PredictHQ's Intelligent Event Data Sample | Hospitality, Travel & Tourism Data | Customisable Geolocation and POI | Get accurate Polygon Data!
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Verified Data Provider
Verified Data Provider| # | id |
duplicate_of_id |
parent_event_id |
title |
description |
category |
labels |
start |
end |
predicted_end |
timezone |
duration |
scope |
place_hierarchies |
venue_id |
venue_formatted_address |
rank |
local_rank |
phq_attendance |
predicted_event_spend_total |
predicted_event_spend_accommodation |
predicted_event_spend_hospitality |
predicted_event_spend_transportation |
impact_patterns |
first_seen |
updated |
cancelled |
postponed |
deleted_reason |
brand_safe |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
| 2 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx |
| 3 | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx |
| 4 | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
| 5 | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
| 6 | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx |
| 7 | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx |
| 8 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx |
| 9 | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx |
| 10 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx |
| ... | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx |
| # | id |
duplicate_of_id |
parent_event_id |
title |
description |
category |
labels |
start |
end |
predicted_end |
timezone |
duration |
country |
lat |
lon |
scope |
place_hierarchies |
venue_id |
venue_name |
venue_formatted_address |
rank |
local_rank |
phq_attendance |
predicted_event_spend_total |
predicted_event_spend_accommodation |
predicted_event_spend_hospitality |
predicted_event_spend_transportation |
impact_patterns |
first_seen |
updated |
state |
cancelled |
postponed |
deleted_reason |
brand_safe |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
| 2 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx |
| 3 | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx |
| 4 | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
| 5 | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
| 6 | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx |
| 7 | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx |
| 8 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx |
| 9 | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx |
| 10 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx |
| ... | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx |
Volume
10.7K
records
Avail. Formats
.json and .csv
File
Coverage
249
Countries
History
6
months
Data Dictionary
[Sample] PredictHQ-HospitalityTravelTourism-Events-EifelTower-Jan2023-Jul2023.csv
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
id
|
String | 4u7oiZVA6Ub4pqQejM | |
|
duplicate_of_id
|
|||
|
parent_event_id
|
|||
|
title
|
String | Versailles - Christmas School Holidays | |
|
description
|
|||
|
category
|
String | school-holidays | |
|
labels
|
String | holiday,school,christmas-holiday | |
|
start
|
DateTime | 2022-12-17T00:00:00+00:00 | |
|
end
|
DateTime | 2023-01-02T23:59:59+00:00 | |
|
predicted_end
|
|||
|
timezone
|
|||
|
duration
|
Integer | 1468799 | |
| String | FR | Country Name | |
| Float | 48.801408 | Latitude | |
| Float | 2.130122 | Longitude | |
| String | 222-22t@5yv-j89-g6k | Placekey | |
|
scope
|
String | region | |
|
place_hierarchies
|
String | 6295630>6255148>3017382>3012874 | |
|
venue_id
|
|||
| String | Location Name | ||
|
venue_formatted_address
|
|||
|
rank
|
Integer | 90 | |
|
local_rank
|
|||
|
phq_attendance
|
|||
|
predicted_event_spend_total
|
|||
|
predicted_event_spend_accommodation
|
|||
|
predicted_event_spend_hospitality
|
|||
|
predicted_event_spend_transportation
|
|||
|
impact_patterns
|
|||
|
first_seen
|
DateTime | 2021-08-08T20:51:27+00:00 | |
|
updated
|
DateTime | 2023-08-12T15:02:05+00:00 | |
| String | active | State Name | |
|
cancelled
|
|||
|
postponed
|
|||
|
deleted_reason
|
|||
|
brand_safe
|
