# Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19

arXiv: https://arxiv.org/abs/2401.10648 · HTML: https://mlsanity.com/paper/2401.10648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Understanding Urban Human Mobility through Crowdsensed Data](https://mlsanity.com/paper/1805.00628/) (2018)
- [Traj2User: exploiting embeddings for computing similarity of users mobile behavior](https://mlsanity.com/paper/1808.00554/) (2018)

## Built on by (most cited first, 1 of 1)

- [OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns](https://mlsanity.com/paper/2407.19872/) (2024)

## Abstract (arXiv)

Understanding how people use area in a city can be a valuable information in a wide range of fields, from marketing to urban planning. Area usage is subject to change over time due to various events including seasonal shifts and pandemics. Before the spread of smartphones, this data had been collected through questionnaire survey. However, this is not a sustainable approach in terms of time to results and cost. There are many existing studies on area modeling, which characterize an area with some kind of information, using Point of Interest (POI) or inter-area movement data. However, since POI is data that is statically tied to space, and inter-area movement data ignores the behavior of people within an area, existing methods are not sufficient in terms of capturing area usage changes. In this paper, we propose a novel area modeling method named Area2Vec, inspired by Word2Vec, which models areas based on people's location data. This method is based on the discovery that it is possible to characterize an area based on its usage by using people's stay information in the area. And it is a novel method that can reflect the dynamically changing people's behavior in an area in the modeling results. We validated Area2vec by performing a functional classification of areas in a district of Japan. The results show that Area2Vec can be usable in general area analysis. We also investigated area usage changes due to COVID-19 in two districts in Japan. We could find that COVID-19 made people refrain from unnecessary going out, such as visiting entertainment areas.