Spatio-Temporal Evolution Characteristics, Development Patterns, and Ecological Effects of “Production-Living-Ecological Space” at the City Level in China

Abstract

Effective production, living, and ecological space allocation is essential for advancing territorial policy optimization and improving the sustainability of land resource use. Based on the theory of the “production-living-ecological” space (PLES), the present study uses the spatial transfer matrix model, the coupling degree model, and ecosystem service value measurement to analyze the changes in the number and structural characteristics of the PLES and the evolution pattern of development in 336 cities in China from 2000 to 2020 and to evaluate the resulting ecological effects. The results are as follows: the living space is growing; the agricultural production space is decreasing; and the ecological space has been decreasing and then increasing. The evolution of the city space structure has five distinct patterns of development. Cities in the southeast with high urbanization rates have shifted from the pure economic expansion development pattern to the coordinated diversified development pattern. In contrast, the cities in the northeast and northwest, where ecological space accounts for an absolute proportion, still prefer the economic expansion development pattern. There is still a struggle between the “impulse of local development” and the “objective of central coordination”. The development patterns of ecological protection and the coordinated diversified development patterns have higher ecological effects among the five development approaches, confirming the effectiveness of the territorial spatial planning policy under the coordinated development objective. Meanwhile, the optimization of future spatial planning policies should consider not only the rational allocation of space but also the quality development of space.

Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China

“…In this study, the PLES was identified from the perspective of the dominant function of land. According to the definition of the PLES, the PS refers to the specific functional area where people engage in production activities; the LS carries the residence, consumption, leisure, and entertainment of residents; and the ES refers to the area that can provide ecological products and ecological services with ecological protection functions [2,29,37]. Due to the significant differences between agricultural production and nonagricultural production in production methods, land use, and ecological impacts, the PS is further divided into agricultural production space (APS) and non-agricultural production space (NAPS).…”

The Role of Cloud Computing in Modernizing Healthcare IT Infrastructure

“…Figure 9 shows a middling accuracy score for logistic regression, whereas Figures 10 and 11 show comparable accuracy as well as recall scores for decision trees and KNN, respectively. The comparison of the categorization reports (Figure 12) highlights the improved performance of Decision Tree [14]. The three models' differences in precision, accuracy, and recall are graphically shown in the continuous plot (Figure 13), which facilitates a comprehensive comprehension of their respective advantages and disadvantages.…”

The Coordinated Development of Ecosystem Services and Farming Household Livelihood Security: A Case Study of the Dongting Lake Area in China

“…In addition, according to the needs of the present study and with reference to the existing research results [42], the land use data were reclassified (Table 1), and the normalized vegetation index and rainfall raster data used to evaluate the livelihood security level of farmers were counted to the county by the average value, and the national average value of NPP of each secondary PLES was also counted to meet the need to adjust the value of ESs. The land use information chart is a composite research method for studying "spatial patterns" and "time processes" [43]. This article uses the "raster calculator" tool of ArcGIS to construct land use information charts for two time periods: 2000-2010 and 2010-2020.…”