Landuse/landcover dataset in the middle reaches of the Heihe River Basin (2011)

The land use / land cover data set of Heihe River Basin in 2011 is the Remote Sensing Research Office of Institute of cold and drought of Chinese Academy of Sciences. Based on the remote sensing data of landsatm and ETM in 2011, combined with field investigation and verification, a 1:100000 land use / land cover image and vector database of Heihe River Basin is established. The data set mainly includes 1:100000 land use graph data and attribute data in the middle reaches of Heihe River Basin. The land cover data of 1:100000 (2011) in Heihe River Basin and the previous land cover are classified into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural residents, industrial and mining land and unused land) and 25 second-class categories by the same hierarchical land cover classification system. The data type is vector polygon and stored in shape format. Land cover classification attributes: Level 1 type level 2 type attribute code spatial distribution location Cultivated land: plain dry land 123 is mainly distributed in basin, piedmont, river alluvial, proluvial or lacustrine plain (poor irrigation conditions due to water shortage). The upland and land 122 is mainly distributed in the hilly area, and generally, the plot is distributed on the gentle slope of the hill, as well as on the top of the ridge and the base. The dry land 121 is mainly distributed in the mountainous area, the hillside (gentle slope, hillside, steep slope platform, etc.) and the Piedmont belt below 4000 m above sea level. Woodland: there are woodland (Arbor) 21 mainly distributed in high mountains (below 4000 meters above sea level) or middle mountain slopes, valley slopes, mountain tops, plains, etc. Shrub land 22 is mainly distributed in the higher mountain area (below 4500m), most of which are hillside, valley and sandy land. Sparse forest land 23 is mainly distributed in mountainous areas, hills, plains and sandy land, Gobi (Loamy, sandy conglomerate) edge. Other forest lands 24 are mainly distributed around the oasis ridge, riverside, roadside and rural residential areas. Grassland: high cover grassland 31 is generally distributed in mountainous area (gentle slope), hilly area (steep slope), river beach, Gobi, sandy land, etc. The middle cover grassland 32 is mainly distributed in dry areas (low-lying land next door and land between Sandy Hills, etc.). Low cover grassland 33 mainly grows in dry areas (loess hills and sand edge). Water area: channel 41 is mainly distributed in plain, inter Sichuan cultivated land and inter mountain valley. Lake 42 is mainly distributed in low-lying areas. Reservoir pond 43 is mainly distributed in plain and valley between rivers, surrounded by residential land and cultivated land. Glaciers and permanent snow cover 44 are mainly distributed on the top of (over 4000) mountains. The beach land 46 is mainly distributed in the valley, piedmont, plain lowland, the edge of river lake basin and so on. Residential land: urban land 51 is mainly distributed in plain, mountain basin, slope and gully platform. Rural residential land 52 is mainly distributed in oasis, cultivated land and roadside, tableland, slope, etc. Industrial and mining land and traffic land 53 are generally distributed in the periphery of cities and towns, more developed traffic areas and industrial mining areas. Unused land: sand 61 is mostly distributed in the basin, both sides of the river, the river bay and the periphery of the mountain front Gobi. Gobi 62 is mainly distributed in the Piedmont belt with strong wind erosion and sediment transport. Salt alkali 63 is mainly distributed in relatively low and easy to accumulate water, dry lakes and lakeside. Swamp 64 is mainly distributed in relatively low and easy to accumulate water. Bare soil 65 is mainly distributed in the arid areas (mountain steep slopes, hills, Gobi), and the vegetation coverage is less than 5%. Bare rock 66 is mainly distributed in the extremely dry stone mountain area (windy, light rain). The other 67 are mainly distributed in the exposed rocks formed by freezing and thawing over 4000 meters, also known as alpine tundra. Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 /* 1st standard parallel 47 00 0.000 /* 2nd standard parallel 105 00 0.000 /* central meridian 0 0 0.000 /* latitude of projection's origin 0.00000 /* false easting (meters) 0.00000 /* false northing (meters)

0 2020-07-28

Data of trade volume between China and BRI Countries (2013-2018)

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.

0 2020-07-26

Gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015(WorldPop1.0)

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.

0 2020-07-23

Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2010(WorldPop1.0)

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.

0 2020-07-23

Coastal ports and airports distribution map in the Belt and Road region (2018)

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.

