I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data

摘要: This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations geo-location data using different visual representations (textual, static animated visualizations) along with amounts (1, 3 or 5 day(s)). We first collected real life from tweets. then asked owners to tag their location points, resulting ground truth data. Using this dataset we conducted an empirical study involving 45 participants analyze how accurately they could infer original under conditions, i.e., three representations, densities four types. The results indicate that while techniques perform better than textual ones, activities can be inferred a relatively high accuracy even only low density points. Workplace was more easily home transport highest accuracy. Our also showed it easier exhibiting stable consistent mobility patterns, which are thus vulnerable privacy disclosures. discuss implications our findings context preservation provide guidelines users companies help preserve safeguard people's privacy.