Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Overview

The Daily Travel data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

Notes

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Data in the charts and graphs above is updated weekly on Mondays. The data lags one week behind the current date.

Data analysis is conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips.

Key Features

1.Level : Indicates National, State, or County level metrics.

2.Date : The date when the data was recorded.

3.State FIPS : A two-digit code representing the FIPS state code.

4.State Postal Code : State postal code.

5.County FIPS : Five-digit FIPS county code.

6.County Name : County name.

7.Population Staying at Home : Number of residents staying at home, i.e., persons who make no trips with a trip end more than one mile away from home.

8.Population Not Staying at Home : Number of residents not staying at home.

9.Number of Trips : Number of trips made by residents, i.e., movements that include a stay of longer than 10 minutes at an anonymized location away from home.

10.Number of Trips <1 : Number of trips by residents shorter than one mile.

11.Number of Trips 1-3 : Number of trips by residents greater than one mile and shorter than 3 miles (1 ≤ trip distance < 3 miles).

12.Number of Trips 3-5 : Number of trips by residents greater than 3 miles and shorter than 5 miles (3 ≤ trip distance < 5 miles).

13.Number of Trips 5-10 : Number of trips by residents greater than 5 miles and shorter than 10 miles (5 ≤ trip distance < 10 miles).

14.Number of Trips 10-25 : Number of trips by residents greater than 10 miles and shorter than 25 miles (10 ≤ trip distance < 25 miles).

15.Number of Trips 25-50 : Number of trips by residents greater than 25 miles and shorter than 50 miles (25 ≤ trip distance < 50 miles).

16.Number of Trips 50-100 : Number of trips by residents greater than 50 miles and shorter than 100 miles (50 ≤ trip distance < 100 miles).

17.Number of Trips 100-250 : Number of trips by residents greater than 100 miles and shorter than 250 miles (100 ≤ trip distance < 250 miles).

18.Number of Trips 250-500 : Number of trips by residents greater than 250 miles and shorter than 500 miles (250 ≤ trip distance < 500 miles).

19.Number of Trips >=500 : Number of trips by residents greater than 500 miles (trip distance ≥ 500 miles).

20.Row ID : Unique row identifier.

21.Week : The week number corresponding to the recorded date.

22.Month : The month number corresponding to the recorded date.

If this was helpful, a vote is appreciated 😄!

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 of origin (15 items: New York; Washington; Michigan; California; ...) Traveller characteristics (3 items: Trips; Nights; Spending in Canada).

This dataset provides a detailed inventory of tourist attractions in the United States, India, and Iran, gathered through an extensive crawl of Google Maps. It encompasses essential details such as attraction names, location, ratings, category, and visitor feedback. Designed to facilitate exploratory data analysis and predictive modeling, this dataset is a valuable tool for researchers and analysts.

For an enhanced analytical experience, interactive Tableau dashboards have been created for each country's dataset. These dashboards offer visual interpretations and trend analyses of tourist activities. Access the dashboards at the following link:

US Tourist Attractions Dashboard India Tourist Attractions Dashboard Iran Tourist Attractions Dashboard

Your feedback is invaluable for enhancing the utility and accuracy of this dataset and its associated dashboards. Please share your insights and suggestions.

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 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) Countries visited (15 items: United States; Mexico; United Kingdom; France; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

Content

Male Flight Attendants

This folder contains the data behind the story Dear Mona, How Many Flight Attendants Are Men?

male-flight-attendants.tsv contains the percentage of U.S. employees that are male in 320 different job categories.

Source: IPUMS, 2012

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

Polylines advising of the estimated warning time for lahars of 10,000,000 to 100,000,000 cubic meters of volume, based on fastest estimated travel time. This data is used to estimate evacuation time for people within the volcanic hazard area. Read the full metadata for more information about the time zones (https://matterhorn.co.pierce.wa.us/GISmetadata/pdbplan_volcanic_time_of_travel.html). Any data download constitutes acceptance of the Terms of Use (https://matterhorn.co.pierce.wa.us/Disclaimer/PierceCountyGISDataTermsofUse.pdf).

The absolute economic contribution of tourism in Latvia was forecast to continuously increase between 2024 and 2029 by in total 1.2 billion U.S. dollars (+42.97 percent). After the ninth consecutive increasing year, the economic contribution is estimated to reach 3.8 billion U.S. dollars and therefore a new peak in 2029. Depited is the economic contribution of the tourism sector in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the absolute economic contribution of tourism in countries like Lithuania and Estonia.

The international tourism expenditure in Latvia was forecast to continuously increase between 2024 and 2029 by in total 422.3 million U.S. dollars (+17.94 percent). After the fifteenth consecutive increasing year, the expenditure is estimated to reach 2.8 billion U.S. dollars and therefore a new peak in 2029. Notably, the international tourism expenditure of was continuously increasing over the past years.Covered are expenditures of international outbound visitors to other countries from the selected region, including payments to foreign carriers for international transport. Domestic tourism expenditures are not included. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the international tourism expenditure in countries like Estonia and Lithuania.

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.

Content

The following Airbnb activity is included in this Seattle dataset: * Listings, including full descriptions and average review score * Reviews, including unique id for each reviewer and detailed comments * Calendar, including listing id and the price and availability for that day

Inspiration

• Can you describe the vibe of each Seattle neighborhood using listing descriptions?
• What are the busiest times of the year to visit Seattle? By how much do prices spike?
• Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?

For more ideas, visualizations of all Seattle datasets can be found here.

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here.

The absolute economic contribution of tourism in Israel was forecast to continuously increase between 2024 and 2029 by in total 8.5 billion U.S. dollars (+37.06 percent). After the ninth consecutive increasing year, the economic contribution is estimated to reach 31.6 billion U.S. dollars and therefore a new peak in 2029. Depited is the economic contribution of the tourism sector in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the absolute economic contribution of tourism in countries like Bahrain and Jordan.

This dataset provides monthly totals of US airline passengers from 1949 to 1960. This dataset is taken from a built-in dataset of R called AirPassengers. Analysts often employ various statistical techniques, such as decomposition, smoothing, and forecasting models, to analyze patterns, trends, and seasonal fluctuations within the data. Due to its historical nature and consistent temporal granularity, the Air Passengers dataset serves as a valuable resource for researchers, practitioners, and students in the fields of statistics, econometrics, and transportation planning.

Tripadvisor, Inc. is an American online travel company that offers online hotel reservations and bookings for transportation, lodging, travel experiences, and restaurants. TripAdvisor is the most popular travel website and it stores data for almost all restaurants, showing locations (even latitude and longitude coordinates), restaurant descriptions, user ratings and reviews, and many more aspects.

For any queries please contact me, I would love to help you.

Introduction

The Datathon is a weekend-long competition where you are challenged to work on a real-world business case from different areas of Machine Learning, AI, and Data Science.

Context

Argo Solutions - A leading technology company in Latin America, developing solutions to facilitate expense management and corporate travel using technology as an enabler of these processes. ​Our team is committed in simplifying our customers' routine, providing an efficient, innovative and seamless experience.​

Challenge

In this competition, we provided a dataset simulating real corporate travel systems - focusing on flights and hotels.
Competitors must analyze this set with over one thousand users and 250 thousand travels to produce insights. How can Argo offer the best travel experience for its customers? Explore, invent and surprise us! See an online BI report.

Inspiration

Now is publicly available to you enjoy and explore!
How generate this dataset? See the link

Acknowledgements

• Argo Solutions - website.
• Photo by Nils Nedel on Unsplash.