In 2023, the most visited city in the United States by international tourists was ********, attracting just under **** million visitors. ********************* followed in the ranking, with roughly *** million and *** million international visitors, respectively.

The Mexican city of Cancún was the top Latin American destination for foreign visitors in 2018, with approximately **** million international tourist arrivals. This number was expected to increase by *** percent in 2019, reaching **** 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 *** million international tourist arrivals in 2018 and an estimate of **** 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 United States cities for summer travel in 2016. During the survey, ** 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.

Indian Tourist Destinations Dataset Dataset Overview This dataset provides a comprehensive list of popular tourist destinations across India, categorized by city, with additional information on ratings, descriptions, and the best time to visit. The data is compiled from various travel survey platforms such as MakeMyTrip, Holidify, and other reliable travel resources.

Columns Description ID: A unique identifier for each city in the dataset. City: The name of the tourist destination city or region in India. Rating: An aggregate rating for the city, derived from surveys conducted on various travel platforms. The rating reflects the overall popularity, quality of tourist experience, and visitor satisfaction. About the City: A brief description of the city, highlighting its cultural, historical, or natural significance. This includes information on key attractions, local culture, and why it's a must-visit destination. Best Time to Visit: The recommended period or season to visit the city for the best tourist experience. This could be based on weather conditions, local festivals, or other seasonal factors that enhance the travel experience. Source of Data The ratings are based on aggregated data from well-known travel platforms such as:

MakeMyTrip Holidify TripAdvisor Other travel blogs and survey websites Potential Use Cases Travel Recommendations: Use the dataset to build travel recommendation systems or itinerary planning tools for tourists. Tourism Analysis: Analyze tourism trends, popular destinations, and visitor preferences across different regions of India. Sentiment Analysis: Combine this dataset with reviews and feedback from tourists to perform sentiment analysis and gain deeper insights into visitor experiences. Seasonal Trends: Study the impact of seasonal variations on tourism by analyzing the 'Best Time to Visit' column. Data Visualization: Create visual dashboards showcasing top-rated destinations, best times to visit, and key attractions for each city. Additional Information Data Format: CSV Total Records: 100 rows (one for each city/region) Data Refresh: This dataset can be periodically updated with more recent ratings and information as new data becomes available from travel platforms. Acknowledgments Special thanks to the platforms MakeMyTrip, Holidify, and other travel resources for providing the ratings and information used to compile this dataset. This dataset aims to promote travel and tourism in India by providing valuable insights into popular tourist destinations.

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

In 2025, San Miguel de Allende in Mexico was named the top destination in Latin America by travelers, receiving a score of 93.33. The Mexican capital followed in second place with a score of nearly 91 points.

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) State visited (15 items: Florida; New York; Washington; California; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

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.

Context

This dataset contains some data to try to answer the question, "What's the best city to live and work in as a Data Scientist?" I include data from the U.S. News & World Report Best Places to Live and Best States Rankings; city scores from Nomad List; rent indices from Zillow; and the number of jobs openings on Indeed.com. All data is publicly available online and manually compiled by myself.

Data Sources

• U.S. News Best Places to Live in the U.S.
• U.S. News Best States Rankings
• Nomad List
• Zillow Research Housing Data
• Wikipedia List of metropolitan statistical areas
• Indeed.com

The U.S. News Best Places and Best States Rankings are updated annually. They were last updated in Dec 2019, so I assume the next update will come in Dec 2020.

Dataset Info

Data from Best Places

For data points from the U.S. News Best Places, drill down into the page for each metro area. I have to manually collect these data points, and not all of them are fully populated. If there's interest, I'll upload a new version with more data filled in.

About Nomad Scores

Nomad List publishes scores for each city that update in real time every 10 mins. These scores are affected by the current weather in each city. Therefore, the scores vary quite a bit seasonally as well as during the day. Learn more here. I've sampled the scores at different times of year and different times of day. Timestamps are in ISO 8601 format. Nomad Scores are not available for all metros in the dataset.

Inspiration

1. Can you combine the data for each metro into an aggregate score (or multiple scores) to determine the best places to live?
2. Should climate be factored into your scores? If so, how?
3. Can you aggregate violent and property crime rates into an aggregate crime rate?
4. Can you combine a quantitative score with a ranking measure to obtain a total score?
5. Can you aggregate all the sampled Nomad Scores to obtain an overall Nomad Score for the metro? How should you weight each score?
6. Should you include the number of Data Scientist job postings in your total score for the metro? If so, how?

