Urban modeling of shrinking cities through Bayesian network analysis using economic, social, and educational indicators: Case of Japanese cities

Kato, Haruka

doi:10.1371/journal.pone.0284134

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…The absolute aging of death-related factors was most dominant and birthrelated factors secondary, with migration-related factors playing a crucial moderating effect. Excessive aging and low fertility will lead to urban contraction, urban contraction will lead to urban economic recession, and urban economic recession will deepen the aging of the population [62,63]. Few studies have studied the driving factors of population aging in China from the perspective of geospatial change.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020

Zhang

Han

2023

Sustainability

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Aging involves the transformation of the population reproduction mode under the rapid development of the social economy. We studied population survey data based on the WorldPop population statistics website and used ArcGIS to construct a spatial database and implement spatial analysis methods. In this study, we analyzed the spatiotemporal evolution characteristics of population aging and its main influencing factors in counties of China, in order to provide a reference for the formulation of a national population development policy and the construction of a pension system. The results are as follows: ① The situation of population aging in China is becoming more serious, showing a point-line-area spatial pattern and two core–periphery aging patterns of high core–low periphery and low core–high periphery. ② The speed of population aging in China is characterized by rapid growth, large scale, and a high degree. Large areas of growing old before getting rich have emerged in the central and western regions. ③ The aging of the population has gradually spread to the northeast, southwest, northwest, and other regions. Influenced by factors such as population migration, population structure change, transportation facility construction, and geographic environment changes, a trend of aging that has spread across the Hu Huan-Yong line has appeared.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020

Zhang

Han

2023

Sustainability

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“…However, one limitation of existing the literature concerns its geographical scope: the intensity of shrinkage and its impacts on the housing sector vary considerably from one locality to another [13][14][15], but Japanese regions have not received equal attention. Indeed, most studies that have categorized the factors leading to different trajectories of decline [16][17][18] or analyzed the impacts of shrinkage on the built environment and real estate markets [19][20][21][22][23] base their fieldwork upon cases situated either in Tokyo or a non-metropolitan municipality. Metropolitan-level studies centered on cities as big as Osaka and Nagoya are absent from this stream of investigations.…”

Section: Introductionmentioning

confidence: 99%

How Are Tier 2 Metropolises Affected by Housing Asset Value Deflation in the Depopulation Era? A Comparison between the Tokyo and Kansai Metropolitan Areas

Uto,

Buhnik,

Okazawa

2024

Land

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This study analyzes the differences and similarities between Tier 1 (Tokyo) and Tier 2 (Kansai) metropolitan areas due to shrinking city problems. Both metropolitan areas will see a dramatic decrease in the housing asset value (HAV). Kansai is declining at a faster pace than Tokyo: it is projected that HAVs will register a further decrease of around 38% by 2045, and the decline will be quantitatively more important in the northern suburbs of Osaka. These results raised the question of whether Kansai would be more impoverished by HAV deflation. By focusing on the income multiplier of HAV per household, we find that Tokyo has a higher income multiplier of around 4 (against 2 for Osaka), thus causing much greater HAV deflation per household in Tokyo. Greater HAV deflation per household entails more severe problems for elderly households that need to finance their retirement. Considering our findings, despite earlier and faster trends of HAV deflation in the Tier 2 metropolitan area, the Tier 1 metropolitan area could face big socioeconomic challenges in the future. We conclude that HAV deflation leads to problems of different nature depending on metropolitan rank, rather than just knowing which one is losing more through HAV deflation.

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Shifting Migration Determinants for Family Households With Children From a Life‐Course Perspective

Kato

2024

Population Space and Place

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During the coronavirus disease 2019 pandemic, many urban planners focused on urban exodus. However, a research gap exists as to whether the spread of the infection affected changes in the migration destination determinants of family households. This study aims to clarify the shifting determinants of the migration destinations of family households with children who migrated from the urban centre of the Japanese metropolitan areas because of the spread of the infection during the pandemic. This study adopted a life‐course perspective of the shift in migration destination determinants from the pre‐pandemic to the pandemic periods. The participants were recruited through a web‐based questionnaire survey. As a result, this study concluded that family households with children who mentioned the spread of COVID‐19 infection as a migration motive exhibited significant shifts in their migration destination determinants, emphasising the importance of social interaction‐related factors. The social interaction factors are the favorability of communities, community ties, returning to their hometown, and proximity to relatives. Conversely, the influence of work, living environment, and housing factors on family migration did not change much from the pre‐pandemic to the pandemic periods. This means that the pandemic did not affect migration motives related to work, the living environment, and housing. Therefore, the unique contribution of this study lies in its revelation of the temporal precedence of shifts in the prioritisation of social interaction determinants from pre‐pandemic to pandemic periods. Our findings suggest that they might migrated from urban centres to other cities that offer opportunities for social interaction.

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Urban modeling of shrinking cities through Bayesian network analysis using economic, social, and educational indicators: Case of Japanese cities

Cited by 9 publications

References 39 publications

Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020

Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020

How Are Tier 2 Metropolises Affected by Housing Asset Value Deflation in the Depopulation Era? A Comparison between the Tokyo and Kansai Metropolitan Areas

Shifting Migration Determinants for Family Households With Children From a Life‐Course Perspective

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