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

TL;DR
This paper introduces Area2Vec, a novel area modeling method based on stay information from people's location data, capturing dynamic usage changes, and analyzes COVID-19's impact on urban activity patterns.
Contribution
Proposes Area2Vec, a new dynamic area modeling technique inspired by Word2Vec that utilizes stay information to reflect behavioral changes over time.
Findings
Area2Vec effectively classifies areas based on usage.
COVID-19 reduced visits to entertainment areas.
Method captures temporal changes in urban activity.
Abstract
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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Geographic Information Systems Studies · Urban Transport and Accessibility
