Uncovering the socioeconomic facets of human mobility.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 04 2021
21 04 2021
Historique:
received:
15
01
2021
accepted:
25
03
2021
entrez:
22
4
2021
pubmed:
23
4
2021
medline:
23
4
2021
Statut:
epublish
Résumé
Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is little agreement on the linkage between socioeconomic status and its influence on movement patterns, in particular, the role of inequality. Here, we analyze a heavily aggregated and anonymized summary of global mobility and investigate the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil. We uncover two types of relationships, finding either a clear connection or little-to-no interdependencies. The former tend to be characterized by low levels of public transportation usage, inequitable access to basic amenities and services, and segregated clusters of communities in terms of income, with the latter class showing the opposite trends. Our findings provide useful lessons in designing urban habitats that serve the larger interests of all inhabitants irrespective of their economic status.
Identifiants
pubmed: 33883580
doi: 10.1038/s41598-021-87407-4
pii: 10.1038/s41598-021-87407-4
pmc: PMC8060260
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8616Références
Department of Economic and Social Affairs, United Nations. The speed of urbanization around the world. Popul. Facts 20, 1–2 (2018).
Danan, G., Gerland, P., Pelletier, F. & Cohen, B. Risk of exposure and vulnerability to natural disasters at the city level: A global overview. United Nations Depart. Econ. Soc. Affairs 2, 1–40 (2015).
Ford, A. et al. A multi-scale urban integrated assessment framework for climate change studies: A flooding application. Comput. Environ. Urban Syst. 75, 229–243 (2019).
doi: 10.1016/j.compenvurbsys.2019.02.005
Bassolas, A. et al. Hierarchical organization of urban mobility and its connection with city livability. Nat. Commun. 10, 20 (2019).
doi: 10.1038/s41467-019-12809-y
Bischoff, K. & Reardon, S. F. Residential segregation by income, 1970–2009. Divers. Dispar. Am. Enters New Century 43, 20 (2014).
Massey, D. S. The age of extremes: Concentrated affluence and poverty in the twenty-first century. Demography 33, 395–412 (1996).
pubmed: 8939412
doi: 10.2307/2061773
Brueckner, J., Thisse, J. & Zenou, Y. Why is downtown Paris so rich and Detroit so poor? An amenity based explanation. Eur. Econ. Rev. 43, 91–107 (1999).
doi: 10.1016/S0014-2921(98)00019-1
Andersen, H. S. Excluded places: The interaction between segregation, urban decay and deprived neighbourhoods. Hous. Theory Soc. 19, 153–169 (2002).
doi: 10.1080/140360902321122860
Brueckner, J. K. & Rosenthal, S. S. Gentrification and neighborhood housing cycles: Will America’s future downtowns be rich?. Rev. Econ. Stat. 91, 725–743 (2009).
doi: 10.1162/rest.91.4.725
Killeen, D. & Caro, R. A. The Power Broker: Robert Moses and the Fall of New York (Knopf, 1975).
Ostendorf, W., Musterd, S. & De Vos, S. Social mix and the neighbourhood effect. Policy ambitions and empirical evidence. Hous. Stud. 16, 371–380 (2001).
doi: 10.1080/02673030120049724
Musterd, S. Segregation, urban space and the resurgent city. Urban Stud. 43, 1325–1340 (2006).
doi: 10.1080/00420980600776418
Eagle, N., Macy, M. & Claxton, R. Network diversity and economic development. Science 328, 1029–1031 (2010).
pubmed: 20489022
doi: 10.1126/science.1186605
Lobmayer, P. & Wilkinson, R. G. Inequality, residential segregation by income, and mortality in us cities. J. Epidemiol. Community Health 56, 183–187 (2002).
pubmed: 11854338
pmcid: 1732095
doi: 10.1136/jech.56.3.183
Henderson, V. Urbanization in developing countries. World Bank Res. Observ. 17, 89–112 (2002).
doi: 10.1093/wbro/17.1.89
Henderson, J. V. Cities and development. J. Region. Sci. 50, 515–540 (2010).
doi: 10.1111/j.1467-9787.2009.00636.x
Gauvin, L., Vignes, A. & Nadal, J.-P. Modeling urban housing market dynamics: Can the socio-spatial segregation preserve some social diversity?. J. Econ. Dyn. Control 37, 1300–1321 (2013).
doi: 10.1016/j.jedc.2013.03.001
Bettencourt, LMa. The origins of scaling in cities. Science 340, 1438–41 (2013).
pubmed: 23788793
doi: 10.1126/science.1235823
Pan, W., Ghoshal, G., Krumme, C., Cebrian, M. & Pentland, A. Urban characteristics attributable to density-driven tie formation. Nat. Commun. 4, 1961 (2013).
pubmed: 23736887
doi: 10.1038/ncomms2961
Youn, H. et al. Scaling and universality in urban economic diversification. J. R. Soc. Interface 13, 20150937 (2016).
pubmed: 26790997
pmcid: 4759798
doi: 10.1098/rsif.2015.0937
Lee, M., Barbosa, H., Youn, H., Holme, P. & Ghoshal, G. Morphology of travel routes and the organization of cities. Nat. Commun. 8, 2229 (2017).
pubmed: 29263392
pmcid: 5738436
doi: 10.1038/s41467-017-02374-7
Kirkley, A., Barbosa, H., Barthelemy, M. & Ghoshal, G. From the betweenness centrality in street networks to structural invariants in random planar graphs. Nat. Commun. 9, 2501 (2018).
pubmed: 29950619
pmcid: 6021391
doi: 10.1038/s41467-018-04978-z
Barbosa, H. et al. Human mobility: Models and applications. Phys. Rep. 734, 1–74 (2018).
doi: 10.1016/j.physrep.2018.01.001
Yuan, J., Zheng, Y. & Xie, X. Discovering regions of different functions in a city using human mobility and POIs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD ’12 186 (2012). arxiv:1010.0436 .
