Chicago Burglary Data for Spatio-Temporal Point Process Modeling

Parameter estimates support the application of space-time self-exciting point process models to crime data. The 5.5 KB Excel file contains results from a study analyzing burglary patterns in Chicago, Illinois. Edward Appau Nketiah authored the work, which was last updated in April 2026.

Use Cases

• Calibrating self-exciting point process models using the provided parameter value estimates for data size.
• Analyzing the strong clustering phenomenon in burglary events as revealed by the model's conditional intensity function.
• Testing the multi-dimensional Gaussian-type exponent approximation method against other computational approaches for large crime datasets.
• Studying space-time patterns of burglaries in Chicago using the model's flexible estimation results.

Strengths

• Model results demonstrate a strong clustering phenomenon in the burglary data.
• The proposed method addresses computational difficulties associated with large datasets.
• Data is shared under a permissive CC-BY-4.0 license.

Limitations

• The dataset is very small at 5.5 KB, containing only parameter estimates, not the underlying raw crime records.
• The analysis is geographically limited to burglaries in Chicago, Illinois, United States.
• The number of rows and specific columns are unknown, limiting reproducibility.

Provenance

Source

Edward Appau Nketiah via Figshare.

Collection Method

Results from applying a proposed multi-dimensional Gaussian-type exponent approximation method to study burglaries.

Freshness

Last updated in April 2026.

Geography

Chicago, Illinois, United States.