📊 Borough-wise Injuries: Bar chart revealed that Brooklyn and Queens had the highest number of injuries, highlighting them as key risk zones.
🗺️ Geographical Hotspots: Scatter plots of latitude/longitude showed concentrated clusters of collisions, with Manhattan showing dense patterns.
🧭 Injury Severity Mapping: Color-coded scatter plot emphasized areas with higher injury counts, useful for identifying dangerous intersections.
⚠️ Contributing Factors: Driver distraction, unsafe speeds, and failure to yield were among the top causes of accidents, as seen in the bar graph.
⏳ Temporal Trends: Time series analysis revealed seasonal peaks — summer months and weekends showed higher collision-related injuries.
🔗 Correlation Analysis: Heatmap showed strong relationships between Persons Injured and Motorists Injured, confirming consistent reporting patterns.
Python (Pandas, Matplotlib, Seaborn)
Data Cleaning & EDA
Visualization & Trend Analysis
Impact:
This analysis highlights critical patterns in NYC traffic collisions, providing actionable insights for policy makers, traffic authorities, and urban planners to improve road safety.
For Source Code: