col()+
coord_flip()+
labs(x = NULL, y = 'Counts')
Boston_Crime %>% count(STREET) %>% arrange(-n) %>% head(15) %>%
ggplot(aes(reorder(STREET,n), n))+
geom_col()+
coord_flip()+
labs(x = NULL, y = NULL)
Boston_Crime %>% filter(DISTRICT %in% (Boston_Crime %>% count(DISTRICT) %>% arrange(-n) %>% pull(DISTRICT)),
OFFENSE_CODE_GROUP %in% (Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(5) %>% pull(OFFENSE_CODE_GROUP))
) %>%
ggplot(aes(DISTRICT, fill = OFFENSE_CODE_GROUP))+
geom_bar()+
scale_fill_ordinal()+
coord_flip()
Boston_Crime %>% filter(STREET %in% (Boston_Crime %>% count(STREET) %>% arrange(-n) %>% head(5) %>% pull(STREET)),
OFFENSE_CODE_GROUP %in% (Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(5) %>% pull(OFFENSE_CODE_GROUP))
) %>%
ggplot(aes(STREET, fill = OFFENSE_CODE_GROUP))+
geom_bar(position = "fill")+
scale_fill_ordinal()+
coord_flip()
Boston_Crime %>% filter(STREET %in% (Boston_Crime %>% count(STREET) %>% arrange(-n) %>% head(5) %>% pull(STREET))) %>%
ggplot(aes(DISTRICT, fill = STREET))+
geom_bar(position = "fill")+
coord_flip()+
scale_fill_ordinal()
Boston_Crime %>% filter(STREET %in% (Boston_Crime %>% count(STREET) %>% arrange(-n) %>% head(5) %>% pull(STREET))) %>%
ggplot(aes(DAY_OF_WEEK, fill = STREET))+
geom_bar()+
coord_flip()+
scale_fill_ordinal()
Boston_Crime %>% filter(OFFENSE_CODE_GROUP %in% (Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(5) %>% pull(OFFENSE_CODE_GROUP))) %>%
ggplot(aes(HOUR, fill = OFFENSE_CODE_GROUP))+
geom_bar()+
coord_flip()+
scale_fill_ordinal()
Boston_Crime %>% filter(OFFENSE_CODE_GROUP %in% (Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(5) %>% pull(OFFENSE_CODE_GROUP))) %>%
ggplot(aes(HOUR, fill = OFFENSE_CODE_GROUP))+
geom_bar()+
facet_wrap(~YEAR)+
coord_flip()+
scale_fill_ordinal()
Boston_Crime %>% filter(OFFENSE_CODE_GROUP %in% (Boston_Crime %>% count(OFFENSE_CODE_GROUP) %>% arrange(-n) %>% head(5) %>% pull(OFFENSE_CODE_GROUP))) %>%
ggplot(aes(DISTRICT, fill = OFFENSE_CODE_GROUP))+
geom_bar()+
facet_wrap(~YEAR)+
coord_flip()+
scale_fill_ordinal()
Modelling and Training a Naive Bayes Classifier
Load libraries
library(e1071)
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftImport the Data
Choose the columns we needed and filter them our; meanwhile, clean the null value
Boston_Crime_UCR <- Boston_Crime %>% filter(UCR_PART !='') %>% select(UCR_PART, OFFENSE_CODE_GROUP, DISTRICT, DAY_OF_WEEK, HOUR)Check and study the dataset
summary(Boston_Crime_UCR)## UCR_PART OFFENSE_CODE_GROUP
## : 0 Motor Vehicle Accident Response:17733
## Other : 578 Larceny :12268
## Part One :29375 Medical Assistance :11193
## Part Three:75654 Investigate Person : 8883
## Part Two :46115 Other : 8502
## Drug Violation : 7702
## (Other) :85441
## DISTRICT DAY_OF_WEEK HOUR
## B2 :23659 Friday :23236 Min. : 0.00
## C11 :20334 Monday :21887 1st Qu.: 9.00
## D4 :19926 Saturday :22232 Median :14.00
## A1 :17072 Sunday :19897 Mean :13.08
## B3 :16867 Thursday :21207 3rd Qu.:18.00
## C6 :11202 Tuesday :22046 Max. :23.00
## (Other):42662 Wednesday:21217str(Boston_Crime_UCR)## 'data.frame': 151722 obs. of 5 variables:
## $ UCR_PART : Factor w/ 5 levels "","Other","Part One",..: 5 4 5 3 4 5 4 4 5 4 ...
## $ OFFENSE_CODE_GROUP: Factor w/ 66 levels "Aggravated Assault",..: 14 52 46 1 2 63 43 64 61 62 ...
## $ DISTRICT : Factor w/ 13 levels "","A1","A15",..: 12 9 5 2 4 7 1 5 12 10 ...
## $ DAY_OF_WEEK : Factor w/ 7 levels "Friday","Monday",..: 7 5 7 7 7 6 7 7 7 7 ...
## $ HOUR : int 20 20 19 20 20 20 20 19 19 20 ...Quick Exploratory Data Analysis
Boston_Crime_UCR %>% filter(UCR_PART %in% (Boston_Crime_UCR %>% count(UCR_PART) %>% arrange(-n) %>% head(5) %>% pull(UCR_PART))) %>%
ggplot(aes(HOUR, fill = UCR_PART))+
geom_bar()+
coord_flip()+
scale_fill_ordinal()
Boston_Crime_UCR %>%
ggplot(aes(x=UCR_PART, y=HOUR, fill=UCR_PART))+
geom_boxplot() +
ggtitle('Box Plot')
Boston_Crime_UCR %>%
ggplot(aes(x=HOUR, fill=UCR_PART))+
geom_density(alpha = 0.8, color = 'black') +
coord_flip()+
ggtitle('Density Plot')
Data Modelling
Partition the dataset in the train set and the test set
set.seed(1234)
id <- sample(2, nrow(Boston_Crime_UCR), replace = T, prob = c(0.8, 0.2))
train <- Boston_Crime_UCR[id == 1,]
test <- Boston_Crime_UCR[id == 2,]Round 1：
Create Naive Bayes model by using the training data set
crime_nb1 <- naiveBayes(as.factor(HOUR)~., data=train, )
crime_nb1##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1 2 3 4 5
## 0.04822577 0.03008044 0.02420748 0.01427114 0.01049567 0.01076710
## 6 7 8 9 10 11
## 0.01509369 0.02760459 0.04034580 0.04667939 0.05239607 0.05247010
## 12 13 14 15 16 17
## 0.05902578 0.05275799 0.05310346 0.05066873 0.06107391 0.06396104
## 18 19 20 21 22 23
## 0.06386234 0.05537368 0.04855479 0.04454077 0.04040338 0.03403688
##
## Conditional probabilities:
## UCR_PART
## Y Other Part One Part Three Part Two
## 0 0.000000000 0.004434590 0.187105577 0.457274433 0.351185400
## 1 0.000000000 0.004648619 0.185124419 0.458299152 0.351927810
## 2 0.000000000 0.004077472 0.183486239 0.476044852 0.336391437
## 3 0.000000000 0.003458213 0.207492795 0.511239193 0.277809798
## 4 0.000000000 0.003918495 0.224137931 0.518025078 0.253918495
## 5 0.000000000 0.002291826 0.203208556 0.572192513 0.222307105
## 6 0.000000000 0.003814714 0.196185286 0.586376022 0.213623978
## 7 0.000000000 0.003575685 0.170143027 0.611740167 0.214541120
## 8 0.000000000 0.005300714 0.168807339 0.571661570 0.254230377
## 9 0.000000000 0.005991189 0.159471366 0.555418502 0.279118943
## 10 0.000000000 0.005651491 0.169387755 0.532496075 0.292464678
## 11 0.000000000 0.005329989 0.174321994 0.522182160 0.298165857
## 12 0.000000000 0.003483835 0.187151616 0.484253066 0.325111483
## 13 0.000000000 0.003741815 0.185687558 0.504521360 0.306049267
## 14 0.000000000 0.003717472 0.203531599 0.490706320 0.302044610
## 15 0.000000000 0.003084416 0.201623377 0.488474026 0.306818182
## 16 0.000000000 0.002693603 0.198383838 0.478114478 0.320808081
## 17 0.000000000 0.002443416 0.192644033 0.460519547 0.344393004
## 18 0.000000000 0.003219990 0.198093766 0.451571355 0.347114889
## 19 0.000000000 0.002673797 0.203802733 0.460190137 0.333333333
## 20 0.000000000 0.003049297 0.219718787 0.473657462 0.303574454
## 21 0.000000000 0.003878116 0.214219760 0.507848569 0.274053555
## 22 0.000000000 0.003257329 0.218851792 0.511604235 0.266286645
## 23 0.000000000 0.004108265 0.216288062 0.521991300 0.257612373
##
