In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

Population in the largest city (% of urban population) in North America was reported at 8.1557 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. North America - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

The statistic depicts the ten largest cities in Mexico in 2020. In 2020, Mexico City had around 8.84 million residents which made it the largest city in Mexico. Population of Mexico Mexico is a federal republic located in North America, sharing borders with the United States to the north, and to the southeast with Guatemala and Belize. With a total area of over 1.9 million square kilometers, it is the fourteenth largest nation in the world and the fifth largest in the Americas. In 2014, Mexico’s total population amounted to approximately 120 million people. A little under two thirds of Mexico’s total population is of Mestizo ethnicity. The total population has steadily grown over the past decade, despite being the source to the largest migration flow between countries in the world; in 2010, around 11.6 million immigrants from Mexico lived in the United States. The migration flow between the United States and Mexico has however, decreased over the past ten years: Between 1995 and 2000, over 2.9 million migrants emigrated from Mexico to the United States. This was more than the double of migrants who emigrated from Mexico to the United States between 2005 and 2010. Each year, Mexico's population grows by about 1.24 percent compared to the previous year. Mexico City, the country’s capital and largest city, is home to approximately 8.6 million people.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

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.

Cost of living can vary wildly depending on the city that people live in. Some are hugely expensive while others are comparatively cheaper. This variety can also be seen in the average overnight price of hotels. In September 2023, New York held the highest hotel rate in North America when compared to other selected cities. The average overnight price for a hotel room in New York was 504 U.S. dollars.

About this dataset

Context

This list ranks the 354 cities in the North Dakota 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 North Dakota 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 North Dakota Black Population: This tells us how much of the entire North Dakota 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 551 cities in the North Carolina 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 North Carolina 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 North Carolina Black Population: This tells us how much of the entire North Carolina 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/.

Context

In this dataset, you will find the public data unified of the trips done with the public bike-sharing systems in important North American cities.

Content

The structure of the dataset is: - One folder per city - On each folder: - Trips.csv: Text file that is containing all the trips done in bike - Stations.csv: text file that is containing the id of the station, the name of the station and location (latitude, longitude) if available - Weather.csv: daily weather data for the city collected from the POWER data access viewer (from the SSE Renewable energy) of NASA

The detail on the columns of the files will be provided in the Kaggle metadata.

Acknowledgements

Currently, the cities concerns are : - Montreal (CA), data provided by Bixi on their open data platform - Toronto (CA), data provided by the city of Toronto - Washington (USA), data provided by Capital bike share

Inspiration

This data can be found in multiple datasets online (usually outdated), so I will try to do my best to update it regularly.

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

A dataset listing Florida cities by population for 2024.

The map title is Brandon. Tactile map scale. 2 centimetres = 3 kilometres North arrow pointing to the north. Brandon and surrounding area. Main roads, Route 1 and Route 10. A circle with a dot in the middle indicates a bus terminal at the northern edge of the city. A circle with the shape of an airplane in it indicates Brandon Municipal Airport to the north of the city. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

In a 2024 study of urban dark sky destinations in North America, Querétaro in Mexico reported the highest increase in hotel searches between July 2023 and June 2024 compared to the previous year, at *** percent. American Fork in Utah, United States, ranked second, with growth in hotel searches of ** percent.

North America Bus Shelters Market size was valued at USD 924.84 Million in 2023 and is projected to reach USD 1,561.77 Million by 2031, growing at a CAGR of 7.77% from 2024 to 2031.

North America Bus Shelters Market Overview Bus shelters are covered buildings usually seen at bus stops and intended to protect and soothe people waiting for public transportation. These shelters provide commuters with a dedicated and protected area and typically have a roof, side walls, and seating arrangements. Depending on the infrastructure and location, bus shelters can have basic forms or sophisticated constructions with contemporary amenities like digital displays, illumination, and connectivity options. They act as a conduit between passengers and public transportation systems, making them essential for suburban and urban transportation networks.

North America Smart City Surveillance Market is projected to grow around USAD 3.6 billion by 2031, at a CAGR of 13.2% during the forecast period.

North America Smart City ICT Infrastructure Market is expected to experience significant growth, with a CAGR of 15-18% from 2025 to 2030.

Snapshot img

AI In Smart Cities Market Size 2025-2029

The AI in smart cities market size is valued to increase by USD 109.95 billion, at a CAGR of 30% from 2024 to 2029. Data privacy, security, and ethical concerns will drive the ai in smart cities market.

