The historical storm surge events data of the 34 key areas along One Belt One Road were first collected from Internet and then re-processed. First, a Web crawler was coded by python language. Using several key words about storm surge, web pages were then collected by Google and Baidu search engine. Last, important information about the storm surge events (e.g., place, time, affected area, affected population, count of death) were extracted from web pages. This data can be used for risk assessment of storm surge in the 34 key areas along One Belt One Road.
GE Yong LING Feng
The 10m level elevation data set of Yangon deep water port area is the DEM data of the main urban area of Yangon deep water port. DEM is the abbreviation of digital elevation model, which is the important original data of watershed terrain and feature recognition. The data set can reflect the local topographic features of the main urban area of Yangon deep-water port with 10m resolution. Therefore, a large amount of surface morphological information can be extracted from the data set, which includes the slope, aspect and the relationship between cells of the basin grid. It can provide accurate topographic data and reliable verification data for the study of the main urban area of Yangon deep-water port.
GE Yong LI Qiangzi LI Yi
High spatial and temporal resolution remote sensing image plays a very important role in land use change detection, disaster monitoring and bio-geochemical parameter estimation.Currently, Landsat multi-spectral series satellite data (including Landsat TM, ETM+ and OLI multi-spectral bands) is one of the most widely used multi-spectral data.Taking the One Belt And One Road key node area as the research area, and based on the data of Landsat TM/ETM+/OLI series with good quality from 2000 to 2016, python was used to clip the data in the research area with the masks .To solve the partial data missing problem, MODIS imagery on the missing date and Landsat-MODIS data pair of adjacent phases are combined for spatio-temporal fusion to obtain Landsat-like data.Finally, the high spatial and temporal resolution remote sensing images of 34 key node area during 2001 to 2016 lasted for 8 to 16 days was obtained.
YIN Zhixiang LING Feng
Remote sensing image refers to the film or photo recording the electromagnetic wave size of various ground objects, mainly divided into aerial photo and satellite photo. The 1-5m remote sensing data set of Yangon deep water port area is from gaofen-2 satellite, with the highest resolution of 1m and the lowest resolution of 5m, including a total of 7 regional images. There are four images in each region, which are band composite images of 5m level and 1m level. The accuracy of 5m level image can meet the needs of most research purposes, and the amount of data is smaller; the accuracy of 1m level image is higher, which can be used for synthesis, verification and other purposes, but the amount of data is larger than 5m level data. In practical use, we can choose 5m or 1m images according to the needs of researchers.
GE Yong LI Qiangzi LI Yi
The historical extreme precipitation events data of the 34 key areas along One Belt One Road were first collected from Internet and then re-processed. First, a Web crawler was coded by python language. Using several key words about extreme precipitation, web pages were then collected by Google and Baidu search engine. Last, important information about the extreme precipitation events (e.g., place, time, affected area, affected population, count of death) were extracted from web pages. This data can be used for risk assessment of extreme precipitation in the 34 key areas along One Belt One Road.
GE Yong LING Feng
The evaluation area of the data set is the central urban area of Yangon deepwater port. The data set is based on the extreme precipitation disaster risk spatial distribution data set (2019) and its evaluation index system. The data set considers both precipitation risk and terrain risk. Among them, precipitation risk index includes extreme precipitation intensity index and extreme precipitation frequency index, both of which are obtained from GPM precipitation data. Terrain risk mainly considers elevation index. Finally, the risk assessment results of extreme precipitation disaster are obtained. The probability and intensity of extreme precipitation disaster in high risk area are higher than those in low risk area.
GE Yong LI Qiangzi LI Yi
The area of the data set is the central urban area of Yangon deep water port. The data set is based on the spatial distribution data set of extreme precipitation disaster vulnerability (2019) and refers to its evaluation index system. When evaluating the vulnerability of extreme precipitation disaster in Yangon deepwater port area, the disaster reduction ability and sensitivity index are considered. The disaster reduction ability is negatively correlated with vulnerability, and the sensitivity is positively correlated with vulnerability. Disaster reduction capacity considers the density of impervious surface, road network and emergency rescue facilities; sensitivity considers the local land cover types, including farmland, urban and road crisscross. When extreme precipitation disaster occurs, high vulnerability areas will suffer more serious losses, and the reconstruction is more difficult.
GE Yong LI Qiangzi LI Yi
One belt, one road level, is set up. The data set is based on the 100 meter risk assessment data set and the 100m level vulnerability assessment dataset. The risk assessment data set of 34 nodes and 100 meters in the key area of the whole area is calculated based on the international definition of risk, risk (R) = hazard (H) * vulnerability (V). The data set assessed one belt, one road, the extreme precipitation risk under extreme precipitation events, and provided the basis for local government departments' decision-making. At the same time, it could make early warning before the flood disaster, so that we could gain valuable time to take measures to prevent and reduce disasters and reduce the loss of lives and property of people caused by floods.
GE Yong LI Qiangzi LI Yi
One belt, one road, 34 key nodes, is used to assess the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters and reduce the lives of the people. Loss of property. The data set takes one belt, one road, 34 key nodes, and the ratio of cultivated land to land, the proportion of urban land, the proportion of interlaced zone, the density of road network and the impervious surface. Based on the spatial analysis method in ArcGIS, the weights of each index are assigned. The vulnerability of 34 key nodes under extreme precipitation conditions is evaluated, and the vulnerability is determined by natural breakpoint method. Sex is divided into five levels, which represent no vulnerability, low vulnerability, medium vulnerability, high vulnerability and extremely high vulnerability.
