The Data Appeal | Company Data | API, Dataset | 251M POI Data Mapped | Coverage from 2019 | Estimate POI Sentiment and Footfall
# | poi_id |
type |
value |
date_last_checked |
---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx |
2 | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx |
3 | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx |
4 | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx |
5 | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx |
6 | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx |
7 | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx |
8 | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx |
9 | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
10 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx |
... | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx |
# | poi_id |
name |
street_address |
latitude |
longitude |
industry |
category |
date_refreshed |
country |
state |
county |
city |
stars |
rooms |
price_class |
sentiment |
popularity |
hours_popular |
main_clusters |
most_discussed_topics |
spoken_languages |
traveler_origin |
traveler_type |
website |
date_first_presence |
date_closed |
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx |
4 | 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 |
5 | 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 |
6 | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
7 | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx |
8 | 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 |
9 | 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 |
10 | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | 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 | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx |
# | poi_id |
day_of_week |
period_time |
open_time |
close_time |
date_last_checked |
---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx |
2 | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx |
4 | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx |
5 | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx |
6 | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
7 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx |
8 | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx |
9 | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx |
10 | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
... | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx |
# | poi_id |
date |
period |
time_period |
popularity |
---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx |
2 | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx |
4 | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx |
5 | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx |
6 | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx |
7 | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx |
8 | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx |
9 | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx |
10 | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx |
... | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx |
# | poi_id |
date |
popularity |
---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx |
2 | xxxxxxxxxx | Xxxxx | Xxxxxx |
3 | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx |
4 | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx |
5 | xxxxxxxxx | Xxxxxxx | xxxxxx |
6 | Xxxxx | xxxxxxxxxx | xxxxxx |
7 | Xxxxxxxxxx | xxxxxx | Xxxxx |
8 | Xxxxxx | xxxxx | xxxxxxxx |
9 | xxxxxxx | Xxxxx | Xxxxxxxx |
10 | xxxxxxxxxx | xxxxxx | Xxxxxxxxx |
... | xxxxxx | Xxxxxxxxx | Xxxxxxxxx |
# | poi_id |
date |
reviews |
sentiment |
---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx |
2 | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx |
3 | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx |
4 | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx |
5 | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx |
6 | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx |
7 | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx |
8 | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx |
9 | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
10 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx |
... | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0017057322b7865f99b3f927c393a7c8ee82fae9 | |
type
|
String | atmosphere | |
value
|
String | family_friendly | |
date_last_checked
|
DateTime | 2023-09-12T00:00:00+00:00 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 4eb6aaf43f81905019576f3b22d2fe3189429bdc | |
name
|
String | % Arabacia Plaza Indonesia | |
street_address
|
String | Plaza Indonesia, Jl. M.H. Thamrin No.Kav. 28-30, RT.9/RW.... | |
latitude
|
Float | -6.1929989 | |
longitude
|
Float | 106.8222671 | |
industry
|
String | Food & Beverage | |
category
|
String | Cafe | |
date_refreshed
|
DateTime | 2023-09-14T00:00:00+00:00 | |
country
|
String | indonesia | |
state
|
String | jakarta raya | |
county
|
String | jakarta pusat | |
city
|
String | menteng | |
stars
|
|||
rooms
|
|||
price_class
|
|||
sentiment
|
Float | 86.2 | |
popularity
|
Float | 60.25 | |
hours_popular
|
String | {"monday":"evening","tuesday":"afternoon","wednesday":"ev... | |
main_clusters
|
String | [{"cluster": "Appearance","sentiment": 70.00},{"cluster":... | |
most_discussed_topics
|
String | [{"topic": "coffee","sentiment": 87.18},{"topic": "latte"... | |
spoken_languages
|
String | [{"language": "en","sentiment": 81.44,"percentage": 58.82... | |
traveler_origin
|
|||
traveler_type
|
|||
String | Phone Number | ||
website
|
|||
date_first_presence
|
DateTime | 2021-11-04T00:00:00+00:00 | |
date_closed
|
