PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs
# | device_id |
id_type |
horizontal_accuracy |
timestamp |
device_os |
source_id |
publisher_id |
app_id |
location_context |
geohash |
year |
month |
geohash3 |
geohash5 |
geohash6 |
geohash7 |
geohash8 |
geohash9 |
clave_device_dia |
day_of_week |
day |
hour |
distance_center |
day_type |
hour_type |
distance_type |
clave_device_dia_hora |
in_polygon |
kmh |
ob_type |
device_home_geohash |
device_work_geohash |
device_work_distance_center |
rwi |
administrative_area_level_1 |
administrative_area_level_2 |
administrative_area_level_3 |
route |
political |
locality |
sublocality |
|||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | xxxxxx | Xxxxxxxx | Xxxxxxx | 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 |
3 | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | 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 |
4 | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | 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 | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx |
5 | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | 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 | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx |
6 | Xxxxxxxxxx | Xxxxxxx | 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 | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
7 | 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 | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx |
8 | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | 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 |
9 | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx |
10 | 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 | 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 |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
device_id
|
2a9d0617-355b-4f38-92e1-e7b77ec301b3 | ||
id_type
|
aaid | ||
Float | 12.9110403060913 | Latitude | |
Float | 77.5614013671875 | Longitude | |
horizontal_accuracy
|
6 | ||
timestamp
|
1625553768000 | ||
device_os
|
Android | ||
source_id
|
33 | ||
publisher_id
|
7aae836893ea29f8299763b7759f64fd599766eb543dc7b7d7d80ba7e... | ||
app_id
|
2d901e3e08149bcede68d4563cf24058f8f4949c7e5f06b2e6b165851... | ||
location_context
|
0 | ||
geohash
|
tdr1kyqkr8ke | ||
year
|
2021 | ||
month
|
7 | ||
geohash3
|
tdr | ||
geohash5
|
tdr1k | ||
geohash6
|
tdr1ky | ||
geohash7
|
tdr1kyq | ||
geohash8
|
tdr1kyqk | ||
geohash9
|
tdr1kyqkr | ||
clave_device_dia
|
2a9d0617-355b-4f38-92e1-e7b77ec301b318814 | ||
day_of_week
|
2 | ||
day
|
6 | ||
hour
|
1 | ||
distance_center
|
2014 | ||
day_type
|
weekday | ||
hour_type
|
work_hour | ||
distance_type
|
<200 | ||
clave_device_dia_hora
|
2a9d0617-355b-4f38-92e1-e7b77ec301b3188141 | ||
in_polygon
|
1 | ||
kmh
|
1.14440916824341 | ||
ob_type
|
pedestrian | ||
device_home_geohash
|
tdr1kyqk | ||
device_work_geohash
|
tdr1kyqk | ||
device_work_distance_center
|
<200 | ||
rwi
|
25-35 | ||
administrative_area_level_1
|
Karnataka | ||
administrative_area_level_2
|
Bangalore Urban | ||
administrative_area_level_3
|
bangalore | ||
route
|
6th Main Road | ||
political
|
Bendre Nagar | ||
String | India | Country Name | |
locality
|
Bengaluru | ||
sublocality
|
Bengal | ||
String | little India | Neighborhood Name | |
String | 560070 | Postal Code |
Description
Country Coverage
History
Pricing
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs?
Data to identify and understand consumer behavior patterns and trends with Foot traffic and anonymized and aggregated mobility data.
What is PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs used for?
This product has 5 key use cases. Predik Data-driven recommends using the data for Location Intelligence, Location Analytics, Foot Traffic Analytics, Retail Analytics, and Customer Data Insights. Global businesses and organizations buy Location Data from Predik Data-driven to fuel their analytics and enrichment.
Who can use PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Location 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 Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs go?
This product has 4 years of historical coverage. It can be delivered on a monthly, quarterly, and yearly basis.
Which countries does PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs cover?
This product includes data covering 247 countries like USA, Japan, Germany, India, and United Kingdom. Predik Data-driven is headquartered in United States of America.
How much does PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs cost?
Pricing information for PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs 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 Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs?
Businesses can buy Location Data from Predik Data-driven and get the data via S3 Bucket, SFTP, Email, and Feed API. Depending on your data requirements and subscription budget, Predik Data-driven can deliver this product in .json, .csv, .xls, and .sql format.
What is the data quality of PREDIK Data-Driven Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs?
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 Aggregated Foot Traffic Data: Custom Datasets with Enriched Raw Mobility Data and Visitation at POIs?
This product has 3 related products. These alternatives include Real-Time Foot Traffic Data Aggregated Foot Traffic Data Location & Mobility Data Global 600+ Customers, Europe Location Data POI, Geospatial, Foot Traffic Data, Sentiment data, Business Listings Data & Store Location 251 Millions+ POI Data Mapped, and Point of Interest (POI) Data Global Location Data 6M+ POIs. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.