全球工业增加值公里网格数据集

工业增加值空间化数据是评估气候变化对工业经济系统影响的一项重要指标，特别是对气候变化背景下工业经济的暴露评估具有重要意义。作者利用2010年全球植被指数数据（EVI，来自MODIS月平均数据）和2010年夜间灯光遥感数据（DMSP/OLS），通过构建夜间灯光调整指数（EANTLI）处理夜间灯光数据过饱和问题，得到全球最佳灯光数据；然后利用最佳灯光数据和各国工业增加值数据（来自全球各个国家统计局）构建回归模型，进行工业增加值空间化反演，生成了全球工业增加值公里网格数据集。在全球范围内，随机选择了178个省级（州）区域的工业增加值和空间化后的工业增加值进行相关性检验。结果表明，数据集的工业增加值与统计的工业增加值相关系数为0.93；以统计数据作为真值，178个区域的工业增加值平均精度为80.14%。数据集包括全球工业增加值空间分布（公里网格）数据和验证精度的表格数据。数据集存储为.tif、xlsx格式，原始数据文件个数为6个，数据量1.79 GB，压缩为1个文件，90.8 MB。

Spatially explicit industrial added value data is a critical indicator for assessing the impacts of climate change on industrial economic systems, and particularly vital for exposure assessment of industrial economies under climate change. The authors utilized 2010 global vegetation index data (EVI, derived from MODIS monthly average products) and 2010 nighttime light remote sensing data (DMSP/OLS), constructed the Enhanced Adjusted Nighttime Light Index (EANTLI) to address the oversaturation issue of nighttime light data, and obtained the optimal global nighttime light dataset. Subsequently, a regression model was developed using the optimal nighttime light dataset and national industrial added value data (sourced from national statistical bureaus across the globe) to conduct spatial inversion of industrial added value, thereby generating a global kilometer-grid industrial added value dataset. Globally, 178 provincial (state) regions were randomly selected for correlation testing between their statistical industrial added value and the spatially inverted industrial added value. The results demonstrate that the correlation coefficient between the industrial added value derived from this dataset and the statistical industrial added value is 0.93; taking the statistical data as the ground truth, the average accuracy of industrial added value across the 178 regions is 80.14%. The dataset includes two components: global spatially distributed industrial added value data (kilometer-grid format) and tabular data for validation accuracy assessment. The dataset is stored in .tif and .xlsx formats. There are 6 original raw data files with a total size of 1.79 GB, which are compressed into a single file of 90.8 MB.

搜集汇总

背景与挑战

背景概述

该数据集是2010年全球1公里分辨率工业增加值空间化网格数据，通过整合夜间灯光、植被指数和统计资料构建回归模型生成，验证显示与统计数据的相关系数达0.93，平均精度80.14%。