Rethinking Urban Socio-Spatial Differentiation using Social Media Open Data

Big data is a newly emerging digital research source in urban studies which has become increasingly available. We paid attention to big data in reflecting socio-spatial differentiation problem because it has the advantage ofbeinghighdynamic, user-generatedandcontinuouslyevolvingtowards traditional statistics sources. We finally choose Twitterdatain our studyon socio-spatial differentiation. We draw on methodsfrom SPSS and GIScienceto geo-locate thesetweetsandutilizea way of mapping to illustrate the daily mobility of the users and the possible spatial links revealed by these movements, andfurthermore manifest urbanspatial differentiationbased on our data. In the case study of Strasbourg, we finally draw three conclusions: 1) Through the analysis of individual mobility activity, we get a general idea that Strasbourg city center and its surrounding areas are best connected while peripheral districts demonstrate diverse connectivity level.2) There are correlations between socio-spatial separation and physical obstacles like high-level roads and railways which go across a densely populated area in Strasbourg.3) We argue against the conventional notion that people in ZUS (Zones Urbaines Sensibles) are isolated and segregated from the rest of city. We point out by our observation on daily mobility of people in Hautepierre and of people outside Hautepierre that, it is actually people from outside ZUS who rarely travel to these neighborhoodsthat are spatially confined and ‘segregated’ in the urban area out of ZUS.