The resilience of road traffic development in countries along the Belt and Road reflects the level of resilience of road traffic development in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of road traffic development in the countries along the Belt and Road. The road traffic development resilience data product is prepared by referring to the World Bank statistical database, using the year-by-year data of four indicators, namely road mileage, railway mileage, air traffic and container terminal throughput of the countries along the "Belt and Road" from 2000 to 2019, and based on the year-by-year changes of each indicator, based on sensitivity Based on the sensitivity and adaptability analysis, the road traffic development resilience product is prepared through comprehensive diagnosis. The data set of road traffic development resilience of countries along the "Belt and Road" is an important reference for analysing and comparing the current road traffic development resilience of countries.
XU Xinliang
Spatial distribution map of roads, railways and pipelines in China Mongolia Russia economic corridor from 1990 to 2020 1) Spatial data of highway, railway and pipeline in 1990; Spatial data of roads, railways and pipelines in China Mongolia Russia economic corridor in 2015; Spatial data of roads, railways and pipelines in China Mongolia Russia economic corridor in 2020; 2) Download the remote sensing images within the China Mongolia Russia economic corridor on NASA website and use arcgis10 2 software manual interpretation and extraction of highway and railway; Map elements are marked with the help of Russian atlas; The pipeline data shall be manually marked with reference to relevant maps; 3) The scale of the atlas is 1:2500000, which clearly reflects the changes of traffic and pipelines in the China Mongolia Russia economic corridor in recent 30 years,; 4) The data shows in detail the changes of traffic and pipelines in the China Mongolia Russia economic corridor in recent 30 years, which provides a data basis for the later study of the impact of traffic and pipeline construction on the change of ecological environment.
BU Xiaoyan
The data set is the basic data of the Qinghai Tibet Plateau in 2015. The original data comes from the National Basic Geographic Information Center, and the data of the Qinghai Tibet plateau region is formed by splicing and clipping the segmented data. The data content includes 1:1 million provincial administrative divisions, 1:1 million roads and 1:250000 water system. The data attributes of administrative divisions include name, code and Pinyin; Road data attributes include: GB, RN, name, rteg and type (basic geographic information classification code, road code, road name, road grade and road type); Water system data attributes include: GB, hydc, name, period (basic geographic information classification code, water system name code, name, season).
YANG Yaping
1) In mountainous areas, due to the complex topographic and geological background conditions, landslides are very easy to occur triggered by external factors such as rainfall, snow melting, earthquake and human engineering activities, resulting in the loss of life and property and the destruction of the natural environment. In order to meet the safety of project site construction, the rationality of land use planning and the urgent needs of disaster mitigation, it is necessary to carry out regional landslide sensitivity evaluation. When many different evaluation results are obtained by using a variety of different methods, how to effectively combine these results to obtain the optimal prediction is a technical problem that is still not difficult to solve at present. It is still very lack in determining the optimal strategy and operation execution of the optimal method for landslide sensitivity evaluation in a certain area. 2) Using the traditional classical multivariate classification technology, through the evaluation of model results and error quantification, the optimal evaluation model is combined to quickly realize the high-quality evaluation of regional landslide sensitivity. The source code is written based on the R language software platform. The user needs to prepare a local folder separately to read and store the software operation results. The user needs to remember the folder storage path and make corresponding settings in the software source code. 3) The source code designs two different modes to display the operation results of the model. The analysis results are output in the standard format of text and graphic format and the geospatial mode that needs spatial data and is displayed in the standard geographic format. 4) it is suitable for all people interested in landslide risk assessment. The software can be used efficiently by experienced researchers in Colleges and universities, and can also be used by government personnel and public welfare organizations in the field of land and environmental planning and management to obtain landslide sensitivity classification results conveniently, quickly, correctly and reliably. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development
YANG Zhongkang
Based on the damage rate of each disaster collected in the Qinghai Tibet scientific research, the relative risk level of a single disaster is divided. The comprehensive natural disaster risk grade evaluation method is adopted. Based on the risk evaluation results of single disaster, the comprehensive evaluation is carried out according to the weight obtained by the occurrence frequency of each disaster. The comprehensive risk data of road traffic around the Himalayas includes the vector data of roads around the Himalayas and the comprehensive risk level of each road section, It is divided into five levels: low risk (1), medium and low risk (2), medium risk (3), medium and high risk (4) and high risk (5). It represents the relative size of possible loss or damage to the road traffic system under the comprehensive impact of various natural disasters in the study area. It can provide a reference basis for road risk prevention and emergency management.