0 2020-07-13

Spatial distribution and dynamic change of soil, water and heat in Central Asia (1995-2015)

Research on the spatial distribution and dynamic change of soil and water heat in Central Asian countries under the background of climate change can provide support for the development of water and soil resources and agricultural production in Central Asian countries, which is of great significance for the social stability of the core region of the "Silk Road Economic Belt". Based on meteorological, water resources, land use and remote sensing data, this paper analyzes the current situation of water and soil resources development and utilization in Central Asia, and introduces the water and heat product index as the water and heat factor, and uses linear trend analysis and partial correlation analysis to study the spatial and temporal variation characteristics of water and soil heat resources in Central Asia in 1995, 2005 and 2015 Equivalent coefficient is used to evaluate the matching characteristics and shortage degree of agricultural water and soil resources. The data set adopts Albers projection, including the spatial distribution of annual precipitation resources, heat resources and cultivated land resources in Central Asia. This data set is intended to provide basic data for the follow-up analysis of agricultural resources, natural regionalization and vulnerability of water, soil and heat resources in Central Asia.

0 2020-07-03

Pan third pole socio economic data set (1960-2018)

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.

0 2020-06-30

Data set of spatial optimization results of irrigation water use in Zhangye basin of Heihe River Basin

Zhangye basin mainly includes 20 irrigation areas. Under the restriction of water diversion, the surface water consumption of the irrigation area is under control, but the groundwater exploitation is increased, resulting in the groundwater level drop in the middle reaches, resulting in potential ecological environment risks. Due to the complex and frequent exchange of surface water and groundwater in the study area, it is possible to realize the overall water resource saving by optimizing the utilization ratio of surface water and groundwater in each irrigation area. In this project, on the premise of not changing the water demand of the middle reaches irrigation area, the two problems of maximizing the outflow of Zhengyi Gorge (given groundwater reserve constraint) and maximizing the outflow of Zhengyi Gorge (given groundwater reserve constraint) are studied.

0 2020-06-11

Land use change in the midstream of Heihe River Basin

According to the statistical yearbook, different types of land use change areas in the middle reaches of China since liberation were collected and sorted out.

0 2020-06-11

The landuse map of Tianshui at 1:500,000 scale (1978)

This data is digitized from the "Tianshui Land Use Status Map" of the drawing. This map is a key scientific and technological research project of the "Seventh Five-Year Plan" of the country: "Three North" Shelterbelt Remote Sensing Comprehensive Survey, one of the series maps of Ganqingning Type Area. The information is as follows: * Chief Editor: Wang Yimou * Deputy Editors: Feng Yushun, You Xianxiang, Shen Yuancun * Editors: Wang Xian, Wang Jingquan, Qiu Mingxin, Quan Zhijie, Mou Xindai, Qu Chunning, Yao Fafen, Qian Tianjiu, Huang Autonomy, Mei Chengrui, Han Xichun, Li Yujiu, Hu Shuangxi * Responsible Editor: Huang Meihua * Manuscript: Mou Xin-shi, Cui Sai-hua, Wang Xian. He Shouhua * Compiling: He Shouhua, Wang Xian, Quan Zhijie, Cui Saihua, Long Yaping, Mu Xinshi, He Shouhua, Mao Xiaoli, Cui Saihua, Wang Changhan * Editors: Feng Yushun and Wang Yimou * Qing Hua: Feng Yushun, Zhang Jingqiu, Yang Ping * Cartography: Feng Yushun, Yao Fafen, Wang Jianhua, Zhao Yanhua, Li Weimin * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Xi 'an Map Publishing House * Scale: 1: 500000 * Publication time: not yet available 2. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Tianshui landuse map (landuse), River, Road, point-like residential land and area-like residential land 3. Data Fields and Attributes Type number land resource class Land_type 88 Exposedrock 86 bare soil Bareground 85 sandy beach and dry ditch Sandy flat and dryvally 446 Artemisia ordosica, miscellaneous grass G1. Artemisia subdingata mixed herbs 445 fern, miscellaneous grass G1. pterideumaquilumvar. latiusculummixedherbs444 Polygonum viviparum, grass G1. G1.Polygonumriciparum,grasses 443 Huang Qiangwei, Spiraea shrub miscellaneous grass G1. Rosa Hugo NIS, Spiraea Canes Cens Scrub Mixed Weeds 442 honeysuckle, elaeagnus pungens shrub miscellaneous grass g1.lonicera japonica eluegas pungens shurb mixed herbs 441 Tiger Hazelnut, Shrub Miscellaneous Grass G1. Ostryopsis Daridiana Scrub Mixed Herbs ............. Please refer to the data document for details. 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000

0 2020-06-11