Functional Tradeoffs and Feature Recognition of Rural Production–Living–Ecological Spaces

Abstract

Recently, the spatial contradiction in rural construction has intensified. Production–living–ecological (PLE) spaces, as important load-bearing spatial patterns of rural revitalization, have become a research focus of territorial space planning. Because of the lack of studies on the scale and accuracy at the township level in rural PLE spaces, the objective of this study is to quantify the subfunctional and functional areas of PLE spaces, weigh the relationship between PLE functions, and conduct feature identification and strategy formulation of the PLE synergistic functional areas. Combined with multitype measurement methods, the study constructed a township-level PLE space evaluation system composed of 12 subfunctional indicators. Taking Guli Street in Nanjing city as an example, Spearman correlation analysis, spatial local autocorrelation analysis, and cold/hot spot identification were used to analyze the synergies and tradeoffs between PLE functions. On this basis, the evaluation model of the PLE synergies area was constructed. The results showed that the production function showed a fragmented distribution pattern. The proportion of high-intensity living function areas was very small. The ecological function area had good patch integrity. In the PLE functions, obvious synergies exist between any two functions, and the tradeoff between the third one and any of the two functions. The seven types of PLE synergistic potential areas were dominated by the dual-function high synergistic zone (DF-H-Z). The zoning scheme and governance strategy proposed in this paper have important practical value for solving the contradiction of sustainable and coordinated development of township-scale spatial resources.

Study on the Evolution and Optimization of the Spatial Structure of the Oasis in the Arid Area: A Case Study of the Aksu River Basin in China

“…The rigid bottom line is the blueprint for conducting spatial use control of the territorial space, which mainly has institutional rigidity, spatial rigidity, and scale rigidity [ 59 ]. The corresponding flexible space mainly has governance, functional, and structural flexibility.…”

Spatiotemporal evolution and Sustainably comprehensive zoning optimization of production–living–ecological functions in the Mountain–Flatland areas

“…The clustering characteristics of the PLEFs were assessed by using the Global Moran's I, and the significance test should be conducted The value exceeding 0 indicated negative correlation, equal to 0 was no correlation, less than 0 was positive correlation. The correlation degree of the PLEFs in ArcGIS software was classified by using the Local Moran's I index, so as to create LISA maps based on homogeneous and heterogeneous agglomeration areas [ 24 , 49 ]. The spatial functions clustering degrees of the PLEF are classified into five types: high-high cluster (H–H), low-low cluster (L-L), high-low cluster (H-L), low-high cluster (L-H), and non-significant area.…”

Research on the Coupling of Regional Low-Carbon Economy and Energy Structure Based on Multi Algorithm Optimization: A Case Study of Anhui Province

“…The conceptual models commonly used by scholars to study internal evolution laws include: PSR model [11], DPSIR model [12], DPSIRM model [13], TQR model [14], EES model [15], PLE model [16], etc. Secondly, the models commonly used to study the prediction between the two include: BP neural network model [17,18,21], ARIMA model [18,19], grey model [20], Markov chain model [21], etc.…”