YANG Saini
Main railway lines of China-Mongolia-Russia Economic Corridor: Manzhouli-Chita; Hohhot-Erlian-Ulaanbaatar; Suifenhe-Vladivostok/Khabarovsk; Erlian-Zamen Uud; Dalian-Harbin; Harbin-Manzhouli; Jining-Erlian; Changchun-Huichun; Zamen Udda-Ulaanbaatar-Sukhbaatar; Zabaikalsk-Chita; Novosibirsk-Ulan-Ude; Ulan-Ude-Chaktu-Darhan-Bayan Gol-Ulaanbaatar-Bayantar-Gobi Sumber-Joy Er-Sinshanda-Zamyn-Uud-Erenhot-Jining-Yanggao-Zhangjiakou-Langfang-Tianjin Port; Inner Mongolia-Erenhot-Zamyn-Uud-Joyel-Ulaanbaatar-Dalkhan-A Letan Bragg-Chaktu-Ulan-Ude; Naushki-Ulan-Ude; Changchun-Hunchun; Sino-Russian oil pipeline: The first and second lines of the Sino-Russian crude oil pipeline (Linyuan-Daqing-Lindian-Nehe-Nenjiang-Dayangshu-Uerqi-Jagedaqi-Mohe-Songling-Jingsong-Xinlin-Tahe-Walagan- 22nd Station-Xing'an Town-Skovorodino (Siberia-Pacific Crude Oil Pipeline System) East Siberia-Pacific Pipeline ((Daqing-Taishe 1, 2) Taishet-Skovorodino-Magdagazi-Khabarovsk-Perevoznaya-Kozimino) Sino-Russian crude oil pipeline (Taishet-Lensk-Olyekminsk-Ardan-Tenda-Skovorodino-Mohe-Qiqihar-Daqing) Sino-Russian Far East pipeline (Tashet-Lensk-Olyekminsk-Ardan-Tenda-Khabarovsk-Vladivostok)
BU Xiaoyan
The road data of 34 key areas along the Belt and Road is first collected from the Internet and then re-processed. Road data can be obtained from the OpenStreetMap open source wiki map. OpenStreetMap is a project designed to create and provide free geographic data (such as street maps) to anyone. First, we download the road data with the country where the key area along the One Belt One Road is located, then clip and extract according to the area, and then calculate the road length in each unit to obtain. Based on OpenStreetMap, it is finally integrated into a road length infrastructure element data product. The road length data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.
GE Yong, LING Feng
The data is the railway distribution map of the chaidamu river basin, with a scale of 25,000 and coordinates of longitude and latitude. The data includes spatial data and attribute data. The attribute field is code.
National Basic Geographic Information Center
Third pole 1:100,000 road data set includes: main road (Tibet_main_highways), road (Tibet_Road)and railway (Tibet_railway) vector space data set and its related attribute data :road names(Name), Type(Type) The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
The 1:1,000,000 road data set of the North Pole includes the Arctic_Major_Routes, the Arctic_Minor_Routes, the Arctic_railway vector space data and the related attribute data: road Name and Type. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，It's most comprehensive, current and seamless geographic digital data for the whole earth. The world map coordinate system is latitude and longitude, WGS84 datum surface，Arctic specific projection parameters（North_Pole_Stereographic）.
ADC WorldMap
The data is the railway distribution map of the north slope of Tianshan River Basin, with a scale of 25000 and the projection is longitude and latitude. the data includes spatial data and attribute data, and the attribute field is code (railway code).
