  Short-term future population prediction is a crucial problem in urban
computing. Accurate future population prediction can provide rich insights for
urban planners or developers. However, predicting the future population is a
challenging task due to its complex spatiotemporal dependencies. Many existing
works have attempted to capture spatial correlations by partitioning a city
into grids and using Convolutional Neural Networks (CNN). However, CNN merely
captures spatial correlations by using a rectangle filter; it ignores urban
environmental information such as distribution of railroads and location of
POI. Moreover, the importance of those kinds of information for population
prediction differs in each region and is affected by contextual situations such
as weather conditions and day of the week. To tackle this problem, we propose a
novel deep learning model called Attention-based Contextual Multi-View Graph
Convolutional Networks (ACMV-GCNs). We first construct multiple graphs based on
urban environmental information, and then ACMV-GCNs captures spatial
correlations from various views with graph convolutional networks. Further, we
add an attention module to consider the contextual situations when leveraging
urban environmental information for future population prediction. Using
statistics population count data collected through mobile phones, we
demonstrate that our proposed model outperforms baseline methods. In addition,
by visualizing weights calculated by an attention module, we show that our
model learns an efficient way to utilize urban environment information without
any prior knowledge.

Machine Learning;Artificial Intelligence;Computers and Society