The Library
A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics
Tools
Tools
Tools
de Andrade, S. C., Restrepo-Estrada, C., Nunes, L. H., Rodrigues, C. A. M., Estrella, J. C., Delbem, A. C. B. and de Albuquerque, João Porto (2021) A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics. International Journal of Geographical Information Science , 35 (1). pp. 43-62. doi:10.1080/13658816.2020.1755039 ISSN 1365-8816.
|
PDF
WRAP-multicriteria-optimization-framework-definition-spatial-granularity-urban-social-media-analytics-deAlbuquerque.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3312Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-multicriteria-optimization-framework-definition-spatial-granularity-urban-social-media-analytics-deAlbuquerque.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1425Kb) |
Official URL: https://doi.org/10.1080/13658816.2020.1755039
Abstract
The spatial analysis of social media data has recently emerged as a significant source of knowledge for urban studies. Most of these analyses are based on an areal unit that is chosen without the support of clear criteria to ensure representativeness with regard to an observed phenomenon. Nonetheless, the results and conclusions that can be drawn from a social media analysis to a great extent depend on the areal unit chosen, since they are faced with the well-known Modifiable Areal Unit Problem. To address this problem, this article adopts a data-driven approach to determine the most suitable areal unit for the analysis of social media data. Our multicriteria optimization framework relies on the Pareto optimality to assess candidate areal units based on a set of user-defined criteria. We examine a case study that is used to investigate rainfall-related tweets and to determine the areal units that optimize spatial autocorrelation patterns through the combined use of indicators of global spatial autocorrelation and the variance of local spatial autocorrelation. The results show that the optimal areal units (30 km2 and 50 km2) provide more consistent spatial patterns than the other areal units and are thus likely to produce more reliable analytical results.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Arts > School for Cross-faculty Studies Faculty of Arts > School for Cross-faculty Studies > Institute for Global Sustainable Development |
||||||||
| Journal or Publication Title: | International Journal of Geographical Information Science | ||||||||
| Publisher: | Taylor & Francis | ||||||||
| ISSN: | 1365-8816 | ||||||||
| Official Date: | 2021 | ||||||||
| Dates: |
|
||||||||
| Volume: | 35 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 43-62 | ||||||||
| DOI: | 10.1080/13658816.2020.1755039 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 20 April 2020 | ||||||||
| Date of first compliant Open Access: | 19 July 2020 | ||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
