Predictive Analytics in Law Enforcement: Unveiling Patterns in NYPD Crime through Machine Learning and Data Mining

Abstract

Urban crime poses multifaceted challenges to cities' socio-economic structures. This study employs machine learning and data mining to bolster predictive policing in New York City. Using a comprehensive NYPD crime dataset spanning 2006 to 2017, the analysis identifies historical patterns and forecasts future crime trends. Rigorous methodologies ensure data fidelity, with algorithms like Random Forest and K-Means clustering parsing the intricate spatio-temporal crime dynamics. Results pinpoint crime hotspots and track criminal activity evolution, informing strategic law enforcement resource allocation and community involvement. Ethical considerations, including data privacy and algorithmic biases, are scrutinized alongside their impacts on community-police relations. The study recommends operational improvements and advocates for ongoing innovation in data-driven public safety strategies, advocating for the integration of new data sources and analytical methods in advancing smart city infrastructures.