Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19
This work addresses the need for sustainable and dynamic area modeling for urban planning and marketing, though it appears incremental as it adapts Word2Vec to a new domain.
The paper tackles the problem of modeling area usage in cities by proposing Area2Vec, a method that uses people's stay information to characterize areas dynamically, and validates it through functional classification in Japan, showing it can be used for general area analysis and detecting changes like reduced visits to entertainment areas during COVID-19.
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.