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Operational Local Join Count Statistics for Cluster Detection.
Journal of Geographical Systems ( IF 2.417 ) Pub Date : 2019-05-02 , DOI: 10.1007/s10109-019-00299-x
Luc Anselin 1 , Xun Li 1
Affiliation  

This paper operationalizes the idea of a local indicator of spatial association for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern-based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open-source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

中文翻译:

用于群集检测的操作性本地加入计数统计信息。

本文针对感兴趣的变量为二进制的情况,实现了空间关联的局部指标的想法。这产生了本地联接计数统计信息的条件版本。通过对共址的显式处理,该统计信息扩展到双变量和多变量上下文。该方法为事件的所有可能位置(例如,城市中的所有地块)均可用的情况提供了一种基于点模式的统计数据的替代方法。统计信息在开源GeoDa软件中实现,并生成了二进制变量的本地群集以及两个(或多个)二进制变量的共置群集的产量图。实证插图调查了2013年和2014年底特律的房屋销售局部群以及2017年芝加哥人口普查区的城市设计特征。
更新日期:2019-05-02
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