Gender gaps in urban mobility

Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile. We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners.

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

[2]  Nathan Eagle,et al.  Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda , 2010, ICTD.

[3]  Hyungwon Choi,et al.  Moving beyond P values: Everyday data analysis with estimation plots , 2018, bioRxiv.

[4]  Y. N. Wong World Development Report 2012: Gender equality and development , 2012 .

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

[6]  Ingmar Weber,et al.  Using Facebook ad data to track the global digital gender gap , 2018, World Development.

[7]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

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

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

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

[11]  Damien C. Jacques Mobile Phone Metadata for Development , 2018, ArXiv.

[12]  Srikanth Gottipati,et al.  Extensive local adaptation within the chemosensory system following Drosophila melanogaster's global expansion , 2016, Nature Communications.

[13]  Kevin J. Gaston,et al.  Global distribution and conservation of rare and threatened vertebrates , 2009, Nature.

[14]  N. Grassly,et al.  United Nations Department of Economic and Social Affairs/population Division , 2022 .

[15]  Alex Rutherford,et al.  On the privacy-conscientious use of mobile phone data , 2018, Scientific Data.

[16]  Elsevier Sdol,et al.  Transportation Research Part C: Emerging Technologies , 2009 .

[17]  Zbigniew Smoreda,et al.  Performance and sensitivities of home detection from mobile phone data , 2018, ArXiv.

[18]  R. Law Beyond ‘women and transport’: towards new geographies of gender and daily mobility , 1999 .

[19]  Wei-Shiuen Ng,et al.  Understanding Urban Travel Behaviour by Gender for Efficient and Equitable Transport Policies , 2018 .

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

[21]  R. Fisher 035: The Distribution of the Partial Correlation Coefficient. , 1924 .

[22]  Zbigniew Smoreda,et al.  Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics , 2018, Journal of Official Statistics.

[23]  Ciro Cattuto,et al.  Shopping mall attraction and social mixing at a city scale , 2018, EPJ Data Science.

[24]  Iyad Rahwan,et al.  Analyzing gender inequality through large-scale Facebook advertising data , 2018, Proceedings of the National Academy of Sciences.

[25]  Marta C. González,et al.  Understanding congested travel in urban areas , 2016, Nature Communications.

[26]  Munmun De Choudhury,et al.  Big data and the well-being of women and girls: applications on the social scientific frontier , 2017 .

[27]  Anastasia Loukaitou-Sideris,et al.  Fear and safety in transit environments from the women’s perspective , 2014 .

[28]  M. Couper A REVIEW OF ISSUES AND APPROACHES , 2000 .

[29]  Cristian Alejandro Silva Lovera Urban sprawl and infrastructural lands: Revamping internal spaces in Santiago de Chile , 2015 .

[30]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[31]  F. Meza,et al.  Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010-2045 , 2014 .

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

[33]  Marijn Janssen,et al.  Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development , 2017, HICSS.

[34]  Susan Hanson,et al.  Gender and mobility: new approaches for informing sustainability , 2010 .

[35]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[36]  P. Ohadike Urbanization , 1968, Encyclopedia of the UN Sustainable Development Goals.

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

[39]  Leo Ferres,et al.  The effect of Pokémon Go on the pulse of the city: a natural experiment , 2016, EPJ Data Science.

[40]  Tim Cresswell,et al.  Gendered Mobilities: Towards an Holistic Understanding , 2016 .

[41]  R. Groves Nonresponse Rates and Nonresponse Bias in Household Surveys , 2006 .

[42]  M. Dix,et al.  Error and uncertainty in travel surveys , 1981 .

[43]  Philip J. Reed,et al.  Observing gender dynamics and disparities with mobile phone metadata , 2016, ICTD.

[44]  Ann Frye Research on Women's Issues in Transportation, Report of a Conference, Volume 1: Conference Overview and Plenary Papers. Keynote Address , 2006 .

[45]  Margaret Nichols Trans , 2015, De-centering queer theory.

[46]  Sylvia Chant,et al.  Cities through a “gender lens”: a golden “urban age” for women in the global South? , 2013 .

[47]  Piotr Sapiezynski,et al.  The role of gender in social network organization , 2017, PloS one.

[48]  Jenq-Neng Hwang,et al.  Nonparametric multivariate density estimation: a comparative study , 1994, IEEE Trans. Signal Process..

[49]  C. Quensel The distribution of the partial correlation coefficient in samples from multivariate universesin a special case of non-normally distributed random variables , 1953 .

[50]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[51]  M. Kwan Gender, the Home-Work Link, and Space-Time Patterns of Nonemployment Activities , 1999 .

[52]  Mayra Buvinic,et al.  Closing the gender data gap , 2016 .

[53]  Eduardo Graells-Garrido,et al.  Inferring modes of transportation using mobile phone data , 2018, EPJ Data Science.

[54]  Vanessa Frías-Martínez,et al.  A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records , 2010, AAAI Spring Symposium: Artificial Intelligence for Development.

[55]  Hyungwon Choi,et al.  Moving beyond P values: data analysis with estimation graphics , 2019, Nature Methods.