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 002bb746b324b9db24cde7cbff4c853c7113f233 | |
day_of_week
|
Integer | 0 | |
period_time
|
Integer | 1 | |
open_time
|
String | Closed | |
close_time
|
|||
date_last_checked
|
DateTime | 2023-09-12T00:00:00+00:00 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0017057322b7865f99b3f927c393a7c8ee82fae9 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
period
|
String | weekend | |
time_period
|
String | (12-15) Early Afternoon | |
popularity
|
Float | 29.21 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0017057322b7865f99b3f927c393a7c8ee82fae9 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
popularity
|
Float | 51.19 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0017057322b7865f99b3f927c393a7c8ee82fae9 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
reviews
|
Integer | 5 | |
sentiment
|
Integer | 100 |
Attribute | Type | Example | Mapping |
---|---|---|---|
String | 9fbf6902-3259-43e0-b84d-c802b1940899 | POI ID | |
String | POI Name | ||
String | Address | ||
Decimal | 40.786342970476895 | Latitude | |
Decimal | -119.2065156609571 | Longitude | |
String | Advertising | Company Industry | |
String | POI Category | ||
date_refreshed
|
Date | ||
String | United States of America | Country Name | |
String | California | State Name | |
String | Bernalillo County | County Name | |
String | Berlin | City Name | |
stars
|
Integer | ||
rooms
|
Integer | ||
price_class
|
Integer | ||
sentiment
|
Decimal | ||
popularity
|
Decimal | ||
hours_popular
|
String | {"monday":null,"tuesday":"afternoon","wednesday":"late_mo... | |
main_clusters
|
Decimal | [{"cluster": "Atmosphere","sentiment": 76.99},{"cluster":... | |
most_discussed_topics
|
Decimal | [{"topic": "service","sentiment": 78.57},{"topic": "staff... | |
spoken_languages
|
Decimal | [{"language": "it","sentiment": 85.93,"percentage": 94.39... | |
traveler_origin
|
Decimal | [{"country": "it","sentiment": 84.67,"percentage": 19.93}... | |
traveler_type
|
Decimal | [{"traveler_type": "couple","sentiment": 83.51,"ercentage... | |
String | Company Phone Number | ||
String | https://www.ibm.com | Company Website | |
date_first_presence
|
Date | ||
date_closed
|
Date |
Description
Country Coverage
History
Volume
137 | Online Sources Monitored |
195 | Countries Mapped |
251 | Million Points of Interest Mapped |
320 | Billion Pieces of Online Content Analyzed Each Day |
Pricing
License | Starts at |
---|---|
One-off purchase | Available |
Monthly License | Available |
Yearly License | Available |
Usage-based | Available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall?
Contact our team to harness the power of Company Data and Point of Interest (POI) Data solutions! Unlock unparalleled insights with our GIS Data. Explore Business Location Data across 180+ countries. Coverage since 2019.
What is The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall used for?
This product has 5 key use cases. The Data Appeal Company recommends using the data for Location Intelligence, Retail Site Selection, Sentiment Analysis, Marketing Data Enrichment, and Point of Interest (POI) Mapping. Global businesses and organizations buy Company Data from The Data Appeal Company to fuel their analytics and enrichment.
Who can use The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Company Data. Get in touch with The Data Appeal Company to see what their data can do for your business and find out which integrations they provide.
How far back does the data in The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall go?
This product has 4 years of historical coverage. It can be delivered on a daily, weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. The Data Appeal Company is headquartered in Italy.
How much does The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall cost?
Pricing information for The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall is available by getting in contact with The Data Appeal Company. Connect with The Data Appeal Company to get a quote and arrange custom pricing models based on your data requirements.
How can I get The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall?
Businesses can buy Company Data from The Data Appeal Company and get the data via S3 Bucket, SFTP, Email, and REST API. Depending on your data requirements and subscription budget, The Data Appeal Company can deliver this product in .csv and .xls format.
What is the data quality of The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall?
The Data Appeal Company has reported that this product has the following quality and accuracy assurances: 80% match rate. You can compare and assess the data quality of The Data Appeal Company using Datarade’s data marketplace. The Data Appeal Company has received 3 reviews from clients. The Data Appeal Company appears on selected Datarade top lists ranking the best data providers, including Who’s New on Datarade? .
What are similar products to The Data Appeal Company Data API, Dataset 251M POI Data Mapped Coverage from 2019 Estimate POI Sentiment and Footfall?
This product has 3 related products. These alternatives include The Data Appeal Global Geographic Data API, Dataset 251M POI Data Mapped Coverage from 2019, Xverum Location Data Point of Interest (POI) Data 230M+ Global / 24M+ US Records Continuous Refresh Specific Business Location Data, and OpenWeb Ninja Business Listings Data, Reviews, Photos, Emails, Social + More 200M+ Local Businesses & POI Google Maps Global API Real-Time. You can compare the best Company Data providers and products via Datarade’s data marketplace and get the right data for your use case.