AI 中文总结
该研究针对城市犯罪预防挑战，构建基于模糊逻辑的框架用于社区感知犯罪热点检测和实时通知，结合多种数据与规则估计风险，经探索性验证，虽未证明能减少犯罪或预测准确，但为相关研究提供了以人为本的框架。
AI 中文摘要
城市犯罪预防对市政当局、执法机构和公民来说一直是一项社会技术挑战。传统的报告和应对流程往往依赖延迟的事件报告和被动的资源分配，而社区层面的信号和模糊的早期预警指标可能未得到充分利用。本文将一个城市计算研讨会项目重新构建为基于模糊逻辑的框架，用于社区感知城市犯罪热点检测和实时通知。该平台结合公民报告、历史犯罪数据、上下文城市指标和可配置的模糊规则来估计局部风险水平并支持有针对性的意识通知。与二元分类方法不同，模糊逻辑可以表示部分风险、不确定性和不完整信息，适用于风险渐进且依赖上下文的城市环境。本文介绍了系统架构、模糊风险模型、通知工作流程、隐私保护措施，并对25名参与者进行了探索性验证。评估重点关注感知有用性、通知相关性、可用性、信任、隐私问题、多语言可访问性以及对社区报告的接受度。研究结果表明，这样的平台可能会提高态势感知能力，支持报告和规划讨论，并有助于解决现有城市安全工作流程中的薄弱环节，但尚未构成减少犯罪或实际预测准确性的证据。其贡献在于为未来关于模糊逻辑、城市计算和社区感知犯罪预防系统的研究提供了一个以人为本的负责任框架。
英文摘要
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.
Comments12 pages, 6 tables, 1 figure. Framework paper with Python prototype and exploratory validation with 25 participants
