In 2023, the most visited city in the United States by international tourists was New York, attracting just under nine million visitors. Miami and Los Angeles followed in the ranking, with roughly 4.4 million and 3.6 million international visitors, respectively.

This statistic shows the most popular United States cities for summer travel in 2016. During the survey, 16 percent of the respondents stated that they planned to visit New York for their summer city trip in 2016, making it the most popular city trip destination.

This statistic shows the leading city destinations in the United States in 2019, by number of international arrivals. New York was the leading city destination in the U.S. in terms of international arrivals with approximately ** million visitors from foreign countries in 2019.

In the fourth quarter of 2024, San Francisco was the most popular city among millennials in the United States, with 71 percent saying they liked the city. Meanwhile, San Diego was the most popular among Generation X, and Nashville among Baby Boomers.

The Mexican city of Cancún was the top Latin American destination for foreign visitors in 2018, with approximately 6.04 million international tourist arrivals. This number was expected to increase by 1.8 percent in 2019, reaching 6.15 million foreign arrivals that year. Argentina's capital, Buenos Aires, ranked second on the list of top city destinations for international tourism in Latin America, with nearly 2.7 million international tourist arrivals in 2018 and an estimate of 2.77 million arrivals for 2019. During the depicted period, the most visited city in the whole American continent was New York.

This statistic shows the most popular domestic summer city destinations for travelers in the United States in 2015, according to travel agent members of the ASTA Research Family. During the survey, 17 percent of the respondents forecasted that Orlando would be a popular domestic city destination for U.S. travelers in summer 2015.

This feature layer provides access to OpenStreetMap (OSM) tourist attraction point data for North America, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes tourism features defined as a query against the hosted feature layer (i.e. tourism is not blank).In OSM, tourism features are places and things of specific interest to tourists including places to see, places to stay, things and places providing information and support to tourists. These features are identified with a tourism tag. There are hundreds of different tag values used in the OSM database. In this feature layer, unique symbols are used for several of the most popular tourism types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Cities level or 1:160k scale) to see the tourism features display. You can click on a feature to get the name of the tourism feature. The name of the feature will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this tourism layer displaying just one or two tourism types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. tourism is ruin), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

In 2021, the most popular cities in the U.S. for Gen Z renters had over 40 percent share of Gen Z applicants. Boulder, CO had the highest share of Gen Z renter applicants of 65 percent.

While these cities had the highest share of Gen Z apartment seekers in 2021, different cities registered a significant growth in the share of Gen Z applicants between 2020 and 2021. These cities are likely to join this list in the near future.

In 2015, Dubai was the city with the highest number of tourists staying at least one night per capita. That same year, international overnight visitor spending in Dubai reached 28.5 billion US dollars. The city, which is the largest and most populous city in the United Arab Emirates, was also one of the most expensive holiday destinations in the world in 2018. In 2016, 14.87 million people visited Dubai from abroad.

Europe and Asia: top destinations

According to the source, most of the 20 tourist cities with the highest number of visitor arrivals per capita were Asian or European cities. After Dubai, Amsterdam and Prague were the second and third cities with the greatest number of tourists spending at least one night per capita. With respectively, 2.7 and 2.5 visitors per capita, the two European cities were followed by another one: London. Singapore, ranked fifth with 2.1 visitors per capita, was the first Asian city in the list. Thus, New-York the first North American city of this ranking was 17th. It appears that Europe remains one of the most popular tourist destinations. Asia was also a very attractive destinations for international visitors with Hong-Kong being the leading city destination in 2017.

Focus on tourism in Europe

Since the nineties, the number of international tourist arrivals in Europe keeps increasing. France is the European country with the largest number of international visitors’ arrivals, while Spain and Italy are also two of the leading tourist destinations on the continent. Travel and tourism have an important contribution to GDP in Europe, reaching 781.6 billion US dollar and 2018 and expected to attain 991.4 billion in 2028.

This statistic shows the number of international overnight visitors in the most popular North American city destinations in 2016. New York had the largest number of international overnight visitors in 2016 with 12.7 million.

Enjoy the most reliable, compliant point of interest Visit Data dataset on the market.

Veraset Visits (Visit Data) integrates traditional movement data with point-of-interest (POI) enrichment and uncovers consumer behavior across millions of branded and retail sites in the US. Visits is an invaluable resource for marketers, retailers, and planners seeking to attribute consumer visits and interactions with various points of interest.

Featuring coverage of ~6% of the USA population, or approximately 15 million pseudonymous devices visiting nearly 4 million places each day, our proprietary machine learning model merges raw GPS signal with precise polygons to understand which POI pseudonymous devices visit when. (In other words, “X pseudonymous device id visited Y Starbucks at Z date and time.")

The result is reliable, accurate, actionable, device-level visits or Visit Data, refreshed and delivered daily.

Use Cases: - Placement - Dynamic Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Placement and Targeting - Attribution - Segmentation and Audience Building - Advertising - Credit Card Loyalty - Competitive Analysis

For up-to-date schema, visit: https://www.veraset.com/docs/visits

The number of international arrivals in tourist accommodation establishments in Rome, Italy, rose significantly in 2023 over the previous year, but remained below pre-pandemic levels. Overall, inbound tourist arrivals in the city reached around *** million in 2023. Which nationalities visit Rome the most? In 2023, the United States was by far the leading inbound travel market in Rome based on tourist arrivals, with over *** million arrivals of U.S. travelers to the city’s accommodation services. In the same year, the United Kingdom, Spain, and France followed in the ranking. How many people visit the Colosseum every year? The Colosseum archaeological park, one of Rome’s most famous landmarks, ranked as the most visited cultural attraction in Italy in 2023, with over ** million visits. That year, the iconic site also topped the list of Italian museums and archaeological areas with the highest income, ranking ahead of Pompeii and the Uffizi Galleries.

New York, Las Vegas, Washington, Chicago, Los Angeles, Branson, Philadelphia, San Francisco, Stratford, Houston, Boston, Atlanta, Orlando, Toronto, Sandy, Dallas, Ashland, St. Louis, Montreal, Minneapolis

This data package consists of 26 datasets all containing statistical data relating to the population and particular groups within it belonging to different countries, mostly the United States.

Bangkok had the most international overnight visitors in 2018 with 22.78 million, followed by Paris and London with 19.1 and 19.09 million, respectively.
 

 Is Bangkok the world’s number one tourist destination?

Bangkok has become increasingly popular as a tourist attraction over the past years, and its popularity looks unlikely to wane any time soon. The number of overnight visitors to Thailand’s capital has more than doubled since 2010. The city has many attractions on offer, including temples, local markets, a varied nightlife and shopping. Therefore, it may come as no surprise that Bangkok was also one of the leading cities in international visitor spending worldwide in 2018, with visitors to the city spending over 20 billion U.S. dollars. Although tourists spend a lot of money when traveling to Bangkok, it still ranked among the 30 cheapest holiday destinations in the world by average price per night per person in 2018.

Global tourism industry 

Globally, the tourism industry has experienced continuous year-over-year growth for the past decade. In 2018, the number of international tourist arrivals worldwide was estimated to reach approximately 1.4 billion – the figure having more than doubled over the past 15 years.

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.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

This data set includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December, 2003, data set.

This layer is sourced from maps.bts.dot.gov.

This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures (all teeth lost, dental visits, mammograms, pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name