Social media generated by location-services-enabled cellular devices produce enormous amounts of location-based content. Spatiotemporal analysis of such data facilitate new ways of modeling human behavior and mobility patterns. In this paper, we use over 10 millions geo-tagged tweets from the city of Los Angeles as observations of human movement and apply them to understand the relationships of geographical regions, neighborhoods and gang territories. Using a graph based-representation of street gang territories as vertices and interactions between them as edges, we train a machine learning classifier to tell apart rival and non-rival links. We correctly identify 89% of the true rivalry network, which beats a standard baseline by about 30%. Looking at larger neighborhoods, we were able to show that the direction of displacement, i.e, the distribution of movement direction, can be used as a profile to identify physical (or geographic) barriers when it is not uniform. Finally, considering the temporal dimension of tweets, we detect events taking place around the city by identifying irregularities in tweeting patterns.

Jeff Brantingham is as close as it gets to putting a face on the controversial practice of "predictive policing." Over the past decade, the University of California-Los Angeles anthropology professor adapted his Pentagon-funded research in forecasting battlefield casualties in Iraq to predicting crime for American police departments, patenting his research and founding a for-profit company named PredPol, LLC. PredPol quickly became one of the market leaders in the nascent field of crime prediction around 2012, but also came under fire from activists and civil libertarians who argued the firm provided a sort of "tech-washing" for racially biased, ineffective policing methods. Now, Brantingham is using military research funding for another tech and policing collaboration with potentially damaging repercussions: using machine learning, the Los Angeles Police Department's criminal data, and an outdated gang territory map to automate the classification of "gang-related" crimes. Being classified as a gang member or related to a gang crime can result in additional criminal charges, heavier prison sentences, or inclusion in a civil gang injunction that restricts a person's movements and ability to associate with other people.