## OFFENSE_CODE_GROUP
## Y Aggravated Assault Aircraft Arson
## 0 0.0301893229 0.0000000000 0.0003411223
## 1 0.0418375718 0.0000000000 0.0002734482
## 2 0.0496092423 0.0000000000 0.0006795787
## 3 0.0351585014 0.0000000000 0.0000000000
## 4 0.0384012539 0.0000000000 0.0007836991
## 5 0.0213903743 0.0007639419 0.0007639419
## 6 0.0152588556 0.0000000000 0.0005449591
## 7 0.0140047676 0.0000000000 0.0002979738
## 8 0.0156982671 0.0002038736 0.0002038736
## 9 0.0119823789 0.0000000000 0.0003524229
## 10 0.0152276295 0.0001569859 0.0000000000
## 11 0.0169305534 0.0003135288 0.0000000000
## 12 0.0165830546 0.0000000000 0.0002787068
## 13 0.0208917992 0.0001559089 0.0000000000
## 14 0.0192069393 0.0001548947 0.0003097893
## 15 0.0215909091 0.0001623377 0.0001623377
## 16 0.0268013468 0.0000000000 0.0002693603
## 17 0.0198045267 0.0001286008 0.0002572016
## 18 0.0202215353 0.0001287996 0.0002575992
## 19 0.0251039810 0.0004456328 0.0004456328
## 20 0.0293071320 0.0001694054 0.0005082162
## 21 0.0323176362 0.0000000000 0.0001846722
## 22 0.0348127036 0.0000000000 0.0000000000
## 23 0.0413243113 0.0002416626 0.0002416626
## OFFENSE_CODE_GROUP
## Y Assembly or Gathering Violations Auto Theft Auto Theft Recovery
## 0 0.0121098414 0.0144976974 0.0039229064
## 1 0.0103910309 0.0131255127 0.0038282745
## 2 0.0050968400 0.0125722052 0.0033978933
## 3 0.0046109510 0.0161383285 0.0028818444
## 4 0.0031347962 0.0242946708 0.0023510972
## 5 0.0000000000 0.0175706646 0.0015278839
## 6 0.0005449591 0.0212534060 0.0032697548
## 7 0.0026817640 0.0116209774 0.0029797378
## 8 0.0014271152 0.0120285423 0.0038735984
## 9 0.0015859031 0.0126872247 0.0049339207
## 10 0.0021978022 0.0122448980 0.0053375196
## 11 0.0017244082 0.0112870356 0.0050164603
## 12 0.0033444816 0.0115663322 0.0029264214
## 13 0.0009354537 0.0121608980 0.0034299969
## 14 0.0015489467 0.0147149938 0.0034076828
## 15 0.0017857143 0.0116883117 0.0027597403
## 16 0.0016161616 0.0117171717 0.0020202020
## 17 0.0010288066 0.0122170782 0.0020576132
## 18 0.0011591963 0.0142967543 0.0027047913
## 19 0.0011883541 0.0164884135 0.0019310755
## 20 0.0010164323 0.0225309165 0.0023716754
## 21 0.0016620499 0.0236380425 0.0035087719
## 22 0.0030537459 0.0250407166 0.0032573290
## 23 0.0125664572 0.0212663122 0.0033832769
## OFFENSE_CODE_GROUP
## Y Ballistics Bomb Hoax Burglary - No Property Taken
## 0 0.0027289783 0.0003411223 0.0000000000
## 1 0.0054689636 0.0000000000 0.0000000000
## 2 0.0071355759 0.0000000000 0.0000000000
## 3 0.0109510086 0.0000000000 0.0000000000
## 4 0.0070532915 0.0000000000 0.0000000000
## 5 0.0045836516 0.0000000000 0.0000000000
## 6 0.0038147139 0.0000000000 0.0000000000
## 7 0.0011918951 0.0005959476 0.0000000000
## 8 0.0016309888 0.0000000000 0.0000000000
## 9 0.0015859031 0.0000000000 0.0000000000
## 10 0.0032967033 0.0006279435 0.0000000000
## 11 0.0015676438 0.0001567644 0.0001567644
## 12 0.0029264214 0.0005574136 0.0000000000
## 13 0.0021827253 0.0003118179 0.0000000000
## 14 0.0020136307 0.0000000000 0.0000000000
## 15 0.0017857143 0.0003246753 0.0000000000
## 16 0.0020202020 0.0002693603 0.0000000000
## 17 0.0020576132 0.0002572016 0.0000000000
## 18 0.0023183926 0.0002575992 0.0000000000
## 19 0.0028223411 0.0001485443 0.0000000000
## 20 0.0015246485 0.0000000000 0.0000000000
## 21 0.0025854109 0.0001846722 0.0000000000
## 22 0.0048859935 0.0000000000 0.0000000000
## 23 0.0070082165 0.0002416626 0.0000000000
## OFFENSE_CODE_GROUP
## Y Commercial Burglary Confidence Games Counterfeiting
## 0 0.0056285178 0.0274603445 0.0080163739
## 1 0.0068362045 0.0051955154 0.0013672409
## 2 0.0067957866 0.0057764186 0.0003397893
## 3 0.0195965418 0.0034582133 0.0023054755
## 4 0.0297805643 0.0000000000 0.0031347962
## 5 0.0183346066 0.0038197097 0.0007639419
## 6 0.0147138965 0.0076294278 0.0021798365
## 7 0.0071513707 0.0077473182 0.0020858164
## 8 0.0053007136 0.0099898063 0.0040774720
## 9 0.0054625551 0.0149779736 0.0065198238
## 10 0.0025117739 0.0095761381 0.0048665620
## 11 0.0018811726 0.0083085123 0.0064273397
## 12 0.0023690078 0.0154682274 0.0072463768
## 13 0.0009354537 0.0124727159 0.0067040848
## 14 0.0023234201 0.0117719950 0.0063506815
## 15 0.0019480519 0.0125000000 0.0060064935
## 16 0.0018855219 0.0082154882 0.0045791246
## 17 0.0012860082 0.0086162551 0.0047582305
## 18 0.0025759918 0.0099175683 0.0028335909
## 19 0.0031194296 0.0077243018 0.0026737968
## 20 0.0022022700 0.0050821616 0.0028798916
## 21 0.0022160665 0.0053554940 0.0029547553
## 22 0.0030537459 0.0065146580 0.0012214984
## 23 0.0053165781 0.0041082649 0.0009666506
## OFFENSE_CODE_GROUP
## Y Criminal Harassment Disorderly Conduct Drug Violation Embezzlement
## 0 0.0005116834 0.0126215248 0.0320654955 0.0017056115
## 1 0.0002734482 0.0169537873 0.0273448182 0.0002734482
## 2 0.0000000000 0.0220863065 0.0227658852 0.0000000000
## 3 0.0005763689 0.0178674352 0.0161383285 0.0000000000
## 4 0.0000000000 0.0078369906 0.0274294671 0.0007836991
## 5 0.0007639419 0.0061115355 0.0160427807 0.0000000000
## 6 0.0005449591 0.0070844687 0.0207084469 0.0000000000
## 7 0.0002979738 0.0047675805 0.0172824791 0.0000000000
## 8 0.0000000000 0.0069317023 0.0185524975 0.0010193680
## 9 0.0003524229 0.0066960352 0.0232599119 0.0015859031
## 10 0.0006279435 0.0065934066 0.0365777080 0.0010989011
## 11 0.0004702931 0.0062705753 0.0429534410 0.0004702931
## 12 0.0005574136 0.0072463768 0.0436176143 0.0016722408
## 13 0.0004677268 0.0067040848 0.0559713128 0.0007795447
## 14 0.0004646840 0.0044919455 0.0565365551 0.0015489467
## 15 0.0003246753 0.0042207792 0.0639610390 0.0009740260
## 16 0.0005387205 0.0057912458 0.0857912458 0.0005387205
## 17 0.0001286008 0.0069444444 0.1028806584 0.0005144033
## 18 0.0003863988 0.0061823802 0.0994332818 0.0005151984
## 19 0.0005941771 0.0068330362 0.0861556744 0.0007427213
## 20 0.0005082162 0.0096561071 0.0592918855 0.0005082162
## 21 0.0000000000 0.0072022161 0.0326869806 0.0003693444
## 22 0.0002035831 0.0107899023 0.0219869707 0.0002035831
## 23 0.0000000000 0.0089415176 0.0154664089 0.0007249879
## OFFENSE_CODE_GROUP
## Y Evading Fare Explosives Fire Related Reports Firearm Discovery
## 0 0.0020467338 0.0001705611 0.0051168344 0.0015350503
## 1 0.0038282745 0.0002734482 0.0084768936 0.0013672409
## 2 0.0047570506 0.0000000000 0.0074753653 0.0010193680
## 3 0.0028818444 0.0000000000 0.0109510086 0.0017291066
## 4 0.0031347962 0.0000000000 0.0109717868 0.0023510972
