China Urban Statistical Yearbook, China County Statistical Yearbook, China Fiscal Statistical Yearbook, China Tax Statistical Yearbook, China Science and Technology Statistical Yearbook, China Health Statistical Yearbook

         Data download link: Baidu cloud download link  

        Statistical yearbook refers to a large-scale reference book that is mainly based on statistical charts and analysis descriptions, and obtains statistical data through highly intensive statistical data to comprehensively, systematically and continuously record the annual economic and social development.

        Statistical yearbook is a necessary prerequisite for conducting various economic and social studies. With the help of statistical yearbooks, it is a common way for researchers. At present, the commonly used statistical yearbooks in China are the " China Statistical Yearbook " and the provincial (city) statistical yearbooks.

        At present, the geographic remote sensing ecological network www.gisrs.cn has compiled the statistical yearbook data of various provinces and cities in historical years.

1. China Statistical Yearbook (provinces 2000-2021)
2. China Urban Statistical Yearbook (1996-2020)
3. China County Statistical Yearbook (2000-2020)
4. China Environmental Statistical Yearbook (1999-2020)
5. China Fiscal Yearbook , China Fiscal Statistical Yearbook (1999-2019)
6. China Tax Yearbook, China Tax Statistical Yearbook (2003-2020)
7. China Science and Technology Statistical Yearbook (1991-2021)
8. China Health Statistical Yearbook, China Health and Health Statistical Yearbook (2006 -2021)
9. China Financial Statistical Yearbook (2000-2019)
10. China Insurance Yearbook, China Insurance Statistical Yearbook (2001-2020)
11. China Population and Employment Statistical Yearbook (1998-2020)
12. Bulletin on National Economic and Social Development (2006-2018)
13. China Labor Statistics Yearbook (1999-2019)
14. China Household Finance Survey Database CHFS (2011-2017)
15. China High-tech Industry Statistical Yearbook (1995-2021, if the official website did not announce 2018, everyone can choose The average of 2017 and 2019)
16. China Torch Statistical Yearbook (2008-2021)
17. National Agricultural Product Cost Compilation (2000-2020)
18. If the official website of China Industrial Statistical Yearbook (2000-2020) did not announce 2018 and 2019, everyone can take it before Average value of several years
19. China Urban Construction Statistical Yearbook (1999-2019)
20. China Energy Statistical Yearbook (2007-2020)
21. China Health and Pension Tracking Survey (2018)

22. China Rural Statistical Yearbook (1985-2020)

23. China Industrial Enterprise Database + Patent Matching Data (1998-2014, panel data)

 

 Source of data acquisition:

1. Geographic Remote Sensing Ecological Network www.gisrs.cn

At the same time, the geographic remote sensing ecological network www.gisrs.cn shares a lot of scientific data in the field of geographic remote sensing (land use data, npp net primary productivity data, NDVI data, meteorological data (precipitation, temperature, evapotranspiration, radiation, humidity) , sunshine hours, wind speed, water vapor pressure data), runoff data, night light data, statistical yearbook, road network, POI point of interest data, GDP distribution, population density distribution, three-level watershed vector boundary, geological disaster distribution data, soil Type, soil texture, soil organic matter, soil PH value, soil texture, soil erosion, vegetation type, distribution of nature reserves, building outline distribution, etc. geographic data, as well as operation tutorials on gis and remote sensing).

2. Geospatial Data Cloud
(1) Global Land Cover Plan 2000 (GLC2000)

(2) ESA Global Land Cover Data (ESA GlobCover)

3. Geographical Science Ecology Network
Website address www.csdn.store

4. The University of Maryland data set
UMd based on the 5 bands of AVHRR data and NDVI data has been combined to propose a data matrix again, and the global land cover classification has been carried out with the method of classification tree. Its purpose is to hope to build a dataset with higher accuracy than past data

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Origin blog.csdn.net/m0_66892427/article/details/124371400