This data set records the main data bulletin of the third national agricultural census in Qinghai Province, and the data is from the Qinghai Provincial Bureau of statistics. The data set contains five word document files, which are: the main data bulletin of the third national agricultural census of Qinghai Province (No.1), the main data bulletin of the third national agricultural census of Qinghai Province (No.2), the main data bulletin of the third national agricultural census of Qinghai Province (No.3), the main data bulletin of the third national agricultural census of Qinghai Province (No.4), and the main data bulletin of the third national agricultural census of Qinghai Province (No.4) Main data bulletin of the third National Agricultural Census (No.5). The survey objects include agricultural operators, households who live in rural areas and have the right to (contract) land or agricultural means of production, agricultural operators, villagers' committees and Township People's governments. The main contents of the census are agricultural production capacity and output, rural infrastructure and basic social services, and farmers' living conditions. Agricultural census adopts the method of comprehensive survey, and the census personnel check and fill in all census objects one by one.
Qinghai Provincial Bureau of Statistics
1) China's investment on BRI countries from 2003 to 2019. 2) The Data comes from UNCTAD database. 3) The data quality is good. However, the data of Syria, Tajikistan, Nepal, Myanmar, Brunei and Maldives are missing. 4) The data could reveal China's investment on BRI countries since 2003.
This data set records the division of Qinghai Province from 2000 to 208 according to urban and rural areas, as well as economic types and quantitative statistics. The data are collected from the statistical yearbook: Qinghai statistical yearbook, and the accuracy is the same as the statistical yearbook extracted from the data. The data set contains three data tables, which are 2005-2006, 2006-2007 and 2007-2008 year-end employment statistics by urban and rural areas. The data table structure is the same. Each data table has five fields, such as the number of employed persons at the end of the year by urban and rural areas in 2005-2006: Field 1: towns in 2005 Field 2: 2005 rural Field 3: towns in 2006 Field 4: villages in 2006 Field 5: 2005 total Field 6: 2006 total
ZHAO Hu
Gross domestic product (GDP) refers to the sum of the value of all the final products and services produced by all the resident units of a country (or region) in a certain period of time. It is an important indicator to measure the overall economic situation of a country. The source data of economic development degree comes from Matti. After cutting the original data and obtaining the data of the main urban area of Yangon deep water port, downscaling processing is carried out to reduce the resolution of the data to 10m level. The data of economic development degree can be used to measure the development degree of Yangon deep water port area, and can be used in urban planning, election, risk assessment, disaster relief, disease prevention and control, poverty alleviation and other fields.
GE Yong, LI Yi
Gross domestic product (GDP) refers to the sum of the value of all the final products and services produced by all the resident units of a country (or region) in a certain period of time. It is an important indicator to measure the overall economic situation of a country. The source data of economic development degree comes from Matti. After cutting the data and obtaining the data of the main urban area of Yangon deep water port, downscaling processing is carried out to reduce the resolution of the data to 10m level. The data of economic development degree can be used to measure the development degree of Yangon deep water port area, and can be used in urban planning, election, risk assessment, disaster relief, disease prevention and control, poverty alleviation and other fields.
GE Yong, LI Yi
The refined spatial distribution data set of GDP in Hambantota port area is obtained by downscaling the GDP data of Sri Lanka in 2015 with 100m spatial resolution based on the land use data and POI data obtained from high-resolution remote sensing images. The land use data are obtained by interactive correction after classification of high-resolution (0.5m) satellite images of Digital Globe and the POI data is obtained through the Internet map. The functional areas are determined based on the POI data, and the weight is determined by the statistics of 100-meter scale GDP of different functional areas. Finally, under the control of regional GDP, the GDP of different functional areas is allocated according to the weight proportion, and the fine scale GDP distribution data with 30 meters spatial resolution is obtained.
Economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the statistics of economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between economic data and covariables (e.g.,night lighting NPP-VIIRS, road network density). Then, spatial regression analysis method is used to model relationship between the economic data and covariables, and economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at county level were downscaled and predicted. Based on statistical data and spatial analysis, the data of economic adult is finally integrated. The economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) can provide important basic data for the development of social and economic research on key areas and regions along the Belt and Road.
GE Yong, LING Feng
Data content: annual GDP statistics (1990-2019), quarterly cumulative GDP statistics (1990-2019) and GDP (2010-2019) Data sources and processing methods: the original macroeconomic data of China (including the third pole) from the official website of the world bank and sina.com from 1990 to 2019 are obtained through data sorting, screening and cleaning. The data are stored in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: Foreign Economic and trade_ Total import and export of goods (1952-2019) and foreign economic and trade_ Total import and export by trade (1981-2019) Data sources and processing methods: the original data of China's foreign trade and investment from 2015 to 2019 (including the third pole) were obtained from the official website of the world bank and sina.com, and the foreign trade and investment data set of China (including the third pole) from 1952 to 2019 was obtained through data sorting, screening and cleaning. The data start time is from 1952 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: national economy_ Industrial value added (monthly) (2010-2019) Data source and processing method: the original industrial economic data of China (including the third pole) from the official website of the world bank and sina.com from 2010 to 2019 are obtained through data sorting, screening and cleaning. The data start time is from 2010 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Da Wei
Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. We have collected the published GDP data. Collect the public GDP data. On the basic of 1-kilometer scale global GDP grid data in 2010 released by the United Nations, the total GDP of the node area was obtained. The lighting data and land use data of node areas are took as auxiliary data, after data preprocessing, data interpolation and multiple regression analysis, establish the relationship between GDP and the hundred meter scale multiple data, and then, the GDP data of 34 key node areas are obtained.
