Fighting inequality with the help of network science

The way we live in cities—where we go or shop—can have deep effects on the dynamics and wealth of different communities. In this guest post, Thomas Louail and Maxime Lenormand explain how they analyzed credit card transactions data to explore the flow of money in urban areas, discussing the ways in which this tool can help spread wealth more evenly among neighborhoods.

© Louail et al. 2017

Guest post by Thomas Louail and Maxime Lenormand

The geographical footprints that people passively produce while using their mobile devices contain information that have proved useful to coordinate their actions in space and time. This is particularly true in the case of daily mobility practices, which nowadays integrate these pervasive data in feedback loops: individuals produce data when moving, and their travel decisions are partly guided by the data produced by others. Examples include GPS navigation using real-time traffic data, local search and discovery of new places, or location-based social apps.

So far these footprints have been mainly used in services intended to enhance individual satisfaction (e.g. time savings, discover a restaurant, encounter of a partner), but they have also fostered large-scale, spontaneous solidarity movements (e.g. Facebook’s safety check, or the use of dedicated Twitter hashtags).

An important question is thus whether we can scale up, and address complex issues through distributed, coordinated approaches relying on such data. Here we refer to social issues for which improvements would necessarily occur on longer timescales. There is a need to relate smart technology with sustainability and spatial justice in cities, and this implies building upon the spatial behaviors of individuals. We developed this idea by focusing on a hard problem: the reduction of spatial inequality among neighborhoods in large cities.

In our manuscript, we perform numerical experiments to evaluate to what extent spatial inequality of money flows could be modulated thanks to small changes in the citizens shopping practices. Two main reasons have motivated our choice of Applied Network Science as the appropriate journal for publishing our work: the potential of our method to generate new tools and research questions in applied network science applied to human mobility, and the timeliness of the method that we develop in the manuscript.

Our paper presents counter-intuitive and challenging results, that we obtained by linking two old research questions which had never been considered together. On one hand, spatial inequality in urban areas; on the other hand, the spatial and statistical properties of intra-urban mobility, and in particular of shopping mobility. The availability of a database containing anonymized and geotagged credit card transactions allowed us to link these two questions.

The data indicate that money flows resulting from the daily expenses of individuals are heterogeneously distributed in the city space. The question that motivated this research was: what fraction of our individual shopping trips should be rewired toward alternative shops, located in other neighborhoods, in order to distribute money flows more evenly in the city?

To answer this question we took advantage of a large database containing the credit card transactions performed during an entire year by approx. 150,000 anonymized residents of Madrid and Barcelona, the two largest Spanish metropolitan areas. We performed experiments in which we rewired the spatial networks linking individuals and businesses in the city. Our results include an empirical demonstration that rewiring only a very limited fraction of each individuals shopping trips, as low as 5%, generates a dramatic reduction of spatial inequality among the neighborhoods. This result suggests another option to fight inequality, thanks to a distributed, bottom-up process that would rely upon small changes in the daily spatial behavior of individuals.

Read the full article here.

 


Thomas Louail holds a permanent research position at the CNRS Institute for Humanities and Social Sciences (INSHS) and he currently works at Géographie-cités in Paris (www.parisgeo.cnrs.fr). His work mainly focuses on the uses of pervasive, anonymized data to measure, model and hopefully better understand social dynamics. He is particularly interested in the determinants of our spatial behaviors.

Maxime Lenormand is currently working at Irstea as a permanent member of the TETIS laboratory based in Montpellier (France). His research interests focus on big data analysis and modelling of complex systems in a multidisciplinary context, with particular applications to urban systems and human mobility.

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