The total population in China was estimated at 1409.7 million people in 2023, according to the latest census figures and projections from Trading Economics. This dataset provides - China Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Hong Kong HK: Population in Largest City: as % of Urban Population data was reported at 99.637 % in 2017. This records an increase from the previous number of 99.540 % for 2016. Hong Kong HK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 99.382 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 100.000 % in 2010 and a record low of 94.548 % in 1974. Hong Kong HK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong – Table HK.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Hong Kong HK: Population in Largest City data was reported at 7,364,883.000 Person in 2017. This records an increase from the previous number of 7,302,843.000 Person for 2016. Hong Kong HK: Population in Largest City data is updated yearly, averaging 5,581,213.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 7,364,883.000 Person in 2017 and a record low of 2,611,539.000 Person in 1960. Hong Kong HK: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

Additional file 2: Table S2. The pairwise Fst values (below diagonal lines) and p-values (above diagonal lines) of Guizhou Buyei population and 53 other published populations worldwide. The paired Fst and p-values of the Guizhou Buyei population and 13 other published populations in China were calculated. The genetic differentiation between the Guizhou Buyei and Guizhou Miao was the smallest (with the closest genetic affinity, Fst= 0.01508), followed by the Henan Han nationality (Fst= 0.01799). The genetic distance between the northwest Hui and Guizhou Buyei was the largest (with the farthest genetic affinity, Fst= 0.05908).The paired Fst genetic distance and correlation coefficient p-values between Guizhou Buyei nationality and 40 other reference populations in the world (except China) showed that Guizhou Buyei nationality and Pakistan Hazara nationality had the smallest genetic distance (with the closest genetic affinity, Fst= 0.01783), followed by Kashmiri (Fst= 0.02084), and had the largest genetic differentiation (with the farthest genetic affinity, Fst= 0.12165) with the Gdansk people in Poland.

With a population of over 1.4 billion and 56 ethnic groups, China is the largest country in the world in terms of population. Han is the main ethnic group of China (93%). To provide genetic data of Y chromosomal STRs from Anshan City, Northeast of China, for the first time, which will serve as a reference database for forensic and population studies. We report data of 20 Y-chromosomal short tandem repeats (YSTRs) genotyped with the Goldeneye® 20Y kit in 270 Han individuals residing in Anshan City of China. A total of 170 alleles were observed on 20 Y-STRs. The gene diversities varied from 0.3460 (DYS391) to 0.9692 (DYS385). Overall haplotype diversity was almost 1 with 261 unique haplotypes, while the discrimination capacity (DC) was 0.9814. Pairwise Rst and Fst genetic analyses, MDS plot, N-J tree and PCA showed the genetic structure of Anshan Han population was significantly different from other minority groups like Tibetans and Kazakhs. Results of this study showed that Goldeneye® 20Y system loci have strong discriminatory power in the Anshan Han population of China which makes this kit suitable for forensic applications in this ethnic group.

