City-driven open data initiatives have made spatially referenced crime and risk factor data more readily available online, allowing for significance tests to determine the relationship between environment and crime. This paper uses a variety of open source data to assess risk factors for specific violent crime types (assault, homicide, rape, robbery) in three different cities. The results contribute to our understanding of 1) variation in intra-city risk factors for each violent crime type, 2) the degree of spatial overlap for high-risk places for each violent crime type within a city, and 3) the generalizability of risk factors across crime types and cities. Risk Terrain Modeling (RTM) was used to determine the risk factors associated with each crime type at the micro-level and conjunctive analyses of case configurations (CACC) determined the unique behavior settings at the highest risk for each specific violent crime in each city. The findings indicate that intra-city risk factors vary greatly for each violent crime, highrisk places for different violent crimes tend to not overlap spatially within a city, and risk factors are not generalizable across crime types or across cities. Researchers and law enforcement need to examine local, crime-specific contexts when assessing crime problems and generating solutions.

中文翻译：

---

我们可以信任犯罪预测因素和犯罪类别吗？关于泛化的潜在问题的扩展

城市驱动的开放数据计划使在线参考的犯罪和危险因素数据更易于在线获得，从而允许进行重要度测试以确定环境与犯罪之间的关系。本文使用各种开源数据来评估三个不同城市中特定暴力犯罪类型（袭击，凶杀，强奸，抢劫）的风险因素。结果有助于我们理解以下方面：1）每种暴力犯罪类型的城市内风险因素的变化，2）城市内每种暴力犯罪类型的高风险场所的空间重叠程度，以及3）风险的可概括性犯罪类型和城市的因素。风险地形模型（RTM）用于在微观级别确定与每种犯罪类型相关的风险因素，案例配置的联合分析（CACC）确定了每个城市中每种特定暴力犯罪在最高风险下的独特行为设置。研究结果表明，每种暴力犯罪的城市内部风险因素差异很大，针对不同暴力犯罪的高风险场所在城市范围内往往不会在空间上重叠，并且风险因素无法在犯罪类型或城市之间推广。研究人员和执法部门在评估犯罪问题和制定解决方案时，需要检查当地针对特定犯罪的情况。不同暴力犯罪的高风险场所在城市中往往不会在空间上重叠，并且危险因素无法在犯罪类型或城市之间推广。研究人员和执法部门在评估犯罪问题和制定解决方案时，需要检查当地针对特定犯罪的情况。不同暴力犯罪的高风险场所在城市中往往不会在空间上重叠，并且危险因素无法在犯罪类型或城市之间推广。研究人员和执法部门在评估犯罪问题和制定解决方案时，需要检查当地针对特定犯罪的情况。