This project focuses on analyzing and visualizing crime data across multiple cities to uncover actionable insights. It aims to understand crime patterns, resolution times, geographic trends, and more, using advanced data analysis and visualization techniques.
- Exploratory Data Analysis (EDA): Initial exploration and cleaning of the dataset.
- 12 Analytical Questions: Detailed insights into specific crime-related patterns.
- Visualizations: Charts and graphs for data representation.
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Which sectors handle incidents most effectively in terms of resolution time?
Figures 24 and 25 show Sector T with the lowest average resolution time of 85.1 hours, validated by consistent distribution in a box plot. -
Are there specific times of day when incidents occur more frequently?
Figures 26 and 27 indicate peak incidents at midnight and Fridays, with activity increasing into weekends. -
Which police districts or agencies have the most missing resolution time entries?
Figures 28 and 29 highlight Montgomery Village, Silver Spring, and Wheaton as areas with significant missing data, linked primarily to the MCFM agency. -
How does the time required to respond (Dispatch Date/Time to Start Date/Time) vary by Beat?
Figures 30 and 31 reveal Beats 3G5, 5N3, and 1A4 as having the highest average response times, with varying data distributions. -
How is crime distributed geographically across various cities, and what patterns emerge when examining incidents by type, frequency, and seasonal variations?
Figures 32 and 33 show Silver Spring as a high-crime area, with incidents peaking in summer across all cities. -
How do crime incident distributions vary across different directional regions of the city?
Figures 34 and 35 reveal high crime densities in the northwest and southeast regions despite smaller geographic areas. -
What are the key crime hotspots in different cities, and how do they vary by crime type?
Figures 36 and 37 show Silver Spring as a hotspot for diverse crime types, with Chevy Chase prominent for property crimes. -
How do response times vary across cities and crime types?
Figures 38 and 39 indicate cities like Rockville and Chevy Chase with high response time variability, particularly for property crimes. -
How do high-density crime areas, identified geographically by Zip Codes, correlate with the types of crimes occurring within those regions?
Figures 40 and 41 highlight Zip Codes 20910, 20904, and others with high crime densities, dominated by property crimes. -
How do 'Not a Crime' incidents vary across different Zip Codes and Place Types?
Figures 42 and 43 show Zip Code 20906 with the most 'Not a Crime' incidents, concentrated in residential areas. -
How does the distribution of crime incidents across cities compare to the number of police stations allocated?
Figures 44 and 45 show no significant need for additional police resources, as the distribution aligns with incident counts. -
Which streets experience the highest frequency of crime incidents, and what are the most common types of crimes on these streets?
Figures 46 and 47 identify Georgia Street and Rockville Street as high-crime areas, dominated by property and societal crimes.
- Programming Language: Python
- Libraries: Pandas, Matplotlib, Seaborn, Plotly
- Data Visualization Techniques: Heatmaps, Box Plots, Hexbin Maps, Bar Charts, Violin Plots
This repository includes a variety of visualizations to represent findings effectively, such as:
- Bar charts
- Line graphs
- Heatmaps
- Hexbin maps
- Violin plots
- Sector T is the most efficient in handling incidents.
- Summer and weekends experience the highest crime rates.
- Northwest and southeast regions are high-density crime areas despite smaller geographic coverage.
- Silver Spring stands out as a crime hotspot across multiple categories.
- Most cities do not exhibit an immediate need for additional police resources.
- Clone this repository:
git clone <https://github.com/Patric-1613/Crime-Analysis-Visualization>