Zhan, X., Hasan, S., Ukkusuri, S. V. & Kamga, C. Urban link travel time estimation using large-scale taxi data with partial information. Transport. Res. Part C Emerg. Technol. 33, 37–49 (2013).
doi: 10.1016/j.trc.2013.04.001
Lenormand, M. et al. Influence of sociodemographic characteristics on human mobility. Sci. Rep. 5, 10075 (2015).
pubmed: 25993055
pmcid: 4438721
doi: 10.1038/srep10075
Wang, W., Pan, L., Yuan, N., Zhang, S. & Liu, D. A comparative analysis of intra-city human mobility by taxi. Phys. A 420, 134–147 (2015).
doi: 10.1016/j.physa.2014.10.085
Luo, F., Cao, G., Mulligan, K. & Li, X. Explore spatiotemporal and demographic characteristics of human mobility via twitter: A case study of chicago. Appl. Geogr. 70, 11–25 (2016).
doi: 10.1016/j.apgeog.2016.03.001
Louail, T., Lenormand, M., Murillo Arias, J. & Ramasco, J. J. Crowdsourcing the Robin Hood effect in cities. Appl. Netw. Sci. 2, 11 (2017).
pubmed: 30443566
pmcid: 6214245
doi: 10.1007/s41109-017-0026-3
González, M. C., Hidalgo, C. A. & Barabási, A. L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008) (arxiv:0806.1256).
pubmed: 18528393
doi: 10.1038/nature06958
Di Clemente, R. et al. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nat. Commun. 9, 20 (2018).
doi: 10.1038/s41467-018-05690-8
Alessandretti, L., Sapiezynski, P., Lehmann, S. & Baronchelli, A. Multi-scale spatio-temporal analysis of human mobility. PLoS One 12, e0171686 (2017).
pubmed: 28199347
pmcid: 5310761
doi: 10.1371/journal.pone.0171686
Xu, Y., Belyi, A., Bojic, I. & Ratti, C. Human mobility and socioeconomic status: Analysis of Singapore and Boston. Comput. Environ. Urban Syst. 72, 51–67 (2018).
doi: 10.1016/j.compenvurbsys.2018.04.001
Shelton, T., Poorthuis, A. & Zook, M. Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landsc. Urban Plan. 142, 198–211 (2015).
doi: 10.1016/j.landurbplan.2015.02.020
Frias-Martinez, V. & Virseda, J. On the relationship between socio-economic factors and cell phone usage. In Proceedings of the Fifth International Conference on Information and Communication Technologies and Development, 76–84 (ACM, 2012).
Lotero, L., Hurtado, R. G., Floría, L. M. & Gómez-Gardeñes, J. Rich do not rise early: Spatio-temporal patterns in the mobility networks of different socio-economic classes. R. Soc. Open Scie. 3, 150654 (2016).
doi: 10.1098/rsos.150654
Llorente, A., Garcia-Herranz, M., Cebrian, M. & Moro, E. Social media fingerprints of unemployment. PLoS One 10, e0128692 (2015).
pubmed: 26020628
pmcid: 4447438
doi: 10.1371/journal.pone.0128692
Almaatouq, A., Prieto-Castrillo, F. & Pentland, A. Mobile Communication Signatures of Unemployment. In Encyclopedia of Library and Information Sciences, Third Edition, vol. 1, 4814–4819 (CRC Press, 2009). arxiv:9780201398298 .
Pappalardo, L., Pedreschi, D., Smoreda, Z. & Giannotti, F. Using big data to study the link between human mobility and socio-economic development. Proceedings—2015 IEEE International Conference on Big Data, IEEE Big Data 2015 871–878 (2015).
Gabrielli, L. et al. An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal. 2, 75–92 (2016).
doi: 10.1007/s41060-016-0013-2
Wilson, R. et al. Differentially private SQL with bounded user contribution (2020).
United States Census Bureau. 2016 5-year American community survey [s0601]. https://www.census.gov/programs-surveys/acs (2016).
Brazilian Institute of Geography and Statistics (IBGE). 2010 population census summary. http://ghdx.healthdata.org/record/brazil-demographic-census-2010 (2010).
Openstreetmap contributors. https://www.openstreetmap.org . Accessed 2019.
Guénoche, A., Hansen, P. & Jaumard, B. Efficient algorithms for divisive hierarchical clustering with the diameter criterion. J. Classif. 8, 5–30 (1991).
doi: 10.1007/BF02616245
United States Census Bureau. Longitudinal employer-household dynamics. https://lehd.ces.census.gov/data/ (2016).
Fowlkes, E. B. & Mallows, C. L. A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78, 553–569 (1983).
doi: 10.1080/01621459.1983.10478008
Teunissen, T., Sarmiento, O., Zuidgeest, M. & Brussel, M. Mapping equality in access: The case of bogotá’s sustainable transportation initiatives. Int. J. Sustain. Transport. 9, 457–467 (2015).
doi: 10.1080/15568318.2013.808388
Graves, E. M. The structuring of urban life in a mixed-income housing community. City Community 9, 109–131 (2010).
doi: 10.1111/j.1540-6040.2009.01305.x
Joseph, M. & Chaskin, R. Living in a mixed-income development: Resident perceptions of the benefits and disadvantages of two developments in chicago. Urban Stud. 47, 2347–2366 (2010).
pubmed: 20845592
doi: 10.1177/0042098009357959