China's GDP spatial distribution kilometer grid data set is based on the country's county-level GDP statistics, taking into account the spatial interaction patterns of land use types, nighttime light brightness, residential density data and GDP that are closely related to human activities, and is generated through spatial interpolation. spatial grid data. The data includes 6 issues including 1995, 2000, 2005, 2010, 2015 and 2019. This data set reflects the detailed spatial distribution of GDP data across the country. The data is 1Km raster data. Each raster represents the total GDP output value within the grid range (1 square kilometer). The unit is 10,000 yuan/square meter. km. Preface – Artificial Intelligence Tutorial
GDP is one of the important indicators for measuring social and economic development, regional planning and resource and environmental protection. Administrative regions are usually used as the basic statistical unit. GDP spatialization replaces traditional administrative statistical units with spatial statistical units, which facilitates data sharing and spatial statistical analysis among multiple fields. China's GDP spatial distribution kilometer grid data set is based on national county GDP statistics, taking into account multiple factors such as land use type, nighttime light brightness, residential density, etc., and using the multi-factor weight allocation method to use administrative districts as the basic statistical unit. GDP data is spread out on grid cells, thereby realizing the spatialization of GDP. The data set includes data from 6 time periods from 1995 to 2019. Each raster represents the total GDP output value within 1 square kilometer, with the unit of 10,000 yuan/square kilometer. The data format is grid, the base ellipsoid is the Krassovsky ellipsoid, and the projection method is the Albers projection, which reflects the detailed spatial distribution of GDP across the country.
In the spatialization process, the GDP distribution weights of land use type, nighttime light brightness, and residential density are first calculated, and then the total weight of each county-level administrative unit is calculated based on the standardized processing of the weights of the above three aspects, and then the total weight of each county is calculated. On the basis of the GDP proportion of the unit weight of the level administrative unit, grid space calculation is used to combine the population number on the unit weight with the total weight distribution map to spatialize the population. The calculation formula is:
GDPij = GDP × (Qij/Q)
In the formula, GDPij is the value of the grid unit after spatialization; GDP is the GDP statistical value of the county-level administrative unit where the grid unit is located; Qij is the land use type, nighttime light brightness, and residential density of the grid unit. The total weight; Q is the total weight of the land use type, nighttime light brightness, and residential density of the county-level administrative unit where the raster unit is located.
Dataset ID:
RESDC/CHINA_GDP
Time range: 1995-2019
Scope: Nationwide
Source: Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences
Copy code snippet:
var images = pie.ImageCollection("RESDC/CHINA_GDP")
Band:
| name | type | unit | Spatial resolution (meters) | Invalid value | Description |
|---|---|---|---|---|---|
| B1 | Int32 | Ten thousand yuan/square kilometer | 1000 | -1 | GDP total output value |
Attributes
| date |
string |
image time |
Code:
var img = pie.ImageCollection("RESDC/CHINA_GDP")
.select("B1")
;
print(img)
//设置显示参数
visParams = {min:0, max:600,
palette: ['000000', '023858', '006837', '1a9850', '66bd63', 'a6d96a',
'd9ef8b', 'ffffbf', 'fee08b', 'fdae61', 'f46d43', 'd73027']};
//加载显示影像
Map.addLayer(img.mean(), visParams, "img"); 
Data citation:
Xu Xinliang. China’s GDP spatial distribution kilometer grid data set. Resource and Environmental Science Data Registration and Publishing System (http://www.resdc.cn/DOI), 2017.DOI:10.12078/2017121102.