When Urban Safety Index Inference Meets Location-Based Data

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban location-based data. We first extract spatially-related and temporally-related features from various urban location-based data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are fed into a novel sparse auto-encoder (SAE) framework with feature correlation constraint to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments by utilizing various real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.