Input output table of 11 districts and counties in Heihe River Basin in 2012
1. Data overview Based on the collected statistical yearbooks and survey data of counties and districts in Zhangye City in the middle reaches of Heihe River, the social and economic database in the middle reaches is constructed to reflect the basic situation of regional social economy. 2. Data content The database includes two data sets: (1) statistical yearbook data; (2) survey data of human factors in river basin. The statistical yearbook data mainly includes a number of relevant statistical data such as the gross product, financial revenue, construction of villages and towns, industrial output value, grain output, etc. of Zhangye City and its towns. The survey data of human factors in Heihe River Basin mainly include the survey data of social capital, cultural theory, happiness index and sustainable consumption in Heihe River Basin. 3. Time and space The statistical yearbook data is the statistical data of Ganzhou District, Linze County, Gaotai County, Sunan County, Shandan County, Minle county and towns under the jurisdiction of each county from 1990 to 2010. The survey data of human factors in the basin is the corresponding survey data of counties in the upper, middle and lower reaches in 2005.
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.
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.
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.
The data content of the Belt and Road Food Consumption includes: The data of Food Consumption for “B&R” includes: 1. Total food consumption in regions and countries along the “B&R”, mainly including Cereals-Excluding Beer, Meat, Eggs, Milk-Excluding Butter, Fruits - Excluding Wine, Vegetables, Pulses, Starchy Roots, Oilcrops, Sugar& Sweeteners; 2.Nutrient intake in regions and countries along the “B&R”, mainly including energy, protein, fat. Source：FAO. Data application：According to the data provided，the basic characteristics analysis of food consumption, the analysis of the nutrient intake level and the analysis of food consumption pressure can be carried out in the Belt and Road region and the countries along the route, so that the dietary pattern evaluation analysis and the basic characteristics food requirement can be carried out.
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, then obtained the 1-km gridded datasets for GDP of 2015 in five key nodes over Bengal and Myanmar, including Dacca, Chittagong, Kyaukpyu, Rangoon and Mandalay. To solve the problem of missing data existing in the current datasets, we will apply kriging and function interpolation methods to fill gaps. We will also develop the multi-source data fusion method based on geostatistics to achieve the GDP predictions of time continuously and high spatial resolution.
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.
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.
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.