PREDIK Data-Driven: Geospatial Data | USA | Tailor-made datasets: Foot traffic & Places Data
# | 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 |
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Frequently asked questions
What is PREDIK Data-Driven: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data?
Data to identify and understand consumer behavior patterns and trends with Foot traffic and anonymized and aggregated mobility data.
What is PREDIK Data-Driven: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data used for?
This product has 5 key use cases. Predik Data-driven recommends using the data for Location Intelligence, Audience Insights, Retail Analytics, Customer Data Insights, and Address Data Enrichment. Global businesses and organizations buy Location Data from Predik Data-driven to fuel their analytics and enrichment.
Who can use PREDIK Data-Driven: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data?
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: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data go?
This product has 5 years of historical coverage. It can be delivered on a weekly, monthly, quarterly, and yearly basis.
Which countries does PREDIK Data-Driven: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data cover?
This product includes data covering 235 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: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data cost?
Pricing information for PREDIK Data-Driven: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data 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: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data?
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: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data?
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: Geospatial Data USA Tailor-made datasets: Foot traffic & Places Data?
This product has 3 related products. These alternatives include The Data Appeal Global Business Location Data 200 Million+ POI Data Mapped Coverage from 2019 API, Dataset, Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location, and Echo Analytics POI Data Europe 82M Locations Available Worldwide Points of Interest (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.