Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Tools
- Tools
+ Tools

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 ISSN 1365-8816.

[img]
Preview
PDF
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

Request Changes to record.

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
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:
DateEvent
2015Published
13 February 2015Available
Volume: 29
Number: 5
Page Range: pp. 868-887
DOI: 10.1080/13658816.2014.1002499
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): “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
Date of first compliant deposit: 4 March 2019
Date of first compliant Open Access: 4 March 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDKlaus Tschira Stiftunghttp://dx.doi.org/10.13039/501100007316

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us