Global population survey data set (1950-2018)

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

0 2020-08-24

Dataset of soil relative humidity and drought index in 2014-2015

Water scarcity,food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative moisture index is one of the indicators that characterize soil drought. It is the ratio of soil relative humidity to field water holding capacity, which can directly reflect the availability of water for crops.The soil moisture data is obtained from the SMAP remote sensing soil moisture data product through the downscaling method, and the field water holding capacity data comes from the Hamonized World Soil Database (HWSD). For detailed calculation formulas and methods, please refer to: "National Standard for Agricultural Drought Grades of China" No.: GB/T 32136-2015. The data covers 34 key node areas along the Belt and Road.

0 2020-08-16

Relative wetness index dataset in Pan-Third Pole (2011-2015)

Water scarcity,food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The relative moisture index is the difference between the precipitation in a certain period of time and the potential evapotranspiration in the same period and then divided by the potential evapotranspiration in the same period.The precipitation data comes from the downscaling of the TRMM/GPM satellite precipitation data, and the potential evapotranspiration is estimated using the Thornthwaite method. For detailed algorithm, please refer to "National Standard for Meteorological Drought of China" (GB/T 20481-2017). The data only covers 34 key node areas along the Belt and Road.

0 2020-08-06

Heat wave vulnerability data set of 34 key nodes in 2015

Vulnerability refers to a property of the system that is susceptible to changes in structure and function due to the system's sensitivity to internal and external disturbances and its lack of ability to respond, that is, the ability of the region to cope with disasters to reduce losses when heat waves occur. This dataset is based on the pan-third pole regional road network data, GDP data, medical facility spatial distribution data, vegetation coverage data, and water distribution data as basic data,and takes 2015 as the base year. The Euclidean Metric calculation method is adopted to determine the spatial distribution of road networks, water and medical facilities in the area. The distance from roads, water bodies, medical facilities, GDP, and vegetation coverage are used as evaluation indicators. The equal-weight overlapping addition is used to evaluate the vulnerability of heat waves at each node. In order to eliminate the impact of unit differences, the data of each index layer is normalized before the evaluation.Finally, the vulnerability level of each node is divided by the natural Jenks method.

0 2020-07-23

Meteorological drought index data set of 34 key nodes of Pan third pole precipitation anomaly percentage (2014-2015)

Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).

0 2020-07-21

Slope and aspect data of 34 key nodes of Pan third pole (2000-2016)

"Digital data including slope and aspect (slope and aspect) data are the basic data of GIS, and can be used as two important indicators to describe the terrain feature information, which can not only indirectly express the relief shape and structure of the terrain, It includes hydrological model, landslide monitoring and analysis, surface material movement, soil erosion, land use planning, etc The basic data of geoscience analysis model. At present, slope and aspect data are generally calculated by certain calculation model on digital elevation model (DEM). This data takes 34 key nodes of Pan third pole as the research area, takes DEM data with resolution of 30 meters as the base, realizes the digital simulation of slope and aspect in terrain data (that is, the digital expression of slope and aspect in terrain surface data), and finally obtains the slope and aspect data of pan third pole key nodes. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).

0 2020-07-21

high temperature heat wave risk dataset at 34 key nodes of the third pole (2015)

Apparent temperature refers to the degree of heat and cold that the human body feels, which is affected by temperature, wind speed and humidity. The spatial scope of the data covers 34 key nodes in the pan-third pole region (Vientiane, Yangon, Kolkata, Warsaw, Karachi, Yekaterinburg, Chittagong, Tashkent, etc.). The spatial resolution is 100m, and the temporal resolution is year. Processing process: Based on the monitoring data of the meteorological station, calculate the apperant temperature based on the Humidex index, and then use the temperature correction method based on elevation correction to obtain 1km gridded data of the entire area, and downscale it to 100m. The heat wave risk dataset mainly uses intensity as the evaluation index. The spatial range and spatial resolution are consistent with the somatosensory temperature data set, and the temporal resolution is years. The criterion for judging the heat wave is: the weather process in which the somatosensory temperature exceeds 29℃ for three consecutive days is judged to be a high-temperature heat wave.

0 2020-06-18

Dataset of precipitation anomaly in percentage at 34 key nodes of Pan-Third Pole (2011-2015)

Water scarcity,food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The percentage of precipitation anomaly is the percentage of the precipitation between a certain period of time and the average climate precipitation of the same period divided by the average climate precipitation of the same period.Based on the daily rainfall data of GPM IMERG Final Run(GPM), this data set calculates the precipitation of the corresponding region, adopts the evaluation index of precipitation anomaly percentage grade, and analyzes the distribution characteristics of drought of different grades. The data area is 34 key nodes of the pan-third pole (Abbas, Astana, Colombo, Gwadar, Mamba, Tehran, Vientiane, etc.).

0 2020-06-18

34 key nodes of Pan third pole historical extreme precipitation dataset (2000-2018)

The pan third pole historical extreme precipitation data set includes 2000-2018 extreme precipitation identification data. One belt, one road, was used to assess the rainfall in the important area along the GPM IMERG Final Run (GPM) daily rainfall. The extreme precipitation threshold of 34 important nodes was evaluated by percentile method. The daily precipitation period was identified by the calculated threshold, and the surface inundation area was produced on the basis of extreme precipitation. The data range mainly includes 34 key nodes of Pan third pole (Vientiane, Alexandria, Yangon, Calcutta, Warsaw, Karachi, yekajerinburg, Chittagong, Djibouti, etc.) The data set can provide the basis for local government decision-making, so as to correctly identify extreme precipitation and reduce the loss of life and property caused by extreme precipitation.

0 2020-06-18

Dataset of surface inundation caused by historical extreme precipitation for The 34 critical nodes of the pan third pole (2014-2018)

Data set of surface inundation caused by historical extreme precipitation evaluated the surface inundation range of One Belt And One Road key areas under extreme precipitation, providing a basis and reference for the decision-making of local government departments, so as to give early warning before the occurrence of extreme precipitation and reduce the loss of life and property caused by extreme precipitation.This data set to the extreme precipitation threshold set "and" the extreme precipitation recognition "as the foundation, to confirm the extreme precipitation time node and the area, and then to NASA's web site to download the submerged range products corresponding to the time and region, combining ArcGIS spatial analysis was used to connect the above data, build the data sets of historical extreme precipitation caused surface submerged range for 34 key nodes. The data mainly includes 34 key nodes (Vientiane, China-Myanmar oil and gas pipeline, China-Laos Thai-Cambodia railway, Alexandria, Yangon, Kwantan, Kolkata, Warsaw, Karachi, Yekaterinburg, Yekaterinburg and other regions).

0 2020-06-17