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A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets
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Westerholt, René, Resch, Bernd and Zipf, Alexander (2015) A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets. International Journal of Geographical Information Science , 29 (5). pp. 868-887. doi:10.1080/13658816.2014.1002499
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WRAP-local-scale-sensitive-indicator-spatial-autocorrelation-high-low-Westerholt-2015.pdf - Accepted Version - Requires a PDF viewer. Download (2993Kb) | Preview |
Official URL: http://dx.doi.org/10.1080/13658816.2014.1002499
Abstract
Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.
| Item Type: | Journal Article | ||||||
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| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) | ||||||
| Divisions: | Faculty of Social Sciences > Centre for Interdisciplinary Methodologies | ||||||
| Library of Congress Subject Headings (LCSH): | Geography -- Statistical methods, Geographic information systems., Information storage and retrieval systems -- Geography, Spatial analysis (Statistics), Social media | ||||||
| Journal or Publication Title: | International Journal of Geographical Information Science | ||||||
| Publisher: | Taylor & Francis | ||||||
| ISSN: | 1365-8816 | ||||||
| Official Date: | 2015 | ||||||
| Dates: |
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| Date of first compliant deposit: | 4 March 2019 | ||||||
| Volume: | 29 | ||||||
| Number: | 5 | ||||||
| Page Range: | pp. 868-887 | ||||||
| DOI: | 10.1080/13658816.2014.1002499 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | “This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 13/02/2019, available online: http://www.tandfonline.com/ 10.1080/13658816.2014.1002499 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
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