I 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, I build a gravity model on an unprecedent scale to estimate the heterogeneity in travel costs.
Lino Galiana
Data Scientist
I am data scientist in French national statistical institute, Insee. I study how emerging data or new computational methods help to renew the production of statistical knowledge.
Publications
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.