Mobility & Foot traffic Enriched Data by PREDIK Data-Driven product image in hero

Mobility & Foot traffic Enriched Data by PREDIK Data-Driven

A dataset by Predik Data-driven
device_id id_type latitude longitude +42 more
2a9d0617-355b-4f38-92e1-e7b77ec301b3 aaid 12.9110403060913 77.5614013671875 ...
2a9d0617-355b-4f38-92e1-e7b77ec301b3 aaid 12.9110403060913 77.5614013671875 Sample
Request Free Data Sample Request Mobility & Foot traffic Enriched Data by PREDIK Data-Driven sample icon
Pricing available upon request

Description

Data to identify and understand consumer behavior patterns and trends with Foot traffic and anonymized and aggregated mobility data.
This Mobility & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:  -How often do people visit a location? (daily, monthly, absolute, and averages).  -What type of places do they visit ? (parks, schools, hospitals, etc)  -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors.  -What's their mobility like enduring night hours & day hours?   -What's the frequency of the visits partition by day of the week and hour of the day? Extra insights -Visitors´ relative income Level.  -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.  Overview & Key Concepts  Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.  We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.  Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.  Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.  Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.  Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.  POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.  Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others).  Coverage: Worldwide.  Delivery schemas  We can deliver the data in three different formats:  Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.  Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.  Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users. 

Data Attributes

Attribute & Description Example
device_id
Unique advertising identifier from the device
2a9d0617-355b-4f38-92e1-e7b77ec301b3
id_type
Advertising ID device type: IDFA (iOS) and ADID (Android)
aaid
latitude
Latitude of the event
12.9110403060913
longitude
Longitude of the event
77.5614013671875
horizontal_accuracy
GPS accuracy in meters as reported by the device OS
6
timestamp
Unix timestamp of the event (millisecond)
1625553768000
device_os
Operating system of the device: "iOS" or "android"
Android
source_id
Quadrant unique identifier for the data source
33
publisher_id
Unique developer identifier
7aae836893ea29f8299763b7759f64fd599766eb543dc7b7d7d80ba7e2097970
app_id
Unique application identifier
2d901e3e08149bcede68d4563cf24058f8f4949c7e5f06b2e6b165851d4f34ab
location_context
0= foreground, the event was captured when the app was open 1= background, the event was captured when the app was not open
0
geohash
Unique alphanumeric string to express a location
tdr1kyqkr8ke
year
Year of the event
2021
month
Month of the event
7
geohash3
Unique alphanumeric string to express a location in 3 character format
tdr
geohash5
Unique alphanumeric string to express a location in 5 character format
tdr1k
geohash6
Unique alphanumeric string to express a location in 6 character format
tdr1ky
geohash7
Unique alphanumeric string to express a location in 7 character format
tdr1kyq
geohash8
Unique alphanumeric string to express a location in 8 character format
tdr1kyqk
geohash9
Unique alphanumeric string to express a location in 9 character format
tdr1kyqkr
clave_device_dia
Concatenation of device_id and day number derived from unix timestamp
2a9d0617-355b-4f38-92e1-e7b77ec301b318814
day_of_week
ISO day of the week from x. The value ranges from 1 (Monday) to 7 (Sunday).
2
day
Day of the month of the event
6
hour
Hour of the event
1
distance_center
Distance in meters from the centroid of the initial area
2014
day_type
Type of day: workweek (Monday, Tuesday, Wednesday, Thursday,or Friday) or weekend (Saturday, Sunday)
weekday
hour_type
"Time type: work_hour (8 am-6pm), rest_hour (6 pm-8pm)"
work_hour
distance_type
"Distance range from center of starting location: 25mt, 100mt, 200mt, 300mt, 400mt, 500mt, 1000mt,> 1000mt"
<200
clave_device_dia_hora
Concatenation of device_id and day number derived from unix timestamp and hour of day
2a9d0617-355b-4f38-92e1-e7b77ec301b3188141
in_polygon
1 if it is within the initial zone, 0 if not
1
kmh
Calculated speed of the event. The speed is taken by calculating the time and distance of the previous event of the same device
1.14440916824341
ob_type
Type of the event. Using a speed-based algorithm, it is determined whether the event is moving by vehicle or on foot.
pedestrian
device_home_geohash
Calculation of the possible home location in geohash format. Analyzing in which area the device spends most of the time at nighttimeon different days, a possible home base is determined.
tdr1kyqk
device_work_geohash
Calculation of thepossible work location in geohash format. Analyzing in which area the device spends most of the time in working hours on different days, a possible work location is determined.
tdr1kyqk
device_work_distance_center
Distance in meters of the possible home base from the center of the starting polygon.
<200
rwi
Calculation of the Relative Wealth Index: https://dataforgood.facebook.com/dfg/tools/relative-wealth-index
25-35
administrative_area_level_1
ndicates a first-order civil entity below the country level. Within the United States, these administrative levels are states. Not all nations exhibit these administrative levels. In most cases, administrative_area_level_1 short names will closely match ISO 3166-2 subdivisions and other widely circulated lists; however this is not guaranteed as our geocoding results are based on a variety of signals and location data.
Karnataka
administrative_area_level_2
Indicates a second-order civil entity below the country level. Within the United States, these administrative levels are counties. Not all nations exhibit these administrative levels.
Bangalore Urban
administrative_area_level_3
Indicates a third-order civil entity below the country level. This type indicates a minor civil division. Not all nations exhibit these administrative levels.
bangalore
route
Indicates a named route (such as "US 101").
6th Main Road
political
Indicates a political entity. Usually, this type indicates a polygon of some civil administration.
Bendre Nagar
country
Indicates the national political entity andis typically the highest order type returned by the Geocoder
India
locality
Indicates an incorporated city or town political entity.
Bengaluru
sublocality
Indicates a first-order civil entity below a locality. For some locations may receive one of the additional types: sublocality_level_1 to sublocality_level_5. Each sublocality level is a civil entity. Larger numbers indicate a smaller geographic area.
Bengal
neighborhood
Indicates a named neighborhood
little India
postal_code
Indicates a postal code as used to address postal mail within the country.
560070