## 5 0.0007639419 0.0000000000 0.0084033613 0.0022918258
## 6 0.0005449591 0.0000000000 0.0038147139 0.0005449591
## 7 0.0008939213 0.0000000000 0.0053635280 0.0008939213
## 8 0.0006116208 0.0000000000 0.0032619776 0.0030581040
## 9 0.0003524229 0.0001762115 0.0054625551 0.0028193833
## 10 0.0007849294 0.0001569859 0.0056514914 0.0021978022
## 11 0.0006270575 0.0000000000 0.0040758740 0.0031352877
## 12 0.0004180602 0.0000000000 0.0058528428 0.0033444816
## 13 0.0012472716 0.0003118179 0.0045213595 0.0035859058
## 14 0.0006195787 0.0000000000 0.0068153656 0.0038723668
## 15 0.0008116883 0.0000000000 0.0060064935 0.0016233766
## 16 0.0008080808 0.0000000000 0.0076767677 0.0029629630
## 17 0.0010288066 0.0000000000 0.0050154321 0.0018004115
## 18 0.0007727975 0.0000000000 0.0068263782 0.0016743946
## 19 0.0019310755 0.0000000000 0.0063874034 0.0020796197
## 20 0.0008470269 0.0000000000 0.0084702694 0.0018634593
## 21 0.0020313943 0.0000000000 0.0075715605 0.0018467221
## 22 0.0016286645 0.0000000000 0.0061074919 0.0002035831
## 23 0.0026582890 0.0002416626 0.0060415660 0.0007249879
## OFFENSE_CODE_GROUP
## Y Firearm Violations Fraud Gambling Harassment
## 0 0.0071635681 0.0409346751 0.0000000000 0.0165444312
## 1 0.0084768936 0.0046486191 0.0000000000 0.0060158600
## 2 0.0084947333 0.0047570506 0.0000000000 0.0037376826
## 3 0.0074927954 0.0028818444 0.0000000000 0.0069164265
## 4 0.0086206897 0.0039184953 0.0000000000 0.0047021944
## 5 0.0061115355 0.0061115355 0.0007639419 0.0068754775
## 6 0.0010899183 0.0070844687 0.0000000000 0.0049046322
## 7 0.0017878427 0.0080452920 0.0000000000 0.0125148987
## 8 0.0024464832 0.0156982671 0.0000000000 0.0101936799
## 9 0.0031718062 0.0262555066 0.0000000000 0.0142731278
## 10 0.0037676609 0.0270015699 0.0000000000 0.0164835165
## 11 0.0048596959 0.0246120082 0.0000000000 0.0125411507
## 12 0.0052954292 0.0377647715 0.0000000000 0.0174191750
## 13 0.0067040848 0.0210477081 0.0001559089 0.0151231681
## 14 0.0044919455 0.0255576208 0.0000000000 0.0140954151
## 15 0.0047077922 0.0202922078 0.0000000000 0.0146103896
## 16 0.0049831650 0.0183164983 0.0000000000 0.0123905724
## 17 0.0061728395 0.0169753086 0.0001286008 0.0132458848
## 18 0.0065687790 0.0135239567 0.0000000000 0.0124935600
## 19 0.0077243018 0.0136660725 0.0000000000 0.0115864528
## 20 0.0055903778 0.0111807555 0.0000000000 0.0155852956
## 21 0.0053554940 0.0094182825 0.0001846722 0.0101569714
## 22 0.0052931596 0.0032573290 0.0000000000 0.0091612378
## 23 0.0053165781 0.0038666022 0.0000000000 0.0070082165
## OFFENSE_CODE_GROUP
## Y Harbor Related Incidents HOME INVASION Homicide HUMAN TRAFFICKING
## 0 0.0003411223 0.0000000000 0.0006822446 0.0000000000
## 1 0.0005468964 0.0000000000 0.0010937927 0.0000000000
## 2 0.0006795787 0.0000000000 0.0006795787 0.0000000000
## 3 0.0000000000 0.0000000000 0.0011527378 0.0000000000
## 4 0.0015673981 0.0000000000 0.0007836991 0.0000000000
## 5 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## 6 0.0005449591 0.0000000000 0.0010899183 0.0000000000
## 7 0.0002979738 0.0000000000 0.0002979738 0.0000000000
## 8 0.0002038736 0.0000000000 0.0002038736 0.0000000000
## 9 0.0003524229 0.0000000000 0.0000000000 0.0000000000
## 10 0.0004709576 0.0000000000 0.0003139717 0.0000000000
## 11 0.0007838219 0.0000000000 0.0001567644 0.0000000000
## 12 0.0005574136 0.0000000000 0.0000000000 0.0000000000
## 13 0.0007795447 0.0000000000 0.0001559089 0.0000000000
## 14 0.0000000000 0.0000000000 0.0001548947 0.0000000000
## 15 0.0003246753 0.0000000000 0.0001623377 0.0000000000
## 16 0.0010774411 0.0000000000 0.0002693603 0.0000000000
## 17 0.0009002058 0.0000000000 0.0005144033 0.0000000000
## 18 0.0007727975 0.0000000000 0.0003863988 0.0000000000
## 19 0.0014854427 0.0000000000 0.0004456328 0.0000000000
## 20 0.0003388108 0.0000000000 0.0008470269 0.0000000000
## 21 0.0005540166 0.0000000000 0.0007386888 0.0000000000
## 22 0.0002035831 0.0000000000 0.0010179153 0.0000000000
## 23 0.0004833253 0.0000000000 0.0019333011 0.0000000000
## OFFENSE_CODE_GROUP
## Y HUMAN TRAFFICKING - INVOLUNTARY SERVITUDE Investigate Person
## 0 0.0000000000 0.0499744158
## 1 0.0000000000 0.0451189500
## 2 0.0000000000 0.0489296636
## 3 0.0000000000 0.0593659942
## 4 0.0000000000 0.0611285266
## 5 0.0000000000 0.0519480519
## 6 0.0000000000 0.0446866485
## 7 0.0000000000 0.0482717521
## 8 0.0000000000 0.0470948012
## 9 0.0000000000 0.0579735683
## 10 0.0000000000 0.0588697017
## 11 0.0000000000 0.0638031039
## 12 0.0000000000 0.0617335563
## 13 0.0000000000 0.0671967571
## 14 0.0000000000 0.0628872367
## 15 0.0000000000 0.0665584416
## 16 0.0000000000 0.0615488215
## 17 0.0000000000 0.0565843621
## 18 0.0000000000 0.0622102009
## 19 0.0000000000 0.0655080214
## 20 0.0000000000 0.0598001016
## 21 0.0000000000 0.0633425669
## 22 0.0000000000 0.0629071661
## 23 0.0000000000 0.0563073949
## OFFENSE_CODE_GROUP
## Y INVESTIGATE PERSON Investigate Property Landlord/Tenant Disputes
## 0 0.0000000000 0.0411052362 0.0027289783
## 1 0.0000000000 0.0388296418 0.0008203445
## 2 0.0000000000 0.0530071356 0.0006795787
## 3 0.0000000000 0.0680115274 0.0005763689
## 4 0.0000000000 0.0611285266 0.0000000000
## 5 0.0000000000 0.0481283422 0.0015278839
## 6 0.0000000000 0.0441416894 0.0010899183
## 7 0.0000000000 0.0390345650 0.0023837902
## 8 0.0000000000 0.0250764526 0.0024464832
## 9 0.0000000000 0.0320704846 0.0033480176
## 10 0.0000000000 0.0324960754 0.0047095761
## 11 0.0000000000 0.0308825835 0.0048596959
## 12 0.0000000000 0.0333054627 0.0043199554
## 13 0.0000000000 0.0293108824 0.0037418148
## 14 0.0000000000 0.0294299876 0.0030978934
## 15 0.0000000000 0.0336038961 0.0025974026
## 16 0.0000000000 0.0432323232 0.0026936027
## 17 0.0000000000 0.0324074074 0.0033436214
## 18 0.0000000000 0.0280783101 0.0030911901
## 19 0.0000000000 0.0271836007 0.0037136067
## 20 0.0000000000 0.0315094020 0.0042351347
## 21 0.0000000000 0.0360110803 0.0029547553
## 22 0.0000000000 0.0337947883 0.0018322476
## 23 0.0000000000 0.0478492025 0.0031416143
## OFFENSE_CODE_GROUP
## Y Larceny Larceny From Motor Vehicle
## 0 0.0617431349 0.0392290636
## 1 0.0462127427 0.0330872300
## 2 0.0441726130 0.0258239891
## 3 0.0409221902 0.0368876081
## 4 0.0407523511 0.0352664577
## 5 0.0404889228 0.0504201681
## 6 0.0485013624 0.0441416894
## 7 0.0536352801 0.0455899881
## 8 0.0662589195 0.0373088685
## 9 0.0669603524 0.0352422907
## 10 0.0857142857 0.0287284144
## 11 0.0940586299 0.0230443643
## 12 0.1040969900 0.0252229654
## 13 0.1049267228 0.0219831618
## 14 0.1096654275 0.0252478315
## 15 0.1084415584 0.0267857143
## 16 0.0996632997 0.0292255892
## 17 0.0949074074 0.0340792181
## 18 0.0909325090 0.0383822772
## 19 0.0846702317 0.0405525847
## 20 0.0828392343 0.0406572929
## 21 0.0664819945 0.0474607572
## 22 0.0549674267 0.0531351792
## 23 0.0480908652 0.0502658289
## OFFENSE_CODE_GROUP
## Y License Plate Related Incidents License Violation Liquor Violation
## 0 0.0028995395 0.0252430496 0.0049462732
## 1 0.0008203445 0.0216024063 0.0030079300
## 2 0.0010193680 0.0129119946 0.0013591573
## 3 0.0000000000 0.0040345821 0.0017291066
## 4 0.0000000000 0.0015673981 0.0007836991
## 5 0.0015278839 0.0000000000 0.0000000000
## 6 0.0010899183 0.0000000000 0.0005449591
## 7 0.0008939213 0.0002979738 0.0002979738
## 8 0.0040774720 0.0000000000 0.0002038736
## 9 0.0022907489 0.0005286344 0.0021145374
## 10 0.0009419152 0.0007849294 0.0039246468
## 11 0.0012541151 0.0036055808 0.0070543972
## 12 0.0022296544 0.0029264214 0.0073857302
## 13 0.0015590895 0.0042095416 0.0026504521
## 14 0.0015489467 0.0018587361 0.0015489467
## 15 0.0008116883 0.0011363636 0.0009740260
## 16 0.0014814815 0.0012121212 0.0030976431
## 17 0.0015432099 0.0037294239 0.0057870370
## 18 0.0018031942 0.0018031942 0.0045079856
## 19 0.0014854427 0.0013368984 0.0038621509
## 20 0.0025410808 0.0028798916 0.0027104862
## 21 0.0020313943 0.0055401662 0.0024007387
## 22 0.0016286645 0.0122149837 0.0048859935
## 23 0.0009666506 0.0289995167 0.0053165781
## OFFENSE_CODE_GROUP
## Y Manslaughter Medical Assistance Missing Person Located
## 0 0.0000000000 0.0622548184 0.0167149923
## 1 0.0002734482 0.0776592836 0.0131255127
## 2 0.0000000000 0.0750934421 0.0118926266
## 3 0.0005763689 0.0870317003 0.0109510086
## 4 0.0007836991 0.1042319749 0.0101880878
## 5 0.0000000000 0.1077158136 0.0168067227
## 6 0.0000000000 0.0801089918 0.0261580381
## 7 0.0000000000 0.0682359952 0.0208581645
## 8 0.0002038736 0.0678899083 0.0189602446
## 9 0.0000000000 0.0703083700 0.0130396476
## 10 0.0000000000 0.0839874411 0.0139717425
## 11 0.0000000000 0.0736792601 0.0128546794
## 12 0.0000000000 0.0714882943 0.0139353400
## 13 0.0000000000 0.0834112878 0.0152790770
## 14 0.0000000000 0.0755885998 0.0154894672
## 15 0.0000000000 0.0711038961 0.0125000000
## 16 0.0001346801 0.0649158249 0.0146801347
## 17 0.0000000000 0.0653292181 0.0128600823
## 18 0.0000000000 0.0575734158 0.0140391551
## 19 0.0000000000 0.0701128936 0.0148544266
## 20 0.0001694054 0.0738607488 0.0198204303
## 21 0.0000000000 0.0877192982 0.0193905817
## 22 0.0000000000 0.0804153094 0.0270765472
## 23 0.0000000000 0.0775737071 0.0193330111
## OFFENSE_CODE_GROUP
## Y Missing Person Reported Motor Vehicle Accident Response
## 0 0.0129626471 0.0776053215
## 1 0.0109379273 0.0976210008
## 2 0.0115528372 0.1281005776
## 3 0.0080691643 0.1279538905
## 4 0.0054858934 0.1285266458
## 5 0.0122230710 0.1611917494
## 6 0.0234332425 0.1820163488
## 7 0.0172824791 0.1576281287
## 8 0.0132517839 0.1445463812
## 9 0.0096916300 0.1207048458
## 10 0.0108320251 0.1006279435
## 11 0.0116005644 0.1094215394
## 12 0.0117056856 0.1020066890
## 13 0.0123168070 0.1114748987
## 14 0.0148698885 0.1140024783
## 15 0.0108766234 0.1310064935
## 16 0.0111784512 0.1239057239
## 17 0.0101594650 0.1225565844
## 18 0.0103039670 0.1150180319
## 19 0.0092097445 0.1088829471
## 20 0.0120277825 0.1080806370
## 21 0.0156971376 0.1165281625
## 22 0.0207654723 0.1209283388
## 23 0.0132914451 0.1227646206
## OFFENSE_CODE_GROUP
## Y Offenses Against Child / Family Operating Under the Influence
## 0 0.0010233669 0.0032406618
## 1 0.0013672409 0.0090237900
## 2 0.0000000000 0.0088345226
## 3 0.0005763689 0.0040345821
## 4 0.0007836991 0.0039184953
## 5 0.0000000000 0.0022918258
## 6 0.0005449591 0.0000000000
## 7 0.0026817640 0.0002979738
## 8 0.0014271152 0.0002038736
## 9 0.0019383260 0.0001762115
## 10 0.0015698587 0.0004709576
## 11 0.0018811726 0.0007838219
## 12 0.0022296544 0.0006967670
## 13 0.0020268163 0.0004677268
## 14 0.0017038414 0.0006195787
## 15 0.0014610390 0.0000000000
## 16 0.0026936027 0.0008080808
## 17 0.0029578189 0.0010288066
## 18 0.0016743946 0.0005151984
## 19 0.0014854427 0.0019310755
## 20 0.0011858377 0.0027104862
## 21 0.0020313943 0.0027700831
## 22 0.0016286645 0.0032573290
## 23 0.0002416626 0.0021749638
## OFFENSE_CODE_GROUP
## Y Other Other Burglary Phone Call Complaints
## 0 0.0521917107 0.0017056115 0.0001705611
## 1 0.0587913590 0.0008203445 0.0002734482
## 2 0.0533469249 0.0010193680 0.0000000000
## 3 0.0489913545 0.0028818444 0.0000000000
## 4 0.0446708464 0.0023510972 0.0000000000
## 5 0.0427807487 0.0030557678 0.0000000000
## 6 0.0376021798 0.0027247956 0.0000000000
## 7 0.0435041716 0.0014898689 0.0000000000
## 8 0.0591233435 0.0020387360 0.0002038736
## 9 0.0629074890 0.0017621145 0.0003524229
## 10 0.0616954474 0.0003139717 0.0003139717
## 11 0.0689763286 0.0020379370 0.0001567644
## 12 0.0661928651 0.0012541806 0.0001393534
## 13 0.0639226692 0.0003118179 0.0001559089
## 14 0.0509603470 0.0015489467 0.0000000000
## 15 0.0590909091 0.0019480519 0.0000000000
## 16 0.0544107744 0.0014814815 0.0001346801
## 17 0.0480967078 0.0018004115 0.0000000000
## 18 0.0582174137 0.0006439979 0.0001287996
## 19 0.0564468212 0.0007427213 0.0000000000
## 20 0.0543791293 0.0020328646 0.0000000000
## 21 0.0500461681 0.0012927054 0.0001846722
## 22 0.0445846906 0.0012214984 0.0000000000
## 23 0.0420492992 0.0016916385 0.0000000000
## OFFENSE_CODE_GROUP
## Y Police Service Incidents Prisoner Related Incidents Property Found
## 0 0.0081869350 0.0006822446 0.0071635681
## 1 0.0084768936 0.0008203445 0.0046486191
## 2 0.0054366293 0.0003397893 0.0074753653
## 3 0.0046109510 0.0005763689 0.0069164265
## 4 0.0047021944 0.0000000000 0.0054858934
## 5 0.0244461421 0.0000000000 0.0091673033
## 6 0.0092643052 0.0005449591 0.0119891008
## 7 0.0083432658 0.0011918951 0.0119189511
## 8 0.0120285423 0.0012232416 0.0165137615
## 9 0.0128634361 0.0012334802 0.0186784141
## 10 0.0172684458 0.0009419152 0.0163265306
## 11 0.0133249726 0.0006270575 0.0128546794
## 12 0.0112876254 0.0005574136 0.0149108138
## 13 0.0082631743 0.0000000000 0.0157468039
## 14 0.0103779430 0.0012391574 0.0147149938
## 15 0.0071428571 0.0012987013 0.0134740260
## 16 0.0083501684 0.0005387205 0.0091582492
## 17 