Crime Mapping through Geo-Spatial Social Media Activity

The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction

Fearsquare: hacking open crime data to critique, jam and subvert the 'aesthetic of danger'

We present a critical evaluation of a locative media application, Fearsquare, which provocatively invites users to engage with personally contextualized risk information drawn from the UK open data crime maps cross-referenced with geo-located user check-ins on Foursquare. Our analysis of user data and a corpus of #Fearsquare discourse on Twitter revealed three cogent appraisals ('Affect', 'Technical' and 'Critical') reflecting the salient associations and aesthetics that were made between different components of the application and interwoven issues of technology, risk, danger, emotion by users. We discuss how the varying strength and cogency of these public responses to Fearsquare call for a broader imagining and analysis of how risk and danger are interpreted; and conclude how our findings reveal important challenges for researchers and designers wishing to engage in projects that involve the computer-mediated communication of risk

Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization

Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80\% of public tweets.Comment: 9 pages, 8 figures, accepted to IEEE BigData 2014, Compton, Ryan, David Jurgens, and David Allen. "Geotagging one hundred million twitter accounts with total variation minimization." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 201