This project analyzes real-world 911 emergency call data to uncover patterns in call volume, call types, and temporal trends. The goal is to derive insights that can support public safety planning, resource allocation, and operational decision making.
Rather than focusing only on modeling, this project emphasizes exploratory analysis and interpretation of data to understand how emergency services are utilized over time.
Emergency response agencies receive large volumes of calls daily, making it difficult to identify trends that inform staffing, preparedness, and response strategies. Understanding when calls peak, which types of emergencies are most common, and how patterns vary over time is critical for effective decision support.
- Cleaned and prepared raw 911 call data for analysis
- Performed exploratory data analysis to understand call distributions
- Analyzed temporal patterns (hourly, daily, monthly trends)
- Examined frequency of different emergency call categories
- Visualized key patterns to make insights easily interpretable
- Python
- pandas, NumPy
- Data visualization libraries (matplotlib / seaborn)
- Identified peak call times that may require increased staffing
- Observed dominant emergency call categories and their trends
- Revealed seasonal and time-based variations in emergency activity
- Demonstrated how exploratory analytics can support operational decisions
- Incorporate geographic analysis to identify regional hotspots
- Apply predictive modeling to forecast future call volumes
- Integrate external factors (weather, events) to enrich insights