0.0061728395 0.0009002058 0.0101594650
## 18 0.0078567749 0.0006439979 0.0109479650
## 19 0.0059417706 0.0000000000 0.0109922757
## 20 0.0050821616 0.0006776215 0.0105031340
## 21 0.0064635272 0.0003693444 0.0108956602
## 22 0.0052931596 0.0000000000 0.0103827362
## 23 0.0041082649 0.0002416626 0.0074915418
## OFFENSE_CODE_GROUP
## Y Property Lost Property Related Damage Prostitution
## 0 0.0395701859 0.0015350503 0.0010233669
## 1 0.0213289582 0.0016406891 0.0027344818
## 2 0.0231056745 0.0013591573 0.0010193680
## 3 0.0155619597 0.0028818444 0.0005763689
## 4 0.0062695925 0.0023510972 0.0054858934
## 5 0.0076394194 0.0030557678 0.0015278839
## 6 0.0234332425 0.0038147139 0.0000000000
## 7 0.0202622169 0.0041716329 0.0000000000
## 8 0.0246687054 0.0034658512 0.0004077472
## 9 0.0296035242 0.0035242291 0.0001762115
## 10 0.0324960754 0.0029827316 0.0000000000
## 11 0.0384072739 0.0042326383 0.0009405863
## 12 0.0457079153 0.0023690078 0.0005574136
## 13 0.0377299657 0.0040536327 0.0023386342
## 14 0.0393432466 0.0043370508 0.0009293680
## 15 0.0366883117 0.0027597403 0.0012987013
## 16 0.0360942761 0.0026936027 0.0008080808
## 17 0.0304783951 0.0038580247 0.0007716049
## 18 0.0287223081 0.0028335909 0.0005151984
## 19 0.0264408794 0.0040106952 0.0002970885
## 20 0.0291377266 0.0016940539 0.0008470269
## 21 0.0238227147 0.0011080332 0.0000000000
## 22 0.0250407166 0.0014250814 0.0004071661
## 23 0.0280328661 0.0021749638 0.0007249879
## OFFENSE_CODE_GROUP
## Y Recovered Stolen Property Residential Burglary
## 0 0.0037523452 0.0173972369
## 1 0.0084768936 0.0153130982
## 2 0.0040774720 0.0115528372
## 3 0.0063400576 0.0282420749
## 4 0.0054858934 0.0235109718
## 5 0.0045836516 0.0252100840
## 6 0.0043596730 0.0310626703
## 7 0.0032777116 0.0277115614
## 8 0.0040774720 0.0238532110
## 9 0.0051101322 0.0197356828
## 10 0.0045525903 0.0182103611
## 11 0.0043894027 0.0164602602
## 12 0.0041806020 0.0183946488
## 13 0.0040536327 0.0132522607
## 14 0.0049566295 0.0164188352
## 15 0.0032467532 0.0150974026
## 16 0.0044444444 0.0158922559
## 17 0.0041152263 0.0169753086
## 18 0.0054095827 0.0181607419
## 19 0.0046048723 0.0167855021
## 20 0.0050821616 0.0157547010
## 21 0.0038781163 0.0179132041
## 22 0.0052931596 0.0173045603
## 23 0.0036249396 0.0198163364
## OFFENSE_CODE_GROUP
## Y Restraining Order Violations Robbery Search Warrants
## 0 0.0030701006 0.0160327477 0.0028995395
## 1 0.0032813782 0.0267979218 0.0002734482
## 2 0.0016989467 0.0312606184 0.0013591573
## 3 0.0046109510 0.0265129683 0.0005763689
## 4 0.0039184953 0.0289968652 0.0117554859
## 5 0.0030557678 0.0267379679 0.0084033613
## 6 0.0038147139 0.0174386921 0.0065395095
## 7 0.0044696067 0.0086412396 0.0026817640
## 8 0.0053007136 0.0061162080 0.0016309888
## 9 0.0054625551 0.0056387665 0.0037004405
## 10 0.0043956044 0.0061224490 0.0047095761
## 11 0.0058002822 0.0084652767 0.0050164603
## 12 0.0076644370 0.0076644370 0.0033444816
## 13 0.0057686311 0.0110695354 0.0046772685
## 14 0.0061957869 0.0142503098 0.0038723668
## 15 0.0050324675 0.0139610390 0.0029220779
## 16 0.0061952862 0.0114478114 0.0035016835
## 17 0.0050154321 0.0110596708 0.0027006173
## 18 0.0051519835 0.0124935600 0.0018031942
## 19 0.0038621509 0.0158942365 0.0013368984
## 20 0.0055903778 0.0235473488 0.0011858377
## 21 0.0040627886 0.0221606648 0.0016620499
## 22 0.0034609121 0.0282980456 0.0012214984
## 23 0.0041082649 0.0265828903 0.0009666506
## OFFENSE_CODE_GROUP
## Y Service Simple Assault Towed Vandalism Verbal Disputes
## 0 0.0005116834 0.0573085451 0.0266075388 0.0562851782 0.0363295241
## 1 0.0008203445 0.0855892808 0.0262510254 0.0639868745 0.0377358491
## 2 0.0003397893 0.0893645940 0.0220863065 0.0625212368 0.0346585117
## 3 0.0000000000 0.0605187320 0.0305475504 0.0657060519 0.0461095101
## 4 0.0000000000 0.0501567398 0.0313479624 0.0595611285 0.0391849530
## 5 0.0000000000 0.0473644003 0.0466004584 0.0641711230 0.0290297937
## 6 0.0010899183 0.0348773842 0.0588555858 0.0697547684 0.0376021798
## 7 0.0005959476 0.0396305125 0.1495828367 0.0551251490 0.0300953516
## 8 0.0006116208 0.0397553517 0.1092762487 0.0503567788 0.0338430173
## 9 0.0005286344 0.0320704846 0.0881057269 0.0431718062 0.0324229075
## 10 0.0012558870 0.0367346939 0.0635792779 0.0425431711 0.0373626374
## 11 0.0012541151 0.0412290328 0.0473428437 0.0396613889 0.0337043424
## 12 0.0004180602 0.0457079153 0.0257803790 0.0366499443 0.0321906355
## 13 0.0009354537 0.0447458684 0.0249454319 0.0366386031 0.0383536015
## 14 0.0012391574 0.0529739777 0.0165737299 0.0368649318 0.0381040892
## 15 0.0009740260 0.0508116883 0.0128246753 0.0428571429 0.0436688312
## 16 0.0008080808 0.0486195286 0.0162962963 0.0424242424 0.0363636364
## 17 0.0012860082 0.0495113169 0.0176183128 0.0432098765 0.0340792181
## 18 0.0009015971 0.0477846471 0.0153271510 0.0458526533 0.0440494590
## 19 0.0011883541 0.0536244801 0.0172311349 0.0506535948 0.0510992276
## 20 0.0013552431 0.0489581569 0.0176181603 0.0581060478 0.0547179400
## 21 0.0009233610 0.0541089566 0.0238227147 0.0629732225 0.0585410896
## 22 0.0010179153 0.0604641694 0.0223941368 0.0639250814 0.0590390879
## 23 0.0004833253 0.0645239246 0.0161913968 0.0671822136 0.0575157081
## OFFENSE_CODE_GROUP
## Y Violations Warrant Arrests
## 0 0.0179089203 0.0213201433
## 1 0.0363686081 0.0281651627
## 2 0.0343187224 0.0224260958
## 3 0.0149855908 0.0184438040
## 4 0.0172413793 0.0219435737
## 5 0.0084033613 0.0267379679
## 6 0.0076294278 0.0239782016
## 7 0.0086412396 0.0184743743
## 8 0.0242609582 0.0356778797
## 9 0.0276651982 0.0440528634
## 10 0.0274725275 0.0353218210
## 11 0.0238281862 0.0377802163
## 12 0.0170011148 0.0277313266
## 13 0.0176177113 0.0271281572
## 14 0.0176579926 0.0294299876
## 15 0.0128246753 0.0254870130
## 16 0.0150841751 0.0242424242
## 17 0.0217335391 0.0344650206
## 18 0.0251159196 0.0312982998
## 19 0.0161913250 0.0261437908
## 20 0.0121971879 0.0242249704
## 21 0.0151431210 0.0175438596
## 22 0.0152687296 0.0126221498
## 23 0.0130497825 0.0130497825
##
## DISTRICT
## Y A1 A15 A7 B2
## 0 0.009039741 0.129967593 0.018761726 0.035817841 0.144976974
## 1 0.004922067 0.180749248 0.012031720 0.052502051 0.157506153
## 2 0.007815155 0.201495073 0.009174312 0.051987768 0.159021407
## 3 0.006340058 0.141210375 0.021325648 0.044380403 0.180979827
## 4 0.003918495 0.115987461 0.014890282 0.038401254 0.151253918
## 5 0.009167303 0.099312452 0.017570665 0.050420168 