Demo2Vec: Learning Region Embedding with Demographic Information
Ya Wen, Yulun Zhou · 2024
Demographic data, such as income, education level, and employment rate, contain valuable information about urban regions, yet few studies have integrated demographic information to generate region embedding. In this study, we show how simple and easy-to-access demographic data can enhance the quality of state-of-the-art region embedding and improve predictive performance in urban areas across three common urban tasks, namely check-in prediction, crime rate prediction, and house price prediction. We find that existing pre-training methods based on KL divergence are potentially biased towards mobility information and propose using Jenson-Shannon divergence as a more appropriate loss function for multi-view representation learning. Experimental results from both New York and Chicago indicate that Mobility + Income is the best pre-training data combination, providing up to 10.22% improvement in predictive performance compared to existing models. Since mobility big data is hard to access in many developing cities, Income + Neighbor may serve as an alternative data combination for effective region embedding learning.