[Sample] PredictHQ-HospitalityTravelTourism-Events-EifelTower-Sep2023-Mar2024.csv
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
id
|
String | 3xhKQX4EYeFe6fK4Kh | |
|
duplicate_of_id
|
|||
|
parent_event_id
|
|||
|
title
|
String | Paris - Summer School Holidays | |
|
description
|
|||
|
category
|
String | school-holidays | |
|
labels
|
String | holiday,school,summer-holiday | |
|
start
|
DateTime | 2023-07-08T00:00:00+00:00 | |
|
end
|
DateTime | 2023-09-03T23:59:59+00:00 | |
|
predicted_end
|
|||
|
timezone
|
|||
|
duration
|
Integer | 5011199 | |
|
country
|
String | FR | |
|
lat
|
Float | 48.856614 | |
|
lon
|
Float | 2.3522219 | |
| String | 222-22t@5yv-j89-g6k | Placekey | |
|
scope
|
String | region | |
|
place_hierarchies
|
String | 6295630>6255148>3017382>3012874 | |
|
venue_id
|
|||
|
venue_name
|
|||
|
venue_formatted_address
|
|||
|
rank
|
Integer | 90 | |
|
local_rank
|
|||
|
phq_attendance
|
|||
|
predicted_event_spend_total
|
|||
|
predicted_event_spend_accommodation
|
|||
|
predicted_event_spend_hospitality
|
|||
|
predicted_event_spend_transportation
|
|||
|
impact_patterns
|
|||
|
first_seen
|
DateTime | 2023-01-19T20:13:30+00:00 | |
|
updated
|
DateTime | 2024-01-11T04:18:37+00:00 | |
|
state
|
String | active | |
|
cancelled
|
|||
|
postponed
|
|||
|
deleted_reason
|
|||
|
brand_safe
|
Description
Free sample dataset of attended, non-attended, and unscheduled events around your preferred location. These categories play a crucial role in generating domestic, inbound, and outbound tourism activity - which drives transportation, accommodation, retail, restaurants, and other demand.
Events such as concerts, school holidays or conferences are powerful tourism attractions. Used by hospitality, travel, and tourism industry providers, PredictHQ’s data can help quickly identify key events that impact locations and behaviors to enhance targeted event-based marketing or foster local tourism partnerships. By better understanding which types of events attract the most tourists, you’re able to easily adjust your strategy to maximize demand and improve forecasting.
Data also includes Predicted Event Spend, a dollar figure that reflects the predicted amount of retail, accommodation, and transportation spending in a specific area as a result of a major event. At the core of this figure sits our market leading global event coverage, predicted event attendance, local accommodation demand, aviation demand, third party data and more to give you greater geographical context of the scale of the economic impact an event will have.
Location: Pick your favourite location
Visibility Window: 6-Months Historical
Categories: community, concerts, conferences, expos, festivals, performing arts, sports, academic, daylight savings, observances, politics, public holidays, school holidays, airport delays, disasters, health warnings, severe weather, terror
Fields Included:
- Title
- Category
- Labels
- Description
- Start date and time
- End date and time
- Predicted end time
- Timezone
- Country
- Lat / Lon
- Venue Name
- Venue Address
- Rank (PHQ Rank, Local Rank)
- PHQ Attendance
- Event status
- Place Hierarchy
- Created/updated timestamps
- Predicted event spend total
- Predicted event spend accommodation
- Predicted event spend hospitality
- Predicted event spend transportation
Polygon information: PredictHQ's polygons enable you to see the full area impacted by an event represented as a shape, because many types of events don't occur neatly at a point on a map. That means you will get a much more accurate picture of impact. Data samples including polygons are available upon request.
Data quality:
PredictHQ's data quality is one of its key strengths:
1) We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality.
2) We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites.
3) We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event.
4) On average we process 28 million events and 422,000 entities every day
5) We track the quality of our data over time and make improvements as needed.
About PredictHQ:
PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best.
Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/
Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics, hospitality, travel, tourism, aviation, flight, ride-sharing, transportation, mobility, school holiday, observances, public holidays, election, campaign, holiday, delays, polygon, mapping, location, weather, severe weather, historical weather,
Country Coverage
Africa
(58)
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo
Congo (Democratic Republic of the)
Côte d'Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Réunion
Saint Helena, Ascension and Tristan da Cunha
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Swaziland
Tanzania, United Republic of
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
Asia
(51)
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
China
Cyprus
Georgia
Hong Kong
India
Indonesia
Iran (Islamic Republic of)
Iraq
Israel
Japan
Jordan
Kazakhstan
Korea (Democratic People's Republic of)
Korea (Republic of)
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Lebanon
Macao
Malaysia
Maldives
Mongolia
Myanmar
Nepal
Oman
Pakistan
Palestine, State of
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Syrian Arab Republic
Taiwan
Tajikistan
Thailand
Timor-Leste
Turkey
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
Europe
(51)
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Germany
Gibraltar
Greece
Guernsey
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia (the former Yugoslav Republic of)
Malta
Moldova (Republic of)
Monaco
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Svalbard and Jan Mayen
Sweden
Switzerland
Ukraine
United Kingdom
Åland Islands
North America
(13)
Belize
Bermuda
Canada
Costa Rica
El Salvador
Greenland
Guatemala
Honduras
Mexico
Nicaragua
Panama
Saint Pierre and Miquelon
United States of America
Oceania
(25)
American Samoa
Australia
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia (Federated States of)
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
Vanuatu
Wallis and Futuna
Other
(9)
Antarctica
Bouvet Island
British Indian Ocean Territory
Christmas Island
Cocos (Keeling) Islands
French Southern Territories
Heard Island and McDonald Islands
South Georgia and the South Sandwich Islands
United States Minor Outlying Islands
South America
(42)
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Bolivia (Plurinational State of)
Bonaire, Sint Eustatius and Saba
Brazil
Cayman Islands
Chile
Colombia
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
Falkland Islands (Malvinas)
French Guiana
Grenada
Guadeloupe
Guyana
Haiti
Jamaica
Martinique
Montserrat
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Vincent and the Grenadines
Sint Maarten (Dutch part)
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela (Bolivarian Republic of)
Virgin Islands (British)
Virgin Islands (U.S.)
History
6 months of historical data
Volume
| 10,700 | records |
Pricing
Free sample available
PredictHQ has not published pricing information for this product yet.
You can request detailed pricing information below.
Suitable Company Sizes
Small Business
Medium-sized Business
Enterprise
Delivery
Methods
S3 Bucket
UI Export
REST API
Frequency
hourly
daily
weekly
real-time
on-demand
Format
.json
.csv
Use Cases
Categories
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101 countries covered
6 months of historical data
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Frequently asked questions
What is PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data!?
Free sample dataset of attended, non-attended, and unscheduled events around your preferred location. These categories play a crucial role in generating domestic, inbound, and outbound tourism activity - which drives transportation, accommodation, retail, restaurants, and other demand.
What is PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data! used for?
This product has 5 key use cases. PredictHQ recommends using the data for Demand Forecasting, Delivery Optimization, Workforce Optimization, Event Visibility, and Dynamic Pricing. Global businesses and organizations buy Event Data from PredictHQ to fuel their analytics and enrichment.
Who can use PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data!?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Event Data. Get in touch with PredictHQ to see what their data can do for your business and find out which integrations they provide.
How far back does the data in PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data! go?
This product has 6 months of historical coverage. It can be delivered on a hourly, daily, weekly, real-time, and on-demand basis.
Which countries does PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data! cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. PredictHQ is headquartered in United States of America.
How much does PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data! cost?
Pricing information for PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data! is available by getting in contact with PredictHQ. Connect with PredictHQ to get a quote and arrange custom pricing models based on your data requirements.
How can I get PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data!?
Businesses can buy Event Data from PredictHQ and get the data via S3 Bucket, UI Export, and REST API. Depending on your data requirements and subscription budget, PredictHQ can deliver this product in .json and .csv format.
What is the data quality of PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data!?
You can compare and assess the data quality of PredictHQ using Datarade’s data marketplace. PredictHQ appears on selected Datarade top lists ranking the best data providers, including June Provider Spotlight.
What are similar products to PredictHQ’s Intelligent Event Data Sample Hospitality, Travel & Tourism Data Customisable Geolocation and POI Get accurate Polygon Data!?
This product has 3 related products. These alternatives include PredictHQ’s Intelligent Event Data Hospitality, Travel & Tourism Data Tourist Attraction Data Eiffel Tower, Paris September 2023 - March 2024, The Data Appeal Hospitality, Travel and Tourism Data API, Dataset 200 Million+ POI Data Mapped Coverage from 2019, and Grepsr Yelp Resturants Address and Reviews Data Global Coverage with Custom and On-demand Datasets. You can compare the best Event Data providers and products via Datarade’s data marketplace and get the right data for your use case.
