Uncovering the socioeconomic structure of spatial and social interactions in cities

The relationship between urban mobility, social networks and socioeconomic status is complex and difficult to apprehend, notably due to the lack of data. Here we use mobile phone data to analyze the socioeconomic structure of spatial and social interaction in the Chilean’s urban system. Based on the concept of geographical and social events, we develop a methodology to assess the level of spatial and social interactions between locations according to their socioeconomic status. We demonstrate that people with the same socioeconomic status preferentially interact with locations and people with a similar socioeconomic status. We also show that this proximity varies similarly for both spatial and social interactions during the course of the week. Finally, we highlight that these preferential interactions appear to be holding when considering city-city interactions.

[1]  Maarten Vanhoof,et al.  Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities , 2018, ISPRS Int. J. Geo Inf..

[2]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[3]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[4]  On the importance of trip destination for modelling individual human mobility patterns , 2020, Journal of the Royal Society Interface.

[5]  P. Marquet,et al.  Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile , 2021, Science.

[6]  Piotr Sapiezynski,et al.  Evidence for a conserved quantity in human mobility , 2016, Nature Human Behaviour.

[7]  Xin Lu,et al.  Mapping poverty using mobile phone and satellite data , 2017, Journal of The Royal Society Interface.

[8]  M. Barthelemy,et al.  Patterns of Residential Segregation , 2015, PloS one.

[9]  S. Lehmann,et al.  The scales of human mobility , 2020, Nature.

[10]  Matias Garreton,et al.  Exploring the correlation between city size and residential segregation: comparing Chilean cities with spatially unbiased indexes , 2020, Environment and Urbanization.

[11]  James P. Bagrow,et al.  Information flow reveals prediction limits in online social activity , 2017, Nature Human Behaviour.

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

[13]  P. Jirón.  Mobility on the move: Examining urban daily mobility practices in Santiago de Chile. , 2009 .

[14]  Frederic Bartumeus,et al.  New Approaches to Human Mobility: Using Mobile Phones for Demographic Research , 2013, Demography.

[15]  Matthew Zook,et al.  Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information , 2015 .

[16]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[17]  John Urry,et al.  Social networks, travel and talk. , 2003, The British journal of sociology.

[18]  Kai Zhao,et al.  Urban Pulse: Capturing the Rhythm of Cities , 2016, IEEE Transactions on Visualization and Computer Graphics.

[19]  H. Yeung.  Capital, State and Space: Contesting the Borderless World , 1998 .

[20]  Teodoro Dannemann,et al.  The time geography of segregation during working hours , 2018, Royal Society Open Science.

[21]  Geoffrey Paterson,et al.  Annals of the American association of geographers , 2016 .

[22]  Vanessa Frías-Martínez,et al.  On the relationship between socio-economic factors and cell phone usage , 2012, ICTD.

[23]  Henry Kautz,et al.  Uncovering the socioeconomic facets of human mobility , 2020, Scientific Reports.

[24]  Gourab Ghoshal,et al.  Contrasting social and non-social sources of predictability in human mobility , 2021, Nature Communications.

[25]  Barbara Entwisle,et al.  Putting people into place , 2007, Demography.

[26]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[27]  Ulrik Brandes,et al.  Social Networks , 2013, Handbook of Graph Drawing and Visualization.

[28]  Emmanuel Alloa.  Capital , 2018, The Creativity Complex.

[29]  Eric J. Miller,et al.  AGENCY IN SOCIAL ACTIVITY INTERACTIONS: THE ROLE OF SOCIAL NETWORKS IN TIME AND SPACE , 2008 .

[30]  Marc Barthelemy,et al.  Is spatial information in ICT data reliable? , 2016, ArXiv.

[31]  Matthew J. Salganik,et al.  Bit by bit: social research in the digital age , 2019, The Journal of mathematical sociology.

[32]  Henry A. Kautz,et al.  Hierarchical organization of urban mobility and its connection with city livability , 2019, Nature Communications.

[33]  Paolo Santi,et al.  Beyond Distance Decay: Discover Homophily in Spatially Embedded Social Networks , 2021, Annals of the American Association of Geographers.

[34]  Emmanuel Saez,et al.  World inequality report 2018 , 2018 .

[35]  Enrique Frías-Martínez,et al.  Comparing and modelling land use organization in cities , 2015, Royal Society Open Science.

[36]  A. El-geneidy,et al.  The Cost of Equity: Assessing Transit Accessibility and Social Disparity Using Total Travel Cost , 2016 .

[37]  Alexandre G. Evsukoff,et al.  Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area , 2020, Entropy.

[38]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[39]  Vinicius M. Netto,et al.  Social Interaction and the City: The Effect of Space on the Reduction of Entropy , 2017, Complex..

[40]  J. Vallée,et al.  Social segregation around the clock in the Paris region (France) , 2017 .

[41]  Gabriel Cadamuro,et al.  Predicting poverty and wealth from mobile phone metadata , 2015, Science.

[42]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[43]  Jukka-Pekka Onnela,et al.  Geographic Constraints on Social Network Groups , 2010, PloS one.

[44]  Francisco Sabatini,et al.  The Social Spatial Segregation in the Cities of Latin America , 2006 .

[45]  A. Vespignani,et al.  Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile , 2020, Nature Communications.

[46]  Frank H. P. Fitzek,et al.  The Medium is the Message , 2006, 2006 IEEE International Conference on Communications.

[47]  A. Fawcet.  [Urban and regional planning]. , 1974, Arhiv za higijenu rada i toksikologiju.

[48]  Luis Mario Floría,et al.  Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes , 2016, Royal Society Open Science.

[49]  Anne R. Pebley,et al.  Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared , 2014, Demography.

[50]  Zbigniew Smoreda,et al.  An analytical framework to nowcast well-being using mobile phone data , 2016, International Journal of Data Science and Analytics.

[51]  M. Barthelemy,et al.  Human mobility: Models and applications , 2017, 1710.00004.