Summary of Demo2vec: Learning Region Embedding with Demographic Information, by Ya Wen et al.
Demo2Vec: Learning Region Embedding with Demographic Information
by Ya Wen, Yulun Zhou
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to improve the quality of region embeddings by integrating demographic data from urban regions, such as income, education level, and employment rate. By using Jenson-Shannon divergence as a loss function, this study shows that mobility + income is the best pre-train data combination for improving predictive performances in tasks like check-in prediction, crime rate prediction, and house price prediction. Experimental results on New York and Chicago datasets demonstrate up to 10.22% better predictive performances compared to existing models. The authors suggest geographic proximity + income as a simple yet effective alternative for region embedding pre-training, which can be accessible even in developing cities where mobility data may not be available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how demographic data from urban regions can help create better region embeddings. By combining different types of data, like income and education level, the study finds that this information can improve predictive performances for tasks like checking in to specific locations or predicting crime rates. The results show that using a specific type of loss function, Jenson-Shannon divergence, helps create more accurate region embeddings. This research could help cities make better predictions about what will happen in different areas and how people will behave. |
Keywords
» Artificial intelligence » Embedding » Loss function
