This Crime Data Analysis project in Power BI explores crime trends in Washington, D.C., visualizing crime distribution by location, time, and type. It includes week-on-week trends, UCR severity rankings, and geospatial crime hotspots. The interactive dashboard aids law enforcement and policymakers in making data-driven decisions for public safety.
✅ Data Cleaning & Transformation: Processed raw crime data using Power Query, handling missing values and ensuring data consistency.
✅ Data Modeling: Established relationships between crime records, locations, and time dimensions for accurate analysis.
✅ DAX Calculations: Created measures for total crimes, week-on-week trends, high-crime areas, and UCR severity ranking.
✅ Visualizations Used: Bar Chart, Heatmap, Treemap, KPI Cards, and Geospatial Maps for insightful crime analysis.
🔹 Homicides are most frequent in the 7th Police District, occurring exclusively during the Midnight shift, with guns being the primary method.
🔹 Robbery is the most common offense within the Violent Crimes category.
🔹 Property Crimes account for the highest number of offenses, with Theft/Other being the most prevalent.
🔹 Downtown records the highest crime incidents in the BID areas.
🔹 The 6th week of the year witnessed the highest number of reported crimes.
🔹 The Evening shift experiences the highest crime rate.
🔹 The 5th Police District, Sector 5D1, and PSA 501 report the highest overall crimes.
🔹 3rd Police District, Sector 5D1, and PSA 106 have the highest number of late-reported crimes.
🔹 The average reporting time for crimes is 7.95 days.
🔹 The average resolution time for crimes is 19.78 hours.
🔹 Ward 1, Location (38.9295168861, -77.0327298633) is a high-crime hotspot, with 63 reported crimes.
🔹 A total of 28 locations have been identified as crime hotspots, each recording more than 10 incidents.
🔹 Power BI | 🔹 Power Query | 🔹 DAX | 🔹 Data Visualization | 🔹 Business Intelligence
This project provided valuable insights into crime data analysis, enhancing my ability to work with real-world datasets. It strengthened my analytical skills, particularly in identifying crime trends, prime locations, and high-risk areas using Power BI. Additionally, it reinforced the importance of data-driven decision-making in law enforcement and urban safety planning. 🚔📊
🔗 - Washington DC Jan-Feb Crime Report
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