Operational local join count statistics for cluster detection
Operational local join count statistics for cluster detection
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DOI：
10.1007/s10109-019-00299-x
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发表时间：
2019-06-01
影响因子：
2.9
通讯作者：
Li, Xun
中科院分区：
文献类型：
--
作者：
Anselin, Luc;Li, Xun
本文针对感兴趣的变量为二元的情况，提出了空间关联局部指标的概念。这将产生本地连接计数统计信息的条件版本。统计数据扩展到双变量和多变量上下文，并明确处理共定位。对于事件的所有潜在位置（例如，城市中的所有包裹）都可用的情况，该方法提供了基于点模式的统计的另一种选择。统计数据是在开源的GeoDa软件中实现的，并生成二进制变量的本地集群的地图，以及两个（或更多）二进制变量的共定位集群。实证分析了2013年和2014年底特律的房屋销售集群，以及2017年芝加哥人口普查区的城市设计特征。
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.