PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom
# | geohash8 |
unique_pedestrians |
avg_pedestrian_staying_time |
pedestrians_devices_morning |
avg_pedestrian_staying_time_morning |
pedestrians_devices_afternoon |
avg_pedestrian_staying_time_afternoon |
pedestrians_devices_evening |
avg_pedestrian_staying_time_evening |
pedestrians_devices_dawn |
avg_pedestrian_staying_time_dawn |
unique_pedestrians_monday |
unique_pedestrians_tuesday |
unique_pedestrians_wednesday |
unique_pedestrians_thursday |
unique_pedestrians_friday |
unique_pedestrians_saturday |
unique_pedestrians_sunday |
unique_pedestrians_monday_morning |
unique_pedestrians_monday_afternoon |
unique_pedestrians_monday_evening |
unique_pedestrians_monday_dawn |
unique_pedestrians_tuesday_morning |
unique_pedestrians_tuesday_afternoon |
unique_pedestrians_tuesday_evening |
unique_pedestrians_tuesday_dawn |
unique_pedestrians_wednesday_morning |
unique_pedestrians_wednesday_afternoon |
unique_pedestrians_wednesday_evening |
unique_pedestrians_wednesday_dawn |
unique_pedestrians_thursday_morning |
unique_pedestrians_thursday_afternoon |
unique_pedestrians_thursday_evening |
unique_pedestrians_thursday_dawn |
unique_pedestrians_friday_morning |
unique_pedestrians_friday_afternoon |
unique_pedestrians_friday_evening |
unique_pedestrians_friday_dawn |
unique_pedestrians_saturday_morning |
unique_pedestrians_saturday_afternoon |
unique_pedestrians_saturday_evening |
unique_pedestrians_saturday_dawn |
unique_pedestrians_sunday_morning |
unique_pedestrians_sunday_afternoon |
unique_pedestrians_sunday_evening |
unique_pedestrians_sunday_dawn |
geohash_lat |
geohash_lon |
geohash_geometry |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx |
2 | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | 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 | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
3 | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | 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 | xxxxxxxxxx | xxxxxx | Xxxxx |
4 | 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 | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx |
5 | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | 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 | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx |
6 | 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 |
7 | 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 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx |
8 | 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 |
9 | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx |
10 | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx |
... | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | Xxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
geohash8
|
String | gcpvjbx0 | |
unique_pedestrians
|
Integer | 999 | |
avg_pedestrian_staying_time
|
Float | 2918.8571 | |
pedestrians_devices_morning
|
Integer | 295 | |
avg_pedestrian_staying_time_morning
|
Float | 139.6667 | |
pedestrians_devices_afternoon
|
Integer | 567 | |
avg_pedestrian_staying_time_afternoon
|
Float | 6512.6667 | |
pedestrians_devices_evening
|
Integer | 182 | |
avg_pedestrian_staying_time_evening
|
Integer | 475 | |
pedestrians_devices_dawn
|
Integer | 23 | |
avg_pedestrian_staying_time_dawn
|
Integer | 2315 | |
unique_pedestrians_monday
|
Integer | 45 | |
unique_pedestrians_tuesday
|
Integer | 113 | |
unique_pedestrians_wednesday
|
Integer | 227 | |
unique_pedestrians_thursday
|
Integer | 159 | |
unique_pedestrians_friday
|
Integer | 295 | |
unique_pedestrians_saturday
|
Integer | 91 | |
unique_pedestrians_sunday
|
Integer | 91 | |
unique_pedestrians_monday_morning
|
Integer | 318 | |
unique_pedestrians_monday_afternoon
|
Integer | 23 | |
unique_pedestrians_monday_evening
|
Integer | 23 | |
unique_pedestrians_monday_dawn
|
Integer | 45 | |
unique_pedestrians_tuesday_morning
|
Integer | 68 | |
unique_pedestrians_tuesday_afternoon
|
Integer | 68 | |
unique_pedestrians_tuesday_evening
|
Integer | 182 | |
unique_pedestrians_tuesday_dawn
|
Integer | 113 | |
unique_pedestrians_wednesday_morning
|
Integer | 68 | |
unique_pedestrians_wednesday_afternoon
|
Integer | 136 | |
unique_pedestrians_wednesday_evening
|
Integer | 23 | |
unique_pedestrians_wednesday_dawn
|
Integer | 0 | |
