Milano, città d' arte: Urban residents preferences clusters from tweets

Cities are complex systems evolving constantly. Thus, it is necessary to improve the way we collect intra-urban data in order to quantify such evolution. We propose a methodology to transform geo-located tweets into labels for different areas of a given city using DBPedia, Wikipedia and Foursquare categories. We conduct experiments using 77K geolocated tweets posted in Milan during November and December 2013 and feed a clustering algorithm with the annotated tweets to produce dynamic thematic maps. Since, Twitter is the most popular platform for publishing short public messages, to generate crowd-sourced city maps. The results suggest that we can accurately find different functional areas on different temporal bands.