Identifying the natural and anthropogenic factors influencing the spatial disparity of population hollowing in traditional villages within a prefecture-level city

Abstract

In developing countries, the phenomena of rural depopulation have been an intense continuing, which have become a major bottleneck for the sustainable revitalization of traditional villages. However, the factors influencing the spatial disparity of population hollowing (SDPH) in traditional villages within a prefecture-level city have not been fully quantitatively researched. Based on the factors that influence general villages, this study incorporated historical and cultural factors related to traditional village characteristics to construct a targeted influencing factor index system and then identified the key factors by applying the geo-detector method. With the percentage of resident population (PRP) used as a metric, this study examined Lishui, one of China’s traditional village agglomeration regions, as an example to explore SDPH in traditional villages. The results of this study were revealed in the following. (1) The average PRP value in traditional villages in Lishui was 0.68, with clear spatial disparities between the northern region (0.73) and the southern region (0.57). (2) The factors driving the SDPH included both natural and anthropogenic factors; of these, altitude, the number of public facilities, and the number of communication base stations were the most significant influencing factors. In contrast, historical and cultural factors have relatively low impacts. (3) The interaction relationships of pair factors were often enhanced on a bivariate basis, with the highest enhanced impact occurring from the interaction of two variables: the degree of intangible cultural inheritance and altitude. (4) The intervals of the variables leading to the hollowing of the population above a moderate level can be detected. This method can effectively analyze the factors influencing SDPH in traditional villages; can help reveal the interaction impact of pair factors; and can help identify the factors’ risk intervals.

Exploring Village Spatial Patterns for Sustainable Development: A Case Study of Diqing Prefecture

“…In the formula: S-spatial shape index P-the length of the se lement boundary obtained by surveying (unit: m) P0-the perimeter of an ellipse with the same area (unit: m) A-the projected area of the village se lement space on the plane (unit: m 2 ) λ-the aspect ratio of the circumscribed rectangle of the se lement boundary N1-the number of building groups within the village boundary (unit: pcs) N2-the number of buildings in a unit building group (unit: pcs) D-the average relative distance of buildings within the se lement (unit: m) The village settlement space, as the material foundation of the human living environment, needs the relevant vernacular resource conditions for spatial planning. The main factors affecting the village settlement spatial agglomeration pattern in Diqing Prefecture are natural factors [44].…”

Identifying the Spatial Structure and Influence Mechanism of Characteristic Protection Villages under the background of Rural Revitalization: A case study of Hubei Province, China

“…China have implemented the development strategy focusing on cities since 1978 (Ahmad et al, 2018), resulting in many problems in the development of rural areas. It has led to the phenomenon of village-hollowing and even recession of rural areas (Liu et al, 2021;Long et al, 2012), and some villages constantly decline and even die out (Tan et al, 2021). Facing the problems of urban-rural segregation and increasingly severe rural decline in the process of rapid urbanization, rural revitalization is accelerated to be the national strategies in China.…”

“…However, limited by the influence of policy orientation, most studies mainly focuses on a single type of CPVs, such as the FHCVs, the TVs, the EVs, etc. In terms of research scale, the existing research can be mainly divided into administrative regions, such as national (Wang et al, 2021;Su et al, 2022;Sato et al, 2019;Zhang et al, 2021;Wu et al, 2020), provincial (Wang et al, 2022;Xu et al, 2021;Yang et al, 2022;Zheng et al, 2022;Li et al, 2023), prefecture-level city (Liu et al, 2021), and typical village Dai et al, 2019;Gao et al, 2017); and geographical units, such as the low hilly region (Tan et al, 2021), the Yellow River basin (Gao et al, 2021), the Tibetan Plateau (Qi et al, 2022;Jin et al, 2022), the hilly and gully areas of Northern Shaanxi (Li et al, 2022).…”

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

“…However, identifying the population hollowing out issues are more complicated than a single socio-economic phenomenon, and the concept of population hollowing out needs to be comprehensively described using multi-source data based on multiple perspectives. Additionally, previous studies on population hollowing out were mainly concentrated on its connotation and formation mechanism [34], evolution law and stage features [35], affecting factors and driving forces [36,37], reclamation potential and measure [38], policy-making, et al [39][40][41][42]. Overall, most published research focused on the quantitative determination and regional spatial heterogeneity of population hollowing out at political boundary scale on a single time cross-section.…”