A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

This table contains data for gross domestic product (GDP), in current dollars, for all census metropolitan area and non-census metropolitan areas.

GDP: per Capita: Shandong: Liaocheng data was reported at 46,995.000 RMB in 2022. This records an increase from the previous number of 44,485.000 RMB for 2021. GDP: per Capita: Shandong: Liaocheng data is updated yearly, averaging 32,968.200 RMB from Dec 2001 (Median) to 2022, with 21 observations. The data reached an all-time high of 51,935.000 RMB in 2018 and a record low of 5,733.780 RMB in 2001. GDP: per Capita: Shandong: Liaocheng data remains active status in CEIC and is reported by Liaocheng Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Heilongjiang: Heihe data was reported at 53,250.000 RMB in 2023. This records an increase from the previous number of 52,575.550 RMB for 2022. GDP: per Capita: Heilongjiang: Heihe data is updated yearly, averaging 18,892.000 RMB from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 53,250.000 RMB in 2023 and a record low of 4,789.890 RMB in 2001. GDP: per Capita: Heilongjiang: Heihe data remains active status in CEIC and is reported by Heihe Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

Annual estimates of balanced UK regional gross domestic product (GDP). Current price estimates and chained volume measures for combined authorities and city regions.

GDP: per Capita: Shandong: Dongying data was reported at 164,430.000 RMB in 2022. This records an increase from the previous number of 155,279.000 RMB for 2021. GDP: per Capita: Shandong: Dongying data is updated yearly, averaging 23,267.000 RMB from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 164,430.000 RMB in 2022 and a record low of 1,167.000 RMB in 1981. GDP: per Capita: Shandong: Dongying data remains active status in CEIC and is reported by Dongying Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

GDP: per Capita: Guangxi: Liuzhou data was reported at 74,679.000 RMB in 2023. This records an increase from the previous number of 74,322.000 RMB for 2022. GDP: per Capita: Guangxi: Liuzhou data is updated yearly, averaging 47,794.986 RMB from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 77,056.000 RMB in 2019 and a record low of 9,337.000 RMB in 2003. GDP: per Capita: Guangxi: Liuzhou data remains active status in CEIC and is reported by Liuzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

As of 2024, Mumbai had a gross domestic product of 368 billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around 167 billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

GDP per Capita: Metropolitan Region of São Paulo: Previous Year Price data was reported at 74,198.582 BRL in 2023. This records an increase from the previous number of 67,309.735 BRL for 2022. GDP per Capita: Metropolitan Region of São Paulo: Previous Year Price data is updated yearly, averaging 44,147.340 BRL from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 74,198.582 BRL in 2023 and a record low of 15,640.787 BRL in 2003. GDP per Capita: Metropolitan Region of São Paulo: Previous Year Price data remains active status in CEIC and is reported by State System of Data Analysis Foundation. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AG022: SNA 2008: Gross Domestic Product per Capita: Southeast: São Paulo.

Nightlights (NTL) have been widely used as a proxy for economic activity, despite known limitations in accuracy and comparability, particularly with outdated Defense Meteorological Satellite Program (DMSP) data. The emergence of newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) data offers potential, yet challenges persist due to temporal and spatial disparities between the two datasets. Addressing this, we employ a novel harmonized NTL dataset (VIIRS + DMSP), which provides the longest and most consistent database available to date. We evaluate the association between newly available harmonized NTL data and various indicators of economic activity at the subnational level across 34 countries in sub-Saharan Africa from 2004 to 2019. Specifically, we analyze the accuracy of the new NTL data in predicting socio-economic outcomes obtained from two sources: 1) nationally representative surveys, i.e., the household Wealth Index published by Demographic and Health Surveys, and 2) indicators derived from administrative records such as the gridded Human Development Index and Gross Domestic Product per capita. Our findings suggest that even after controlling for population density, the harmonized NTL remain a strong predictor of the wealth index. However, while urban areas show a notable association between harmonized NTL and the wealth index, this relationship is less pronounced in rural areas. Furthermore, we observe that NTL can also significantly explain variations in both GDP per capita and HDI at subnational levels.

