Crowdsourcing the Robin Hood effect in cities

Socioeconomic inequalities in cities are embedded in space and result in neighborhood effects, whose harmful consequences have proved very hard to counterbalance efficiently by planning policies alone. Considering redistribution of money flows as a first step toward improved spatial equity, we study a bottom-up approach that would rely on a slight evolution of shopping mobility practices. Building on a database of anonymized card transactions in Madrid and Barcelona, we quantify the mobility effort required to reach a reference situation where commercial income is evenly shared among neighborhoods. The redirections of shopping trips preserve key properties of human mobility, including travel distances. Surprisingly, for both cities only a small fraction (∼5%) of trips need to be modified to reach equality situations, improving even other sustainability indicators. The method could be implemented in mobile applications that would assist individuals in reshaping their shopping practices, to promote the spatial redistribution of opportunities in the city.

[1]  W. Goldsmith,et al.  Separate Societies: Poverty and Inequality in U.S. Cities. , 1992 .

[2]  Jean-François Ruault.  L’effet de la consommation de passage sur le développement et l’intégration métropolitaine des territoires en Ile-de-France , 2014 .

[3]  John C. Berg.  Sharing Cities: A Case for Truly Smart and Sustainable Cities , 2017 .

[4]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[5]  Raj Chetty,et al.  The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates , 2015 .

[6]  Raj Chetty,et al.  The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects , 2016 .

[7]  Greg J. Duncan,et al.  Neighborhood poverty I: Context and consequences for children , 1997 .

[8]  Adam D. Read,et al.  Recycling behaviour in a London Borough: Results from large-scale household surveys , 2005 .

[9]  Josep Blat,et al.  Urban association rules: Uncovering linked trips for shopping behavior , 2016, ArXiv.

[10]  Xianfeng Huang,et al.  Understanding metropolitan patterns of daily encounters , 2013, Proceedings of the National Academy of Sciences.

[11]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[12]  Robert Kirkpatrick,et al.  Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data , 2016, ArXiv.

[13]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[14]  George Galster,et al.  Neighbourhood effects on social opportunities: the European and American research and policy context , 2003 .

[15]  Geoff Vigar,et al.  Mobility and Social Cohesion in the Splintered City: Challenging Technocentric Transport Research and Policy-making Practices , 2012 .

[16]  Chris J. Martin.  Sharing cities – a case for truly smart and sustainable cities , 2018 .

[17]  T. Warnecke,et al.  The Great Divide: Unequal Societies and What We Can Do About Them , 2017 .

[18]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[19]  Pierre Chauvin,et al.  The combined effects of activity space and neighbourhood of residence on participation in preventive health-care activities: The case of cervical screening in the Paris metropolitan area (France). , 2010, Health & place.

[20]  G. Duncan,et al.  Neighborhood Poverty, Volume 1: Context and Consequences for Children , 2000 .

[21]  W. Recker,et al.  Factors influencing destination choice for the urban grocery shopping trip , 1978 .

[22]  Judith R. Blau,et al.  The Cost of Inequality: Metropolitan Structure and Violent Crime , 1982 .

[23]  C. Kesteloot,et al.  Socio-spatial Inequalities and Social Cohesion in European Cities , 2012 .

[24]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[25]  Anthony J. Venables,et al.  Spatial Inequality and Development , 2005 .

[26]  D. Satterthwaite,et al.  Reducing Urban Poverty in the Global South , 2013 .

[27]  Y. de Montjoye,et al.  Unique in the shopping mall: On the reidentifiability of credit card metadata , 2015, Science.

[28]  Kevin Lewis,et al.  Three fallacies of digital footprints , 2015, Big Data Soc..

[29]  T. Piketty.  Capital in the twenty-first century: a multidimensional approach to the history of capital and social classes. , 2013, The British journal of sociology.

[30]  J. Pinol,et al.  Histoire de l'Europe urbaine , 2011 .

[31]  Maxi San Miguel,et al.  Influence of sociodemographics on human mobility , 2015 .

[32]  Marc Barthelemy,et al.  A stochastic model of randomly accelerated walkers for human mobility , 2015, Nature Communications.

[33]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[34]  S. Musterd,et al.  Social Mix and the Neighbourhood Effect. Policy Ambitions and Empirical Evidence , 2001 .

[35]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[36]  Jennifer Dill,et al.  Bicycle Commuting and Facilities in Major U.S. Cities: If You Build Them, Commuters Will Use Them , 2003 .