Static indicators may fail to capture spatiotemporal differences in the spatial influence of urban features on different crime types. In this study, with a base station analogy, we introduced crime risk stations that conceptualize the spatial influence of urban features as crime risk signals broadcasted throughout a coverage area. We operationalized these risk signals with two novel risk scores, risk strength and risk intensity, obtained from novel distance-aware risk signal functions. With a crime-specific spatiotemporal approach, through a spatiotemporal influence analysis we examined and compared these risk scores for different crime types across various spatiotemporal models. Using a correlation analysis, we examined their relationships with concentrated disadvantage. The results showed that bus stops had relatively lower risk intensity, but higher risk strength, while fast-food restaurants had a higher risk intensity, but a lower risk strength. The correlation analysis identified elevated risk intensity and strength around gas stations in disadvantaged areas during late-night hours and weekends. The results provided empirical evidence for a dynamic spatial influence that changes across space, time, and crime type. The proposed risk functions and risk scores could help in the creation of spatiotemporal crime hotspot maps across cities by accurately quantifying crime risk around urban features.

中文翻译：

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犯罪风险站：通过距离感知风险信号函数检查城市特征的时空影响

静态指标可能无法捕捉城市特征对不同犯罪类型的空间影响的时空差异。在本研究中，我们以基站为类比，引入了犯罪风险站，将城市特征的空间影响概念化为在整个覆盖区域广播的犯罪风险信号。我们使用从新的距离感知风险信号函数中获得的两个新的风险评分、风险强度和风险强度来操作这些风险信号。使用特定于犯罪的时空方法，通过时空影响分析，我们检查并比较了各种时空模型中不同犯罪类型的风险评分。使用相关分析，我们检查了他们与集中劣势的关系。结果表明，公交车站的风险强度相对较低，但风险强度较高，而快餐店的风险强度较高，但风险强度较低。相关性分析发现，在深夜和周末，贫困地区加油站周围的风险强度和强度升高。结果为跨空间、时间和犯罪类型变化的动态空间影响提供了经验证据。建议的风险函数和风险评分可以通过准确量化城市特征周围的犯罪风险来帮助创建跨城市的时空犯罪热点地图。结果为跨空间、时间和犯罪类型变化的动态空间影响提供了经验证据。建议的风险函数和风险评分可以通过准确量化城市特征周围的犯罪风险来帮助创建跨城市的时空犯罪热点地图。结果为跨空间、时间和犯罪类型变化的动态空间影响提供了经验证据。建议的风险函数和风险评分可以通过准确量化城市特征周围的犯罪风险来帮助创建跨城市的时空犯罪热点地图。