A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

Camargo de Andrade, S., Restrepo-Estrada, C., Henrique Nunes, L., Augusto Morales Rodriguez, C., Cézar Estrella, J., Cláudio Botazzo Delbem, A. and Porto de Albuquerque, J. (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)

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: Articles
• Additional Information: This work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) through the Global Challenges Research Fund, Sao Paulo Research Foundation (FAPESP) under Grant [2019/01717-2, 2017/15413-0], the Coordination for the Improvement of Higher Education Personnel (CAPES) under Grant [Pró-Alertas 88887.091742/2014-01], FAPESP-Warwick Joint Fund, and the Alan Turing Institute, UK.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porto de Albuquerque, Professor Joao
• Authors: Camargo de Andrade, S., Restrepo-Estrada, C., Henrique Nunes, L., Augusto Morales Rodriguez, C., Cézar Estrella, J., Cláudio Botazzo Delbem, A., and Porto de Albuquerque, J.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: International Journal of Geographical Information Science
• Publisher: Taylor & Francis
• ISSN: 1365-8816
• ISSN (Online): 1365-8824
• Published Online: 19 June 2020
• Copyright Holders: Copyright © 2020 The Author(s)
• First Published: First published in International Journal of Geographical Information Science 35(1):43-62
• Publisher Policy: Reproduced under a Creative Commons Licence