MobInsight: Understanding Urban Mobility with Crowd-Powered Neighborhood Characterizations

In this paper, we present MobInsight, an interactive visual tool for analyzing urban mobility. The tool aims to reveal the collective intelligence of the spatial choices expressed in the mobility patterns of the people that live in a city. It provides an analyst with a rich characterization of neighborhoods, enabling the analyst to compare the difference and infer possible reasons behind traveling behavior between the neighborhoods. MobInsight builds tailored neighborhood characterizations specific to the analyzed city by harnessing the geo-social annotations of the crowd. For the demonstration, MobInsight will feature Barcelona where the conference venue is located. Mobility patterns between the 70 neighborhoods of the city are extracted from real mobile network data of a large sample of residents, and the neighborhood characteristics are profiled by mining various online geo-social services and open government data.