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.

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 world is a crowded place, with more than 7 billion people on the planet as of 2014. About half of this population lives in urban areas, and ongoing migration into city centers has given rise to the megacity—a metropolitan area with 10 million people or more. This story map was produced by Esri's story map team. It is a customization of the Esri Story Map Journal app, and was created in collaboration with the Smithsonian Institution. This story map was also published on Smithsonian.com:https://www.smithsonianmag.com/science-nature/make-cities-explode-size-these-interactive-maps-180952832/

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.

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 the population of the largest urban agglomerations in the world from 1950 to 2010, with projected figures for 2020. By 2020, the population of the Shanghai agglomeration in China is projected to be roughly ***** million people.

The average variation of key urban indicators and GHGs of 41 megacities.

Urban open space (UOS) plays an important role, especially in areas characterized by intense social and economic activity. However, UOS mapping products for major cities around the world are lacking. To fill this gap, we used a deep learning–based method based on a tiny-manual annotation strategy to map UOS and produced a 1.19 m resolution UOS map of 2021 in urban areas of global 169 megacities with populations of over 3,000,000 people (namely the OpenspaceGlobal product). It contains five urban open space categories, namely "park and green spaces," "outdoor sports spaces," "transportation spaces," "water body spaces", and "background". The OpenspaceGlobal product can promote a better understanding of human-made space surfaces in major cities worldwide and facilitate functions such as urban livability estimation, urban planning, and sustainable development. The OpenspaceGlobal product is free to use for non-commercial forms including scientific research and science promotion under proper citation.

This database provides 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 area 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 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, 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 Area (MUA), Functional Urban Area (FUA).

This statistic provides the share of the total land area that is built-up in the ** largest cities around the world in 2015. As of this year, about ***** percent of Los Angeles' land area was considered built-up.

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 shows the projected population of the largest urban agglomerations worldwide in 2035. In that year, the population of the New York-Newark agglomeration in the United States is projected to be **** million people.

Cities ranking and mega citiesTokyo is the world’s largest city with an agglomeration of 37 million inhabitants, followed by New Delhi with 29 million, Shanghai with 26 million, and Mexico City and São Paulo, each with around 22 million inhabitants. Today, Cairo, Mumbai, Beijing and Dhaka all have close to 20 million inhabitants. By 2020, Tokyo’s population is projected to begin to decline, while Delhi is projected to continue growing and to become the most populous city in the world around 2028.By 2030, the world is projected to have 43 megacities with more than 10 million inhabitants, most of them in developing regions. However, some of the fastest-growing urban agglomerations are cities with fewer than 1 million inhabitants, many of them located in Asia and Africa. While one in eight people live in 33 megacities worldwide, close to half of the world’s urban dwellers reside in much smaller settlements with fewer than 500,000 inhabitants.About the dataThe 2018 Revision of the World Urbanization Prospects is published by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It has been issued regularly since 1988 with revised estimates and projections of the urban and rural populations for all countries of the world, and of their major urban agglomerations. The data set and related materials are available at: https://esa.un.org/unpd/wup/

This statistic shows the population growth rate of the top twenty largest urban agglomerations in the United States from 2000 to 2030. Between 2025 and 2030, the average annual population growth rate of the New York-Newark agglomeration is projected to be roughly **** percent.

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.

SummaryThis metadata record provides details of the data supporting the claims of the related manuscript: “Projecting future populations of urban agglomerations: around the world and through the 21st century ”.The data consist of HTML files with interactive maps for future populations projections of urban agglomerations, and HTML file displaying figures for postdictions of urban agglomerations, as well as 5 .csv files containing the raw data.The related study estimated population trends throughout the 21st century for approximately 20,000 urban agglomerations in 151 countries by working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban growth model.Data accessThe following resources, which were among the sources of the data analyzed in the related study, are available from the links below.- Postdiction results for 1794 urban agglomerations http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#postdiction-for-1794-agglomerations-link- Temporal evolution from 2010 to 2100 of the geographical distribution of urban agglomerations, arranged by population scale, as predicted within the various SSP scenarios http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.htmlThese data are also available in raw .csv form via the 'Raw data' link on the same page, and also in the 5 files included as part of this data record.- Available urban-population data include the UN’s World Urbanization Prospects 2018 (https://population.un.org/wup/) and Gridded Population of the World, v4 (https://doi.org/10.7927/H4BC3WMT). Available settlement-point data include, in addition to the above urban population sources, World Gazetteer (https://www.arcgis.com/home/item.html?id=346ce13fa2d4468a9049f71bcc250f37) and GeoNames (https://www.geonames.org/). GDP per capita data is available from OECD.stat (https://stats.oecd.org/), Global Metro Monitor (https://www.brookings.edu/research/global-metro-monitor/), and World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/). OpenStreetMap is available at https://www.openstreetmap.org/. Scenario data for SSPs are available at the IIASA-SSP database (https://doi.org/10.1016/j.gloenvcha.2016.05.009). CodeCode used for the analysis can be downloaded from the author's lab's website: http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#codes. These are written in R. They are provided only for the purpose of tracing the analytical procedure. They are not executable without appropriate datasets.

The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urban development. Multi-objective optimization methods are widely applied to tackle such challenges, providing decision-makers with a set of optimal solutions that balance multiple objectives. However, existing studies often lack quantitative analyses of the relationships among these solutions, limiting their applicability in accommodating decision-makers with varying preferences. This study focuses on Jing’an District in Shanghai, a representative region of a Chinese megacity, to address this global issue. Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. Furthermore, the Pareto solution set is quantitatively analyzed from a cost-benefit perspective by integrating marginal benefit theory. This approach provides robust support for decision-makers seeking an optimal balance between cost and benefit, offering scenario-specific strategies. The findings of this study not only present an innovative, systematic, and flexible solution to the “parking dilemma” in high-density residential areas but also provide practical guidance and insights for other large cities in the planning and implementation of smart underground parking facilities.

Trace metals, as constituents of ambient air, can have impacts on human and environmental health. The Global Atmospheric Passive Sampling (GAPS) and GAPS Megacities (GAPS-MC) networks investigated trace metals in the air at 51 global locations by deploying polyurethane foam disk passive air samplers (PUF–PAS) for periods of 3–12 months. Aluminum and iron exhibited the highest concentrations in air (x̅ = 3400 and 4630 ng/m3, respectively), with notably elevated values at a rural site in Argentina thought to be impacted by resuspended soil. Urban sites had the highest levels of toxic Pb and Cd, with enrichment factors suggesting primarily anthropogenic influences. High levels of As at rural sites were also observed. Elevated trace metal concentrations in cities are associated with local emissions and higher PM2.5 and PM10 concentrations. Brake and tire wear-associated metals Sb, Cu, and Zn are significantly correlated and elevated at urban locations relative to those at background sites. These data demonstrate the versatility of PUF–PAS for measuring trace metals and other particle-associated pollutants in ambient air in a cost-effective and simple manner. The data presented here will serve as a global baseline for assessing future changes in ambient air associated with industrialization, urbanization, and population growth.

This data has been collected to parameterize the Multimedia Urban Model for 19 different mega or major cities. The data collected here can be used with the model, which is available from https://github.com/tfmrodge/FugModel, to estimate the transport and fate of organic contaminants from urban areas.