BackgroundClimate change is likely to further worsen ozone pollution in already heavily polluted areas, leading to increased ozone-related health burdens. However, little evidence exists in China, the world’s largest greenhouse gas emitter and most populated country. As China is embracing an aging population with changing population size and falling age-standardized mortality rates, the potential impact of population change on ozone-related health burdens is unclear. Moreover, little is known about the seasonal variation of ozone-related health burdens under climate change. We aimed to assess near-term (mid-21st century) future annual and seasonal excess mortality from short-term exposure to ambient ozone in 104 Chinese cities under 2 climate and emission change scenarios and 6 population change scenarios.Methods and findingsWe collected historical ambient ozone observations, population change projections, and baseline mortality rates in 104 cities across China during April 27, 2013, to October 31, 2015 (2013–2015), which included approximately 13% of the total population of mainland China. Using historical ozone monitoring data, we performed bias correction and spatially downscaled future ozone projections at a coarse spatial resolution (2.0° × 2.5°) for the period April 27, 2053, to October 31, 2055 (2053–2055), from a global chemistry–climate model to a fine spatial resolution (0.25° × 0.25°) under 2 Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs): RCP4.5, a moderate global warming and emission scenario where global warming is between 1.5°C and 2.0°C, and RCP8.5, a high global warming and emission scenario where global warming exceeds 2.0°C. We then estimated the future annual and seasonal ozone-related acute excess mortality attributable to both climate and population changes using cause-specific, age-group-specific, and season-specific concentration–response functions (CRFs). We used Monte Carlo simulations to obtain empirical confidence intervals (eCIs), quantifying the uncertainty in CRFs and the variability across ensemble members (i.e., 3 predictions of future climate and air quality from slightly different starting conditions) of the global model. Estimates of future changes in annual ozone-related mortality are sensitive to the choice of global warming and emission scenario, decreasing under RCP4.5 (−24.0%) due to declining ozone precursor emissions but increasing under RCP8.5 (10.7%) due to warming climate in 2053–2055 relative to 2013–2015. Higher ambient ozone occurs under the high global warming and emission scenario (RCP8.5), leading to an excess 1,476 (95% eCI: 898 to 2,977) non-accidental deaths per year in 2053–2055 relative to 2013–2015. Future ozone-related acute excess mortality from cardiovascular diseases was 5–8 times greater than that from respiratory diseases. Ozone concentrations increase by 15.1 parts per billion (10−9) in colder months (November to April), contributing to a net yearly increase of 22.3% (95% eCI: 7.7% to 35.4%) in ozone-related mortality under RCP8.5. An aging population, with the proportion of the population aged 65 years and above increased from 8% in 2010 to 24%–33% in 2050, will substantially amplify future ozone-related mortality, leading to a net increase of 23,838 to 78,560 deaths (110% to 363%). Our analysis was mainly limited by using a single global chemistry–climate model and the statistical downscaling approach to project ozone changes under climate change.ConclusionsOur analysis shows increased future ozone-related acute excess mortality under the high global warming and emission scenario RCP8.5 for an aging population in China. Comparison with the lower global warming and emission scenario RCP4.5 suggests that climate change mitigation measures are needed to prevent a rising health burden from exposure to ambient ozone pollution in China.

China has the longest terrestrial boundary and the highest number of neighboring countries in the world. At present, there is a lack of research on human activities at the grid scale in China's border areas. Using the land use/cover data, population density data, and night-time light data, we constructed the human activity intensity index (HAI). Taking the 50km buffer zones of China’s land boundary on each side as the study area, we calculated the HAI with a resolution of 1km in 1992, 2000, 2010, and 2020. This dataset is stored as the tif format and composed of four layers with a volume of 469 MB (compressed into one file, 4.37 MB).