GDP: per Capita: Heilongjiang: Qiqihar data was reported at 33,300.524 RMB in 2022. This records an increase from the previous number of 30,837.000 RMB for 2021. GDP: per Capita: Heilongjiang: Qiqihar data is updated yearly, averaging 19,390.000 RMB from Dec 2001 (Median) to 2022, with 22 observations. The data reached an all-time high of 33,300.524 RMB in 2022 and a record low of 5,380.110 RMB in 2001. GDP: per Capita: Heilongjiang: Qiqihar data remains active status in CEIC and is reported by Qiqihar Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Guangdong: Shenzhen data was reported at 195,230.173 RMB in 2023. This records an increase from the previous number of 183,801.000 RMB for 2022. GDP: per Capita: Guangdong: Shenzhen data is updated yearly, averaging 35,390.000 RMB from Dec 1979 (Median) to 2023, with 45 observations. The data reached an all-time high of 195,230.173 RMB in 2023 and a record low of 606.000 RMB in 1979. GDP: per Capita: Guangdong: Shenzhen data remains active status in CEIC and is reported by Shenzhen Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Xinjiang: Turpan data was reported at 84,919.000 RMB in 2023. This records an increase from the previous number of 75,671.000 RMB for 2022. GDP: per Capita: Xinjiang: Turpan data is updated yearly, averaging 37,967.000 RMB from Dec 2005 (Median) to 2023, with 18 observations. The data reached an all-time high of 84,919.000 RMB in 2023 and a record low of 20,580.000 RMB in 2005. GDP: per Capita: Xinjiang: Turpan data remains active status in CEIC and is reported by Turpan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Fujian: Fuzhou data was reported at 152,845.937 RMB in 2023. This records an increase from the previous number of 145,935.886 RMB for 2022. GDP: per Capita: Fujian: Fuzhou data is updated yearly, averaging 15,835.000 RMB from Dec 1950 (Median) to 2023, with 45 observations. The data reached an all-time high of 152,845.937 RMB in 2023 and a record low of 66.000 RMB in 1950. GDP: per Capita: Fujian: Fuzhou data remains active status in CEIC and is reported by Fuzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Jiangxi: Jingdezhen data was reported at 74,146.000 RMB in 2023. This records an increase from the previous number of 73,537.233 RMB for 2022. GDP: per Capita: Jiangxi: Jingdezhen data is updated yearly, averaging 39,151.000 RMB from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 74,146.000 RMB in 2023 and a record low of 7,205.020 RMB in 2001. GDP: per Capita: Jiangxi: Jingdezhen data remains active status in CEIC and is reported by Jingdezhen Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Jiangxi: Fuzhou data was reported at 56,854.000 RMB in 2023. This records an increase from the previous number of 54,359.668 RMB for 2022. GDP: per Capita: Jiangxi: Fuzhou data is updated yearly, averaging 20,892.910 RMB from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 56,854.000 RMB in 2023 and a record low of 3,743.310 RMB in 2001. GDP: per Capita: Jiangxi: Fuzhou data remains active status in CEIC and is reported by Fu zhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Anhui: Huaibei data was reported at 47,654.000 RMB in 2019. This records an increase from the previous number of 43,962.000 RMB for 2018. GDP: per Capita: Anhui: Huaibei data is updated yearly, averaging 22,309.000 RMB from Dec 2001 (Median) to 2019, with 19 observations. The data reached an all-time high of 47,654.000 RMB in 2019 and a record low of 5,435.930 RMB in 2001. GDP: per Capita: Anhui: Huaibei data remains active status in CEIC and is reported by Huaibei Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.

GDP: per Capita: Guangdong: Zhuhai data was reported at 170,306.367 RMB in 2023. This records an increase from the previous number of 163,654.350 RMB for 2022. GDP: per Capita: Guangdong: Zhuhai data is updated yearly, averaging 29,590.000 RMB from Dec 1979 (Median) to 2023, with 45 observations. The data reached an all-time high of 170,306.367 RMB in 2023 and a record low of 579.000 RMB in 1979. GDP: per Capita: Guangdong: Zhuhai data remains active status in CEIC and is reported by Zhuhai Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: per Capita.