0.184873950
## 6 0.002179837 0.087193460 0.027247956 0.045776567 0.148773842
## 7 0.006257449 0.099523242 0.020858164 0.042014303 0.134684148
## 8 0.006320082 0.097043833 0.025688073 0.040163099 0.139449541
## 9 0.003171806 0.105903084 0.023083700 0.043700441 0.140616740
## 10 0.005180534 0.113814757 0.022448980 0.038147567 0.141444270
## 11 0.005800282 0.113967707 0.023044364 0.039504625 0.155353504
## 12 0.004459309 0.111900780 0.022157191 0.040133779 0.157329989
## 13 0.007171812 0.108356720 0.022450889 0.042407234 0.145307141
## 14 0.005731103 0.098358116 0.019206939 0.040737299 0.150402726
## 15 0.006331169 0.110714286 0.019480519 0.040097403 0.156331169
## 16 0.004579125 0.105993266 0.018855219 0.041616162 0.151111111
## 17 0.006430041 0.119341564 0.015432099 0.040380658 0.157664609
## 18 0.007599176 0.108191654 0.019319938 0.040700670 0.161643483
## 19 0.006833036 0.096405229 0.021093286 0.044266191 0.158199643
## 20 0.005759783 0.098424530 0.021853295 0.044214806 0.157885821
## 21 0.004801477 0.084949215 0.022714681 0.038227147 0.169529086
## 22 0.004885993 0.104030945 0.017508143 0.041123779 0.183021173
## 23 0.003866602 0.122281295 0.018124698 0.044465926 0.165780570
## DISTRICT
## Y B3 C11 C6 D14 D4
## 0 0.105747911 0.143100802 0.080334300 0.068736142 0.129285349
## 1 0.105824446 0.122504785 0.068635494 0.069729286 0.114848236
## 2 0.099218485 0.113829426 0.061501869 0.071355759 0.122663948
## 3 0.110086455 0.147550432 0.062824207 0.070317003 0.098559078
## 4 0.130877743 0.170062696 0.077586207 0.066614420 0.115203762
## 5 0.108479756 0.158899924 0.061879297 0.058823529 0.110771581
## 6 0.119891008 0.172207084 0.069209809 0.053405995 0.113896458
## 7 0.112038141 0.120679380 0.077473182 0.087902265 0.138557807
## 8 0.110907238 0.131498471 0.076452599 0.075229358 0.141080530
## 9 0.103964758 0.128634361 0.080000000 0.066079295 0.126696035
## 10 0.110047096 0.130769231 0.075824176 0.059968603 0.145211931
## 11 0.109264775 0.131368553 0.073365731 0.060511052 0.136855306
## 12 0.099637681 0.120958751 0.069537347 0.062709030 0.147296544
## 13 0.116152167 0.122700343 0.075615840 0.058621765 0.142033053
## 14 0.111059480 0.129337051 0.080390335 0.059014870 0.143897150
## 15 0.103896104 0.130357143 0.073863636 0.061688312 0.141233766
## 16 0.108417508 0.138316498 0.080673401 0.058720539 0.131447811
## 17 0.100051440 0.130787037 0.074331276 0.057741770 0.137217078
## 18 0.115018032 0.131633179 0.074446162 0.057315817 0.133436373
## 19 0.121509210 0.136660725 0.076945930 0.060903149 0.127599525
## 20 0.119430798 0.148737930 0.069286803 0.064374047 0.122988311
## 21 0.134441367 0.155493998 0.066112650 0.063711911 0.114681440
## 22 0.123982085 0.137011401 0.067793160 0.067385993 0.116449511
## 23 0.112614790 0.144272595 0.062590623 0.078298695 0.110681489
## DISTRICT
## Y E13 E18 E5
## 0 0.043322531 0.046563193 0.044345898
## 1 0.042384468 0.038556194 0.029805852
## 2 0.036357458 0.034998301 0.030581040
## 3 0.046109510 0.040922190 0.029394813
## 4 0.037617555 0.039184953 0.038401254
## 5 0.049656226 0.047364400 0.042780749
## 6 0.048501362 0.063760218 0.047956403
## 7 0.058402861 0.059892729 0.041716329
## 8 0.058103976 0.053414883 0.044648318
## 9 0.062555066 0.062026432 0.053568282
## 10 0.057456829 0.053689168 0.045996860
## 11 0.055024298 0.055337827 0.040601975
## 12 0.054626533 0.060200669 0.049052397
## 13 0.059245401 0.054568132 0.045369504
## 14 0.054368030 0.065365551 0.042131351
## 15 0.051136364 0.058928571 0.045941558
## 16 0.056969697 0.062626263 0.040673401
## 17 0.063143004 0.060442387 0.037037037
## 18 0.059763009 0.051133436 0.039799073
## 19 0.057338087 0.052139037 0.040106952
## 20 0.054887345 0.054717940 0.037438591
## 21 0.054293629 0.052446907 0.038596491
## 22 0.055781759 0.048452769 0.032573290
## 23 0.053890768 0.044465926 0.038666022
##
## DAY_OF_WEEK
## Y Friday Monday Saturday Sunday Thursday Tuesday
## 0 0.14378305 0.13849565 0.16953778 0.16851441 0.12587413 0.12553300
## 1 0.12852065 0.12031720 0.19770304 0.23543888 0.10637134 0.11375444
## 2 0.12130479 0.10363575 0.23445464 0.26231736 0.09208291 0.08019028
## 3 0.10720461 0.12219020 0.20115274 0.27435159 0.09971182 0.09913545
## 4 0.12539185 0.12774295 0.20141066 0.22257053 0.10971787 0.10344828
## 5 0.14056532 0.12146677 0.12987013 0.18334607 0.15431627 0.12223071
## 6 0.15095368 0.14495913 0.10735695 0.12806540 0.16621253 0.16076294
## 7 0.15494636 0.16299166 0.12127533 0.09505364 0.14630513 0.15941597
## 8 0.16941896 0.16472987 0.11539246 0.09133537 0.14576962 0.16085627
## 9 0.15859031 0.14801762 0.12757709 0.10907489 0.13955947 0.15947137
## 10 0.16483516 0.15808477 0.12621664 0.11601256 0.14191523 0.14348509
## 11 0.15645085 0.14939646 0.12917385 0.11600564 0.15002351 0.14955322
## 12 0.15830546 0.14353400 0.14464883 0.11928651 0.14785396 0.14130435
## 13 0.14733396 0.15060804 0.13673215 0.11973807 0.14343623 0.14795759
## 14 0.14916357 0.14203841 0.14451673 0.12252169 0.14513631 0.15040273
## 15 0.15048701 0.14480519 0.13879870 0.11461039 0.15389610 0.15974026
## 16 0.15326599 0.14976431 0.13117845 0.11299663 0.14922559 0.15973064
## 17 0.16306584 0.15483539 0.12872942 0.11754115 0.13747428 0.15457819
## 18 0.15043792 0.15378671 0.13588357 0.11360124 0.14232354 0.15417311
## 19 0.14676173 0.14750446 0.14438503 0.12329174 0.13398693 0.15240642
## 20 0.14789090 0.14839912 0.13942063 0.13366085 0.14924615 0.15331188
## 21 0.14939982 0.14330563 0.15789474 0.13130194 0.14736842 0.13277932
## 22 0.17772801 0.12764658 0.16775244 0.13823290 0.13680782 0.13070033
## 23 0.18849686 0.12421460 0.20130498 0.12010633 0.13436443 0.12228130
## DAY_OF_WEEK
## Y Wednesday
## 0 0.12826198
## 1 0.09789445
## 2 0.10601427
## 3 0.09625360
## 4 0.10971787
## 5 0.14820474
## 6 0.14168937
## 7 0.16001192
## 8 0.15249745
## 9 0.15770925
## 10 0.14945055
## 11 0.14939646
## 12 0.14506689
## 13 0.15419395
## 14 0.14622057
## 15 0.13766234
## 16 0.14383838
## 17 0.14377572
## 18 0.14979392
## 19 0.15166370
## 20 0.12807047
## 21 0.13795014
## 22 0.12113192
## 23 0.10923151Evaluate the model by using testing set; then, check the confusion matrix
pred1 <- predict(crime_nb1, test)confusionMatrix(table(pred1, test$HOUR))## Confusion Matrix and Statistics
##
##
## pred1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 0 170 62 66 37 18 15 17 25 51 77 75 78 132 103 89 57 79
## 1 66 64 70 34 19 12 13 8 17 20 35 38 38 31 24 36 40
## 2 49 62 64 20 12 4 10 14 22 16 19 22 20 28 21 21 20
## 3 1 1 2 1 1 2 0 1 1 1 1 0 1 3 0 1 0
## 4 8 10 3 7 8 8 5 6 3 4 1 3 5 2 1 1 6
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 1 1 0 0 1 1 1 1 2 0 0 0 0 0 0
