Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law...

Area of Science:

• Data Science
• Geospatial Analytics
• Predictive Modeling

Background:

• Effective resource allocation requires accurate predictions of future activity.
• Challenges include user understanding and appropriate spatiotemporal granularity for statistical models.

Purpose of the Study:

• To develop a visual analytics approach for proactive and predictive resource allocation.
• To address challenges in end-user understanding and spatiotemporal granularity for predictive analytics.

Main Methods:

• Utilizing natural scale templates for geospatial and temporal drill-down.
• Applying Seasonal Trend decomposition based on Loess (STL) for spatiotemporal forecasting.
• Developing a novel kernel density estimation technique influenced by spatial correlation.

Main Results:

• Demonstrated a visual analytics approach for enhanced decision-making in resource allocation.
• Successfully applied spatiotemporal visual analytics and STL forecasting to incident data.
• Integrated spatial correlation into kernel density estimation for improved predictions.

Conclusions:

• The proposed visual analytics approach facilitates effective resource allocation through proactive and predictive insights.
• The methodology improves prediction accuracy by allowing flexible spatiotemporal granularity and incorporating spatial dependencies.
• The techniques are validated using Criminal, Traffic, and Civil (CTC) incident datasets.