Research on the spatiotemporal process and mechanism of metropolitan transition in land use

Dataset Description TranslationThis dataset is exclusively for the research project titled "Research on the spatiotemporal process and mechanism of metropolitan transition in land use". It covers core data supporting the analysis of spatiotemporal evolution, driving mechanisms, and scenario simulation of Jinan's land use metropolitan transition, including remote sensing data, spatial data, driving factor statistical data, vector data, and policy planning data. By leveraging multi-source data fusion technology, machine learning methods, and the PLUS (Patch-generating Land Use Simulation) model, the dataset aims to objectively present the spatial-temporal patterns and stage-based driving characteristics of Jinan's land use transition. It provides data support for optimizing Jinan's land use structure and formulating "spatial control-mechanism optimization-technology empowerment" policies, and also serves as a case reference and basic data resource for land use transition research in other metropolises and regional planning-related analyses. Remote sensing dataRemote sensing data mainly consists of land use classification data (2005-2020). The data set is provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). Taking Landsat remote sensing image data of the United States as the main information source, this data has established a national-scale multi-period land use remote sensing monitoring database (CNLUCC) with a scale of 1:100,000 through manual visual interpretation. The classification system for China's multi-period land use remote sensing monitoring data includes six major categories: cultivated land, forest land, grassland, water area, construction land, and unused land.Spatial dataSpatial data refers to POI (Point of Interest) data, which is sourced from the Amap Platform. A unified data calibration standard is adopted to ensure temporal consistency and spatial accuracy. Based on the function-oriented principle, geographic grid analysis technology is employed to construct an integrated innovative classification framework of "function-space-policy".Driving factor statistical dataThis dataset covers four major dimensions: natural environment, economic development, population change, and transportation construction, providing a comprehensive basis for identifying the driving mechanisms of urbanization transformation in land use in Jinan. The elevation (DEM) data is sourced from the Geospatial Data Cloud platform, while slope and aspect data are derived from the DEM. Water resource distribution data (Euclidean distance to the nearest water system) is obtained from the National Geographic Information Resource Directory Service System. Raster format GDP and population data are sourced from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, used to characterize the demand-driven effects of economic growth and population agglomeration on land use transformation. Data on primary roads, secondary roads, tertiary roads, highways, and railways are all obtained from the National Geographic Information Resource Directory Service System.Vector dataThe administrative boundary data of Jinan and the central points of its districts are sourced from the National Earth System Science Data Sharing Platform—Lower Yellow River Scientific Data Center. The data underwent standardized processing, with a unified coordinate system (consistent with remote sensing data) and spatial resolution, providing a fundamental spatial framework for defining the study area and conducting regional-scale land use analysis.Policy and planning dataPolicy planning data includes the“Development plan for the Jinan start-up zone for converting old and new growth drivers (2021-2035)”, “Ecological protection plan for the southern mountainous area of Jinan (2021-2035)” and “Overall Plan for Territorial Space of Jinan (2021-2035)”, all sourced from the official website of Jinan and development and Reform Commission. Such policies are integrated into the PLUS model as constraints to enhance policy adaptability in multi-scenario simulations, providing a policy basis for predicting future trends in land use transformation in Jinan.