This statistic shows the ten countries with the largest increase in the size of the urban population between 2018 and 2050. Based on forecasted population figures, the urban population of India is projected to be around 416 million more in 2050 than it was in 2018.

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.

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.

In 2024, Bangladesh's capital Dhaka had a pollution index score of 93.9, the highest among megacities in the Asia-Pacific region. In contrast, Japan's capital Tokyo had a pollution index score of 42.2 that year. Megacities on course for growth The United Nations defines megacities as cities with over ten million inhabitants. The population living in megacities has doubled in size in the last twenty years and is expected to rise even more until 2035. Today, the Asia-Pacific region is home to the highest number of megacities, with China and India alone accounting for around half of all megacities worldwide. At the same time, only half of the population in Asia is living in cities. This figure is also expected to rise exponentially over the next years, especially with much of the younger population migrating to larger cities. The growth of megacities and their higher population densities bring along several environmental problems. Exposure to pollution in India The most populated cities in APAC are located in Japan, China and India. As seen above, India's capital also falls among the top three most polluted megacities in the region and ranks second among the most polluted capital cities worldwide with an average PM2.5 concentration. As one of the fastest emerging economies in the world, India's rapid urbanization and industrialization have led to high pollution rates in different areas. The volume of emissions from coal-fired power plants has led to electricity and heat accounting for the largest share of greenhouse gas emissions in India. The country is also among the nations with the highest population share exposed to hazardous concentrations of air pollution worldwide.

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 ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

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).

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience’s theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model’s effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities’ economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in greater New York City. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography including substantial suburban and smaller city patterns. We also found that super-gentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already gentrified.

Japan’s largest city, greater Tokyo, had a staggering 37.19 million inhabitants in 2023, making it the most populous city across the Asia-Pacific region. India had the second largest city after Japan with a population consisting of approximately 33 million inhabitants. Contrastingly, approximately 410 thousand inhabitants populated Papua New Guinea's largest city in 2023. A megacity regionNot only did Japan and India have the largest cities throughout the Asia-Pacific region but they were among the three most populated cities worldwide in 2023. Interestingly, over half on the world’s megacities were situated in the Asia-Pacific region. However, being home to more than half of the world’s population, it does not seem surprising that by 2025 it is expected that more than two thirds of the megacities across the globe will be located in the Asia Pacific region. Other megacities are also expected to emerge within the Asia-Pacific region throughout the next decade. There have even been suggestions that Indonesia’s Jakarta and its conurbation will overtake Greater Tokyo in terms of population size by 2030. Increasing populationsIncreased populations in megacities can be down to increased economic activity. As more countries across the Asia-Pacific region have made the transition from agriculture to industry, the population has adjusted accordingly. Thus, more regions have experienced higher shares of urban populations. However, as many cities such as Beijing, Shanghai, and Seoul have an aging population, this may have an impact on their future population sizes, with these Asian regions estimated to have significant shares of the population being over 65 years old by 2035.

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.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience’s theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model’s effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities’ economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

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.

Web-based Taxi-Sharing Market Outlook

The global web-based taxi-sharing market size was valued at approximately USD 11.2 billion in 2023 and is anticipated to grow at a robust CAGR of 14.7% from 2024 to 2032, reaching an estimated USD 41.4 billion by 2032. This remarkable growth can be attributed to the increasing urbanization and the rising demand for cost-effective and efficient modes of transportation. As cities become more congested and the need for sustainable transport solutions becomes more pressing, web-based taxi-sharing services have emerged as a viable alternative to traditional taxi services. Moreover, the integration of advanced technologies such as artificial intelligence and machine learning in optimizing ride-sharing routes is further propelling the market growth.

One of the primary growth factors for the web-based taxi-sharing market is the increasing penetration of smartphones and internet connectivity across the globe. The proliferation of mobile applications that facilitate easy booking, tracking, and payment processes has made taxi-sharing services more accessible to a broader audience. Additionally, the rising awareness and acceptance of shared mobility solutions as an environmentally friendly alternative to owning personal vehicles are accelerating the market's expansion. Governments and regulatory bodies in various countries are also encouraging ride-sharing as part of their efforts to reduce carbon emissions and traffic congestion, providing a conducive environment for market growth.

Another significant driver of growth in the web-based taxi-sharing market is the economic benefits that these services offer to both service providers and consumers. For consumers, taxi-sharing services offer a more affordable means of transportation compared to traditional taxi or car ownership, which involves high maintenance and fuel costs. On the service provider side, companies can optimize their fleet operations, reduce idle times, and increase their profitability by offering ride-sharing and carpooling options. Moreover, the ability of taxi-sharing platforms to provide personalized and reliable services by leveraging data analytics and consumer preferences is enhancing customer satisfaction and loyalty, further driving market growth.

The growing trend of urbanization and the rising number of megacities are also contributing to the increased adoption of web-based taxi-sharing services. As more people migrate to urban areas, the demand for efficient urban mobility solutions is set to rise. Taxi-sharing services are well-positioned to address this demand by offering flexible transportation solutions that can be tailored to individual needs. In addition, the ongoing trend of digital transformation and the adoption of smart city initiatives are enabling the development of more integrated and intelligent transportation networks, fostering the growth of the web-based taxi-sharing market.

On the regional front, the Asia Pacific region is expected to hold a significant share of the web-based taxi-sharing market during the forecast period. The region's large population base, coupled with rapid urbanization and increasing disposable incomes, is boosting the demand for cost-effective transportation solutions. Moreover, key markets such as China and India are witnessing substantial growth in mobile internet users, enhancing the accessibility and convenience of taxi-sharing services. North America and Europe are also expected to contribute significantly to market growth, driven by the presence of established service providers and the increasing focus on sustainable transportation solutions. Meanwhile, regions like Latin America and the Middle East & Africa are gradually catching up as internet penetration and smartphone adoption rates continue to rise.

Service Type Analysis

The web-based taxi-sharing market can be categorized into two primary service types: ride sharing and carpooling. Ride sharing, which involves multiple individuals sharing a single vehicle without a predetermined network of users, represents a substantial portion of the market. This service type is driven by the convenience and cost-effectiveness that it offers to customers, as it provides a ride that is often cheaper than hiring a traditional taxi. With the aid of sophisticated algorithms and real-time tracking, ride-sharing services can efficiently match drivers with passengers heading in the same direction, optimizing the use of available vehicles and reducing wait times. The increasing customer preference for flexible and affordable ride solutions is thus amplifying the growth of the ride-sharing segment.

To comprehend urban evolution, it is necessary to investigate changes in urban land in megacities. However, volumetric expansion has not been well studied. This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. The approach identifies three processes of urban growth: new urbanization, intensified compactness, and intensified height. The method was applied to four megacities, Beijing, Moscow, Paris, and Houston, from 2000 to 2020. The results showed satisfactory overall accuracy, ranging between 80% and 90%. The expansion of new urbanization was found to be consistently faster than intensified compactness and intensified height in all cities. Analysis of Beijing revealed that during this period, new urbanization increased by 52.8%, while intensified compactness decreased by 25%, and intensified height increased by 87.5%. However, due to the initially small base area of intensified height, this growth was less significant in terms of overall land coverage compared to new urbanization. Additionally, the “diffusion to coalescence” pattern was found to be beneficial for urban intensification. Our research forecasts urban expansion and intensification in 2100 under different Shared Socioeconomic Pathways (SSPs), indicating that stricter sustainability policies may promote concentrated, vertical urban growth, while looser ones may lead to more dispersed expansion, underscoring the crucial role of these policies in shaping future urban development strategies.

The Pearl River Delta (PRD), where several megacities are located, has undergone drastic morphological changes caused by anthropogenic impact during the past few decades. In its main estuary, the water area has been reduced by 21% whilst the average water depth has increased by 2.24 m from 1970s to 2010s. The mainly human-induced morphological change together with sea level rise has jointly led to a remarkable change in the water stratification. However, the spatial and temporal variability of stratification in the estuary and associated driving mechanisms remain less understood. In this study, stratification in the Pearl River Estuary (PRE) in response to morphological change and external forcing is investigated by 3-dimensional numerical modeling. Simulation results indicate that stratification in the PRE exhibits distinct spatial and temporal variabilities. At a tidal-to-monthly time scale, variation of stratification is mainly driven by advection and straining through tidal forcing. At a monthly-to-seasonal scale, monsoon-driven river runoff and associated plume and fronts dominate the variation of stratification. Human-induced morphological change leads to an enhancement of stratification by up to four times in the PRE. Compared to an overwhelming human impact in the past few decades, future sea level rise would further enhance stratification, but to a much lesser extent than past human impacts. In addition, stratification in different areas of the estuary also responds differently to the driving factors. The western shoal of the estuary is most sensitive to changes in morphology and sea level due to its shallowness, followed by the channels and other parts of the estuary, which are less sensitive.

Humankind has historically settled near water. In fact, approximately two thirds of the worlds mega cities are situated on the coast. It comes as no surprise then that owning a waterfront property is more expensive than the average property price. In a report conducted by Knight Frank, they found that across 12 key global cities, properties situated at a harbor had an average added premium of 59.1 percent as of the third quarter of 2018.

In 2023, the congestion level of Bengaluru amounted to 53 percent each, meaning that it took 53 percent more time to get from one point to another compared to a free flow situation. Comparatively, the congestion level in Sydney and Hong Kong amounted to 30 and 29 percent respectively during the same year.

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/

With approximately 9.57 million inhabitants, Tokyo was Japan's most populous city as of 2023, followed by Yokohama, which, in the same year, counted about 3.75 million inhabitants. In total, there were twelve cities with a population of over one million people in Japan.