Session Info
Understanding Urban Economic Status through GNN-based Urban Representation Learning Using Mobility Data
Yuyol Shin, Gwanghwan Seong, Namwoo Kim, Seyun Kim, and Yoonjin Yoon
With growing urban population and urban concentration, various data-driven efforts are being made to achieve sustainable growth to promote equity, inclusion, and well-being. Among abundant urban data, mobility data is a source with rich semantic about urban environments in which social and economic activities are dissolved. In this paper, we employ graph attention network (GAT) to obtain urban representation learning embedding based on taxi trips and subway ridership data in Seoul, South Korea. Our GAT-based region embedding model outperformed all baseline models in predicting the number of employees and housing prices. For the number of employees prediction, our model achieved R-squared value of 0.649 using mobility data only. We also found that increasing the embedding dimensions to stack the elderly and disabled subway user types can further improve the model’s capability in the number of employees and housing prices predictions. Our study results suggest that a transportation network is a key contributor to shaping the economic landscapes of urban regions. Such findings also indicate that understanding people’s activities and movements is essential in achieving sustainable urban growth and promoting equity and inclusion for all. Our research contributes to the growing body of research on the urban region representation learning in understanding the economic impact of transportation systems on urban regions, especially for vulnerable populations.