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Journal of Geographical Systems

, Volume 21, Issue 2, pp 189–210 | Cite as

Operational local join count statistics for cluster detection

  • Luc AnselinEmail author
  • Xun Li
Original Article

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 

JEL Classification

C12 C88 R31 

Notes

Acknowledgements

This research was funded in part by Award 1R01HS021752-01A1 from the Agency for Healthcare Research and Quality (AHRQ), “Advancing spatial evaluation methods to improve healthcare efficiency and quality.” Emily Talen and Hyesun Jeong provided the urban design classifications of the Chicago census block data. Comments by Julia Koschinsky and referees on an earlier version of the paper are greatly appreciated.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Spatial Data ScienceThe University of ChicagoChicagoUSA

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