The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events

Sidgley Camargo De Andrade,João Porto De Albuquerque,Camilo Restrepo-Estrada,René Westerholt,Carlos Augusto Morales Rodriguez,Eduardo Mario Mendiondo,Alexandre Cláudio Botazzo Delbem

doi:10.1080/13658816.2021.1957898

Abstract

ABSTRACT Although it is acknowledged that urban inequalities can lead to biases in the production of social media data, there is a lack of studies which make an assessment of the effects of intra-urban movements in real-world urban analytics applications, based on social media. This study investigates the spatial heterogeneity of social media with regard to the regular intra-urban movements of residents by means of a case study of rainfall-related Twitter activity in São Paulo, Brazil. We apply a spatial autoregressive model that uses population and income as covariates and intra-urban mobility flows as spatial weights to explain the spatial distribution of the social response to rainfall events in Twitter vis-à-vis rainfall radar data. Results show high spatial heterogeneity in the response of social media to rainfall events, which is linked to intra-urban inequalities. Our model performance () provides evidence that urban mobility flows and socio-economic indicators are significant factors to explain the spatial heterogeneity of thematic spatiotemporal patterns extracted from social media. Therefore, urban analytics research and practice should consider not only the influence of socio-economic profile of neighborhoods but also the spatial interaction introduced by intra-urban mobility flows to account for spatial heterogeneity when using social media data.

Highlights

Social media platforms have enabled the generation of spatial information in an unpre cedented way
Our results show that the strength of the rainfall-related Twitter activity follows a radial socio-spatial segregation pattern found in related works about the city of São Paulo (Haddad and NedovićBudić 2006, Haddad 2009), providing evidence of the fact that there is a persistent ‘digital divide’ between wealthy people living in central urban areas – with access to information and communication technologies – and poor people living in peripheral areas with more limited access to technology
There is a mechanism underlying the spatial distribution of social media data which must be fully understood before it can be properly employed

Summary

Introduction

Social media platforms have enabled the generation of spatial information in an unpre cedented way. The analysis of social media data through the intense use of social media platforms in towns and cities, has been found to be a promising way of studying human activities and natural. Researchers have seized on the opportunity to use social media data in a wide range of domains, such as the detection, monitoring and recognition of natural hazards (e.g. floods and typhoons), humanitarian crises (e.g. outbreaks of epidemic diseases), and to assist in urban planning (e.g. restricted mobility and traffic jams) – see Said et al (2019), Martí et al (2019), Martínez-Rojas et al (2018), Nummi (2017) and Steiger et al (2015a)

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