Geospatial characterization of rural settlements and potential targets for revitalization by geoinformation technology

Abstract

To better implement the Strategy of Rural Revitalization, it is essential to characterize the rural settlements and understand their roles in the socio-environmental interactive system. This paper is hence aimed at achieving such a study using different spatial analysis such as kernel density and spatial autocorrelation (SA) and modeling approaches, e.g., simple and multiple linear regression analyses taking Jiangxi, a province in China as an example. Remote sensing, topographic and socioeconomic data were employed for this purpose. Through these analyses, it is found that the rural settlements in the study area appear to have a spatial distribution pattern of “dense north and sparse south” as an “F” type, and are quantitatively characterized as low elevations, flat terrain, high river and road densities, rich cultivated land resources and susceptible to the impact of urban radiation with a R2 of 0.520–0.748. Based on this understanding, a new inequality evaluation indicator of rural development, i.e., socio-environmental evaluation index (SEI), was developed. Areas with SEI lower than 0.40 should be given a priority to implement the revitalization strategy in the province. This index can also be extended to study of the imbalance of rural development in other regions and countries.

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

“…As with elevation, different areas with the same elevat of se lement agglomeration [30]. According to the characte ation in Diqing, we divided the slope into 10 levels: 0-5°, The results reveal the following.…”

“…Through the literature review, we noticed that ArcGIS10.1 has the ability to manage and analyze large amounts of spatial data [29]. In recent years, ArcGIS has been widely employed in village settlement research [30]. This research has used kernel density, spatial autocorrelation (SA), and modeling approaches to analyze village settlements at the macro level, including public service facilities, community governance, rural geography, etc.…”

Recognition and Spatial Distribution of Rural Buildings in Vietnam

“…The rural buildings database has the characteristics of wide coverage, high accuracy, fine scale, correctability, and complementarity, which can effectively make up for the poor availability of rural data at present. At the same time, the national rural buildings database can integrate social, economic, population, and other multi-source data, increase the integrity of rural research data, and promote the transformation of rural research from case field investigation to "case field investigation + global 'computability'" [61], laying the foundation for sustainable research on urbanization development and urban-rural relationship evolution in Southeast Asia [62].…”

Mapping the suitable areas for future urbanization in Jiangxi, China

“…For example, natural environmental factors such as topography (elevation, slope, plain and valley) and hydrology (rainfall, river, etc.) may have different degrees of influence on the formation and development of cities [1][2][3][4][5] . In the past decades, machine learning methods have been widely used in the study of geographic and geological issues [5][6][7][8] .…”

“…may have different degrees of influence on the formation and development of cities [1][2][3][4][5] . In the past decades, machine learning methods have been widely used in the study of geographic and geological issues [5][6][7][8] . A number of machine learning methods can be used for prediction such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Logistic Regression (LR) and Random Forests (RF), etc.…”