Operational local join count statistics for cluster detection

• Published in: Journal of geographical systems Vol. 21; no. 2; pp. 189 - 210
• Main Authors: Anselin, Luc, Li, Xun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019; Springer; Springer Nature B.V
• Subjects: Bivariate analysis; Clusters; Computer Appl. in Social and Behavioral Sciences; Data science; Econometrics; Economics; Economics and Finance; Geographical Information Systems/Cartography; Geospatial data; Landscape/Regional and Urban Planning; Original Article; Public software; Regional/Spatial Science; Source code; Statistics; Urban Economics; Urban planning; R31; Spatial clusters; C88; C12; LISA; Multivariate spatial association; Spatial data science; Join count statistic; spatial data science; multivariate spatial association; join count statistic; spatial clusters
• ISSN: 1435-5930; 1435-5949
• DOI: 10.1007/s10109-019-00299-x

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.