Summary of Area Modeling Using Stay Information For Large-scale Users and Analysis For Influence Of Covid-19, by Kazuyuki Shoji et al.
Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19
by Kazuyuki Shoji, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Area2Vec method models areas based on people’s location data, characterizing an area by its usage through stay information within the area. Inspired by Word2Vec, this novel approach can reflect dynamically changing people’s behavior in an area and is validated through functional classification of areas in a Japanese district. The paper also investigates COVID-19-induced changes in area usage, revealing reduced visits to entertainment areas. This method has potential applications in marketing, urban planning, and other fields where understanding area usage is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Area2Vec is a new way to understand how people use different parts of a city. It’s based on the idea that you can tell what an area is like by looking at where people spend their time there. This method is better than others because it takes into account how people behave in each area, even if they change over time. The researchers tested this approach and found that it worked well for identifying different types of areas. They also used it to see how the COVID-19 pandemic changed how people used different parts of the city. |
Keywords
* Artificial intelligence * Classification * Word2vec
