# Assessing the applicability of big data driven urban vibrancy analysis in mixed urbanized-depopulated contexts: a case study of a Japanese city

## TL;DR

This paper explores how big data can help understand urban vibrancy in cities with both busy and depopulated areas, using Toyota City in Japan as a case study.

## Contribution

The study demonstrates the applicability of big data vibrancy analysis in mixed urbanized-depopulated contexts, where data is limited.

## Key findings

- POI density strongly correlates with vibrancy in urbanized areas.
- Residential density is more critical for vibrancy in depopulated areas.
- Big data methods can provide meaningful insights even in data-scarce urban contexts.

## Abstract

Understanding the relationship between the built environment and urban vibrancy is crucial for effective urban planning and policy development. While recent research using big data and regression analysis has identified built environment factors associated with vibrancy, most studies focus on densely populated urban cores. However, many cities now contain both thriving centers and depopulated areas, which pose sustainability challenges and require targeted strategies. This study investigates whether conventional big data driven approaches can provide reliable, context-sensitive insights when applied to a city encompassing both urbanized and depopulated areas, particularly under data limitations. Using Toyota City, Japan, as a case study, we employed large-scale GPS trajectory data as a proxy for human activity and constructed built environment factors from readily available Geographic Information System data, including land-use maps, building-use/stock information, road and railway networks, and point of interest (POI) locations, to quantify key dimensions of the built environment, namely diversity, density, and accessibility. Global and local regression models were applied to analyze spatial variation in relationships between vibrancy and built environment factors. The results show that these relationships differ markedly between urbanized and depopulated areas; for example, POI density correlates strongly with vibrancy in urbanized areas, whereas residential density is more critical in depopulated areas. These findings demonstrate that big data driven vibrancy analysis can yield meaningful insights even in data-scarce contexts, extending its applicability to diverse urbanized-depopulated settings.