不同共享社会经济路径下中国未来城市土地数量数据集（2020-2070）V1.0

本数据集包括2020-2070年中国280个城市在不同共享社会经济路径（SSPs）下的城市土地数量。在此，城市土地数量表示未来的城市土地面积，单位为km2。本数据集的制作流程主要包括：（1）基于城市土地、GDP、人口、城市化率等历史数据集建立模拟预测指标集。（2）使用套索回归、回归树、相关矩阵的方法，选取特征指标，用于训练历史模型。（3）使用层次聚类、K-means聚类方法对历史特征指标进行聚类，并使用决策树训练历史模型。（4）采用蒙特卡洛方法和迭代方式，以十年为一期动态迭代模拟未来城市土地数量。该数据集有望广泛应用于中国未来社会经济发展和气候变化研究。本数据集的具体制作方法和流程可参考论文Hou, Y., Li, Y., Li, J., Huang, Q., Duan, X., Feng, X., & Zhu, G. (2023). Simulating the dynamics of urban land quantity in China from 2020 to 2070 under the Shared Socioeconomic Pathways. Applied Geography, 159, 103094.This dataset contains urban land quantity data for 280 cities in China from 2020 to 2070 under different Shared Socioeconomic Pathways (SSPs). Herein, urban land quantity denotes the future urban land area, measured in km². The production workflow of this dataset mainly includes: (1) Develop a simulation and prediction indicator set based on historical datasets including urban land, GDP, population, urbanization rate and other relevant indicators; (2) Select feature indicators for training historical prediction models using methods such as Lasso Regression, Regression Tree and Correlation Matrix; (3) Cluster the historical feature indicators via Hierarchical Clustering and K-means Clustering algorithms, and train the historical models with Decision Tree algorithms; (4) Adopt the Monte Carlo method and iterative approach to dynamically conduct iterative simulation of future urban land quantity, with 10-year intervals as the simulation cycle. This dataset is expected to be widely utilized in studies on China's future socio-economic development and climate change. The specific production methods and procedures of this dataset can be referenced in the paper: Hou, Y., Li, Y., Li, J., Huang, Q., Duan, X., Feng, X., & Zhu, G. (2023). Simulating the dynamics of urban land quantity in China from 2020 to 2070 under the Shared Socioeconomic Pathways. *Applied Geography*, 159, 103094.