Area Modeling using Stay Information for Large-Scale Users and Analysis
  for Influence of COVID-19
Kazuyuki Shoji Shunsuke Aoki Takuro Yonezawa Nobuo Kawaguchi
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.