{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:54:07Z","timestamp":1771700047741,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T00:00:00Z","timestamp":1546992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/M023583\/1"],"award-info":[{"award-number":["EP\/M023583\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions.<\/jats:p>","DOI":"10.3390\/ijgi8010019","type":"journal-article","created":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T03:22:31Z","timestamp":1547090551000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities"],"prefix":"10.3390","volume":"8","author":[{"given":"Cl\u00e9mentine","family":"Cottineau","sequence":"first","affiliation":[{"name":"CNRS, Centre Maurice Halbwachs, UMR 8097, Paris 75014, France"},{"name":"Centre for Advanced Spatial Analysis, University College London, London W1T 4TJ, UK"}]},{"given":"Maarten","family":"Vanhoof","sequence":"additional","affiliation":[{"name":"Open Lab, University of Newcastle, Newcastle upon Tyne NE4 5TG, UK"},{"name":"Orange Labs France, SENSe, Ch\u00e2tillon 92320, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1038\/nature06958","article-title":"Understanding individual human mobility patterns","volume":"453","author":"Gonzalez","year":"2008","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., and Schmitt, G. 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