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 treatme...
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| Published in | Journal of geographical systems Vol. 21; no. 2; pp. 189 - 210 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2019
Springer Springer Nature B.V |
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| 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. |
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| AbstractList | This paper operationalizes the idea of a local indicator of spatial association (LISA) 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.This paper operationalizes the idea of a local indicator of spatial association (LISA) 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. This paper operationalizes the idea of a local indicator of spatial association (LISA) 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. 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. This paper operationalizes the idea of a local indicator of spatial association (LISA) 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. |
| Audience | Academic |
| Author | Li, Xun Anselin, Luc |
| Author_xml | – sequence: 1 givenname: Luc surname: Anselin fullname: Anselin, Luc email: anselin@uchicago.edu organization: Center for Spatial Data Science, The University of Chicago – sequence: 2 givenname: Xun surname: Li fullname: Li, Xun organization: Center for Spatial Data Science, The University of Chicago |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31171898$$D View this record in MEDLINE/PubMed |
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| Copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2019 COPYRIGHT 2019 Springer Journal of Geographical Systems is a copyright of Springer, (2019). All Rights Reserved. |
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| Keywords | 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 |
| Language | English |
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| SubjectTerms | 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 |
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| Title | Operational local join count statistics for cluster detection |
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