Sensing the city with Instagram: Clustering geolocated data for outlier detection

Abstract

Early detection of unusual events in urban areas is a priority for city management departments, which usually deploy specific complex video-based infrastructures typically monitored by human staff. However, and with the emergence and quick popularity of Location-based social networks (LBSNs), detecting abnormally high or low number of citizens in a specific area at a specific time could be done by an expert system that automatically analyzes the public geo-tagged posts. Our approach focuses exclusively on the location information linked to these posts. By applying a density-based clustering algorithm, we obtain the pulse of the city (24 h–7 days) in a first training phase, which enables the detection of outliers (unexpected behaviors) on-the-fly in an ulterior test or monitoring phase. This solution entails that no specific infrastructure is needed since the citizens are the ones who buy, maintain, carry the mobile devices and freely disclose their location by proactively sharing posts. Besides, location analysis is lighter than video analysis and can be automatically done. Our approach was validated using a dataset of geo-tagged posts obtained from Instagram in New York City for almost six months with good results. Actually, not only all the already previously known events where detected, but also other unknown events where discovered during the experiment.