title: Alternative spatial weights matrices: Methodology and application in calculating LISA
metadata.name: dc.contributor.author
metadata.value: Okunev, Igor Yu.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kushnareva, Anna E.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-10-05T11:56:21Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-10-05T11:56:21Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Okunev, I. Yu., Kushnareva, A. E. (2023). Alternative spatial weights matrices: Methodology and application in calculating LISA. Vestnik of Saint Petersburg University. Earth Sciences, 68 (2), 390–413. https://doi.org/10.21638/spbu07.2023.210 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu07.2023.210
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44084
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The heuristic potential of spatial methods depends heavily on thechoice ofa spatial weight
  matrix. The article illustrates the creation and use ofabsolute geographic versus relative socio-
  political matrix and tests both by calculating spatial autocorrelation for development indicators.
  Particularly, a comparison is made of spatial association patterns in geometric and
  geopolitical space on a sample of the 193 UN member states. The Moran’s I is calculated to
  assess the degree of the neighborhood effect for both matrices. International clustering patterns
  are explored with local indicators of spatial association (LISA), plotted on a map with
  the two types of matrices. Comparing LISA cartograms helps identify possible opportunities
  for socio-political phenomena to spread throughout space, as well as the trends inthe spatial
  organization of the international politics. Carrying out calculations with different matrices
  allows us to single out groups of observations that constitute the core of the cluster. On the
  contrary, “transitive” observations that change their cluster affiliation in a supposedly homogeneous
  group, can also be detected. Also, the usage of different types of weight matrices can
  help highlight the “gray zones” — parts of the data set that lack spatial autocorrelation and
  may require additional research. Overall, the results suggest that usingboth absolute topological
  and relative socio-economic weight matrices is reasonable for the purposes of exploratory
  spatial analysis. Using matrices based on different types of variables concurrently can assist in
  detecting new trends in spatial organization and providing empirical confirmation for existing
  spatial patterns.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The study was conducted as part of the Russian Science Foundation scholarship project no. 19-78-
  10004.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Earth Sciences;Volume 68; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: spatial weights matrix
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: relative neighbourhood
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: local indicators of spatial association
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: spatial autocorrelation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: political geography
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: system of international relations
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: geopolitical bloc
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Alternative spatial weights matrices: Methodology and application in calculating LISA
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 10.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44084/1/10.pdf
resources.format: Adobe PDF
resources.size: 3,57 MB