2026-10-08T17:25:24Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34021134 2026-10-02T10:23:09Z category_25930 item_type_3 month_year_10_2026

UGS-SDC datasets Xiang Que (25135104) Regolith and landscape evolution URBAN GREEN-SPACE fuzhou city <p dir="ltr"><b>Introduction: </b>This UGS-SDC dataset contains the geospatial inputs and intermediate layers used to evaluate and optimize the equality of urban green-space (UGS) provision in the central urban area of Fuzhou, China, under the supply-demand-connectivity (SDC) framework.</p><p dir="ltr"><b>Data record:</b><b> </b>UGS-SDC includes vector, raster and tabular data organized into supply, demand, transport, analysis-grid and output components:</p><p dir="ltr">1. Supply(Green-space)_layers:</p><p dir="ltr">(1)fuzhou_urban_green_2020 and fuzhou_urban_green_2024: OSM-derived polygon datasets representing parks, grasslands and natural green areas for 2020 and 2024, respectively.</p><p dir="ltr">(2)supply_green_space_2020 and supply_green_space_2024: Green-space area and mean NDVI were calculated for 2020 and 2024, respectively, and combined to estimate green-space supply capacity for accessibility analysis.</p><p dir="ltr">(3)Fuzhou_NDVI_2020.tif and Fuzhou_NDVI_2024.tif: NDVI rasters for 2020 and 2024, respectively, derived from 10m Sentinel-2 surface reflectance imagery acquired from April to November, with scenes restricted to cloud cover below 10%. The imagery was composited, and NDVI was calculated from the red and near-infrared bands to characterize vegetation quality within green-space polygons.</p><p dir="ltr">2. Demand layer: gridded total resident population at 100 m resolution, based on the Seventh National Population Census of China (2020) obtained from the ASPECT dataset (<a href="https://doi.org/10.6084/m9.figshare.27323106.v1)" target="_blank">https://doi.org/10.6084/m9.figshare.27323106.v1)</a> (Ju et al., 2025).</p><p dir="ltr">3. Transport network: the multimodal road network of Fujian Province, based on an OpenStreetMap (OSM) snapshot from February 27, 2026, downloaded from Geofabrik, classified into walking, driving, and bus with mode-specific speeds (walking 5 km/h; driving 20–50 km/h;bus 15–20 km/h).</p><p dir="ltr">4. Analysis grid: the study area aggregated into a uniform 200 m hexagonal grid; each cell carries population demand and all accessibility/equality measures.</p><p dir="ltr">5. Outputs_accessibility_equality: per-capita accessible supply capacity (PCASC) and comparative indicators (CV, Gini coefficient, Spearman's ρ) computed under five models (Buffer, Gravity, 2SFCA, Ga2SFCA, SDC) across walking, driving, bus, and total modes, using a unified 15-minute travel-time threshold.</p><p dir="ltr">6. Outputs_siting_optimization: candidate-site solutions from the maximal covering location problem (MCLP) embedded with dynamic equality metrics, together with p-median, p-center, and a manually selected alternative, and their resulting changes in low-service population.</p><p dir="ltr">7 Outputs_sensitivity_analysis: sensitivity analyses of accessibility results under different travel-time thresholds and PCASC percentiles for SDC-MCLP siting optimization.</p><p dir="ltr">All geographic layers share the coordinate reference system [CRS, EPSG:4326]; vector layers are provided in GeoJSON and ESRI Shapefile (with accompanying CSV attribute tables), rasters in GeoTIFF, and tabular results in CSV and XLSX. The dataset is intended to be used with the open-source interactive planning-support tool UGS-SDC [https://github.com/yuannell/UGS-SDC]. Please cite the article above when using these data. </p> 2026-10-02T10:23:09Z Dataset Dataset 10.6084/m9.figshare.34021134.v1 https://figshare.com/articles/dataset/UGS-SDC_datasets/34021134 CC BY 4.0