GE Yong, LI Qiangzi, DONG Wen
The degree of opening to the outside world refers to the degree of opening to the outside world of a country or region's economy, which is embodied in the degree of opening to the outside world of the market, usually including the amount of import and export, the use of foreign capital, the level of tariff, the convenience of customs clearance, free trade agreements, market access, capital exchange, intellectual property protection, etc. The data are one belt, one road, 64 countries, including the net inflow of foreign direct investment (US $100 million), total import (US $100 million) and total export volume (US $100 million). Data sources include the world bank, the United Nations Conference on Trade and development, and the WTO. The 64 countries along the line include 16 in West Asia and North Africa, 16 in central and Eastern Europe, 5 other CIS countries, 8 in South Asia, 11 in Southeast Asia, including Myanmar, Vietnam and Thailand, and 5 in Mongolia, Russia and Central Asia.
SONG Tao
The data include the coastal ports and airport distribution in the Belt and Road region. The data are from the Natural Earth global port and airport data. The data are cut according to the standard map of the 65 countries along the Belt and road, and further corrected, then the distribution of the ports and airports in the area along the B&R is obtained. This data is mainly one to analyze the B&R area's important spatial layout and main characteristics of the transportation facilities, and to get other attributes data of port and airport in the following research, including the throughput of different port cargo types, the incoming and outgoing throughput, the number of docks and berths, the number of passengers on the airport, the data of the flights and routes of ports and airports, we can get further understanding of the spatial differentiation of the distribution of ports and airports in the B&R region.
WANG Chengjin
"One belt, one road" along the lines of risk rating, credit risk rating and Moodie's national sovereignty rating reflects the structure of sovereign risk in every country. The rating of Moodie's national sovereignty is from the highest Aaa to the lowest C level, and there are twenty-one levels. Data source: organized by the author. Data quality is good. The rating level is divided into two parts, including investment level and speculation level. AAA level is the highest, which is the sovereign rating of excellent level. It means the highest credit quality and the lowest credit risk. The interest payment has sufficient guarantee and the principal is safe. The factors that guarantee the repayment of principal and interest are predictable even if they change. The distribution position is stable. C is the lowest rating, indicating that it cannot be used for real investment.
SONG Tao
It is summarized that the agricultural and socio-economic status of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan) in 2016. This data comes from the statistical yearbook of five Central Asian countries, including six elements: total population, cultivated land area, grain production area, GDP, proportion of agricultural GDP to total GDP, proportion of industrial GDP to total GDP, and forest area. Detailed statistics of the six socio-economic elements of the five Central Asian countries. It can be seen from the statistics that there are different emphases among the six elements of the five Central Asian countries. This data provides basic data for the project, facilitates the subsequent analysis of the ecological and social situation in Central Asia, and provides data support for the project data analysis.
LIU Tie
The trade data between China and BRI Countries, including China's export data to BRI Countries, China's import data from BRI Countries and the total trade volume between China and BRI Countries. BRI Countries refer to the 64 countries along the traditional silk road, including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, Turkmenistan, Mongolia, Russia, Vietnam, Laos, Kampuchea, Thailand, Malaysia, Singapore, Indonesia, Brunei, Indonesia, Indonesia, convergence, and Bangladesh, Afghanistan, Nepal, Bhutan, Sri Lanka, Maldives, Poland, Czech Republic, Slovakia, Hungary, Slovenia, Croatia, Romania, Bulgaria, Serbia, Montenegro, Macedonia, Bosnia and Herzegovina, Albania, Estonia, Lithuania, Latvia, Ukraine, Belarus, Moldova, Turkey Iran, Syria, Iraq, UAE, Saudi Arabia, Qatar, Bahrain, Kuwait, Lebanon, Oman, Yemen, Jordan, Israel, Palestine, Armenia, Georgia, Azerbaijan, Egypt.
Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. According to the collected the published global GDP data of 2015, a downscaling model, named support vector machine regression kriging was established for predicting 100-m GDP in thirty-four key nodes along the Belt and Road. The remote sensed night light data, land cover, vegetation and terrain indices were employed as ancillary variables in downscaling process. To solve the problem of missing data existing in the ancillary datasets, we will apply kriging and function interpolation methods to fill gaps. The aggregation and resampling were used to obtain 1-km and 500-m all ancillary variables, as well as 100-m terrain indices including elevation, slope and aspect. The adopted downscaling model contains trend and residual predictions. The support vector machine regression is used to model the relationship among GDP and its ancillary variables for obtaining GDP trends at fine scale based on scale invariant of the relationship. And then, the kriging interpolation is used to estimate GDP residuals at fine scale. In the downscaling process, the mentioned downscaling model was firstly employed in 1-km and 500-m data for obtaining 500-m GDP predictions; and it was again used in 500-m and 100-m data for achieving 100-m GDP predictions. The 100-m GDP predictions in constant 2011 international US dollars would provide high spatial resolution data for risk assessments.
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
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
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