Next Article in Journal
Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?
Previous Article in Journal
Transport Service Provider Perception of Barriers and Urban Freight Policies in Brazil
Open AccessArticle

Spatiotemporal Differences in Determinants of City Shrinkage Based on Semiparametric Geographically Weighted Regression

1
Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
2
Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(24), 6891; https://doi.org/10.3390/su11246891
Received: 23 October 2019 / Revised: 30 November 2019 / Accepted: 1 December 2019 / Published: 4 December 2019
(This article belongs to the Section Human Geography and Social Sustainability)
City shrinkage, as an ongoing worldwide phenomenon, is an issue for urban planning and regional development. City shrinkage is remarkable in Japan, with over 85% of municipalities experiencing population loss from 2005 to 2015. As Japan’s society ages and with its low fertility rate, city shrinkage has had a tremendous negative effect on economic development and urban planning. Understanding the spatial dependence and spatial heterogeneity of city shrinkage and its determinants is essential for ensuring the sustainable development of a city or region. In this study, a semiparametric geographically weighted regression (SGWR) model was adopted to explore the spatiotemporal differences in determinants of city shrinkage. The results reveal that the SGWR model incorporating the global and local variables is more interpretive compared to ordinary least squares and geographically weighted regression models in exploring the correlates of city shrinkage. We found the spatial dependence and heterogeneity of shrinking cities resulted from demographic, economy, and social factors, and revealed low fertility, the ageing population, and enterprise change ratio influenced city shrinkage in different regions at different times in Japan, whereas foreign population ratio, industry structure, and social welfare had global impacts. The findings provide useful information for understanding city shrinkage at global and local scales.
Keywords: city shrinkage; spatial autocorrelation; population structure; semiparametric geographically weighted regression (SGWR) city shrinkage; spatial autocorrelation; population structure; semiparametric geographically weighted regression (SGWR)
MDPI and ACS Style

Peng, W.; Gao, W.; Yuan, X.; Wang, R.; Jiang, J. Spatiotemporal Differences in Determinants of City Shrinkage Based on Semiparametric Geographically Weighted Regression. Sustainability 2019, 11, 6891.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop