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Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We abstract 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas.
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