The Data Appeal | Location Data, Point of Interest (POI) Data and Map Data | API, Dataset | 200 Million + POI Data Mapped | Coverage from 2019
# | 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 | 000ccc0b9da3bf54ac6c3c1ba744b10ac95719ea | |
type
|
String | openings | |
value
|
String | sunday_closed | |
date_last_checked
|
DateTime | 2023-09-12T00:00:00+00:00 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | e3affb374c66db07539df8c87df0bb3a7875f203 | |
name
|
String | (Perfect balance) Ladies tailor all type of designer work . | |
street_address
|
String | Dainik Shivner Marg, Gandhi Nagar, Upper Worli, Worli, Mu... | |
latitude
|
Float | 18.9940825 | |
longitude
|
Float | 72.82187119999999 | |
industry
|
String | Freelance | |
category
|
String | Tailor | |
date_refreshed
|
DateTime | 2023-09-13T00:00:00+00:00 | |
country
|
String | india | |
state
|
String | maharashtra | |
county
|
String | mumbai city | |
city
|
String | bombay | |
stars
|
|||
rooms
|
|||
price_class
|
|||
sentiment
|
|||
popularity
|
|||
hours_popular
|
|||
main_clusters
|
|||
most_discussed_topics
|
|||
spoken_languages
|
|||
traveler_origin
|
|||
traveler_type
|
|||
String | Phone Number | ||
website
|
String | https://perfect-balance-tailor.business.site/ | |
date_first_presence
|
DateTime | 2021-11-08T00:00:00+00:00 | |
date_closed
|
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 000ccc0b9da3bf54ac6c3c1ba744b10ac95719ea | |
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 | 0163025a87e8e12f41c0555d0b55a9224ff71927 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
period
|
String | weekdays | |
time_period
|
String | (12-15) Early Afternoon | |
popularity
|
Float | 12.89 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0163025a87e8e12f41c0555d0b55a9224ff71927 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
popularity
|
Float | 44.36 |
Attribute | Type | Example | Mapping |
---|---|---|---|
poi_id
|
String | 0163025a87e8e12f41c0555d0b55a9224ff71927 | |
date
|
DateTime | 2021-09-01T00:00:00+00:00 | |
reviews
|
Integer | 3 | |
sentiment
|
Float | 93.33 |
Attribute | Type | Example | Mapping |
---|---|---|---|
String | 9fbf6902-3259-43e0-b84d-c802b1940899 | POI ID | |
name
|
String | ||
street_address
|
String | ||
Decimal | 40.786342970476895 | Latitude | |
Decimal | -119.2065156609571 | Longitude | |
String | Advertising | Company Industry | |
category
|
String | ||
date_refreshed
|
Date | ||
country
|
String | ||
state
|
String | ||
county
|
String | ||
city
|
String | ||
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 | Phone Number | ||
website
|
String | ||
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
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
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Frequently asked questions
What is The Data Appeal Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019?
Connect with us for comprehensive Location Data, Point of Interest (POI) Data, and Map Data solutions! Discover invaluable insights with our Business Location Data, spanning across 180+ countries, and backed by continuous coverage since 2019. Unleash the power of data to drive your business forward.
What is The Data Appeal Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019 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 Location Data from The Data Appeal Company to fuel their analytics and enrichment.
Who can use The Data Appeal Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Location 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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019 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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019 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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019 cost?
Pricing information for The Data Appeal Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019 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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019?
Businesses can buy Location 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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019?
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 Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019?
This product has 3 related products. These alternatives include The Data Appeal Business Location Data Point of Interest (POI) Data Map Data API, Dataset 200 Million + POI Data Mapped, SafeGraph: Location Data - Global Coverage 52M+ POIs, and Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.