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%.

本文介绍了一种名为 SpatialRank 的新颖的空间事件排名方法，通过自适应图卷积层动态地学习位置间的时空依赖关系，并通过优化带有空间组件的混合 NDCG 损失来更好地排序相邻的空间位置。在三个实际数据集上进行的综合实验表明，SpatialRank 能够有效地识别犯罪和交通事故的最高风险地点，并在 NDCG 方面超过最先进的方法多达 12.7%。