Chicago Burglary Spatio-Temporal Parameter Estimates

Edward Appau Nketiah published parameter value estimates for a space-time self-exciting point process model applied to crime data. The 5.5 KB dataset contains results from a study analyzing burglary patterns in Chicago, Illinois, United States. It was last updated in April 2026.

Use Cases

• Calibrate space-time self-exciting point process models using the provided parameter estimates for burglary data.
• Analyze the clustering phenomenon in crime by applying the multi-dimensional Gaussian-type exponent approximation method described.
• Validate computational methods for estimating the conditional intensity function of point processes on crime datasets.
• Study the spatio-temporal patterns of burglaries in Chicago using the model's derived parameters.

Strengths

• Model parameters are derived from a real-world application to Chicago burglary data.
• Dataset is openly licensed under CC-BY-4.0 for reuse.

Limitations

• Dataset is very small at 5.5 KB, indicating limited raw data or only summary parameters.
• Specific row count, column names, and sample data are unavailable for validation.
• Geographic scope is limited to burglaries in Chicago, Illinois.

Provenance

Source

Edward Appau Nketiah via figshare.

Collection Method

Parameters estimated via a proposed multi-dimensional Gaussian-type exponent approximation method applied to crime data.

Freshness

Last updated in April 2026.

Geography

Chicago, Illinois, United States.