Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex
| # | id |
name |
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 |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
|---|---|---|---|
|
id
|
Integer | 108135561 | |
|
name
|
String | Walmart @ Miami 9191 W. Flagler St, Miami, FL 33174, USA | |
| String | Address | ||
| Integer | 33172 | Postal Code | |
|
do_date
|
String | 01-08-2021 | |
|
year
|
Integer | 2021 | |
|
month
|
Integer | 8 | |
|
day_of_week
|
String | Thursday | |
|
part_of_day
|
String | Evening | |
|
n_visitors
|
Integer | 77 | |
|
distance_from_home
|
Float | 19877.16 | |
|
travelled_countries
|
String | {"CAN": 0.5, "MEX": 0.5} | |
|
visitor_country_origin
|
String | {"USA": 1.0} | |
|
visitor_home_origin
|
String | {"03202312110113033": 0.114, "03202310332330223": 0.057, ... | |
|
visitor_work_origin
|
String | {"03202312110113033": 0.114, "03202310332330223": 0.057, ... | |
|
carrier
|
String | {"t-mobile usa": 0.743, "at&t wireless": 0.229, "verizon ... | |
|
brand_visited
|
String | {"Walmart Visitors": 100, "Subway Visitors": 100, "Teavan... | |
|
place_categories
|
String | {"Convenience Store Visitors": 100, "Burger Restaurant Vi... | |
|
geo_behaviour
|
String | {"DIY Enthusiasts": 100, "Foodies": 78, "Frequent Pizza R... | |
|
make
|
String | {"apple": 0.4, "samsung": 0.4, "generic": 0.057, "oneplus... | |
|
model
|
String | {"iphone": 0.4, "sm-g950u": 0.057, "sm-g960u1": 0.057, "a... | |
|
os_version
|
String | {"14": 0.2, "10.0": 0.143, "10": 0.114, "9": 0.114, "14.7... | |
|
ratio_age_18_24
|
Float | 0.25 | |
|
ratio_age_25_34
|
Float | 0.25 | |
|
ratio_age_35_44
|
Float | 0.188 | |
|
ratio_age_45_54
|
Integer | 0 | |
|
ratio_age_55
|
Float | 0.313 | |
|
ratio_female
|
Float | 0.233 | |
|
ratio_male
|
Float | 0.767 |
| 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
Geography
History
Volume
| 870 million | Monthly Active Users |
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 Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex?
We provide Location Intelligence Data, which helps power geographical information system (GIS) tools and provides data-driven insights across a wide range of use cases, from marketing to public planning and fraud detection.
What is Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex used for?
This product has 5 key use cases. Factori recommends using the data for Footfall Analytics, Foot Traffic Analytics, Retail Analytics, Geomarketing, and Retail Intelligence. Global businesses and organizations buy Location Data from Factori to fuel their analytics and enrichment.
Who can use Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Location 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 Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex go?
This Tabular Data has 1 years of historical coverage. It can be delivered on a daily, weekly, monthly, and quarterly basis.
Which countries does Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex 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 Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex cost?
Pricing for Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex 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 Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex?
Businesses can buy Location Data from Factori and get the data via S3 Bucket and SFTP. Depending on your data requirements and subscription budget, Factori can deliver this product in .csv format.
What is the data quality of Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex?
Factori has reported that this product has the following quality and accuracy assurances: 100% 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 Best Data Providers For Location-Based Marketing, Top 10 POI Data Providers & APIs, and Top 6 Clickstream & Click Path Data Providers.
What are similar products to Factori Human Mobility data by time of day, aggregated to Geohash/Quadkey/hex?
This Tabular Data has 3 related products. These alternatives include Factori Mobility/ Raw Location Data Global mobile location data (1 year history), Veraset- Geospatial ‘Visits’ or Point of Interest US Only, and Tamoco Aggregated Footfall Data USA (200M devices, 11M Point-of Interests). You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.
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