This data is the distribution data of the prehistoric era sites on the Qinghai-Tibet Plateau and surrounding areas, which is derived from the Supplementary Maps of the paper: Chen, F.H., Dong, G.H., Zhang, D.J., Liu, X.Y., Jia, X., An, C.B., Ma, M.M., Xie, Y.W., Barton, L., Ren, X.Y., Zhao, Z.J., & Wu, X.H. (2015). Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. SCIENCE, 347, 248-250. The Qinghai-Tibet Plateau, with an average altitude of more than 4000m, is the highestand largest plateau all around the world, and also is one of the most unsuitable areas for human life with long-term on the earth. The remains at the archaeological site are direct evidences left behind the ancient human activities. The original data of this data is digitized from the results of the Qinghai-Tibet Plateau high-textual census and archaeological survey (Qinghai Volume and Tibet Volume of the Chinese Cultural Relics Atlas). The map was digitized mainly based on the distribution maps of the sites, and the latitude and longitude coordinates and altitude were obtained. a total of 6,950 sites, most of which are distributed in the northern part of the plateau. The age range of the site is between 7000BP and 2300BP. This data set is of reference value for the research on the process and power of human diffusion to the Tibetan Plateau in the prehistoric era and other studies related to human activities in the Tibetan Plateau and the prehistoric era.
DONG Guanghui , LIU Fengwen
The main body of the Tibetan Plateau is Qinghai Province and the Tibetan Autonomous Region. The economic and social data of Qinghai Province and the Tibetan Autonomous Region are the basis for the analysis and assessment of the basic data of sustainable development of populations, resources, environment and economic society on the Tibetan Plateau by integrating the basic data of natural sciences. Under normal circumstances, the statistical yearbooks of all provinces and regions are all in paper and CD-ROM versions, and users need to perform secondary editing before they can use them. This data set mainly relies on the raw data of the Statistical Yearbook of Qinghai Province and the Tibetan Autonomous Region to carry out data conversion and integrate the current economic and social data sets. The temporal coverage of the data is from 2007 to 2016, and the temporal resolution is one year. The spatial coverage is Qinghai Province and the Tibetan Autonomous Region of the Tibetan Plateau. The spatial resolution is the administrative unit of the prefecture or city. The data include information on population, economy, finance, agriculture, forestry, animal husbandry and fishery, investment in fixed assets, education and health.
WANG Shijin
The data set contains data on the birth rate, mortality rate and natural growth rate in Tibet. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. The table contains 4 fields. Field 1: Year of the data Field 2: Birth rate, unit: ‰ Field 3: Mortality rate, unit:‰ Field 4: Natural growth rate, unit: ‰
National Bureau of Statistics
The data set contains three tables: demographic data for Tibet, demographic data for each county in Tibet, and data on rural workers. These time series data include the year-end total population, the number of men, the number of women, urban population, rural population, and statistics on workers in various rural industries in Tibet from 1967 to 2016. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. Table 1: The table of demographic data for Tibet contains 10 fields. Field 1: Year Field 2: Year-end total population, unit: 10,000 Field 3: Total number of men, unit: 10,000 Field 4: Male proportion, unit: % Field 5: Total number of women, unit: 10,000 Field 6: Female proportion, unit: % Field 7: Urban population, unit: 10,000 Field 8: Urban population proportion, unit: % Field 9: Rural population, unit 10,000 Field 10: Rural population proportion, unit: %. Table 2: The table of demographic data for each county contains 7 fields. Field 1: Districts and counties Field 2: Year Field 3: Year-end total number of households Field 4: Number of rural households Field 5: Year-end total population, unit: 10,000 Field 6: Rural population, unit: 10,000 Field 7: Year-end number of workers, unit: 10,000 Table 3: The table of rural workers contains 7 fields Field 1: Year Field 2: Districts and counties Field 3: Number of rural workers Field 4: Number of workers in the agricultural, forestry, animal husbandry and fishery sectors Field 5: Number of workers in the industrial sector Field 6: Number of workers in the construction sector Field 7: Number of other non-agricultural workers
Tibet Autonomous Region Provincial Bureau of Statistics
The data set records the proportion of male and female data of 1960-2017 countries along 65 countries along the belt and road. Data sources: (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. The data set contains 4 tables：(1)Population, male;(2)Population, male (% of total);(3)Population, female;(4)Population, female (% of total).
