Burglary Spatio-Temporal Pattern Estimates for Chicago

Parameter estimates for a space-time self-exciting point process model applied to crime data. The dataset contains results from a study analyzing burglary patterns in Chicago, Illinois, United States, authored by Edward Appau Nketiah and published in April 2026. The 5.5 KB size indicates a small, focused set of model outputs rather than raw event data.

Use Cases

• Calibrating self-exciting point process models using estimated parameter values for crime prediction.
• Analyzing the strength and scale of spatio-temporal clustering in burglary events as revealed by the model.
• Benchmarking computational methods for flexible estimation of a conditional intensity function on crime datasets.
• Studying the multi-dimensional Gaussian-type exponent approximation method's output for burglary pattern analysis.

Strengths

• Model results are derived from application to a real-world crime dataset (Chicago burglaries).
• Dataset is openly licensed under CC-BY-4.0 for reuse.

Limitations

• Extremely small scale at 5.5 KB, containing only model parameters, not the underlying event data.
• Lacks raw columns, sample data, or row counts, limiting direct analytical utility.
• Geographic scope is limited to a single city (Chicago).

Provenance

Source

Edward Appau Nketiah via figshare.

Collection Method

Derived from applying a proposed multi-dimensional Gaussian-type exponent approximation method to burglary data.

Freshness

Last updated in April 2026.

Geography

Chicago, Illinois, United States.