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

Lino Galiana (Insee-Dese – Département des études économiques – Division « Redistribution et politiques sociales »), Benjamin Sakarovitch (Insee-Dmcsi – SSP-lab), François Sémécurbe (Insee-Dmcsi – SSP-lab), Zbigniew Smoreda (Orange Labs, SENSE)

Documents de travail
No G2020-12
Paru le : Paru le 09/11/2020
Lino Galiana (Insee-Dese – Département des études économiques – Division « Redistribution et politiques sociales »), Benjamin Sakarovitch (Insee-Dmcsi – SSP-lab), François Sémécurbe (Insee-Dmcsi – SSP-lab), Zbigniew Smoreda (Orange Labs, SENSE)
Documents de travail  No G2020-12 - November 2020

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.