XU Xinliang
"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."
DONG Wen
One belt, one road, 64 countries in 2017 accounted for the total population of the country. Data source: organized by the author. Data quality is good. The data can have one broad prospect in one belt, one road, and the other is comprehensive research on economy, society, population and governance structure. "One belt, one road" covers Asia Pacific, Eurasia, Middle East, Africa, etc., including 65 countries, with a total population of over 4 billion 400 million, accounting for 63% of the world's population. One belt, one road, one belt, one road, one belt, one road, one country, one country, and one country.
SONG Tao
The population data of Zhangye City from 2001 to 2012 include: annual population density and natural population growth rate, Data source: Statistical Bureau of Zhangye City. Statistical yearbook of Zhangye City. 2001-2012, Department of water resources of Gansu Province. Bulletin of water resources of Gansu Province. 2001-2012. Water Affairs Bureau of Zhangye City. Comprehensive annual report of water resources of Zhangye City, 1999-2011
ZHANG Dawei
Gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong, LING Feng
Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2010, which indicates that the population count (Unit: person) per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2010. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong, LI Qiangzi, DONG Wen
1) data content: social and economic data of major countries and regions in the pan third polar region, including four categories: urbanization index, economic and industrial index, population index and social index, including urbanization rate, total population, population in the largest city, population, GDP, life expectancy and other indicators in the urban agglomeration with population over 1 million; 2) data source and processing method: data source World Bank, 65 countries and regions of Pan third pole are extracted, others are not processed; 3) data quality description: some data are missing from 1960-1992; 4) data application results and prospects: it can be used for urbanization and other socio-economic analysis.
LI Guangdong
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize monthly NDVI products and calculate FVC by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
QI Xuebin
Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong, LING Feng
One belt, one road, in 2017, the proportion of religious population in 64 countries is the total population. Data source: organized by the author. Data quality is good. The data can have one broad prospect in one belt, one road, and the other is comprehensive research on economy, society, population and governance structure. "One belt, one road" covers Asia Pacific, Eurasia, Middle East, Africa, etc., including 65 countries, with a total population of over 4 billion 400 million, accounting for 63% of the world's population. One belt, one road, one belt, one road, one belt, one road, one area, and the other two. The first one is to make contributions to the systematic research and comprehensive application of the whole area.
SONG Tao
Gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from socioeconomic data and applications center of Columbia University, USA. The prejection transform and extraction processes were done to generate the gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data by the area weighted method for each administrative unit. Accurate population data at grid level are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong, LING Feng
This dataset, based on night light data and macro statistical data, uses remote sensing inversion method（1km*1km）to obtain the poverty rate in different regions within each country. It has three advantages. a) The calculation unit can be adjusted according to the boundaries of administrative regions to reflect the poverty rate of sub-regions within the large country and scale, which is rare in statistically data. b) The survey and summary cycle limits the updating of national and sub-regional poverty rate, while the method based on night light data is more convenient. c) Due to the continuous annual data of night light, the difficulty of obtaining regional poverty rate in a long period was overcome. In view of the three outstanding advantages mentioned above, this data set can support to achieve the research subjects and provide scientific data for understanding the basic situation of poverty along the Silk Roads.
