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

Author(s)

de Andrade, Sidgley Camargo ^[1] ; Restrepo-Estrada, Camilo ^[2] ; Nunes, Luiz Henrique ^[3] ; Morales Rodriguez, Carlos Augusto ^[4] ; Estrella, Julio Cezar ^[5] ; Botazzo Delbem, Alexandre Claudio ^[5] ; de Albuquerque, Joao Porto ^{[6, 7]}

Affiliation

^[1] Univ Tecnol Fed Parana, Toledo - Brazil

^[2] Univ Antioquia, Fac Econ Sci, Medellin - Colombia

^[3] Fed Inst Sao Paulo, Araraquara, SP - Brazil

^[4] Univ Sao Paulo, Inst Astron Geophys & Atmospher Sci, Sao Paulo - Brazil

^[5] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos - Brazil

^[6] Univ Warwick, Inst Global Sustainable Dev, Coventry, W Midlands - England

^[7] Alan Turing Inst, London - England

• Document type: Journal article
• Source: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE; v. 35, n. 1 JUN 2020.
• Web of Science Citations: 1

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 km(2)and 50 km(2)) provide more consistent spatial patterns than the other areal units and are thus likely to produce more reliable analytical results. (AU)

• FAPESP's process: 19/01717-2 - Building a spatial fairness model in social media analysis
• Grantee: Sidgley Camargo de Andrade
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 17/15413-0 - Fusion of data from social media and physical sensors for rainfall monitoring
• Grantee: Sidgley Camargo de Andrade
• Support Opportunities: Scholarships in Brazil - Doctorate