Operational local join count statistics for cluster detection
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.
KeywordsSpatial clusters LISA Join count statistic Multivariate spatial association Spatial data science
JEL ClassificationC12 C88 R31
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.
- Anselin L (1996) The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In: Fischer M, Scholten H, Unwin D (eds) Spatial analytical perspectives on GIS in environmental and socio-economic sciences. Taylor and Francis, London, pp 111–125Google Scholar
- Anselin L, Rey SJ (2014) Modern spatial econometrics in practice, a guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press, ChicagoGoogle Scholar
- Anselin L, Syabri I, Smirnov O (2002) Visualizing multivariate spatial correlation with dynamically linked windows. In: Anselin L, Rey S (eds) New tools for spatial data analysis: proceedings of the specialist meeting. Center for Spatially Integrated Social Science (CSISS), University of California, Santa Barbara. CD-ROMGoogle Scholar
- Cliff A, Ord JK (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
- Cuzick J, Edwards R (1990) Spatial clustering for inhomogeneous populations. J R Soc B 52:73–104Google Scholar
- Getis A, Ord JK (1996) Local spatial statistics: an overview. In: Longley P, Batty M (eds) Spatial analysis: modeling in a GIS environment. GeoInformation International, pp 261–277Google Scholar
- Moran PA (1948) The interpretation of statistical maps. Biometrika 35:255–260Google Scholar