当前位置: X-MOL 学术Prof. Geogr. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Bayesian Geographic Profiling: A Fundamental Limitation
The Professional Geographer ( IF 1.5 ) Pub Date : 2022-07-11 , DOI: 10.1080/00330124.2022.2075408
D. Kim Rossmo 1
Affiliation  

Geographic profiling is a criminal investigative technique that analyzes the locations of a crime series to determine the most probable area of offender residence. Police agencies employ the methodology for suspect prioritization and information management purposes in serial crime cases. Geoprofiles are probability maps generated by an algorithm that integrates distance decay functions originating from the point pattern of the connected crime sites. A more recent approach, known as empirical Bayes journey-to-crime estimation (or Bayesian geographic profiling), seeks to supplement these models with area-based historical offender and crime data. Spatial information from previous crime trips is used to calibrate analyses following the assumption that the unknown offender likely resides in the same neighborhood as past criminals who offended in the location of the new crime series. Inferring individual suspect rankings from historical area rankings, however, creates an ecological fallacy, and the greater the congruence between past offenders and future suspects, the more tautological the analysis. Although Bayesian models cannot be used to inform suspect prioritization—the main function of geographic profiling—the approach could have applicability for police strategies based on area prioritization. Surprisingly, this major limitation of the Bayes approach to geoprofiling has been ignored in the literature.



中文翻译:

贝叶斯地理剖析:基本限制

地理剖析是一种犯罪调查技术,它分析犯罪系列的位置以确定犯罪者最可能居住的区域。警察机构在连环犯罪案件中采用这种方法对嫌疑人进行优先排序和信息管理。地理剖面是由一种算法生成的概率图,该算法集成了源自相连犯罪现场的点模式的距离衰减函数。一种更新的方法,称为经验贝叶斯犯罪旅程估计(或贝叶斯地理剖析),旨在用基于区域的历史罪犯和犯罪数据来补充这些模型。来自先前犯罪旅行的空间信息用于校准分析,假设未知犯罪者可能与在新犯罪系列地点犯罪的过去犯罪者居住在同一社区。然而,从历史区域排名推断个人嫌疑人排名会产生生态谬误,过去犯罪者和未来嫌疑人之间的一致性越大,分析就越重言式。尽管贝叶斯模型不能用于确定嫌疑人的优先级(地理剖析的主要功能),但该方法可能适用于基于区域优先级的警察策略。令人惊讶的是,贝叶斯方法对地理剖析的这一主要限制在文献中被忽略了。然而,从历史区域排名推断个人嫌疑人排名会产生生态谬误,过去犯罪者和未来嫌疑人之间的一致性越大,分析就越重言式。尽管贝叶斯模型不能用于确定嫌疑人的优先级(地理剖析的主要功能),但该方法可能适用于基于区域优先级的警察策略。令人惊讶的是,贝叶斯方法对地理剖析的这一主要限制在文献中被忽略了。然而,从历史区域排名推断个人嫌疑人排名会产生生态谬误,过去犯罪者和未来嫌疑人之间的一致性越大,分析就越重言式。尽管贝叶斯模型不能用于确定嫌疑人的优先级(地理剖析的主要功能),但该方法可能适用于基于区域优先级的警察策略。令人惊讶的是,贝叶斯方法对地理剖析的这一主要限制在文献中被忽略了。尽管贝叶斯模型不能用于确定嫌疑人的优先级(地理剖析的主要功能),但该方法可能适用于基于区域优先级的警察策略。令人惊讶的是,贝叶斯方法对地理剖析的这一主要限制在文献中被忽略了。尽管贝叶斯模型不能用于确定嫌疑人的优先级(地理剖析的主要功能),但该方法可能适用于基于区域优先级的警察策略。令人惊讶的是,贝叶斯方法对地理剖析的这一主要限制在文献中被忽略了。

更新日期:2022-07-11
down
wechat
bug