This project develops a comprehensive crime forecasting framework for Chicago using advanced time series methodologies applied to 23 years of historical data (2001–2023) from the Chicago Data Portal.
- Objective: Develop and evaluate multiple time series forecasting models using ensemble techniques to improve crime prediction accuracy and inform strategic planning.
- Dataset: Comprehensive analysis of 7M+ crime incidents from Chicago (2001–2023), with deep-dive focus on 1.75M theft and 1.51M battery cases representing Chicago's highest-volume offenses
- Methodological Focus: Multi-model ensemble approach combining traditional time series analysis with advanced neural network architectures
- Strategic Goal: Deliver actionable predictive intelligence to law enforcement leadership, municipal policymakers, and community safety stakeholders for evidence-based decision making.
- Regression Model - Quadratic trend with seasonal decomposition for long-term pattern detection
- Exponential Smoothing (ETS) - Adaptive weighting for recent trend prioritization
- Auto ARIMA - Automated parameter selection with seasonal differencing
- Neural Network (NNAR) - Non-linear pattern recognition with 11-lag architecture
- Seasonal Naïve Baseline - Benchmark model for performance validation
- Ensemble Methods - Multi-model combination strategies:
- Simple averaging for stability
- Trimmed mean for outlier robustness
- Regression-based weighting for optimal model combination
- Dramatic 50% decline in theft and battery arrests over 23-year period, indicating significant long-term crime reduction trends
- COVID-19 anomaly detection - Models successfully identified and quantified pandemic-related crime pattern disruptions
- Robust seasonal intelligence - Consistent 15-20% crime spikes during summer months across all model validations
- Strong individual model performance: ETS achieved 12.59% MAPE, demonstrating reliable short-term forecasting capability
- Superior ensemble methodology: Trimmed mean approach reached 12.79% MAPE with enhanced stability
- Strong predictive performance: Regression combination model achieved 5.72% MAPE - substantially improving upon individual model results
- Tactical Resource Deployment: Implement data-driven 25% patrol increase during identified peak periods (June–August), with targeted allocation to high-probability zones
- Predictive Budget Optimization: Integrate ETS/ARIMA forecasting models into quarterly resource allocation for community policing and intervention programs
- Adaptive Crime Prevention: Deploy monthly regression-combination forecasts for real-time strategy pivots and proactive crime prevention initiatives
- Community Intelligence Integration: Synchronize predictive analytics with neighborhood watch programs and community engagement efforts for comprehensive crime prevention
- Cross-Agency Coordination: Enable police departments, social services, and municipal planning to align strategies based on forecasted crime patterns
/chicago-crime-forecasting
├── chicago-crime.html # Final report with visualizations
├── chicago-crime.Rmd # R Markdown file with code
├── final_presentation.pdf # Presentation of key findings
└── final_report.pdf # Comprehensive report with methodology
- Arrest-only focus may underrepresent total crime incidents (not all crimes result in arrests)
- Excluded external factors such as socioeconomic shifts, weather conditions, and policy changes that influence crime patterns
- Geospatial elements were excluded in favor of pure time-series approach
- Model sensitivity to irregular shocks (e.g., pandemic, economic downturns, policy shifts)
- Spatial Analysis: Incorporate geographic variables for neighborhood-level forecasting and targeted resource allocation
- External Variables: Include socioeconomic indicators, weather data, and demographic factors to enhance model performance
- Advanced Methods: Explore deep learning approaches and additional ensemble techniques for improved pattern detection
- Model Validation: Test methodology on other cities to assess generalizability and transferability of findings
- Real-time Applications: Investigate implementation of continuous forecasting systems for operational planning
- Asad Adnan
- Muhammad Ahmad
- Brian Murphy