Factori AI & ML Training Data | Point of Interest Data (POI) | Global | Machine Learning Data
# | PlaceID |
do_date |
year |
month |
day_of_week |
part_of_day |
n_visitors |
distance_from_home |
travelled_countries |
visitor_country_origin |
visitor_home_origin |
visitor_work_origin |
carrier |
brand_visited |
place_categories |
geo_behaviour |
make |
model |
os_version |
ratio_age_18_24 |
ratio_age_25_34 |
ratio_age_35_44 |
ratio_age_45_54 |
ratio_age_55 |
ratio_female |
ratio_male |
ratio_residents |
ratio_workers |
ratio_others |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | 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 | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx |
3 | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx |
4 | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx |
5 | 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 | xxxxxxxxxx |
6 | 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 |
7 | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
8 | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx |
9 | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx |
10 | 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 | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
PlaceID
|
String | Walmart @ Miami 8400 Coral Way, Miami, FL 33155, USA | |
do_date
|
DateTime | 2024-05-01T00:00:00+00:00 | |
year
|
Integer | 2024 | |
month
|
Integer | 5 | |
day_of_week
|
String | All | |
part_of_day
|
String | Afternoon | |
n_visitors
|
Integer | 1926 | |
distance_from_home
|
Float | 66492.461 | |
travelled_countries
|
String | {"CUB": 0.175, "MEX": 0.14, "BHS": 0.105, "CAN": 0.088, "... | |
visitor_country_origin
|
String | {"USA": 1.0} | |
visitor_home_origin
|
String | {"03202312110131313": 0.019, "03202312111020202": 0.005, ... | |
visitor_work_origin
|
String | {"03202312110131313": 0.038, "03202312110132103": 0.004, ... | |
carrier
|
String | {"t-mobile usa": 0.525, "at&t wireless": 0.336, "verizon ... | |
brand_visited
|
String | {"Ginger People Visitors": 100, "Walmart Visitors": 94, "... | |
place_categories
|
String | {"Parking Area Visitors": 100, "Petroleum Company Visitor... | |
geo_behaviour
|
String | {"DIY Enthusiasts": 100, "Family Leisure Enthusiasts": 10... | |
make
|
String | {"apple": 0.929, "samsung": 0.054, "12": 0.007, "10": 0.0... | |
model
|
String | {"iphone": 0.896, "ipad": 0.032, "sm-g975u": 0.007, "mobi... | |
os_version
|
String | {"16": 0.314, "12": 0.138, "13": 0.133, "11": 0.095, "15"... | |
ratio_age_18_24
|
Float | 0.223 | |
ratio_age_25_34
|
Float | 0.123 | |
ratio_age_35_44
|
Float | 0.145 | |
ratio_age_45_54
|
Float | 0.223 | |
ratio_age_55
|
Float | 0.285 | |
ratio_female
|
Float | 0.068 | |
ratio_male
|
Float | 0.932 | |
ratio_residents
|
Float | 0.021 | |
ratio_workers
|
Float | 0.037 | |
ratio_others
|
Float | 0.942 |
Attribute | Type | Example | Mapping |
---|---|---|---|
String | 108135559 | Location ID | |
Name
|
Walmart @ Walmart, Miami, FL 33162, USA | ||
Day OF Week
|
saturday | ||
Part of Day
|
Morning | ||
n_visitors
|
274 | ||
Distance from home
|
166407 | ||
Vistor home
|
Lat/Long | ||
Visitor work
|
Lat/Long | ||
brand visited
|
Mc Donalds | ||
Demography Ratio
|
.239 |
Description
Country Coverage
History
Volume
5 million | POI's |
420 million | MAU |
Pricing
License | Starts at |
---|---|
One-off purchase |
$25,000$22,500 / purchase |
Monthly License | Not available |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?
We provide POI Data, which can be used to train AI & ML Models on14M physical locations globally, and unlock wide range of use cases, from marketing to public planning and fraud detection.
What is Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data used for?
This product has 5 key use cases. Factori recommends using the data for Geofencing, Location-based Advertising, Urban Mobility Analysis, Foot Traffic Analytics, and Point of Interest (POI) Mapping. Global businesses and organizations buy Foot Traffic Data from Factori to fuel their analytics and enrichment.
Who can use Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Foot Traffic Data. Get in touch with Factori to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data go?
This product has 1 years of historical coverage. It can be delivered on a daily, weekly, monthly, and quarterly basis.
Which countries does Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data cover?
This product includes data covering 248 countries like USA, Japan, Germany, India, and United Kingdom. Factori is headquartered in United States of America.
How much does Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data cost?
Pricing for Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data starts at USD25,000 per purchase. Factori offers a 10% discount when you buy data from them through Datarade. Connect with Factori to get a quote and arrange custom pricing models based on your data requirements.
How can I get Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?
Businesses can buy Foot Traffic Data from Factori and get the data via S3 Bucket. Depending on your data requirements and subscription budget, Factori can deliver this product in .csv format.
What is the data quality of Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?
Factori has reported that this product has the following quality and accuracy assurances: 95% Match rate. You can compare and assess the data quality of Factori using Datarade’s data marketplace. Factori has received 2 reviews from clients. Factori appears on selected Datarade top lists ranking the best data providers, including 10 Best Data Providers for 360 Customer View, 10 Best Data Providers for Customer Segmentation, and Best Data Providers For Location-Based Marketing.
What are similar products to Factori AI & ML Training Data Point of Interest Data (POI) Global Machine Learning Data?
This product has 3 related products. These alternatives include Factori Location Intelligence with Profile POI + People Data , The Data Appeal Point of Interest (POI) Data Location Data Map Data 200 Million + POI Data Mapped Sentiment & Popularity insights, and Global Bar & Restaurant Data Points of Interest (POI). You can compare the best Foot Traffic Data providers and products via Datarade’s data marketplace and get the right data for your use case.