class: center, middle, inverse, title-slide .title[ # The Evolving Landscape of Crime: A Data-Driven Journey ] .author[ ### Ankit Singh ] --- # Introduction ## The Evolving Landscape of Crime: A Data-Driven Journey - **Objective**: To analyze and visualize crime statistics data to understand trends and patterns in different types of offences. - **Data Source**: [https://www.crimestatistics.vic.gov.au/crime-statistics/latest-victorian-crime-data/year-ending-31-december-2023] # Data Overview ## Dataset Description - The dataset contains crime statistics from 2014 to the year ending in December 2023. It includes various types of offences recorded, categorized by divisions and subdivisions. The data covers different geographical regions and provides counts and rates of offences. # Data Pre-Processing Checks ``` ## New names: ## • `` -> `...2` ``` ``` ## ## --------------------------------------- ## Data Check for sheet: Contents ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Contents character ## 2 ...2 character ## ## No Numeric Columns for Summary Statistics ## ## ## ## --------------------------------------- ## Data Check for sheet: Footnotes ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Table character ## 2 Footnotes character ## ## No Numeric Columns for Summary Statistics ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 01 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Division character ## 4 Offence Subdivision character ## 5 Offence Subgroup character ## 6 Offence Count numeric ## 7 Rate per 100,000 population numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count Rate per 100,000 population ## Min. :2014 Min. : 1 Min. : 0.0147 ## 1st Qu.:2016 1st Qu.: 69 1st Qu.: 1.0925 ## Median :2019 Median : 509 Median : 7.9808 ## Mean :2019 Mean : 4518 Mean : 70.7358 ## 3rd Qu.:2021 3rd Qu.: 3384 3rd Qu.: 53.1001 ## Max. :2023 Max. :68893 Max. :1116.0065 ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 02 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Division character ## 4 Offence Subdivision character ## 5 Offence Subgroup character ## 6 Location Division character ## 7 Location Subdivision character ## 8 Location Group character ## 9 Offence Count numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count ## Min. :2014 Min. : 1 ## 1st Qu.:2016 1st Qu.: 2 ## Median :2019 Median : 7 ## Mean :2019 Mean : 121 ## 3rd Qu.:2021 3rd Qu.: 31 ## Max. :2023 Max. :34711 ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 03 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Division character ## 4 Offence Subdivision character ## 5 Family Incident Flag character ## 6 Offence Count numeric ## 7 Rate per 100,000 population numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count Rate per 100,000 population ## Min. :2019 Min. : 1.0 Min. : 0.0147 ## 1st Qu.:2020 1st Qu.: 135.5 1st Qu.: 2.0468 ## Median :2021 Median : 1184.0 Median : 17.8691 ## Mean :2021 Mean : 9617.9 Mean : 145.1790 ## 3rd Qu.:2022 3rd Qu.: 8771.5 3rd Qu.: 131.7747 ## Max. :2023 Max. :181683.0 Max. :2779.1728 ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 04 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Division character ## 4 Offence Subdivision character ## 5 Offence Subgroup character ## 6 Investigation Status character ## 7 Offence Count numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count ## Min. :2014 Min. : 1.0 ## 1st Qu.:2016 1st Qu.: 5.0 ## Median :2019 Median : 34.0 ## Mean :2019 Mean : 956.3 ## 3rd Qu.:2021 3rd Qu.: 248.0 ## Max. :2023 Max. :60688.0 ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 05 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Subdivision character ## 4 Offence Subgroup character ## 5 Offence Code character ## 6 Offence Description character ## 7 Act Name character ## 8 Statutory Reference character ## 9 Offence Count numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count ## Min. :2014 Min. : 1.0 ## 1st Qu.:2016 1st Qu.: 1.0 ## Median :2019 Median : 4.0 ## Mean :2019 Mean : 290.6 ## 3rd Qu.:2021 3rd Qu.: 21.0 ## Max. :2023 Max. :61413.0 ## ## ## ## --------------------------------------- ## Data Check for sheet: Table 06 ## --------------------------------------- ## ## No Missing Values ## ## Number of Duplicate Rows: 0 ## ## Data Types: ## Column Data_Type ## 1 Year numeric ## 2 Year ending character ## 3 Offence Subdivision character ## 4 Offence Group character ## 5 Offence Description character ## 6 CSA Drug Type character ## 7 Offence Count numeric ## ## Summary Statistics (for numeric columns): ## Year Offence Count ## Min. :2014 Min. : 1.0 ## 1st Qu.:2016 1st Qu.: 2.0 ## Median :2019 Median : 6.0 ## Mean :2019 Mean : 150.1 ## 3rd Qu.:2021 3rd Qu.: 27.0 ## Max. :2023 Max. :11464.0 ``` ``` ## [[1]] ## NULL ## ## [[2]] ## NULL ## ## [[3]] ## NULL ## ## [[4]] ## NULL ## ## [[5]] ## NULL ## ## [[6]] ## NULL ## ## [[7]] ## NULL ## ## [[8]] ## NULL ``` --- ### A Peek into the Crime Data - This story is about the evolving landscape of criminal activities in Victoria.We'll explore the data over the years, the trends and key statistics. #### Crime Trends Over Time - let's have a look at the offences recorded from 2014 to 2023. ``` ## `geom_smooth()` using formula = 'y ~ x' ## `geom_smooth()` using formula = 'y ~ x' ``` <!-- -->  --- ## Analysis based on Distribution of crime - After seeing the trends over time let's pay our attention to the crime across different divisions.We'll also try to understand geographical distributions. <!-- --><!-- --> --- ## Impact of Family Incidents on Crime Rates - Further in this story, let's delve into the impact family incidents have on crime rates.Let's also explore how these incidents vary across different regions. <!-- --><!-- --> --- # Specific Crime Types and Status of Investigation Over Time - Now that we have seen different crime trends and their distribution let's delve into the chapter of the specific types of crime and their investigation status. <!-- --> --- <!-- --> --- # Geo Location wise Trends in Crime -This chapter will show you where the attention of law enforcement can be drawn with a heat map showing areas with high to low offence rates. <!-- --> --- # Understanding crimes as per Offence Division - This shall help you visualize the the distribution of crimes across different divisions <!-- --> --- # Summary of the story - This story has highlighted many important trends in the crime data over the years. - The geo-analysis showcased hot spots for criminal activities. - The story also compared crime rates among different divisions and how they impact the society. - These understandings helps in forming a plan of action to mitigate such activities based on trends in the data. ### Thank you