## 7 26 8 7 5 4 4 12 52 74 62 44 30 18 22 14 9 17
## 8 28 16 12 11 6 16 21 55 96 83 68 53 50 55 53 51 49
## 9 38 21 19 18 10 15 23 40 77 67 82 70 69 54 53 47 77
## 10 60 48 43 24 24 29 39 44 64 90 142 111 108 119 119 83 101
## 11 13 8 10 1 4 6 6 3 14 13 14 24 22 23 21 14 14
## 12 180 74 57 31 29 15 38 66 133 201 208 242 300 219 229 213 227
## 13 19 10 13 10 9 7 3 13 26 20 25 43 37 39 38 36 38
## 14 39 22 8 10 8 3 11 20 28 28 61 50 56 57 66 64 57
## 15 2 0 1 0 0 0 0 1 0 3 1 2 1 4 1 1 3
## 16 144 78 59 37 30 38 64 115 147 170 175 188 180 209 193 212 243
## 17 147 99 90 53 40 34 54 114 169 159 177 225 221 208 238 242 306
## 18 164 134 103 54 37 37 87 111 195 207 225 241 265 236 258 221 300
## 19 17 14 9 6 3 3 14 16 14 13 15 19 11 24 22 16 18
## 20 43 23 25 16 8 9 10 15 32 30 22 23 24 31 35 29 41
## 21 63 52 41 19 20 19 19 36 57 70 76 77 82 70 90 88 79
## 22 94 44 39 22 18 13 33 49 58 57 67 66 75 63 84 70 80
## 23 56 32 26 23 9 6 5 2 10 22 21 26 26 26 28 22 32
##
## pred1 17 18 19 20 21 22 23
## 0 67 67 66 83 80 76 57
## 1 31 45 40 53 46 29 43
## 2 31 38 21 24 31 34 25
## 3 0 0 1 1 0 0 2
## 4 5 0 5 6 6 6 5
## 5 0 0 0 0 0 0 0
## 6 0 1 0 1 0 1 1
## 7 18 16 16 14 14 11 8
## 8 67 43 28 38 47 17 18
## 9 53 68 58 43 45 35 29
## 10 106 100 87 88 68 69 36
## 11 21 18 17 10 9 10 5
## 12 249 208 189 168 137 121 95
## 13 34 36 37 22 27 27 20
## 14 60 59 53 46 24 23 23
## 15 1 1 1 1 1 1 2
## 16 212 215 189 176 152 149 139
## 17 350 317 241 211 174 131 130
## 18 293 323 291 264 190 210 141
## 19 15 23 31 27 22 11 15
## 20 48 39 32 51 50 46 46
## 21 85 100 76 102 107 85 60
## 22 88 94 82 76 88 91 76
## 23 29 30 30 34 34 38 49
##
## Overall Statistics
##
## Accuracy : 0.0776
## 95% CI : (0.0746, 0.0807)
## No Information Rate : 0.0618
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.0263
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3 Class: 4
## Sensitivity 0.119131 0.072562 0.083333 2.273e-03 0.0252366
## Specificity 0.948574 0.973075 0.980803 9.993e-01 0.9964466
## Pos Pred Value 0.103218 0.075117 0.101911 4.762e-02 0.0701754
## Neg Pred Value 0.955896 0.972078 0.976152 9.854e-01 0.9897117
## Prevalence 0.047333 0.029256 0.025474 1.459e-02 0.0105148
## Detection Rate 0.005639 0.002123 0.002123 3.317e-05 0.0002654
## Detection Prevalence 0.054630 0.028261 0.020831 6.966e-04 0.0037813
## Balanced Accuracy 0.533853 0.522818 0.532068 5.008e-01 0.5108416
## Class: 5 Class: 6 Class: 7 Class: 8 Class: 9
## Sensitivity 0.000000 2.062e-03 0.064436 0.074476 0.047383
## Specificity 1.000000 9.996e-01 0.984561 0.969334 0.963667
## Pos Pred Value NaN 8.333e-02 0.102970 0.097859 0.060306
## Neg Pred Value 0.990215 9.839e-01 0.974530 0.959098 0.953611
## Prevalence 0.009785 1.609e-02 0.026768 0.042756 0.046902
## Detection Rate 0.000000 3.317e-05 0.001725 0.003184 0.002222
## Detection Prevalence 0.000000 3.980e-04 0.016751 0.032539 0.036852
## Balanced Accuracy 0.500000 5.008e-01 0.524499 0.521905 0.505525
## Class: 10 Class: 11 Class: 12 Class: 13 Class: 14
## Sensitivity 0.09126 0.0147149 0.172315 0.023985 0.039356
## Specificity 0.94194 0.9903216 0.882811 0.980717 0.971550
## Pos Pred Value 0.07880 0.0800000 0.082667 0.066214 0.075342
## Neg Pred Value 0.95012 0.9461605 0.945662 0.946311 0.944964
## Prevalence 0.05161 0.0540998 0.057748 0.053934 0.055626
## Detection Rate 0.00471 0.0007961 0.009951 0.001294 0.002189
## Detection Prevalence 0.05977 0.0099509 0.120373 0.019537 0.029057
## Balanced Accuracy 0.51660 0.5025182 0.527563 0.502351 0.505453
## Class: 15 Class: 16 Class: 17 Class: 18 Class: 19
## Sensitivity 6.519e-04 0.13300 0.18787 0.17545 0.019485
## Specificity 9.991e-01 0.88450 0.86636 0.84937 0.987849
## Pos Pred Value 3.571e-02 0.06915 0.08475 0.07042 0.082011
## Neg Pred Value 9.491e-01 0.94053 0.94185 0.94061 0.947598
## Prevalence 5.088e-02 0.06060 0.06180 0.06107 0.052773
## Detection Rate 3.317e-05 0.00806 0.01161 0.01071 0.001028
## Detection Prevalence 9.288e-04 0.11656 0.13699 0.15215 0.012538
## Balanced Accuracy 4.999e-01 0.50875 0.52711 0.51241 0.503667
## Class: 20 Class: 21 Class: 22 Class: 23
## Sensitivity 0.033138 0.079142 0.074529 0.047805
## Specificity 0.976336 0.949090 0.950358 0.980531
## Pos Pred Value 0.070055 0.068023 0.059594 0.079545
## Neg Pred Value 0.949422 0.956430 0.960519 0.966951
## Prevalence 0.051048 0.044845 0.040500 0.033999
## Detection Rate 0.001692 0.003549 0.003018 0.001625
## Detection Prevalence 0.024148 0.052176 0.050650 0.020433
## Balanced Accuracy 0.504737 0.514116 0.512443 0.514168Round 2：
crime_nb2 <- naiveBayes(UCR_PART ~ ., data=train)
crime_nb2##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## Other Part One Part Three Part Two
## 0.000000000 0.003816606 0.193125175 0.499325514 0.303732706
##
## Conditional probabilities:
## OFFENSE_CODE_GROUP
## Y Aggravated Assault Aircraft Arson
##
## Other 0.000000e+00 0.000000e+00 6.681034e-02
## Part One 1.242813e-01 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 2.470966e-04 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Assembly or Gathering Violations Auto Theft
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 7.730312e-02
## Part Three 5.880899e-03 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Auto Theft Recovery Ballistics Bomb Hoax
##
## Other 8.534483e-01 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00 0.000000e+00
## Part Two 0.000000e+00 9.559660e-03 7.041109e-04
## OFFENSE_CODE_GROUP
## Y Burglary - No Property Taken Commercial Burglary
##
## Other 2.155172e-03 0.000000e+00
## Part One 0.000000e+00 2.074194e-02
## Part Three 0.000000e+00 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Confidence Games Counterfeiting Criminal Harassment
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00 0.000000e+00
## Part Two 3.263283e-02 1.432595e-02 1.218654e-03
## OFFENSE_CODE_GROUP
## Y Disorderly Conduct Drug Violation Embezzlement Evading Fare
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part Two 2.599794e-02 1.674701e-01 2.572713e-03 4.224666e-03
## OFFENSE_CODE_GROUP
## Y Explosives Fire Related Reports Firearm Discovery
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 3.294622e-05 1.181122e-02 4.365374e-03
