Geospatial Crime Pattern Analysis
Pandas, scikit-learn, and GeoPandas for clustering urban crime against zoning.
Summary. Statistical and geospatial analysis of urban crime data, looking for correlations between residential density, commercial zoning, and crime incidence. Built a Python pipeline (Pandas, scikit-learn, GeoPandas) that handles normalization, K-means clustering, and choropleth visualization.
Coursework at Chapman University.
The question
[TODO 1 paragraph framing the question. Be specific. Which city, which crime categories, what years.]
Pipeline
[TODO walk through the steps. Geocoding and spatial join, the normalization choice (per-capita or per-area), and the clustering decision (why K-means rather than DBSCAN here, or vice versa).]
Findings
[TODO 1 to 2 concrete patterns the analysis revealed, with a choropleth or scatter to back them up.]
What I’d do differently now
[TODO this is one of the earlier projects. A short reflection on what an upgraded version would look like is a great signal of growth.]