As of January 2024, Guangzhou had the largest metropolitan population in the Asia-Pacific region, with approximately 70.1 million inhabitants. Tokyo had the second-largest metropolitan population of around 41 million inhabitants. There were a total of 28 megacities with a population of over 10 million inhabitants in the Asia-Pacific region as of January 2024.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

This statistic shows a forecast of the top ten most populous megacities in 2030. By 2030, Tokyo will be the most populous city in the world, with a projected 37 million inhabitants.

This database provides the construction of Large Urban Regions (LUR) in the world. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the areas of economic influence of a core into one statistical unit (see Rozenblat, 2020 or Rogov & Rozenblat, 2020 for Russia). In doing so we use four principal urban concepts (Pumain et al., 1992): local administrative units (Municipality or localities: MUNI), morphological urban area (MUA), functional urban area (FUA), and conurbation that we call Large Urban Region (LUR). The LURs are the spatial extensions of the influence of one or several FUAs or MUAs. MUAs and FUAs are defined by various national or international sources. We implemented LURs using criteria such as the population distribution among one or several MUAs or FUAs, road networks, access to an airport, distance from a core, and presence of multinational firms. FUAs and MUAs perimeters, if they form a part of a LUR, belong to a unique LUR. In this database, we provide the composition of the LURs in terms of local administrative units (MUNI), Morphological Urban Areas (MUA), and Functional Urban Areas (FUA).

This last update provides new LURs for the 54 African Countries (see Rogromel & Rozenblat, 2024) and some corrections for China.

It includes now 1'828 LURs composed of 130'283 localities.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

This statistic provides a projection of the gross domestic product (GDP) of major megacities worldwide in 2030. As of this time, it is projected that the GDP of Tokyo, Japan, will reach 40 billion U.S. dollars.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

A city with a population in excess of 10 million is defined by the UN as a Megacity. The International Hydrological Programme of UNESCO has established the Megacities Alliance for Water and Climate Change to support the exchange of knowledge and best practices in delivering sustainable water and sanitation services in view of Climate Change. Creative Cities, intend to foster international cooperation with and between cities committed to investing in creativity as a driver for sustainable urban development, social inclusion and cultural vibrancy. Inclusive and Sustainable Cities are interested in sharing experiences in order to improve their policies to fight racism, discrimination, xenophobia and exclusion; and Learning cities provide inspiration, know-how and best practices in matter of international policy. The map presents the Cities UNESCO’s Sectors are supporting in their efforts to enhance knowledge exchange and achieve the SDGs of the 2030 Sustainable Agenda.

As of January 2024, Hangzhou in China had the highest annual metropolitan population growth rate among megacities in the Asia-Pacific region, at about **** percent. In contrast, all three Japanese megacities Tokyo, Nagoya, and Osaka had the lowest annual population growth rates across APAC, with Osaka's population shrinking by **** percent as of January 2024.

This dataset lists cities which consists of above 15,000 inhabitants. Each city is associated with its country and sub-country to reduce the number of ambiguities. Subcountry can be the name of a state (eg in the United Kingdom or the United States of America) or the major administrative section (eg "region" in "France").

The COVID-19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non-methane volatile organic compounds (NMVOCs) in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the S-NPP Cross-Track Infrared Sounder (CrIS) over eight of the world’s megacities: Mexico City, Beijing, Los Angeles, Tokyo, São Paulo, Delhi, Lagos, and Karachi. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID-19 lockdowns. Statistically significant changes to PANs occurred over two megacities: Los Angeles (PAN decreased) and Beijing (PAN increased). Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export poten...

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The COVID-19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non-methane volatile organic compounds (NMVOCs) in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the S-NPP Cross-Track Infrared Sounder (CrIS) over eight of the world’s megacities: Mexico City, Beijing, Los Angeles, Tokyo, São Paulo, Delhi, Lagos, and Karachi. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID-19 lockdowns. Statistically significant changes to PANs occurred over two megacities: Los Angeles (PAN decreased) and Beijing (PAN increased). Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export ...

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.

The rapid urbanization in China since the 1970s has led to an exponential growth of metal stocks (MS) in use in cities. A retrospect on the quantity, quality, and patterns of these MS is a prerequisite for projecting future metal demand, identifying urban mining potentials of metals, and informing sustainable urbanization strategies. Here, we deployed a bottom-up stock accounting method to estimate stocks of iron, copper, and aluminum embodied in 51 categories of products and infrastructure across 10 Chinese megacities from 1980 to 2016. We found that the MS in Chinese megacities had reached a level of 2.6–6.3 t/cap (on average 3.7 t/cap for iron, 58 kg/cap for copper, and 151 kg/cap for aluminum) in 2016, which still remained behind the level of western cities or potential saturation level on the country level (e.g., approximately 13 t/cap for iron). Economic development was identified as the most powerful driver for MS growth based on an IPAT decomposition analysis, indicating further increase in MS as China’s urbanization and economic growth continues in the next decades. The latecomer cities should therefore explore a wide range of strategies, from urban planning to economy structure to regulations, for a transition toward more “metal-efficient” urbanization pathways.

The unprecedented volume of urban sensing data has allowed the tracking of individuals at remarkably high resolution. As an example, Telecommunication Service Providers (TSPs) cannot provide their service unless they continuously collect information regarding the location of their customers. In conjunction with appropriate post-processing methodologies, these traces can be augmented with additional dimensions such as the activity of the user or the transport mode used for the completion of journeys. However, justified privacy concerns have led to the enforcement of legal regulations aiming to hinder, if not entirely forbid, the use of such private information even for purely scientific purposes. One of the most widely applied methods for the communication of mobility information without raising anonymity concerns is the aggregation of trips in origin–destination (OD) matrices. Previous work has showcased the possibility to exploit multi-period and purpose-segmented ODs for the synthesis of realistic disaggregate tours. The current study extends this framework by incorporating the multimodality dimension into the framework. In particular, the study evaluates the potential of synthesizing multimodal, diurnal tours for the case where the available ODs are also segmented by the transport mode. In addition, the study proves the scalability of the method by evaluating its performance on a set of time period-, trip purpose-, and transport mode-segmented, large-scale ODs describing the mobility patterns for millions of citizens of the megacity of Tokyo, Japan. The resulting modeled tours utilized over 96% of the inputted trips and recreated the observed mobility traces with an accuracy exceeding 80%. The high accuracy of the framework establishes the potential to utilize privacy-safe, aggregate urban mobility data for the synthesis of highly informative and contextual disaggregate mobility information. Implications are significant since the creation of such granular mobility information from widely available data sources like aggregate ODs can prove particularly useful for deep explanatory analysis or for advanced transport modeling purposes (e.g., agent-based, microsimulation modeling).

About this dataset

Context

This list ranks the 1 cities in the Colonial Heights city, VA by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Colonial Heights city, VA by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Colonial Heights city Black Population: This tells us how much of the entire Colonial Heights city, VA Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 22 cities in the Big Stone County, MN by White population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by White Population: This column displays the rank of cities in the Big Stone County, MN by their White population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• White Population: The White population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Big Stone County White Population: This tells us how much of the entire Big Stone County, MN White population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Population in largest city in Japan was reported at 37115035 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.