Content

Data was pulled from a table in the following Wikipedia article: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population I used Microsoft Excel's PowerQuery function to pull the table from Wikipedia. Lists each city, its rank (based on 2020 population), some data on its area, and population in both 2020 and 2010.

Banner image source: https://unsplash.com/photos/wh-7GeXxItI

Live city rankings with ignore politics political preference weighting applied. Showing 1-50 of 386 cities.

Las Vegas, New York, Washington, Chicago, Toronto, Los Angeles, Denver, San Francisco, Honolulu, Orlando, Cincinnati, Phoenix, Houston, Philadelphia, Atlanta, Boston, Dallas, Minneapolis, Seattle, Cleveland

Real-time city rankings with political preference weighting for Ignore Politics

Latin American cities face many problems that compromise them from different angles such as lack of infrastructure, government fragmentation, and environmental degradation. At the same time, each city tries to come up with its own solutions, but there are so many difficulties that in many cases it is difficult to keep attention and efforts focused on all these directions. For these reasons, this research aims to define some of the most common problems faced by cities in Latin America. Disseminating these similarities could help to face those problems, since, if local governments recognize that they face the same situations as their neighbors, they could organize themselves to study them and find solutions. To achieve these objectives, this research reviewed the diagnoses made by hundreds of Best Practice proposals collected in the libraries of UN Habitat and the Dubai International Award for Best Practices. Based on these results, this research built a proposal for the contest "Participatory Projects in Public Space Contest" organized in commemoration of the 450 years of Caracas. This proposal served as a case study where some of these cross-cutting problems in the region were explored. At the same time, the contest served as a framework to make these results public and promote discussion on some of these important issues. Finally, this research links different stages and synthesizes some important efforts that are intended to serve as a reference framework to better understand serious and everyday problems that are manifested in Latin American cities.

Percent Sunshine by US City

Discovering the Clear Skies for Astronomy and Sunniest Locations in the US

By Gary Hoover [source]

About this dataset

This dataset provides all the information you need to plan a visit to a city that offers plenty of sunshine! With data on average annual percent of possible sunshine for US cities, you'll be able to answer questions like Which cities get the most sunlight on average each year?, What months are best for sunny days in [CITY]? and many more. Whether you're looking for somewhere warm and sunny to take your vacation, or clear skies for nighttime stargazing, this dataset has all the answers! Find out how each city in the US stacks up against each other and make sure your next trip is one filled with bright blue skies!

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Research Ideas

• Creating a City Sunshine Index to compare the average percent of sunshine in different cities
• Estimating the amount of daylight hours available for astronomy stargazing in each city
• Creating a map to visualize monthly sunshine insights for vacation planning to different parts of the U.S

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Average Percent of Possible Sunshine by US City.csv | Column name | Description | |:--------------|:------------------------------------------------------------------| | CITY | Name of the city. (String) | | JAN | Average percent of possible sunshine in January. (Float) | | FEB | Average percent of possible sunshine in February. (Float) | | MAR | Average percent of possible sunshine in March. (Float) | | APR | Average percent of possible sunshine in April. (Float) | | MAY | Average percent of possible sunshine in May. (Float) | | JUN | Average percent of possible sunshine in June. (Float) | | JUL | Average percent of possible sunshine in July. (Float) | | AUG | Average percent of possible sunshine in August. (Float) | | SEP | Average percent of possible sunshine in September. (Float) | | OCT | Average percent of possible sunshine in October. (Float) | | NOV | Average percent of possible sunshine in November. (Float) | | DEC | Average percent of possible sunshine in December. (Float) | | ANN | Average percent of possible sunshine for the entire year. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Gary Hoover.

The city park with the highest annual visitation in 2023 was Central Park in New York, accounting for a total of ********** visitors. The second most visited city park in that year was Golden Gate Park in San Francisco, with nearly half the visitation of Central Park, at **********.

In 2018, Cancún (Mexico) was the Latin American city with the largest number of international visitors, receiving a total of 6.04 million tourists. It was followed by Punta Cana (Dominican Republic) with 3.89 million international tourists that year.

This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

About this dataset

Context

This list ranks the 70 cities in the Bergen County, NJ 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 Bergen County, NJ 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 Bergen County Black Population: This tells us how much of the entire Bergen County, NJ 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/.