ZHANG Qian, Linxiu ZHANG
This set of data mainly includes the demographic data of 12 counties in 6 prefecture-level cities of Qinghai, Gansu and Inner Mongolia in Heihe River Basin, covering the time period of 2000-2009. The data source is the local statistical yearbook, which mainly includes: Statistical Bureau of Suzhou District. Statistical Yearbook of Suzhou. 2004-2009; Yumen Statistical Bureau. Yumen Statistical Yearbook. 2000-2008; Jinta County Statistical Bureau. Jinta County Statistical Yearbook. 2004-2009; Gaotai Statistical Bureau. Gaotai Statistical Yearbook. 2000-2007; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Sunan Yugur Statistical Bureau. Statistical Yearbook of Sunan Yugur Autonomous County. 2004-2009; Minle County Statistical Bureau. Minle County Statistical Yearbook. 2004-2009; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Linze County Statistical Bureau. Linze County Statistical Yearbook. 2000-2009; Ejin Banner Statistical Bureau. Ejin Banner Statistical Yearbook. 1990-2005; Qilian County Statistical Bureau. Qilian County National Economic Statistics. 2004-2009; Part of the data of Zhangye City comes from the basic social and economic situation of townships of Zhangye City in 2005. Data of Jiayuguan City is derived from the CNKI statistical data database of China National Knowledge Infrastructure, and only contains some county-level data. Data Content Description: The data mainly includes three population indicators of 12 counties in the basin, including Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugur Autonomous County, Jinta County, Sunzhou District and Yumen City, Jiayuguan City, Qilian County, and Ejin Banner. The population indicators are permanent population, agricultural population and non-agricultural population at the end of the year. It is divided into two levels: county level and township level. The statistics currently available are: County level: Ejina Banner: 2006-2009: resident population, agricultural population, non-agricultural population at the end of each year Ganzhou District: 2009: agricultural population, non-agricultural population of the year; Gaotai County: 2009: agricultural population, non-agricultural population of the year; Sunan: 2000-2009: permanent population, agricultural population, non-agricultural population at the end of each year; Minle County: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Linze: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of each year; Township level: Ejin Banner: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of the year; Ganzhou District: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Gaotai County: 2000-2004, 2006, 2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Shandan County: 2000-2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Minle County: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Jinta County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2006-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Suzhou District 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Qilian County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Permanent population at the end of the year, agricultural population, non-agricultural population County level township level county level township level county level township level Ejin Banner:2006-2009 2000-2005 2006-2009 2000-2005 2006-2009 2000-2005 Ganzhou District 2000-2009 2009 2000-2008 2009 2000-2008 Gaotai County 2000-2004、 2006、2007、2009 2009 2000-2004、 2006、2007 2009 2000-2004、 2006、2007 Shandan County 2000-2007、2009 2000-2007 2000-2007 Sunan County 2000-2009 2000-2009 2000-2009 Minle County 2009 2000-2008 2009 2000-2008 2009 2000-2008 Linze County 2009 2009 2009 Jinta County 2004-2009 2004-2009 2004-2009 Sunzhou District 2004-2009 2004-2009 2004-2009 Qilian County 2004-2009 2004-2009 2004-2009 Yumen City 2000-2005 2006-2008 2000-2005 2006-2008 2000-2005 2006-2008
ZHAO Jun
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
QI Xuebin
The contents include five Central Asian countries, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The basic socio-economic indicators from 2012 to 2017 are divided into 12 categories: GDP, price, industry, agriculture, animal husbandry, construction, capital investment, transportation, foreign trade, labor market, wages, living standards and the exchange rate of the US dollar. Developments and changes. The data comes from ww. cisstat. com. The original index name is Russian, which is translated and edited. The accurate official data can provide basic data basis for the study of social and economic development in Central Asian countries.
BAO Shaoyong
The data set records the urbanization rate data of each state of Uzbekistan from 2000 to 2017.The data is from Uzbekistan's national statistics bureau. Urbanization is a concept with broad implications.In a narrow sense, it generally refers to the urbanization of population, which refers to the increase of the number of cities and the expansion of the urban scale, and the process of population aggregation to cities in a certain period.Urbanization rate refers to the proportion of permanent urban residents in a region in the total permanent resident population.The name of the original index is Russian, which has been translated and edited.The accuracy of the official data can provide basic data basis for the study of the socio-economic development of central Asian countries.
Kazakhstan National Bureau Of Statistics, HUANG Jinchuan, MA Haitao
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