Chicago Burglary Spatio-Temporal Clustering Parameter Estimates

Parameter estimates model the strong clustering phenomenon in Chicago burglary data using a space-time self-exciting point process. The 5.5 KB Excel file contains results from a study applying a multi-dimensional Gaussian-type exponent approximation method. Edward Appau Nketiah authored this research, which was last updated in April 2026.

Use Cases

• Calibrate the conditional intensity function of a self-exciting point process model for burglary prediction.
• Analyze clustering features in crime datasets using estimated model parameters.
• Validate the multi-dimensional Gaussian-type exponent approximation method against simulation results.
• Study space-time patterns of burglaries in Chicago, Illinois, based on the fitted model parameters.

Strengths

• Model results demonstrate a strong clustering phenomenon in the burglary data.
• The proposed method addresses computational difficulties associated with large datasets.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope or a summary of results.
• Specific row counts, column names, and raw crime event data are unavailable.

Provenance

Source

Edward Appau Nketiah via figshare.

Collection Method

Results from applying a proposed multi-dimensional Gaussian-type exponent approximation method to crime data.

Freshness

Last updated in April 2026.

Geography

Chicago, Illinois, United States.