Independent analysis · built on a real published study
Where the things that predict crime actually change across Chicago
Most crime maps assume one relationship holds true everywhere in a city. This one doesn't: it works
out a separate answer for every neighborhood, so you can see exactly where each real factor matters a
lot, matters a little, or points in the opposite direction than you'd expect. Built on real Chicago
data from 2023–2025 (777 neighborhoods), using the same approach as a real 2025 study,
Cai (2025), PLOS One.
Where each factor matters more, less, or the opposite of what you'd expect
Pick a crime type, then pick what to look at. "How well it fits" shows how much of that crime type these
four factors actually explain in each part of the city. Each factor's own map shows where more of it
lines up with more crime (green) or less crime (red) — not just whether it matters somewhere in
Chicago, but exactly where and how strongly.
Does crime cluster into real patterns, or is it scattered randomly?
All three crime types cluster into clear, real patterns across the city rather than being scattered at
random. That's what justifies breaking this down neighborhood by neighborhood instead of settling for
one citywide average. ("Moran's I" below is the statistical test that confirms this — further from
0 means stronger real clustering, and the tiny p-values mean it isn't a coincidence.)
What else is connected, even outside the main model
Two more real things were checked against each crime type, but kept separate from the main model, the
same way the original study handled them: how dense the street network is, and how many vacant or
abandoned buildings are nearby. Positive numbers mean more of that thing tends to go along with more of
that crime type; negative numbers mean the opposite. Vacant buildings turn out to matter a lot for
criminal damage and battery, but barely at all for theft.
Sources
Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139. https://doi.org/10.1016/j.compenvurbsys.2017.05.004
Cai, Y. (2025). Advancing urban management: Integrating GIS, LLMs, and media narratives into environmental and socio-economic analyses for enhanced urban crime analysis. PLOS ONE. https://doi.org/10.1371/journal.pone.0331788
OpenStreetMap contributors. (2026). Chicago street network and points of interest [Data set]. OpenStreetMap. https://www.openstreetmap.org
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), Article 269. https://doi.org/10.3390/ijgi8060269