Geography

Africa (58)
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo
Congo (Democratic Republic of the)
Côte d'Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Réunion
Saint Helena, Ascension and Tristan da Cunha
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Swaziland
Tanzania, United Republic of
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
Asia (49)
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
Cyprus
Georgia
Hong Kong
India
Indonesia
Iran (Islamic Republic of)
Iraq
Israel
Japan
Jordan
Kazakhstan
Korea (Democratic People's Republic of)
Korea (Republic of)
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Lebanon
Macao
Malaysia
Maldives
Mongolia
Nepal
Oman
Pakistan
Palestine, State of
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Syrian Arab Republic
Taiwan
Tajikistan
Thailand
Timor-Leste
Turkey
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
Europe (51)
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Germany
Gibraltar
Greece
Guernsey
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia (the former Yugoslav Republic of)
Malta
Moldova (Republic of)
Monaco
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Svalbard and Jan Mayen
Sweden
Switzerland
Ukraine
United Kingdom
Åland Islands
North America (13)
Belize
Bermuda
Canada
Costa Rica
El Salvador
Greenland
Guatemala
Honduras
Mexico
Nicaragua
Panama
Saint Pierre and Miquelon
United States of America
Oceania (25)
American Samoa
Australia
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia (Federated States of)
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
Vanuatu
Wallis and Futuna
Other (9)
Antarctica
Bouvet Island
British Indian Ocean Territory
Christmas Island
Cocos (Keeling) Islands
French Southern Territories
Heard Island and McDonald Islands
South Georgia and the South Sandwich Islands
United States Minor Outlying Islands
South America (42)
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Bolivia (Plurinational State of)
Bonaire, Sint Eustatius and Saba
Brazil
Cayman Islands
Chile
Colombia
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
Falkland Islands (Malvinas)
French Guiana
Grenada
Guadeloupe
Guyana
Haiti
Jamaica
Martinique
Montserrat
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Vincent and the Grenadines
Sint Maarten (Dutch part)
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela (Bolivarian Republic of)
Virgin Islands (British)
Virgin Islands (U.S.)

History

3 years of historical data

Pricing

Free sample available
Predik Data-driven has not published pricing information for this product yet. You can request detailed pricing information below.

Suitable Company Sizes

Small Business
Medium-sized Business
Enterprise

Delivery

Methods
S3 Bucket
SFTP
Email
Feed API
Frequency
yearly
quarterly
monthly
Format
.json
.csv
.xls
.sql

Use Cases

Categories

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Frequently asked questions

What is Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

Data to identify and understand consumer behavior patterns and trends with Foot traffic and anonymized and aggregated mobility data.

What are the data attributes of Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

This product has 46 key data attributes. These include device_id , id_type , latitude , longitude , and horizontal_accuracy . Request a data sample from Predik Data-driven to see these attributes in more detail and see what information they can provide.

What is Mobility & Foot traffic Enriched Data by PREDIK Data-Driven 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 Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

This product is best suited if you’re a Enterprise, Medium-sized Business, or Small Business 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 Mobility & Foot traffic Enriched Data by PREDIK Data-Driven go?

This Dataset / Database / Data Feed / Data API has 3 years of historical coverage. It can be delivered on a yearly, quarterly, and monthly basis.

Which countries does Mobility & Foot traffic Enriched Data by PREDIK Data-Driven cover?

This product includes data covering 247 countries like USA, Japan, Germany, India, and United Kingdom. Predik Data-driven is headquartered in Costa Rica.

How much does Mobility & Foot traffic Enriched Data by PREDIK Data-Driven cost?

Pricing information for Mobility & Foot traffic Enriched Data by PREDIK Data-Driven 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 Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

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 Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

You can compare and assess the data quality of Predik Data-driven using Datarade’s data marketplace.

What are similar products to Mobility & Foot traffic Enriched Data by PREDIK Data-Driven?

This Dataset / Database / Data Feed / Data API has 3 related products. These alternatives include Enriched USA Foot Traffic Heat Maps by PREDIK Data-Driven, Enriched Countrywide Foot Traffic Heat Maps by PREDIK Data-Driven, and US Foot Traffic and Shopper Journey Data. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.