In 2022, the total permanent resident population of Tibet autonomous region in China amounted to around 3.64 million inhabitants. Tibet autonomous region is located in Western China.

Population: Census: Tibet: Lhasa data was reported at 867.891 Person th in 12-01-2020. This records an increase from the previous number of 559.423 Person th for 12-01-2010. Population: Census: Tibet: Lhasa data is updated decadal, averaging 559.423 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 867.891 Person th in 12-01-2020 and a record low of 474.499 Person th in 12-01-2000. Population: Census: Tibet: Lhasa data remains active status in CEIC and is reported by Lhasa Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

Population: Average Household Size: Tibet data was reported at 3.270 Person in 2021. This records an increase from the previous number of 3.192 Person for 2020. Population: Average Household Size: Tibet data is updated yearly, averaging 4.600 Person from Dec 1982 (Median) to 2021, with 29 observations. The data reached an all-time high of 6.790 Person in 1999 and a record low of 3.192 Person in 2020. Population: Average Household Size: Tibet data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: No of Person per Household.

Population: Tibet data was reported at 3.843 Person th in 2023. This records an increase from the previous number of 3.730 Person th for 2022. Population: Tibet data is updated yearly, averaging 2.638 Person th from Dec 1982 (Median) to 2023, with 29 observations. The data reached an all-time high of 3,648.100 Person th in 2020 and a record low of 2.473 Person th in 1999. Population: Tibet data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: By Region.

The data set contains three tables: demographic data for Tibet, demographic data for each county in Tibet, and data on rural workers. These time series data include the year-end total population, the number of men, the number of women, urban population, rural population, and statistics on workers in various rural industries in Tibet from 1967 to 2016. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks.

Table 1: The table of demographic data for Tibet contains 10 fields. Field 1: Year Field 2: Year-end total population, unit: 10,000 Field 3: Total number of men, unit: 10,000 Field 4: Male proportion, unit: % Field 5: Total number of women, unit: 10,000 Field 6: Female proportion, unit: % Field 7: Urban population, unit: 10,000 Field 8: Urban population proportion, unit: % Field 9: Rural population, unit 10,000 Field 10: Rural population proportion, unit: %.

Table 2: The table of demographic data for each county contains 7 fields. Field 1: Districts and counties Field 2: Year Field 3: Year-end total number of households Field 4: Number of rural households Field 5: Year-end total population, unit: 10,000 Field 6: Rural population, unit: 10,000 Field 7: Year-end number of workers, unit: 10,000

Table 3: The table of rural workers contains 7 fields Field 1: Year Field 2: Districts and counties Field 3: Number of rural workers Field 4: Number of workers in the agricultural, forestry, animal husbandry and fishery sectors Field 5: Number of workers in the industrial sector Field 6: Number of workers in the construction sector Field 7: Number of other non-agricultural workers

366 (ten thousand persons) in 2021. Resident population refers to the total number of people alive at a certain point of time within a given area. The annual statistics on resident population is taken at midnight, the 3lst of December, not including residents in Taiwan province, Hong Kong SAR and Macao SAR and Chinese national residing abroad.

Forensic parameters of the 12 X-STR loci in Tibetans population residing in Nagqu city in the north of the Tibet Autonomous Region (TAR) in China (n = 549).

Tibetan, one of the largest indigenous populations living in the high-altitude region of the Tibetan Plateau (TP), has developed a suite of physiological adaptation strategies to cope with the extreme highland environment in TP. Here, we reported genome-wide SNP data from 48 Kham-speaking Nagqu Tibetans and analyzed it with published data from 1,067 individuals in 167 modern and ancient populations to characterize the detailed Tibetan subgroup history and population substructure. Overall, the patterns of allele sharing and haplotype sharing suggested (1) the relatively genetic homogeny between the studied Nagqu Tibetans and ancient Nepalese as well as present-day core Tibetans from Lhasa, Nagqu, and Shigatse; and (2) the close relationship between our studied Kham-speaking Nagqu Tibetans and Kham-speaking Chamdo Tibetans. The fitted qpAdm models showed that the studied Nagqu Tibetans could be fitted as having the main ancestry from late Neolithic upper Yellow River millet farmers and deeply diverged lineages from Southern East Asians (represented by Upper Paleolithic Guangxi_Longlin and Laos_Hoabinhian), and a non-neglectable western Steppe herder-related ancestry (∼3%). We further scanned the candidate genomic regions of natural selection for our newly generated Nagqu Tibetans and the published core Tibetans via FST, iHS, and XP-EHH tests. The genes overlapping with these regions were associated with essential human biological functions such as immune response, enzyme activity, signal transduction, skin development, and energy metabolism. Together, our results shed light on the admixture and evolutionary history of Nagqu Tibetan populations.

