DOI: 10.57760/sciencedb.29361
title: Nanzhang County 2000 - 2023 Cultivated Land Spatiotemporal Evolution Dataset
title: Nanzhang County 2000 - 2023 Cultivated Land Spatiotemporal Evolution Dataset

description: Dataset Summary This dataset focuses on the spatiotemporal evolution and driving mechanisms of cultivated land resources in Nanzhang County, Hubei Province, from 2000 to 2023. It includes multi-source geospatial data and attribute data, aiming to provide data support for cultivated land protection, territorial spatial planning, and sustainable development in mountain-plain transition zones. Dataset Content: 1. Land use data: Six phases (2000, 2005, 2010, 2015, 2020, 2023) of land use raster data (30-meter resolution), covering 6 major land types such as cultivated land, forest land, and construction land, sourced from the Resource and Environment Science Data Platform (http://www.resdc.cn). 2. Driving factor data: 10 key indicators, including natural elements (elevation, slope, annual precipitation, etc.) and socioeconomic elements (population density, GDP, distance to highways, etc.), obtained from public data sources such as the National Geographical Information Resource Directory Service System, World Soil Database, and Geospatial Data Cloud Platform. 3. Auxiliary data: Vector boundaries of administrative divisions, township-level dynamic degree of cultivated land change, grid-cell data on cultivated land increase and decrease, etc. Data Values and Structure: The data are mainly in raster (.tif) and vector (.shp) formats. After preprocessing such as coordinate unification (WGS84), resolution standardization (30 meters), and Euclidean distance analysis, they can be directly used for spatial overlay, PLUS model simulation, and other analyses.Reuse Potential: It is applicable to fields such as land use change simulation, analysis of cultivated land driving mechanisms, and research on coordination between ecological protection and food security, providing reference data for land management in similar mountain-plain transition zones.Legal and Ethical Considerations: All data are obtained from public platforms, comply with map usage specifications such as GS (2016) 1600, have no copyright restrictions, do not involve personal privacy or sensitive information, and can be reused under the CC BY 4.0 license. Methods Section 1. Data Collection: - Land use data and administrative division data were obtained from the Resource and Environment Science Data Platform, with secondary land types merged into 6 major dominant types. - Natural driving factors (e.g., DEM, slope) were sourced from the Geospatial Data Cloud Platform; climate data (temperature, precipitation) were obtained from the National Tibetan Plateau Data Center; socioeconomic factors (population, GDP) and infrastructure data (highways, government residences) were derived from the National Geographical Information Resource Directory Service System. 2. Data Processing: - Geographic coordinate systems and projection parameters were unified in ArcGIS 10.8, and raster data were standardized to 30-meter resolution through resampling. - For vector data such as transportation networks and water bodies, Euclidean distance algorithms were used to generate continuous distance factor raster layers. - Multiple rounds of verification were conducted on the consistency of spatiotemporal resolution and coordinate unity to ensure data comparability and model adaptability (meeting the simulation requirements of the PLUS model).Note: The newly added Nanzhang County 2000–2023 Cultivated Land Spatiotemporal Evolution Dataset 2.0 integrates all the core derivative data that support the reproduction of the conclusions of this study. Specifically, it includes reclassified land use raster layers, socioeconomic and environmental driver raster data resampled to a specific resolution, thematic maps of spatial suitability evaluation for cultivated land, and the output results of land use pattern simulation under different future scenarios. This dataset fully preserves the key data processing trajectories and analysis results throughout the research process, and can provide standardized data support for method replication, conclusion verification and extended analysis in subsequent related studies.
description: Dataset Summary This dataset focuses on the spatiotemporal evolution and driving mechanisms of cultivated land resources in Nanzhang County, Hubei Province, from 2000 to 2023. It includes multi-source geospatial data and attribute data, aiming to provide data support for cultivated land protection, territorial spatial planning, and sustainable development in mountain-plain transition zones. Dataset Content: 1. Land use data: Six phases (2000, 2005, 2010, 2015, 2020, 2023) of land use raster data (30-meter resolution), covering 6 major land types such as cultivated land, forest land, and construction land, sourced from the Resource and Environment Science Data Platform (http://www.resdc.cn). 2. Driving factor data: 10 key indicators, including natural elements (elevation, slope, annual precipitation, etc.) and socioeconomic elements (population density, GDP, distance to highways, etc.), obtained from public data sources such as the National Geographical Information Resource Directory Service System, World Soil Database, and Geospatial Data Cloud Platform. 3. Auxiliary data: Vector boundaries of administrative divisions, township-level dynamic degree of cultivated land change, grid-cell data on cultivated land increase and decrease, etc. Data Values and Structure: The data are mainly in raster (.tif) and vector (.shp) formats. After preprocessing such as coordinate unification (WGS84), resolution standardization (30 meters), and Euclidean distance analysis, they can be directly used for spatial overlay, PLUS model simulation, and other analyses.Reuse Potential: It is applicable to fields such as land use change simulation, analysis of cultivated land driving mechanisms, and research on coordination between ecological protection and food security, providing reference data for land management in similar mountain-plain transition zones.Legal and Ethical Considerations: All data are obtained from public platforms, comply with map usage specifications such as GS (2016) 1600, have no copyright restrictions, do not involve personal privacy or sensitive information, and can be reused under the CC BY 4.0 license. Methods Section 1. Data Collection: - Land use data and administrative division data were obtained from the Resource and Environment Science Data Platform, with secondary land types merged into 6 major dominant types. - Natural driving factors (e.g., DEM, slope) were sourced from the Geospatial Data Cloud Platform; climate data (temperature, precipitation) were obtained from the National Tibetan Plateau Data Center; socioeconomic factors (population, GDP) and infrastructure data (highways, government residences) were derived from the National Geographical Information Resource Directory Service System. 2. Data Processing: - Geographic coordinate systems and projection parameters were unified in ArcGIS 10.8, and raster data were standardized to 30-meter resolution through resampling. - For vector data such as transportation networks and water bodies, Euclidean distance algorithms were used to generate continuous distance factor raster layers. - Multiple rounds of verification were conducted on the consistency of spatiotemporal resolution and coordinate unity to ensure data comparability and model adaptability (meeting the simulation requirements of the PLUS model).Note: The newly added Nanzhang County 2000–2023 Cultivated Land Spatiotemporal Evolution Dataset 2.0 integrates all the core derivative data that support the reproduction of the conclusions of this study. Specifically, it includes reclassified land use raster layers, socioeconomic and environmental driver raster data resampled to a specific resolution, thematic maps of spatial suitability evaluation for cultivated land, and the output results of land use pattern simulation under different future scenarios. This dataset fully preserves the key data processing trajectories and analysis results throughout the research process, and can provide standardized data support for method replication, conclusion verification and extended analysis in subsequent related studies.