Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation

Lee Mason; Blanaid Hicks; Jonas Almeida

doi:10.1109/VIS55277.2024.00022

Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation

Lee Mason^*
, Blanaid Hicks
, Jonas Almeida

^*Corresponding author for this work

School of Medicine, Dentistry and Biomedical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The Local Moran's I statistic is a valuable tool for identifying localized patterns of spatial autocorrelation. Understanding these patterns is crucial in spatial analysis, but interpreting the statistic can be difficult. To simplify this process, we introduce three novel visualizations that enhance the interpretation of Local Moran's I results. These visualizations can be interactively linked to one another, and to established visualizations, to offer a more holistic exploration of the results. We provide a JavaScript library with implementations of these new visual elements, along with a web dashboard that demonstrates their integrated use.

Original language	English
Title of host publication	Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	71-75
Number of pages	5
ISBN (Electronic)	9798350354850
ISBN (Print)	9798350354867
DOIs	https://doi.org/10.1109/VIS55277.2024.00022
Publication status	Published - 02 Dec 2024
Event	2024 IEEE Visualization and Visual Analytics Conference, VIS 2024 - St. Pete Beach, United States Duration: 13 Oct 2024 → 18 Oct 2024

Publication series

Name	Proceedings - IEEE Visualization and Visual Analytics (VIS)
ISSN (Print)	2771-9537
ISSN (Electronic)	2771-9553

Conference

Conference	2024 IEEE Visualization and Visual Analytics Conference, VIS 2024
Country/Territory	United States
City	St. Pete Beach
Period	13/10/2024 → 18/10/2024

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

geospatial
GIS
interactive visualization
local indicators of spatial association
Moran's I
Spatial
spatial autocorrelation
spatial clustering
visual analytics

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Software
Media Technology
Modelling and Simulation

Access to Document

10.1109/VIS55277.2024.00022Licence: Unspecified

Interactive visualization of data-driven methods for the exploration of spatiotemporal public health data
Mason, L. (Author), Almeida, J. (Supervisor), Hicks, B. (Supervisor) & Orr, N. (Supervisor), Jul 2025
Student thesis: Doctoral Thesis › Thesis with Publications

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Cite this

Mason, L., Hicks, B., & Almeida, J. (2024). Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation. In Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024 (pp. 71-75). (Proceedings - IEEE Visualization and Visual Analytics (VIS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VIS55277.2024.00022

@inproceedings{b397c8aec86241de965a5523838c1d73,

title = "Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation",

abstract = "The Local Moran's I statistic is a valuable tool for identifying localized patterns of spatial autocorrelation. Understanding these patterns is crucial in spatial analysis, but interpreting the statistic can be difficult. To simplify this process, we introduce three novel visualizations that enhance the interpretation of Local Moran's I results. These visualizations can be interactively linked to one another, and to established visualizations, to offer a more holistic exploration of the results. We provide a JavaScript library with implementations of these new visual elements, along with a web dashboard that demonstrates their integrated use.",

keywords = "geospatial, GIS, interactive visualization, local indicators of spatial association, Moran's I, Spatial, spatial autocorrelation, spatial clustering, visual analytics",

author = "Lee Mason and Blanaid Hicks and Jonas Almeida",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Visualization and Visual Analytics Conference, VIS 2024 ; Conference date: 13-10-2024 Through 18-10-2024",

year = "2024",

month = dec,

day = "2",

doi = "10.1109/VIS55277.2024.00022",

language = "English",

isbn = "9798350354867",

series = "Proceedings - IEEE Visualization and Visual Analytics (VIS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "71--75",

booktitle = "Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024",

address = "United States",

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Mason, L, Hicks, B & Almeida, J 2024, Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation. in Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024. Proceedings - IEEE Visualization and Visual Analytics (VIS), Institute of Electrical and Electronics Engineers Inc., pp. 71-75, 2024 IEEE Visualization and Visual Analytics Conference, VIS 2024, St. Pete Beach, United States, 13/10/2024. https://doi.org/10.1109/VIS55277.2024.00022

Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation. / Mason, Lee; Hicks, Blanaid; Almeida, Jonas.
Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 71-75 (Proceedings - IEEE Visualization and Visual Analytics (VIS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation

AU - Mason, Lee

AU - Hicks, Blanaid

AU - Almeida, Jonas

PY - 2024/12/2

Y1 - 2024/12/2

N2 - The Local Moran's I statistic is a valuable tool for identifying localized patterns of spatial autocorrelation. Understanding these patterns is crucial in spatial analysis, but interpreting the statistic can be difficult. To simplify this process, we introduce three novel visualizations that enhance the interpretation of Local Moran's I results. These visualizations can be interactively linked to one another, and to established visualizations, to offer a more holistic exploration of the results. We provide a JavaScript library with implementations of these new visual elements, along with a web dashboard that demonstrates their integrated use.

AB - The Local Moran's I statistic is a valuable tool for identifying localized patterns of spatial autocorrelation. Understanding these patterns is crucial in spatial analysis, but interpreting the statistic can be difficult. To simplify this process, we introduce three novel visualizations that enhance the interpretation of Local Moran's I results. These visualizations can be interactively linked to one another, and to established visualizations, to offer a more holistic exploration of the results. We provide a JavaScript library with implementations of these new visual elements, along with a web dashboard that demonstrates their integrated use.

KW - geospatial

KW - GIS

KW - interactive visualization

KW - local indicators of spatial association

KW - Moran's I

KW - Spatial

KW - spatial autocorrelation

KW - spatial clustering

KW - visual analytics

U2 - 10.1109/VIS55277.2024.00022

DO - 10.1109/VIS55277.2024.00022

M3 - Conference contribution

AN - SCOPUS:85215310651

SN - 9798350354867

T3 - Proceedings - IEEE Visualization and Visual Analytics (VIS)

SP - 71

EP - 75

BT - Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 IEEE Visualization and Visual Analytics Conference, VIS 2024

Y2 - 13 October 2024 through 18 October 2024

ER -

Mason L, Hicks B, Almeida J. Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation. In Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 71-75. (Proceedings - IEEE Visualization and Visual Analytics (VIS)). doi: 10.1109/VIS55277.2024.00022

Demystifying spatial dependence: interactive visualizations for interpreting local spatial autocorrelation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Student theses

Interactive visualization of data-driven methods for the exploration of spatiotemporal public health data

Cite this