A dataset with over 200k rows analyzed with Python and Tableau, aimed at identifying and addressing the highest priority of crime that is happening in Chicaco using Chicago Police Department's 2021 crime data.
Business Questions
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Do different types of crime occur at different times of the day? If so, which crimes are more likely to occur during which times?
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Has there been a rise or fall in crime over time? By any specific type?
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Which districts have the most crime? Has it changed over time?
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Which offenses are the most and least likely to lead to an arrest?
Result
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The Top 5 Crimes are; Assault, Battery, Criminal Damage, Other Offence, and Theft.
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The rate of the ‘Top 5 Crimes’ peaked at midnight, then steadily declined until 6 am when it picked up once more. This rate increased further starting at 8 am.
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Most other crimes then maintain a steady rising trend for the rest of the day, except for Theft, which peaks at 11 am and remains very active to 5 pm but dropped sharply afterward.
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The ‘Top 5 Crimes' all have a convex distribution of occurrence by Month of Year; the rate of occurrence was low in winter but picked up in the spring, and peaked in Summer and Autumn.
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3 types of Crimes, however, demonstrated an opposite trend; Burglary, Robbery, and Motor Vehicle Theft.
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Ward 28, 6, and 24 were the most prevalent with serious crimes.
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While the amount of theft committed is high throughout the year, ward 42 is particularly prominent with theft with it, especially in July.
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Battery has the highest rate of arrest while Theft is the least likely to lead to an arrest.
Action Summary
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Based on the crime rate shown in the "Top 10 Wards of Crime" dashboard, those wards should receive more attention.
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Ward 42's Theft rate was disproportionately high.
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Speculation into the reason why Burglary, Robbery, and Motor Vehicles Thefts were particularly high during winter months was potentially due to mask mandates which may have encouraged this type of crime.
DASHBOARDS (TABLEAU)
Battery and Theft were the most prevalent crime in Chicago last year, which happened at an above average rate on daily occurrences. Of the two, Battery has a much higher rate of leading to an arrest.
* Tableau Public Server had issues with this dashboard at the time of upload. Therefore a .png file is used.
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Ward 28, 6, and 24 as shown were the most crime rampant wards.
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Ward 42 had always been particularly prone to theft, especially in July.
Data Cleaning Journey
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This dataset wa well maintained. I had no problem reading it with pandas. At first glance, it appeared to be largely intact, though some of the data was plagued by null values and oddly enough, blank space before some columns' names.
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I did a the following to clean the dataset;
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Since most of the missing values were location related and doesn't get in the way of crime quantity analysis, they were given dummy values.
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The columns with empty spaces in the front were trimmed. This is done to avoid confusion for coding purposes.
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"Date of Occurrence" was split into dates and time for easier plotting selection.
After which, the data is ready to be fed into Tableau for further data analysis and plotting.
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