Public crime reporting varies across jurisdictions, which makes direct comparison difficult.
CrimeScore works with public reported-crime records and broad public location context so covered areas can be compared under one scoring convention.
Race and ethnicity are excluded from model features and are not shown as customer-facing score contributors. That does not remove every fairness concern, but it is a core product boundary.
Each result uses a 0 to 100 comparative scale: higher is safer. The score supports local and national comparison without claiming to predict what will happen to a person, property, or exact location.
The final score response is deliberately simple: a score, a grade, resolved geography, component scores, and metadata. Starter and higher teams can also request score details with contributor direction and impact.
There are real limitations. Reported-crime records reflect reporting and enforcement patterns, not every crime that happened. Government estimates can lag rapid change, and some geographies have weaker source coverage. Block-group estimates are more local, but they can also be noisier than county-level estimates.
That is why CrimeScore should be treated as a location intelligence signal. It is useful for context, comparison, maps, and analytics. It is not a policing tool, not a personal score, and not a reason to make an adverse decision about an individual person.