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode. Face-to-face interview: CAPI (Computer Assisted Personal Interview). Face-to-face interview: PAPI (Paper and Pencil Interview). Telephone interview: CATI (Computer Assisted Telephone Interview). Self-administered questionnaire: CAWI (Computer-Assisted Web Interview). Self-administered questionnaire: Paper. In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2022 is face to face (interviewer-administered). Several countries employed self-administered interview or mixed-mode approach to data collection: Australia (CAWI & postal survey); Canada (CAWI); Hong Kong SAR (PAPI & CAWI); Malaysia (CAWI & PAPI); Netherlands (CAWI); USA (CAWI & CATI). The WVS Master Questionnaire was provided in English, Arabic, Russian and Spanish. Each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. WVSA Secretariat and Data archive monitored the translation process; every translation is subject to multi-stage validation procedure before the fieldwork can be started. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country/ territory] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). Research area: Andorra (AD); Argentina (AR); Armenia (AM); Australia (AU); Bangladesh (BD); Bolivia (BO); Brazil (BR); Canada (CA); Colombia (CO); Chile (CL); China (CN); Cyprus (CY); Ecuador (EC); Egypt (EG); Ethiopia (ET); Germany (DE); Greece (GR); Guatemala (GT); Hong Kong SAR PRC (HK); Indonesia (ID); Iran (IR); Iraq (IQ); Japan (JP); Jordan (JO); Kazakhstan (KZ); Kenya (KE); Kyrgyzstan (KG); Lebanon (LB); Libya (LY); Macao SAR PRC (MO); Malaysia (MY); Maldives (MV); Mexico (MX); Mongolia (MN); Morocco (MA); Myanmar (MM); Netherlands (NL); New Zealand (NZ); Nicaragua (NI); Nigeria (NG); Pakistan (PK); Peru (PE); Philippines (PH); Puerto Rico (PR); Romania (RO); Russia (RU); Serbia (RS); Singapore (SG); South Korea (KR); Taiwan ROC (TW); Tajikistan (TJ); Thailand (TH); Tunisia (TN); Turkey (TR); Ukraine (UA); United States (US); Venezuela (VE); Vietnam (VN); Zimbabwe (ZW). The sampling procedures differ from country to country; probability sample: Multistage Sample, Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2021. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Macau MO: Population in Largest City: as % of Urban Population data was reported at 100.000 % in 2017. This stayed constant from the previous number of 100.000 % for 2016. Macau MO: Population in Largest City: as % of Urban Population data is updated yearly, averaging 100.000 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 100.000 % in 2017 and a record low of 100.000 % in 1978. Macau MO: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau SAR – Table MO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Macau MO: Population in Largest City data was reported at 622,567.000 Person in 2017. This records an increase from the previous number of 612,167.000 Person for 2016. Macau MO: Population in Largest City data is updated yearly, averaging 325,850.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 622,567.000 Person in 2017 and a record low of 159,892.000 Person in 1960. Macau MO: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau SAR – Table MO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

ObjectiveLiver cancer is one of the most common causes of cancer-related death. Understanding how demographic factors influence mortality due to liver cancer is crucial for optimizing disease-control strategies. We aimed to characterize the long-term trends in the mortality and years of life lost (YLL) of liver cancer in Shanghai, China, 1973–2019, and quantitatively analyze the contributions of demographic and non-demographic factors on the mortality of liver cancer.MethodsUsing mortality data from the Mortality Registration System of Pudong New Area, the largest district of Shanghai with a population of permanent resident of 5.68 million, during 1973–2019, we analyzed the temporal trends for the mortality rates and YLL by Joinpoint Regression Program. The difference decomposition method was employed to estimate the increasing mortality rates related to demographic and non-demographic factors.ResultsA total of 21,530 deaths from liver cancer occurred from 1973 to 2019. The crude mortality rates (CMR) and age-standardized mortality rate by Segi's world standard population (ASMRW) of liver cancer were 26.73/105 person-years and 15.72/105 person-years, respectively. The CMR, ASMRW, and YLL rates of liver cancer showed significantly decreasing trends in males, females and the total population from 1973 to 2019, whereas the upward trends in the YLL were seen in males, females and the total population (all P < 0.05). A significant upward trend was observed in the increased CMR caused by demographic factors, but the changing rate caused by non-demographic factors decreased.ConclusionsThe CMR and ASMRW of liver cancer continually decreased although YLL increased during 1973–2019 in Pudong New Area, Shanghai. The demographic factors, especially aging, might be responsible for the increase in the mortality of liver cancer. More effective prevention strategies tailored to liver cancer are needed to further reduce its disease burden in the elderly population.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The United States is leading the ranking by number of high networth individuals , recording **** million individuals. Following closely behind is China with **** million individuals, while Lesotho is trailing the ranking with * thousand individuals, resulting in a difference of **** million individuals to the ranking leader, the United States. High Net Worth Individuals are here defined as persons with investible assets of at least *********** U.S. dollars in current exchange rate terms.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).