Integrated county-level dataset of cultural heritage for spatial coupling analysis in Southwest China

README for: Integrated County-level Dataset for Spatial Coupling Analysis of Cultural Heritage in Southwest China================================================================================1. OverviewThis dataset contains integrated county-level data that supports all the key analyses in the associated manuscript, including the Grey Relational Analysis, Bivariate Local Spatial Autocorrelation (LISA), and Geodetector model. It covers the entire Southwest China region, encompassing the provinces of Sichuan, Yunnan, Guizhou, the municipality of Chongqing, and the Xizang Autonomous Region.<br>2. File Description- File Name: `Southwest_China_Heritage_Coupling_Data.csv`- Format: Comma-Separated Values- Rows: 511 (Each row represents one county-level administrative unit).- Columns: 16<br>3. Variable Description| Column Name | Description (English) | Description (中文) | Unit/Values | Data Source &amp; Notes || `ID` | Sequential identifier | 序号 | Integer | Generated for this dataset. || `County_Name` | Name of the county-level unit | 县名称 | Text (in Chinese) | Official administrative divisions. || `Prefecture` | Name of the prefecture-level city or autonomous prefecture | 地州 | Text (in Chinese) | Official administrative divisions. || `Province` | Name of the province, municipality, or autonomous region | 省份 | Text (in Chinese). Values: `四川`, `云南`, `贵州`, `重庆`, `西藏自治区` | Official administrative divisions. || `ICH_Count` | Number of national-level Intangible Cultural Heritage items | 国家级非物质文化遗产数量 | Count (Integer) | Source: Ministry of Culture and Tourism (as of 2023). || `CHCUs_Count`| Number of national-level Cultural Heritage Conservation Units | 国家级文物保护单位数量 | Count (Integer) | Source: National Cultural Heritage Administration (as of 2022). || `LISA_I` | Dependent Variable: Local bivariate Moran's I index value | 双变量局部莫兰指数 | Continuous (Float) | **Calculated in this study.** Measures the spatial coupling intensity between ICH and CHCUs distribution densities. This is the core dependent variable for the Geodetector analysis. || `Elevation` | Average elevation | 平均海拔 | Meters (m) | Source: Derived from 30m DEM (Geospatial Data Cloud, CAS). || `Slope` | Average slope | 平均坡度 | Degrees (°) | Source: Derived from 30m DEM. || `Temperature` | Annual mean temperature | 年平均气温 | Degrees Celsius (°C) | Source: National Tibetan Plateau / Third Pole Environment Data Center (2020). || `NDVI` | Normalized Difference Vegetation Index (mean) | 归一化植被指数 | Index (Range: -1 to 1) | Source: Derived from Landsat 8 OLI images (2020 growing season). || `GDP` | Gross Domestic Product | 地区生产总值 | Yuan | Source: Provincial Statistical Yearbooks (2024 edition, reporting 2023 data). || `Population_Density` | Population density | 人口密度 | Persons per square kilometer (persons/km²) | Source: Provincial Statistical Yearbooks. || `Urbanization_Rate` | Urbanization rate | 城镇化率 | Percentage (%) | Source: Provincial Statistical Yearbooks. || `Road_Density` | Highway density | 公路密度 | Kilometers per square kilometer (km/km²) | Source: Calculated from total highway length (Statistical Yearbooks) and county area. || `Ethnic_Minority_Ratio` | Minority population ratio | 少数民族人口占比 | Percentage (%) / or the ordinal scale you used | Source: Provincial Statistical Yearbooks. [0=None, 1=With ethnic villages, 2=In autonomous prefecture, 3=Autonomous county|| `Distance_to_Routes` | Euclidean distance to historical trade routes | 到历史贸易路线的欧氏距离 | Kilometers (km) | Source: Digitized from historical atlases and academic publications. || `Number_of_Inheritors` | Number of nationally-recognized ICH representative inheritors | 国家级非遗代表性传承人数量 | Count (Integer) | Source: Ministry of Culture and Tourism (as of 2023). |<br>4. Usage Notes- Core Purpose: This dataset is the direct input for the Geodetector model reported in the manuscript. The variable `LISA_I` is the dependent variable, and all other variables (from `Elevation` to `Number_of_Inheritors`) are the independent factors.- Geodetector Analysis: For replicating the Geodetector results, all continuous independent variables should be discretized using the Natural Breaks (Jenks) method into 5 categories within the Geodetector software itself, as described in the manuscript.- Missing Data: Any missing values are represented as blank cells in the CSV file.<br>5. LicensingThis dataset is made available under the CC BY 4.0 license. Please cite both the dataset (using its DOI) and the associated manuscript if you use this data.<br>6. AcknowledgmentsThe compilation of this dataset was supported by the National Natural Science Foundation of China (Grant No. 52168011). We acknowledge the data providers listed in the "Data Source" column.