National Basic Geographic Information Center
The data set records the density of railway and highway transportation lines in Qinghai Province from 1952 to 2004, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains seven data tables: Railway and highway transportation line density 1952-1998.xls, railway and highway transportation line density 1952-1999.xls, railway and highway transportation line density 1952-2000.xls, railway and highway transportation line density 1952-2001.xls, railway and highway transportation line density 1952-2002.xls, railway and highway transportation line density 1952-2003.xls The density of railway and highway transportation lines in 1952-2004.xls. The data table structure is the same. For example, there are three fields in the data table from 1952 to 1998 Field 1: year Field 2: Railway Field 3: Highway
Qinghai Provincial Bureau of Statistics
The data is a dataset of road distribution in Qinghai Lake basin, scale1: 250,000, projection: latitude and longitude, mainly including the spatial distribution and attribute data of main roads in Qinghai Lake basin, attribute fields: code (road code), name (road classification).
National Basic Geographic Information Center
The data set includes the road condition, water system condition and land use situation of Yangon deep water port central city. The road dataset includes both roads and railways, while the water system dataset includes rivers and lakes. The road data set and water system data set are vector data, and the land use data set is grid data with 10m resolution. The classification system of land use is: 10. Forest forest; 20. Cultivated land; 21. Paddy filed paddy field; 22. Dry farmland; 30. Water body; 31. River river river; 32. Lake Lake (including reservoirs and ponds); 33. Wetland; 40. Artificial surface; 43. Mining area; 50. Bareland Bare soil, bare rock, desert and so on, based on the limited sample accuracy analysis of the data, the classification accuracy is about 90%.
GE Yong, LI Qiangzi, LI Yi
The data is the dataset of the road distribution in the qaidam river basin, scale: 250,000, projection: longitude and latitude, mainly including the spatial distribution and attribute data of the main roads in the qaidam river basin, attribute fields: code (road code), Name (road classification).
National Basic Geographic Information Center
Data overview: this set of data mainly includes the spatial distribution of major roads in the heihe river basin, the attributes include road classification and road coding, and the data base year is 2010. Data preparation process: this set of data is based on the topographic map, remote sensing image and the latest road traffic map updated by the transportation department of gansu province in 2009. Data description: there are two important attributes of the data, namely, road classification and road code. The road classification is divided into national road, provincial road, county road, township road and private road. The road code is defined in accordance with the highway grade code of the traffic department.
WU Lizong, NIAN Yanyun
Railway distribution map is the basic data in the mapping process. In order to facilitate the use of users, we compiled the railway data set of Heihe River basin according to the railway data set distributed by the National Basic Geographic Information Center, the atlas of Gansu Province compiled by the Gansu Provincial map Geographic Information Center, the sky map and Guge map published by the China Surveying and Mapping Bureau. This data basically reflects the distribution of Railways around the Heihe River basin around 2010. The national standard of data classification and coding of national basic geographic information system - Classification and code of basic land information data (GB / T 13923-92) is adopted for railway coding, and the code is five digit code (National Basic Geographic Information Center 2010).
National Basic Geographic Information Center
The data is the railway map of Qinghai Lake Basin, with a scale of 250,000, projection: latitude and longitude. The data includes spatial data and attribute data. The attribute field is code (railway code).
National Basic Geographic Information Center
The railway data of 34 key areas along the Belt and Road is collected from the Internet and reprocessed. First, we download the linear railway data from the country where the key node areas along the One Belt One Road are located from the OpenStreetMap, and cut and extracted them by region. Meanwhile, it is compared and analyzed with the railway extraction result based on high resolution remote sensing images, and then updated with data from regional statistical bureaus. It is finally integrated into a railway infrastructure element data product. The format of data is linear shapefile data. The spatial coordinate system of the railway data is WGS84, and it contains attribute fields such as name, class and so on. This data can be used to calculate the length of railways and analyze the distribution of railways in cities. The railway data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.
GE Yong, LING Feng
Based on the Global 1,000,000 Basic Geographic Data (2010) of the Resource and Environment Science Data Center of the Chinese Academy of Sciences, the railway and highway networks of Arctic countries (USA, Canada, Russia, Norway (including Greenland and the Faroe Islands), Denmark, Sweden, Finland, and Iceland) are extracted via ArcGIS. The data are stored separately by nation. The data format is the .shp format of ArcGIS, and the projection mode is GCS_WGS_1984. The railway network data are from http://www.resdc.cn/data.aspx?DATAID=208, and the highway network data are from http://www.resdc.cn/data.aspx?DATAID=207
YANG Linsheng, WANG Li
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
