Chicago city is one of the most populous city in USA, and according to statistics, Chicago has a crime rate of 3,926 per 100,000 people in 2020, which is about 67% higher than the national average. With this statistics, I decided it is important to find meaningful insight and patterns using 2021 crime data. This analysis provided answer to the following questions:
- How did crime change over time?
- What are the top 5 crime by primary type?
- How many arrests were made?
- What is the crime rate in each community?
- What is the relationship between crime rate and unemployment?
The following datasets were used in this project:
Data wrangling is the process of transforming raw datasets to make them more readily and accessible to analyse. I started this process by importing necessary libraries such as pandas, geopandas, matplotlib, etc. Thereafter, I added a geometry column to the crime data frame using Longitude and Latitude columns. Next, I extracted the community area and unemployment columns from the indicator data frame into a new data frame (unemployment), then, the population and unemployment data frames were merge with the community data frame. I ensured there were no duplicates or null values and also removed columns that were not important for the analysis.
Finally, the end of the analysis. Through this analysis, I was able to get the following insights:
- Battery had the highest count of primary type of crime at 40,319, followed by Theft and Criminal Damage. Other Narcotic Violation had the lowest count at 2.
- There is a strong positive correlation between unemployment and crime rate; that is, high unemployment rate leads to high crime rate.
- February had the lowest crime count (12810), while October had the highest crime count (18856).
I hereby recommend that Chicago Police Department should focus on communities with high crime rates, furthermore, government should organise campaign against crime and control unemployment through incentives and fiscal policy.
Thank you for reading!!