Tibetan, one of the largest indigenous populations living in the high-altitude region of the Tibetan Plateau (TP), has developed a suite of physiological adaptation strategies to cope with the extreme highland environment in TP. Here, we reported genome-wide SNP data from 48 Kham-speaking Nagqu Tibetans and analyzed it with published data from 1,067 individuals in 167 modern and ancient populations to characterize the detailed Tibetan subgroup history and population substructure. Overall, the patterns of allele sharing and haplotype sharing suggested (1) the relatively genetic homogeny between the studied Nagqu Tibetans and ancient Nepalese as well as present-day core Tibetans from Lhasa, Nagqu, and Shigatse; and (2) the close relationship between our studied Kham-speaking Nagqu Tibetans and Kham-speaking Chamdo Tibetans. The fitted qpAdm models showed that the studied Nagqu Tibetans could be fitted as having the main ancestry from late Neolithic upper Yellow River millet farmers and deeply diverged lineages from Southern East Asians (represented by Upper Paleolithic Guangxi_Longlin and Laos_Hoabinhian), and a non-neglectable western Steppe herder-related ancestry (∼3%). We further scanned the candidate genomic regions of natural selection for our newly generated Nagqu Tibetans and the published core Tibetans via FST, iHS, and XP-EHH tests. The genes overlapping with these regions were associated with essential human biological functions such as immune response, enzyme activity, signal transduction, skin development, and energy metabolism. Together, our results shed light on the admixture and evolutionary history of Nagqu Tibetan populations.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

Population: Census: Tibet: Xigaze data was reported at 798.153 Person th in 12-01-2020. This records an increase from the previous number of 703.292 Person th for 12-01-2010. Population: Census: Tibet: Xigaze data is updated decadal, averaging 703.292 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 798.153 Person th in 12-01-2020 and a record low of 635.200 Person th in 12-01-2000. Population: Census: Tibet: Xigaze data remains active status in CEIC and is reported by Xigaze Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

131 (ten thousand persons) in 2020. Urban population refers to all people residing in cities and towns, while rural population refers to population other than urban population.

The whole mitochondrial genomes of 68 Tibetan samples were sequenced by high-throughput second-generation sequencing. The average depth of sequencing was 1000 ×, ensuring that the mitochondrial genome of each sample was completely covered (100%). Based on the phylogenetic analysis, we control the quality of these data to ensure that there is no sample pollution and other quality problems. According to the phylogenetic tree, each individual was allocated into haplogroups. The results showed that in Lhasa Tibetan population, M9a1c1b1a was the highest (19.12%), followed by G2 (13.23%), M13a (11.76%), C4a (7.35%), D4 (7.35%), A11a1a (5.88%), M9a1b (5.88%), and F1c, F1g, B4, F1d, M62b, F1a, F1b, G1, M11, M8a, U7a, Z3a. These haplogroups have different originations, including Paleolithic components (M13a, M62b, M9a1b, etc.), northern China millet farmers’ components (M9a1c1b1a and A11a1a), components distributed mainly in southern East Asia (F1a, etc.), northern East Asian haplogroups (C4a, D4, etc.). It is worth noting that the maternal component of Lhasa Tibetans is mainly composed of millet agricultural population in northern China, indicating the important impact of genetic input of millet agricultural population in northern China on the genetic structure of the population in this area. Taken together, the maternal genetic structure of Lhasa Tibetan population exhibits time stratification, which may represent the genetic imprint of different population entering the region in different periods.

preferenceself-reported data of sex preference

8,4 (%) in 2021. Old Dependency Ratio also called old dependency coefficient, refers to the ratio of the elderly population to the working-age population, express in %. It describes the number of the elderly population that every 100 people at working ages will take care of. Old dependency ratio is one of the indicators reflecting the social implication of population aging from the economic perspective. The old dependency ratio is calculated with the following formula: (The elderly population aged 65 and over)/(The working-age population aged 15-64)*100%.

Tibetan and Mongolian Committee Staff Number Statistics Table

Population: Tibet: Lhasa: Nimu data was reported at 32.800 Person th in 2014. This records a decrease from the previous number of 36.000 Person th for 2013. Population: Tibet: Lhasa: Nimu data is updated yearly, averaging 30.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 36.000 Person th in 2013 and a record low of 29.000 Person th in 2006. Population: Tibet: Lhasa: Nimu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.