## Part Two 1.354059e-04 9.207604e-04 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Firearm Violations Fraud Gambling Harassment
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part Two 1.787359e-02 5.941613e-02 1.083248e-04 4.110925e-02
## OFFENSE_CODE_GROUP
## Y Harbor Related Incidents HOME INVASION Homicide
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 2.427701e-03
## Part Three 1.169591e-03 0.000000e+00 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y HUMAN TRAFFICKING HUMAN TRAFFICKING - INVOLUNTARY SERVITUDE
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Investigate Person INVESTIGATE PERSON Investigate Property
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 1.183758e-01 0.000000e+00 7.020838e-02
## Part Two 0.000000e+00 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Landlord/Tenant Disputes Larceny
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 4.184590e-01
## Part Three 6.177415e-03 0.000000e+00
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Larceny From Motor Vehicle License Plate Related Incidents
##
## Other 0.000000e+00 3.879310e-02
## Part One 1.796499e-01 0.000000e+00
## Part Three 0.000000e+00 3.047525e-03
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y License Violation Liquor Violation Manslaughter
##
## Other 0.000000e+00 0.000000e+00 1.293103e-02
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 1.088872e-02 0.000000e+00 0.000000e+00
## Part Two 0.000000e+00 1.145534e-02 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Medical Assistance Missing Person Located
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00
## Part Three 1.469072e-01 3.161189e-02
## Part Two 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Missing Person Reported Motor Vehicle Accident Response
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00
## Part Three 2.434725e-02 2.346100e-01
## Part Two 4.332990e-04 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Offenses Against Child / Family Operating Under the Influence
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00
## Part Two 5.551644e-03 5.497481e-03
## OFFENSE_CODE_GROUP
## Y Other Other Burglary Phone Call Complaints
##
## Other 2.586207e-02 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 7.368287e-03 0.000000e+00
## Part Three 6.292727e-03 0.000000e+00 0.000000e+00
## Part Two 1.719656e-01 0.000000e+00 3.520555e-04
## OFFENSE_CODE_GROUP
## Y Police Service Incidents Prisoner Related Incidents
##
## Other 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00
## Part Three 1.774154e-02 8.236554e-05
## Part Two 0.000000e+00 2.031089e-03
## OFFENSE_CODE_GROUP
## Y Property Found Property Lost Property Related Damage
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 2.375422e-02 6.212009e-02 5.831480e-03
## Part Two 0.000000e+00 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Prostitution Recovered Stolen Property Residential Burglary
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 9.289152e-02
## Part Three 0.000000e+00 0.000000e+00 0.000000e+00
## Part Two 2.681038e-03 1.494882e-02 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Restraining Order Violations Robbery Search Warrants
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 7.687721e-02 0.000000e+00
## Part Three 0.000000e+00 0.000000e+00 5.831480e-03
## Part Two 1.622163e-02 0.000000e+00 0.000000e+00
## OFFENSE_CODE_GROUP
## Y Service Simple Assault Towed Vandalism
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 1.762623e-03 0.000000e+00 7.014249e-02 0.000000e+00
## Part Two 0.000000e+00 1.654119e-01 0.000000e+00 1.618101e-01
## OFFENSE_CODE_GROUP
## Y Verbal Disputes Violations Warrant Arrests
##
## Other 0.000000e+00 0.000000e+00 0.000000e+00
## Part One 0.000000e+00 0.000000e+00 0.000000e+00
## Part Three 8.195371e-02 0.000000e+00 5.480603e-02
## Part Two 0.000000e+00 6.336998e-02 0.000000e+00
##
## DISTRICT
## Y A1 A15 A7 B2
##
## Other 0.004310345 0.064655172 0.010775862 0.064655172 0.183189655
## Part One 0.004173943 0.136079049 0.020358618 0.036287746 0.140295583
## Part Three 0.006688082 0.101177827 0.021464459 0.042088790 0.155324932
## Part Two 0.005497481 0.115690841 0.017684017 0.044304826 0.163353734
## DISTRICT
## Y B3 C11 C6 D14 D4
##
## Other 0.150862069 0.144396552 0.114224138 0.043103448 0.090517241
## Part One 0.081391882 0.110694663 0.071894033 0.066740492 0.202052898
## Part Three 0.120336051 0.139329545 0.073618318 0.069368256 0.109051973
## Part Two 0.115365867 0.141228403 0.074175378 0.053241618 0.123517305
## DISTRICT
## Y E13 E18 E5
##
## Other 0.038793103 0.053879310 0.036637931
## Part One 0.059244431 0.041952383 0.028834277
## Part Three 0.053751750 0.060917552 0.046882464
## Part Two 0.054054054 0.051400097 0.040486378
##
## DAY_OF_WEEK
## Y Friday Monday Saturday Sunday Thursday Tuesday
##
## Other 0.1961207 0.1314655 0.1099138 0.1034483 0.1465517 0.1573276
## Part One 0.1551599 0.1417011 0.1471102 0.1360365 0.1412326 0.1422548
## Part Three 0.1531505 0.1431678 0.1507454 0.1371716 0.1369409 0.1412734
## Part Two 0.1520067 0.1498673 0.1378162 0.1195364 0.1445323 0.1504631
## DAY_OF_WEEK
## Y Wednesday
##
## Other 0.1551724
## Part One 0.1365050
## Part Three 0.1375504
## Part Two 0.1457780
##
## HOUR
## Y [,1] [,2]
## NA NA
## Other 12.24138 6.364888
## Part One 13.42498 6.377021
## Part Three 12.96852 6.267762
## Part Two 13.07715 6.355693pred2 <- predict(crime_nb2, test)confusionMatrix(factor(pred2), factor(test$UCR_PART))## Confusion Matrix and Statistics
##
## Reference
## Prediction Other Part One Part Three Part Two
## Other 97 0 0 0
## Part One 0 5881 2 1
## Part Three 16 15 14858 32
## Part Two 1 0 89 9156
##
## Overall Statistics
##
## Accuracy : 0.9948
## 95% CI : (0.9939, 0.9956)
## No Information Rate : 0.4959
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9917
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Other Class: Part One Class: Part Three
## Sensitivity 0.850877 0.9975 0.9939
## Specificity 1.000000 0.9999 0.9959
## Pos Pred Value 1.000000 0.9995 0.9958
## Neg Pred Value 0.999434 0.9994 0.9940
## Prevalence 0.003781 0.1956 0.4959
## Detection Rate 0.003217 0.1951 0.4928
## Detection Prevalence 0.003217 0.1952 0.4949
## Balanced Accuracy 0.925439 0.9987 0.9949
## Class: Part Two
## Sensitivity 0.9964
## Specificity 0.9957
## Pos Pred Value 0.9903
## Neg Pred Value 0.9984
## Prevalence 0.3048
## Detection Rate 0.3037
## Detection Prevalence 0.3067
## Balanced Accuracy 0.9961