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Spatial autocorrelation-based information visualization evaluation

Authors:

Joseph A. Cottam,

Andrew LumsdaineAuthors Info & Claims

BELIV '12: Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization

Article No.: 8, Pages 1 - 8

https://doi.org/10.1145/2442576.2442584

Published: 14 October 2012 Publication History

Abstract

A data set can be represented in any number of ways. For example, hierarchical data can be presented as a radial node-link diagram, dendrogram, force-directed layout, or tree map. Alternatively, point-observations can be shown with scatter-plots, parallel coordinates, or bar charts. Each technique has different capabilities for representing relationships. These capabilities are further modified by projection and presentation decisions within the technique category. Evaluating the many options is an essential task in visualization development. Currently, evaluation is largely based on heuristics, prior experience, and indefinable aesthetic considerations. This paper presents initial work towards an evaluation technique based in spatial autocorrelation. We find that spatial autocorrelation can be used to construct a separator between visualizations and other image types. Furthermore, this can be done with parameters amenable to interactive use and in a fashion that does not need to take plot schema characteristics as parameters.

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Cited By

Rey SAnselin LAmaral PArribas‐Bel DCortes RGaboardi JKang WKnaap ELi ZLumnitz SOshan TShao HWolf L(2021) The PySAL Ecosystem: Philosophy and Implementation Geographical Analysis10.1111/gean.1227654:3(467-487)Online publication date: Jul-2021
https://doi.org/10.1111/gean.12276

Index Terms

Spatial autocorrelation-based information visualization evaluation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Visual analytics
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

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BELIV '12: Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization

October 2012

94 pages

ISBN:9781450317917

DOI:10.1145/2442576

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2012

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BELIV '12

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Microsoft Research

BELIV '12: Beyond Time and Errors - Novel Evaluation Methods for Visualization

October 14 - 15, 2012

Washington, Seattle, USA

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Overall Acceptance Rate 45 of 64 submissions, 70%

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Cited By

Rey SAnselin LAmaral PArribas‐Bel DCortes RGaboardi JKang WKnaap ELi ZLumnitz SOshan TShao HWolf L(2021) The PySAL Ecosystem: Philosophy and Implementation Geographical Analysis10.1111/gean.1227654:3(467-487)Online publication date: Jul-2021
https://doi.org/10.1111/gean.12276

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