Major Market Trends & Insights

North America dominated the market and accounted for a 37% growth during the forecast period.
By Component - Hardware segment was valued at USD 6.33 billion in 2023
By Deployment - Cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 109947.70 million
CAGR from 2024 to 2029 : 30%

Market Summary

In the realm of urban development, Artificial Intelligence (AI) has emerged as a pivotal catalyst for transforming cities into smart entities. According to recent market intelligence, The market is projected to reach a value of USD19.3 billion by 2025, underscoring its immense potential. This growth is driven by the intensification of urbanization pressures and the sustainability imperative, necessitating innovative solutions to manage resources efficiently and enhance citizen services. AI's role extends beyond mere automation; it is revolutionizing urban applications through generative capabilities. From traffic management and energy optimization to public safety and waste management, AI is redefining city services, delivering real-time insights and predictive analytics.
Its ability to learn and adapt to complex urban environments is enabling cities to become more responsive, resilient, and sustainable. However, the integration of AI in Smart Cities also presents challenges. Data privacy, security, and ethical concerns demand careful consideration as cities embrace this technology. Balancing the benefits of AI with its potential risks requires a collaborative approach between governments, technology providers, and citizens. As the future of urban living unfolds, AI's role in creating smarter, more livable cities will continue to evolve, shaping the way we interact with our urban environments.

What will be the Size of the AI In Smart Cities Market during the forecast period?

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How is the AI In Smart Cities Market Segmented ?

The AI in smart cities industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Hardware
  Software
  Services


Deployment

  Cloud
  On-premises


Technology

  Machine learning
  Computer vision
  Edge AI
  NLP
  Others


Application

  Traffic management
  Infrastructure management
  Public safety and security
  Environmental monitoring
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

The market is witnessing continuous evolution, with advanced technologies like deep learning applications, machine learning models, and computer vision systems transforming urban infrastructure. This market encompasses various sectors, including crime prediction models, public transportation scheduling, energy consumption monitoring, and real-time traffic optimization. For instance, AI-powered traffic management systems can reduce congestion by up to 20%, while predictive policing algorithms enhance public safety. The integration of autonomous vehicle integration, smart parking solutions, and building automation systems further enhances urban efficiency. Moreover, smart infrastructure monitoring, AI-driven urban planning, and smart grid technologies ensure resource optimization and environmental sustainability.

However, data privacy regulations, cybersecurity protocols, and open data initiatives remain critical considerations to ensure ethical and secure implementation. Overall, the market is a dynamic and essential sector, driving innovation and improving the quality of life in urban areas.

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The Hardware segment was valued at USD 6.33 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 37% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How AI In Smart Cities Market Demand is Rising in North America Request Free Sample

The market is witnessing significant evolution, with North America leading the ch

In North America Urban Logistics Market , was valued at approximately USD 10.11 billion in 2022 and is projected to reach USD 12.45 billion by 2029,

Smart City Surveillance Display Market Outlook

According to our latest research, the Global Smart City Surveillance Display market size was valued at $4.2 billion in 2024 and is projected to reach $11.6 billion by 2033, expanding at a robust CAGR of 11.8% during 2024–2033. The primary driver fueling this exceptional growth is the increasing adoption of integrated surveillance solutions in smart city initiatives, particularly as urban centers globally prioritize public safety, efficient traffic management, and infrastructure modernization. As cities continue to digitize and interconnect their critical systems, advanced surveillance display technologies are becoming indispensable for real-time monitoring, rapid response, and proactive urban management, thus propelling the market forward at an unprecedented rate.

Regional Outlook

North America currently holds the largest share of the global Smart City Surveillance Display market, accounting for approximately 38% of the total market value in 2024. This dominance is underpinned by the region’s mature urban infrastructure, widespread adoption of smart city frameworks, and sustained investments in public safety technologies. The United States, in particular, has been at the forefront, with major metropolitan areas deploying sophisticated surveillance networks that leverage high-resolution LED and LCD displays for traffic management, crime prevention, and emergency response. Favorable regulatory policies, substantial public and private sector funding, and a strong ecosystem of technology providers further reinforce North America’s leadership in this sector.

The Asia Pacific region is emerging as the fastest-growing market, projected to expand at a remarkable CAGR of 14.2% through 2033. This rapid growth is driven by aggressive urbanization, government-backed smart city projects, and increasing investments in digital infrastructure across countries such as China, India, Japan, and South Korea. In China, the proliferation of large-scale surveillance initiatives and the integration of AI-powered displays into city management systems are setting new benchmarks for urban safety and efficiency. The region’s burgeoning population and rising concerns over security and urban mobility are further accelerating the adoption of advanced surveillance displays, making Asia Pacific a key focus for market stakeholders.

Emerging economies in Latin America, the Middle East, and Africa are gradually increasing their adoption of smart city surveillance display solutions, though growth is tempered by budgetary constraints, limited digital infrastructure, and varying regulatory landscapes. In these regions, localized demand is often shaped by the need to address specific urban challenges such as traffic congestion, public safety, and infrastructure resilience. While pilot projects and government initiatives are gaining traction, widespread deployment faces hurdles including high upfront costs, skills shortages, and the need for standardized technology frameworks. Nonetheless, these markets represent significant long-term opportunities as policy reforms and international investments begin to stimulate the smart city ecosystem.

Report Scope