Year: 2021
Type: preprint
Abstract: Hour-by-hour variations in spatial distribution of gender, age and social class within cities remain poorly explored and combined in the segregation literature mainly centered on home places from a single social dimension. Taking advantage of 49 mobility surveys compiled together (385,000 respondents and 1,711,000 trips) and covering 60% of France's population, we consider variations in hourly populations of 2,572 districts after disaggregating population across gender, age and education level. We first isolate five district hourly profiles (two 'daytime attractive', two 'nighttime attractive' and one more 'stable') with very unequal distributions according to urban gradient but also to social groups. We then explore the intersectional forms of these everyday geographies. Taking as reference the dominant groups (men, middle-age and high educated people) known as concentrating hegemonic power and capital, we analyze specifically whether district hourly profiles of dominant groups diverge from those of the others groups. It is especially in the areas exhibiting strong increase or strong decrease of ambient population during the day that district hourly profiles not only combine the largest dissimilarities all together across gender, age and education level but are also widely more synchronous between dominant groups than between non-dominant groups (women, elderly and low educated people). These intersectional patterns shed new light on areas where peers are synchronously located over the 24-hour period and thus potentially in better position to interact and to defend their common interests.
Source: arXiv (Cornell University)
Authors: Vall\'ee, Julie, Lenormand, Maxime
Institutions: Centre National de la Recherche Scientifique, Université Paris Cité, UMR Géographie-cités, Université Paris 1 Panthéon-Sorbonne, AgroParisTech, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Territoires, Environnement, Télédétection et Information Spatiale
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4297786158