Predicting Amenities Distributions for Workers from the Built Environment Based on Machine Learning

Abstract

The working population has increased in cities with urbanization. Providing a supportive built environment with reasonable amenities distribution for them is becoming more important. Previous GIS-based approaches to urban planning for this issue tend to be subjective with high labour costs. This paper uses the generative adversarial network (GAN) to explore the relationship between amenities distributions and urban morphology, thus effectively predicting and visualizing the ideal amenities distributions in fastgrowing cities based on the condition of well-developed megacities. In this research, we take Shanghai, one of the global cities in China with a big labour market, as the research sample. First, we use the Point of Interest (POI) data to draw the heatmap of urban amenities that support workers' daily life and collect the corresponding city maps. Then, we cut them into hundreds of image pairs as the training set and train a GAN model for predicting the future amenities distributions in other cities. To implement the model, we further collect the city maps of Jiaxing, one of the second-tier cities near Shanghai, as the testing set. Results show that our trained model can accurately predict amenities distributions for its future. The GAN-based prediction could effectively support detailed urban planning.