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Residential segregation, daytime segregation and spatial frictions : an analysis from mobile phone data

Author

Listed:
  • L. GALIANA

    (Insee)

  • B. SAKAROVITCH

    (Insee)

  • F. SÉMÉCURBE

    (Insee)

  • Z. SMOREDA

    (Orange Labs, SENSE)

Abstract

We bring together mobile phone and geocoded tax data on the three biggest French cities to shed a new light on segregation that accounts for population flows. Mobility being a key factor to reduce spatial segregation, we build a gravity model on an unprecedent scale to estimate the heterogeneity in travel costs. Residential segregation represents the acme of segregation. Low-income people spread more than high-income people during the day. Distance plays a key role to limit population flows. Low-income people live in neighbourhoods where the spatial frictions are strongest.

Suggested Citation

  • L. Galiana & B. Sakarovitch & F. Sémécurbe & Z. Smoreda, 2020. "Residential segregation, daytime segregation and spatial frictions : an analysis from mobile phone data," Documents de Travail de l'Insee - INSEE Working Papers g2020-12, Institut National de la Statistique et des Etudes Economiques.
    • Handle: RePEc:nse:doctra:g2020-12
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    File URL: https://www.bnsp.insee.fr/ark:/12148/bc6p06zrk5j/f1.pdf
    File Function: Document de travail de la DESE numéro G2020/12
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    More about this item

    Keywords

    Segregation; big data; phone data; gravity model; urban economics;
    All these keywords.

    JEL classification:

    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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