  The problem of urban event ranking aims at predicting the top-k most risky
locations of future events such as traffic accidents and crimes. This problem
is of fundamental importance to public safety and urban administration
especially when limited resources are available. The problem is, however,
challenging due to complex and dynamic spatio-temporal correlations between
locations, uneven distribution of urban events in space, and the difficulty to
correctly rank nearby locations with similar features. Prior works on event
forecasting mostly aim at accurately predicting the actual risk score or counts
of events for all the locations. Rankings obtained as such usually have low
quality due to prediction errors. Learning-to-rank methods directly optimize
measures such as Normalized Discounted Cumulative Gain (NDCG), but cannot
handle the spatiotemporal autocorrelation existing among locations. In this
paper, we bridge the gap by proposing a novel spatial event ranking approach
named SpatialRank. SpatialRank features adaptive graph convolution layers that
dynamically learn the spatiotemporal dependencies across locations from data.
In addition, the model optimizes through surrogates a hybrid NDCG loss with a
spatial component to better rank neighboring spatial locations. We design an
importance-sampling with a spatial filtering algorithm to effectively evaluate
the loss during training. Comprehensive experiments on three real-world
datasets demonstrate that SpatialRank can effectively identify the top riskiest
locations of crimes and traffic accidents and outperform state-of-art methods
in terms of NDCG by up to 12.7%.

Machine Learning