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Geospatial Modeling and Simulation of Property Crime in Urban Neighborhoods: An Example Model with Foreclosure

机译:城市社区财产犯罪的地理空间建​​模与仿真:丧失抵押品赎回权的示例模型

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Based on neighborhood life cycles, this paper describes the development and the functions of an Urban Crime Simulator (UCS) that are based on a concept that neighborhood goes through cycles from newly established and energetic neighborhoods to matured and stabilized ones and then to deteriorated neighborhoods that await for new stimuli for revitalization. The UCS was developed to estimate changes in property crime rates as induced by changes in the socioeconomic characteristics of urban neighborhoods. UCS is fully integrated with geographically referenced data and is operational in GIS environment. It offers flexibility in the inclusion of neighborhood attributes that may best fit a specific localized context and knowledge of local neighborhoods and neighborhood attributes as suggested by criminological literature. With UCS, urban neighborhoods are profiled by a selected set of attributes as defined by users. These neighborhoods are first classified into clusters by a hierarchical cluster analysis, which minimizes in-cluster differences and maximizes between-cluster differences. When attribute values of a target neighborhood are updated with projected or planned changes, UCS searches the entire area to find a reference neighborhood with an attribute profile that is the closest to that of the target neighborhood. Once the reference neighborhood is found, all neighborhoods in the reference neighborhood's cluster are statistically analyzed to yield an estimate for what a new crime rate may be for the target neighborhood with the projected changes. UCS has a set of tools to assist its users. Correlation among included attributes can be easily calculated to detect if there is any issue of co-linearity. Global and localized spatial autocorrelation can be calculated to evaluate if any spatial dependency among their data would cause any concern in the simulations. Finally, global and localized regression models enable UCS users to assess how appropriate the selected attributes are with respect to explaining the variation in crime rates among the neighborhoods. UCS is software designed for practical use by law-enforcement agencies that may not be able to take the necessary time to assemble a detailed comprehensive database as other modeling approaches require before carrying out such simulations.
机译:基于邻里的生命周期,本文介绍了开发和基于一个概念,邻里经历来自新成立的,充满活力的地区,为成熟,稳定的人循环,然后恶化市中心的城市犯罪模拟器(UCS)的函数指日可待振兴新的刺激。该UCS是发展成为诱发城市社区的社会经济特征的变化来估计财产犯罪率变化。 UCS与地理参照数据完全整合,并在GIS环境操作。它在包括附近的属性,可能最适合一个特定的本地化环境和当地社区和邻里的知识属性由犯罪文学建议提供了灵活性。与UCS,城市街区被选定的一组属性的轮廓由用户所定义的。这些邻域由层次聚类分析,其最小化在群集的差异并最大化簇间差异第一分类成群集。当目标附近的属性值与预测或计划的变化更新,UCS搜索整个区域查找与属性配置文件是最接近目标附近的一个参考附近。一旦参考附近被发现,参照附近的集群中的所有街区进行统计分析,以产生什么新的犯罪率可能会与预期变化的目标附近的估计。 UCS具有一套工具,以帮助其用户。包括属性之间的关系可以很容易地计算出,以检测是否存在共线性的任何问题。全局和局部空间自相关可以计算来评估,如果他们的数据中的任何空间依赖性会导致在模拟的任何问题。最后,全球和局部回归模型使UCS用户评估适当选择的属性是如何相对于解释在邻里之间犯罪率的变化。 UCS是软件通过执法机构可能不能够采取必要的时间来组装一个详细全面的数据库和其他建模方法进行这种模拟前需要设计的实际使用。

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