Spatio-Temporal Evolution and Driving Factors of Rural Settlements in Low Hilly Region—A Case Study of 17 Cities in Hubei Province, China

Abstract

With the rapid development of the social economy, factors of social and economic development in China’s rural areas have been continuously reorganized, and the pattern and distribution of rural residential areas have undergone significant changes. In rural areas, there have been many peculiar phenomena of “reducing people but not reducing land in rural areas, which has caused tremendous pressure on land resource protection. We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements’ evolution and driving mechanism in Hubei Province from 1990 to 2015. The results show that the kernel density of rural settlements decreased from 1.62 villages/km2 in 1990 to 1.60 villages/km2 in 2015. The scale of rural residential patches has obvious regional differentiation characteristics. From southeast to northwest, there is a wave-like distribution structure of “high-low-high-low-high”, and the clustering characteristics of “cold and hot spots” are strengthened with time. Based on GTWR analysis, the total rural population, total power of agricultural machinery, and rural electricity consumption have promoted the expansion of rural settlements, with the regression coefficients 0.096, 0.484, and 0.878, respectively. Cultivated land, agricultural output value, and rural labor force have negative impacts on the expansion, the regression coefficients of the village were −0.584, −0.510, and −0.109, respectively.

Spatial and temporal distribution of rural settlements and influencing mechanisms in Inner Mongolia, China

“…However, due to the influence of the household registration system, social relations, and hometown nostalgia, the roots of these people remain in rural areas [ 24 , 38 ]. With the increase in economic income, they tend to return to their hometown to build houses [ 4 , 22 , 23 ]. As the people grow older, their labor ability is weakened, and their willingness to return to their hometown increases [ 40 , 43 , 56 ].…”

“…Kernel density analysis is commonly used to describe the spatial distribution density of point elements to visually express their spatial distribution characteristics [ 22 , 28 ]. The formula is as follows: Where f (x , y) denotes the density estimate located at position (x, y); n is the number of rural settlements; h is the bandwidth, that is, the search radius distance; K is the kernel density function, and ( x − x i ) is the distance between two rural settlements.…”

“…Geographic detector, a group of statistical methods, is mainly applied to explore the spatial differentiation of various phenomena to reveal their influencing factors, and multi-factor interactions [ 48 ]. It provides a better expression of the similarities in the same area and the differences in different areas, moreover, it explains the explanatory strength of the independent variable X to the dependent variable Y [ 22 ]. Factor detection determines the extent to which a factor explains the spatial differentiation of variables.…”

“…In recent years, China's rural areas have experienced the phenomenon of population decrease and land use increase [22][23][24]-that is, the rural population is decreasing due to urbanization, but the area occupied by rural settlements is increasing. The increase in rural settlements area is mainly dominated by the occupation of cultivated land, but there are also forest, grassland, and unused land types [4,23,[25][26][27].…”

Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue—An Empirical Research on 31 Chinese Provinces

“…Firstly, an excessively great NEPPI will decrease the number of workers employed in the other links, leading to a decline in overall efficiency. Secondly, as the demand for agricultural products is less elastic, where the other factors remain unchanged, an increase in crop yield or harvest can bring down the income of farmers, discourage workers in the primary industry, and thus hinder the development of the FAPSC [50]. According to Figure 3b, the regions with high values of coefficient, ranging from −0.630 to 0.163, are mainly distributed in Northeast China, Central China and South China, followed by those in Southwest China and Northwest China, whereas the Beijing-Tianjin-Hebei Region and the southwestern and northern regions have values lower than −0.971.…”