A Fuzzy Logic Framework for Community-Aware Crime Hotspot Detection: Prototype Application Architecture and Exploratory Validation for an Urban Computing Platform

Abstract

Urban crime prevention is a persistent socio-technical challenge for municipalities, law enforcement agencies, and citizens. Traditional reporting and response processes often rely on delayed incident reports and reactive resource allocation, while community-level signals and ambiguous early-warning indicators may remain underused. This paper reframes an Urban Computing seminar project into a fuzzy logic-based framework for community-aware urban crime hotspot detection and real-time notification. The proposed platform combines citizen reports, historical crime data, contextual urban indicators, and configurable fuzzy rules to estimate localized risk levels and support targeted awareness notifications. Unlike binary classification approaches, fuzzy logic can represent partial risk, uncertainty, and incomplete information, making it suitable for urban environments where risk is gradual and context-dependent. The paper presents the system architecture, fuzzy risk model, notification workflow, privacy safeguards, and exploratory validation with 25 participants. The evaluation focuses on perceived usefulness, notification relevance, usability, trust, privacy concerns, multilingual accessibility, and acceptance of community reporting. The findings suggest that such a platform may improve situational awareness, support reporting and planning discussions, and help address weak points in existing urban safety workflows, while not yet constituting evidence of deployed crime reduction or real-world predictive accuracy. The contribution is a responsible, human-centered framework for future research on fuzzy logic, urban computing, and community-aware crime prevention systems.