NYC Crime Social and Economic Factors

Crime and motor vehicle collisions are
distinct yet interrelated social phenomena, deeply
influenced by demographic distributions, economic
disparities, and systemic urban dynamics. This study
investigates the spatial and temporal patterns of crime
and motor vehicle collisions (MVCs) across New York
City (NYC), integrating quantitative machine learning
techniques and qualitative socio-economic analysis.
This research constructs a unified, structured feature
set at both borough and ZIP code levels by leveraging
multiple open-source datasets, including crime,
arrests, MVCs, and borough-level census indicators.
The findings emphasized the significance of predictive
analytics for urban safety planning, data governance,
law enforcement targeting, and optimizing resources.
Ultimately, a combination of structured and
unstructured data can give rise to actionable insights
for real-life urban safety problems, as this research has
just demonstrated.