GE Yong LI Qiangzi LI Yi
Based on the global surface water data (wod) from 1984 to 2018, the extreme precipitation frequency index and extreme precipitation intensity index were selected. Combined with the spatial analysis method in ArcGIS, the risk level of flood disaster in 34 key nodes under extreme precipitation conditions was constructed and evaluated. One belt, one road, 34 key nodes, is evaluated for the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events, which provides a basis for local government departments to make decisions and early warning before floods occur, so that we can gain valuable time for disaster prevention and mitigation measures to reduce the lives of the people brought by floods. Loss of property.
GE Yong LI Qiangzi LI Yi
This data set is based on the spatial distribution data set of extreme precipitation disaster risk (2019) and vulnerability spatial distribution data set (2019) in Yangon deep water port area, combined with GDP and population distribution data of Yangon deep water port area, and through the definition of "risk = exposure × vulnerability × risk", the risk of extreme precipitation disaster in Yangon deepwater port area is calculated. The data set can provide a reference for the local disaster prevention and reduction work. By analyzing the distribution and causes of high risk, we can put forward engineering measures or non engineering measures to achieve the purpose of disaster reduction and prevention, and reduce the loss of people's lives and property caused by extreme precipitation disasters.
LI Yi
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
To investigate the paternal genetic structure of Tibetans from Shigatse, 434 male samples were collected from Shigatse, Tibet. Firstly, SNP genotyping was performed to allocate samples into haplogroups. To further evaluate the genetic diversity of the major Y-chromosomal haplogroup in Tibetan populations from Lhasa, eight commonly used Y-chromosomal STR (short tandem repeat) loci (DYS19, DYS388, DYS389I, DYS389II, DYS390, DYS391, DYS392, and DYS393) were genotyped using fluorescence-labeled primers with an ABI 3130XL Genetic Analyzer (Applied Biosystems, USA). The results indicated that haplogroup O-M175 displayed highest frequency in Shigatse Tibetans (47.00%, the majority of its sublineages were O2-M122), followed by haplogroups D-M174 (40.78%, with most of the samples belonging to D-P47 (20.97%) and D-N1(16.82%)). Another relatively rare lineages in Shigatse Tibetans were C-M217 (1.84%), R1a1- M17 (1.61%), N1-LLY22G (5.76%), Q-M242 (0.69%). In combination with the data from Lhasa that released in 2019, our Y chromosome data of Tibetans from different locations on the Tibetan Plateau will be very helpful to understanding the paternal genetic structure of Tibetans. Moreover, the genetic history of Tibetans can also be dissected by phylogeographic and coalescent analyses.
KONG Qingpeng QI Xuebin
The western and northeastern Yunnan is located in the southeast of the Qinghai Tibet Plateau. Previous genetic studies have shown that there are substantial genetic imprints of late Paleolithic human in this region, and these ancient genetic imprints are likely to spread further to the Qinghai Tibet Plateau. Therefore, the genetic study of the population in this area is helpful to clarify the migration history of early human settlement in the Qinghai Tibet Plateau. In this study, we studied the genetics of Dai people in different areas of Yunnan Province. The mitochondrial DNA hypervariable regions of 264 Dai individuals were sequenced by Sanger sequencing. Based on phylogenetic analysis, we control the quality of these data to ensure that there is no sample contamination and other quality problems. According to the revised Cambridge Reference Sequence, the variants were recorded. According to the phylogenetic tree of mitochondrial DNA in the world population (PhyloTree.org), each sample was allocated into certain haplogrop. Based on the published mtDNA data of Dai people in other areas, the maternal genetic structure and formation mechanism of Dai population were systematically studied. The results showed that there was a close genetic relationship among the Dai populations in different regions, and the haplogroups (F1a, M7B and B5a) shared by these populations could be traced back to southern China, suggesting that the Dai population might have originated in southern China and migrated southward to the mainland and Southeast Asia in the Iron or Bronze age. The genetic differentiation of the Dai population in different regions is consistent with the phenomenon that their language and culture have some differences, which indicates that the Dai people and the surrounding populations in the southward migration.
KONG Qingpeng
The spatial distribution data set of disaster prevention and mitigation facilities in hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the fusion of 1-2m remote sensing image data, combined with POI data, the distribution information of hospital, fire protection and refuge facilities were extracted respectively. On this basis, the relevant layers and poi layers of OSM were superimposed with the extracted results and images. Through visual inspection, errors were found and the extracted results were corrected. Finally, hambantuota was formed Vector layer data of disaster prevention and mitigation related facilities in the node and Colombo area.
DONG Wen
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of Hambantota, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using ten meters grid as evaluation unit. Based on statistical method, the tide level of every 20 years, 50 years and 100 years is estimated. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method, which can be used to evaluate the danger level of storm surge in each assessment unit. The data set includes 20-year, 50-year and 100-year hazard assessment results of the port area of Hambantota.
DONG Wen
The spatial distribution data set of infrastructures such as traffic and water system in the areas of hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the 1-2m remote sensing image data, the distribution information of road, water, coastline, and coastal facilities are extracted respectively. On this basis, the road, and other layers of OSM are superimposed with the extracted results and images. Through visual inspection, errors are found and the extracted results are corrected. Finally, the hambantota node area dataset is formed road, water system, coastline, and coastal facilities distribution layer of the region. This data set contains the data information of two key node regions of hambantota and Colombo.
DONG Wen
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
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
The urbanization rate data of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the urbanization rate statistical data at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between urbanization rate and covariables (e.g.,night lighting NPP-VIIRS). The spatial regression analysis method is used to model relationship between the urbanization rate data and covariables, and then the county-level urbanization rate data were downscaled and predicted. Based on statistical data and spatial analysis, it is finally integrated into urbanization rate data. The data can provide important basic data for the development of social and economic research on key area and regions along the Belt and Road.
GE Yong LING Feng
