Revisiting Convolutional Neural Networks for Urban Flow Analytics
Convolutional Neural Networks (CNNs) have been widely adopted in raster based urban flow analytics by virtue of their capability in capturing nearby spatial context. By revisiting CNN based methods for different analytics tasks, we expose two common critical drawbacks in the existing uses: 1) inefficiency in learning global context, and 2) overlooking latent region functions. To tackle these challenges, in this paper we present a novel framework entitled DeepLGR that can be easily generalized to address various urban flow analytics problems. This framework consists of three major parts: 1) a local context module to learn local representations of each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter efficient compared to previous methods. Extensive experiments on two typical urban analytics tasks demonstrate the effectiveness, stability, and generality of our framework.
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