A Voronoi-like model of spatial autocorrelation for characterizing spatial patterns in vector data

Xiang Zhang; Tinghua Ai; Jantien E. Stoter

doi:10.1109/ISVD.2009.19

A Voronoi-like model of spatial autocorrelation for characterizing spatial patterns in vector data

Xiang Zhang, Tinghua Ai, Jantien E. Stoter

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

3 Citations (Scopus)

Abstract

The paper presents a computational model of spatialautocorrelation based on a Voronoi-like auxiliary structure. It shows that the Voronoi-like partition of map objects can be used to discern spatial patterns (e.g. clustered or dispersed) of geographic phenomena. In this paper, we transform the problem of characterizing the patterns for different geometry types (i.e. points, curves, and polygons) into a process of calculating spatial autocorrelation based on the auxiliary partition units. The method is shown to be successful for the designated tasks.

Original language	English
Title of host publication	2009 Sixth International Symposium on Voronoi Diagrams
Subtitle of host publication	23-26 June 2009, Copenhagen, Denmark
Editors	F. Anton
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	118-126
ISBN (Electronic)	978-0-7695-3781-8
ISBN (Print)	978-0-7695-3781-8
DOIs	https://doi.org/10.1109/ISVD.2009.19
Publication status	Published - 2009
Event	6th International Symposium on Voronoi Diagrams 2009 - Copenhagen, Denmark Duration: 23 Jun 2009 → 26 Jun 2009 Conference number: 6

Conference

Conference	6th International Symposium on Voronoi Diagrams 2009
Country	Denmark
City	Copenhagen
Period	23/06/09 → 26/06/09

Keywords

ADLIB-ART-370
GIP

Access to Document

10.1109/ISVD.2009.19

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title = "A Voronoi-like model of spatial autocorrelation for characterizing spatial patterns in vector data",

abstract = "The paper presents a computational model of spatialautocorrelation based on a Voronoi-like auxiliary structure. It shows that the Voronoi-like partition of map objects can be used to discern spatial patterns (e.g. clustered or dispersed) of geographic phenomena. In this paper, we transform the problem of characterizing the patterns for different geometry types (i.e. points, curves, and polygons) into a process of calculating spatial autocorrelation based on the auxiliary partition units. The method is shown to be successful for the designated tasks.",

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Zhang, X, Ai, T & Stoter, JE 2009, A Voronoi-like model of spatial autocorrelation for characterizing spatial patterns in vector data. in F Anton (ed.), 2009 Sixth International Symposium on Voronoi Diagrams: 23-26 June 2009, Copenhagen, Denmark. IEEE, Piscataway, NJ, pp. 118-126, 6th International Symposium on Voronoi Diagrams 2009, Copenhagen, Denmark, 23/06/09. https://doi.org/10.1109/ISVD.2009.19

A Voronoi-like model of spatial autocorrelation for characterizing spatial patterns in vector data. / Zhang, Xiang; Ai, Tinghua; Stoter, Jantien E.

2009 Sixth International Symposium on Voronoi Diagrams: 23-26 June 2009, Copenhagen, Denmark. ed. / F. Anton. Piscataway, NJ : IEEE, 2009. p. 118-126.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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AU - Stoter, Jantien E.

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N2 - The paper presents a computational model of spatialautocorrelation based on a Voronoi-like auxiliary structure. It shows that the Voronoi-like partition of map objects can be used to discern spatial patterns (e.g. clustered or dispersed) of geographic phenomena. In this paper, we transform the problem of characterizing the patterns for different geometry types (i.e. points, curves, and polygons) into a process of calculating spatial autocorrelation based on the auxiliary partition units. The method is shown to be successful for the designated tasks.

AB - The paper presents a computational model of spatialautocorrelation based on a Voronoi-like auxiliary structure. It shows that the Voronoi-like partition of map objects can be used to discern spatial patterns (e.g. clustered or dispersed) of geographic phenomena. In this paper, we transform the problem of characterizing the patterns for different geometry types (i.e. points, curves, and polygons) into a process of calculating spatial autocorrelation based on the auxiliary partition units. The method is shown to be successful for the designated tasks.

KW - ADLIB-ART-370

KW - GIP

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