Operational local join count statistics for cluster detection

Luc Anselin () and Xun Li ()
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Luc Anselin: The University of Chicago
Xun Li: The University of Chicago

Journal of Geographical Systems, 2019, vol. 21, issue 2, No 1, 189-210

Abstract: Abstract 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.

Keywords: Spatial clusters; LISA; Join count statistic; Multivariate spatial association; Spatial data science (search for similar items in EconPapers)
JEL-codes: C12 C88 R31 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10109-019-00299-x

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