ELSA: a new local indicator for spatial association

Nicholas A. S. Hamm; Babak Naimi; Thomas A. Groen

doi:10.5281/zenodo.4665865

ELSA: a new local indicator for spatial association

Nicholas A. S. Hamm, Babak Naimi, Thomas A. Groen

School of Geographical Sciences

Research output: Contribution to conference › Paper › peer-review

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Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

Original language	English
Number of pages	7
DOIs	https://doi.org/10.5281/zenodo.4665865
Publication status	Published - 6 Apr 2021
Event	The 29th Annual GIS Research UK Conference (GISRUK) - Cardiff, Wales, UK (Online) Duration: 14 Apr 2021 → 16 Apr 2021

Conference

Conference	The 29th Annual GIS Research UK Conference (GISRUK)
Period	14/04/21 → 16/04/21

Keywords

LISA, categorical data, hierarchical classification, continuous data
categorical data
hierarchical classification
continuous data

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10.5281/zenodo.4665865Licence: Other

HammEtAl GISRUK2021 paper 58Final published version, 888 KBLicence: CC BY

http://doi.org/10.5281/zenodo.4665865

Cite this

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T1 - ELSA: a new local indicator for spatial association

AU - Hamm, Nicholas A. S.

AU - Naimi, Babak

AU - Groen, Thomas A.

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PY - 2021/4/6

Y1 - 2021/4/6

N2 - There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

AB - There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

KW - LISA, categorical data, hierarchical classification, continuous data

KW - categorical data

KW - hierarchical classification

KW - continuous data

U2 - 10.5281/zenodo.4665865

DO - 10.5281/zenodo.4665865

M3 - Paper

T2 - The 29th Annual GIS Research UK Conference (GISRUK)

Y2 - 14 April 2021 through 16 April 2021

ER -

ELSA: a new local indicator for spatial association

Abstract

Conference

Keywords

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