unique_pedestrians_thursday_morning
|
Integer | 23 | |
unique_pedestrians_thursday_afternoon
|
Integer | 91 | |
unique_pedestrians_thursday_evening
|
Integer | 45 | |
unique_pedestrians_thursday_dawn
|
Integer | 0 | |
unique_pedestrians_friday_morning
|
Integer | 91 | |
unique_pedestrians_friday_afternoon
|
Integer | 136 | |
unique_pedestrians_friday_evening
|
Integer | 68 | |
unique_pedestrians_friday_dawn
|
Integer | 23 | |
unique_pedestrians_saturday_morning
|
Integer | 45 | |
unique_pedestrians_saturday_afternoon
|
Integer | 45 | |
unique_pedestrians_saturday_evening
|
Integer | 0 | |
unique_pedestrians_saturday_dawn
|
Integer | 0 | |
unique_pedestrians_sunday_morning
|
Integer | 0 | |
unique_pedestrians_sunday_afternoon
|
Integer | 68 | |
unique_pedestrians_sunday_evening
|
Integer | 23 | |
unique_pedestrians_sunday_dawn
|
Integer | 0 | |
geohash_lat
|
Float | 51.50673866 | |
geohash_lon
|
Float | -0.089092255 | |
geohash_geometry
|
String | polygon ((-0.089263916015625 51.50665283203125,-0.0889205... |
Description
Country Coverage
History
Volume
55.2 million | rows |
Pricing
License | Starts at |
---|---|
One-off purchase | Not 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 PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom?
Our Aggregated Aggregated Foot Data & Moility Data brings you valuable insights into UK’s pedestrian movement accross different areas, visiting patterns, mobility behavior, travel times and other relevant factors.
What is PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom used for?
This product has 5 key use cases. Predik Data-driven recommends using the data for Foot Traffic Analytics, Foot Traffic Measurement, Site Visitation, Real Estate Insights, and Foot Traffic Analysis. Global businesses and organizations buy Foot Traffic Data from Predik Data-driven to fuel their analytics and enrichment.
Who can use PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom?
This product is best suited if you’re a Medium-sized Business, Enterprise, or Small Business looking for Foot Traffic Data. Get in touch with Predik Data-driven to see what their data can do for your business and find out which integrations they provide.
How far back does the data in PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom go?
This product has 2 years of historical coverage. It can be delivered on a weekly, monthly, quarterly, and yearly basis.
Which countries does PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom cover?
This product includes data covering 1 country like United Kingdom. Predik Data-driven is headquartered in United States of America.
How much does PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom cost?
Pricing information for PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom is available by getting in contact with Predik Data-driven. Connect with Predik Data-driven to get a quote and arrange custom pricing models based on your data requirements.
How can I get PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom?
Businesses can buy Foot Traffic Data from Predik Data-driven and get the data via S3 Bucket, SFTP, Email, UI Export, and REST API. Depending on your data requirements and subscription budget, Predik Data-driven can deliver this product in .bin, .json, .xml, .csv, .xls, .sql, and .txt format.
What is the data quality of PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom?
Predik Data-driven has reported that this product has the following quality and accuracy assurances: 85% quality. You can compare and assess the data quality of Predik Data-driven using Datarade’s data marketplace. Predik Data-driven has received 3 reviews from clients. Predik Data-driven appears on selected Datarade top lists ranking the best data providers, including Best +8 Airport APIs for Travel Data.
What are similar products to PREDIK Data-Driven I UK Aggregated Foot Traffic Data & Mobility Data I Measure Foot Traffic patterns within the United Kingdom?
This product has 3 related products. These alternatives include PREDIK Data-Driven I US Aggregated Foot Traffic Data & Visit Data I Analyze the US South Eastern Area, Unacast Aggregated Foot Traffic Data for U.S. Locations, and Intuizi’s GPS Location Data for the UK 7+mm Unique Daily Devices GDPR Compliant Mobility Data. 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.