RESUMO
Construction land development intensity is a spatial mapping of modern urbanization level, which integrally reflects urban development strategy, land use efficiency, and population carrying intensity. This article analyzed the spatial and temporal evolution of construction land development intensity using panel data of 31 provincial administrative divisions in China from 2002 to 2020, with the application of the Theil index and spatial autocorrelation. To further investigate the relationship between human activities and land development, the article used geographic detectors to analyze the influencing mechanisms. The results showed that: (1) The average intensity of construction land development of Chinese provinces from 2002 to 2020 showed a trend of "steady increase, a short decline, and then a steady increase," and there were significant differences in the characteristics of construction land development intensity changes in different regions. (2) The regional differences in construction land development intensity between provinces showed a decreasing trend. There were uneven differences among regions, with more minor regional differences in Central, South, and North China but more significant differences in Northwest, East, Southwest, and Northeast China. (3) The spatial agglomeration of construction land development intensity in the region increased initially and then decreased during the study period. The overall pattern was "small agglomeration and large dispersion." (4) Economic development factors such as GDP per land, industrial structure, and fixed asset investment completion significantly affect land development intensity. The interaction between the factors was apparent, and the effect of "1 + 1 > 2" was produced. Based on the study's results, it is suggested that scientific regional development planning, guiding inter-provincial factor flow, and rational control of land development efforts are the key to promoting sustainable regional development.
RESUMO
Land development intensity is a comprehensive indicator to measure the degree of saving and intensive land construction and economic production activities. It is also the result of the joint action of natural, social, economic, and ecological elements in land development and utilization. Scientific prediction of land development intensity has particular reference significance for future regional development planning and the formulation of reasonable land use policies. Based on the inter-provincial land development intensity and its influencing factors in China, this study applied four algorithms, XGBoost, random forest model, support vector machine, and decision tree, to simulate and predict the land development intensity, and then compared the prediction accuracy of the four algorithms, and also carried out hyperparameter adjustment and prediction accuracy verification. The results show that the model with the best prediction performance among the four algorithms is XGBoost, and its R2 and MSE between predicted and valid values are 95.66% and 0.16, respectively, which are higher than the other three models. During the training process, the learning curve of the XGBoost model exhibited low fluctuation and fast fitting. Hyperparameter tuning is crucial to exploit the model's potential. The XGBoost model has the best prediction performance with the best hyperparameter combination of max_depth:19, learning_rate: 0.47, and n_estimatiors:84. This study provides some reference significance for the simulation of land development and utilization dynamics.