14,17 (per thousand population) in 2021. Birth Rate (or Crude Birth Rate) refers to the ratio of the number of births to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Birth rate refers to annual birth rate. The following formula is used: (Number of births)/(Annual average population)*1000‰. Number of births in the formula refers to live births, i.e. when a baby has breathed or showed any vital phenomena regardless of the length of pregnancy. Annual average population is the average of the number of population at the beginning of the year and that at the end of the year. Sometimes it is substituted by the mid-year population.

The dataset covers aspects such as transportation, healthcare, and education, which is of great significance for understanding the capacity, accessibility, and support capabilities of transportation, healthcare, and education development in the Qinghai-Tibet Plateau region. By utilizing statistical data from schools and hospitals at various levels and road network data, the dataset provides a comprehensive evaluation of the transportation and infrastructure support capabilities of the Qinghai-Tibet Plateau. The dataset covers the entire Qinghai-Tibet Plateau and is calculated and analyzed at the township unit scale, ensuring the accuracy and practicality of the data. The dataset can provide references for transportation planning, healthcare resource allocation, and education service optimization in the Qinghai-Tibet Plateau region. Through these data, it is possible to identify the weak areas in transportation and infrastructure in the Qinghai-Tibet Plateau and provide more refined references for solving local issues. Transportation Support Capability Evaluation Dataset: This dataset integrates township settlement data obtained from field surveys and road data provided by OpenStreetMap (OSM). It uses the Cost Distance tool in ArcGIS to generate cost raster data. The dataset focuses on township settlements and roads as core elements, aiming to quantify the accessibility and support capabilities of the regional transportation network and provide a scientific basis for transportation planning. The high accuracy and timeliness of township settlement data, combined with the timely updates of OSM road data, ensure the overall quality and reliability of the transportation data. Healthcare Data: This dataset covers the medical service time, population-weighted medical service time, and comprehensive costs of primary, secondary, and tertiary hospitals in the Qinghai-Tibet region. The hospital coordinate data is sourced from Baidu Maps, while the number of health technicians per capita and healthcare expenditure per capita are obtained from the National Health Commission. Based on hospital coordinates and cost raster data in the study area, the Cost Distance tool in ArcGIS is used to calculate the shortest time cost from each raster point to hospitals at various levels. The population-weighted medical service time is calculated through the cost raster fitted to the OSM road network. The comprehensive cost is calculated by integrating three indicators: population-weighted medical service time, the number of health technicians per capita, and healthcare expenditure per capita. The entropy weight method is used to determine the weight of each indicator, and the comprehensive cost values are sorted and classified before being linked to the county-level administrative units in ArcGIS. Education Data: This dataset focuses on the schooling time, population-weighted schooling time, and comprehensive costs of primary schools in the Qinghai-Tibet region. The data on primary school directories, the number of teachers per student, and educational expenditure per student are sourced from the Education Departments of the Tibet Autonomous Region and Qinghai Province, as well as local education bureaus. The population density data is provided by the WORLDPOP website and is corrected using multiple sources of data, including the seventh national population census and land use data, to generate a population density raster with a precision of 1 km × 1 km. The locations of primary schools are determined through Gaode Maps, Tianditu, and Baidu Maps, and their coordinates are corrected and incorporated into a spatial database. Based on primary school coordinates and cost raster data in the study area, the Cost Distance tool in ArcGIS is used to calculate the shortest time cost from each raster point to primary schools. The population-weighted schooling time is also calculated based on the cost raster fitted to the OSM road network. The comprehensive cost is calculated by integrating three indicators: population-weighted schooling time, the number of teachers per student, and educational expenditure per student. The entropy weight method is used to determine the weight of each indicator, and the comprehensive cost values are sorted and classified before being linked to the county-level administrative units in ArcGIS.

Plant adaptation to high altitudes has long been a substantial focus of ecological and evolutionary research. However, the genetic mechanisms underlying such adaptation remain poorly understood. Here, we address this issue by sampling, genotyping, and comparing populations of Tibetan poplar, Populus szechuanica var. tibetica, distributed from low (~2000 m) to high altitudes (~3000 m) of Sejila Mountain on the Qinghai-Tibet Plateau. Population structure analyses allow clear classification of two groups according to their altitudinal distributions. However, in contrast to the genetic variation within each population, differences between the two populations only explain a small portion of the total genetic variation (3.64%). We identified asymmetrical gene flow from high- to low-altitude populations. Integrating population genomic and landscape genomic analyses, we detected two hotspot regions, one containing four genes associated with altitudinal variation, and the other containing ten genes associated with response to solar radiation. These genes participate in abiotic stress resistance and regulation of reproductive processes. Our results provide insight into the genetic mechanisms underlying high-altitude adaptation in Tibetan poplar.