PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market
# | geohash7 |
latitude |
longitude |
cve_ent (entity code) |
nom_ent (entity name) |
cve_mun (municipality code) |
nom_mun (municipality name) |
dwell_unique_dev |
traffic_unique_dev |
unique_dwell_g9 |
unique_traffic_g9 |
count_d_devices |
dwell_total |
traffic_total |
observations |
men_enoe |
women_enoe |
avg_age_possible_enoe |
avg_education_level_enoe |
cannot_read_enoe |
can_read_enoe |
avg_n_children_enoe |
married_enoe |
single_enoe |
weekend_visits |
workday_visits |
work_hours_visits |
nighttime_visits |
avg_hour_visits |
avg_day_of_week_visits |
avg_distance_work_visits |
manufacturing_industry_locations_denue |
wholesale_groceries_food_locations_denue |
educational_visit_osm |
hospital_visit_osm |
mall_visit_osm |
monthly_income_est_census |
total_monthly_income_est_census |
expense_v3_census |
houses_one_room_census |
houses_two_rooms_census |
houses_three_rooms_or_more_census |
population_c_census |
population_d_census |
health_expense_census |
corporate_visits_denue |
hotels_motels_visits_denue |
for_sale_properties_pc |
for_rent_properties_pc |
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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 |
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Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
geohash7
|
String | 9g6hhzb | |
latitude
|
Float | 19.0276337 | |
longitude
|
Float | -98.22807312 | |
cve_ent (entity code)
|
String | 21 | |
nom_ent (entity name)
|
Text | PUEBLA | |
cve_mun (municipality code)
|
String | 119 | |
nom_mun (municipality name)
|
Text | SAN ANDRES CHOLULA | |
dwell_unique_dev
|
Integer | 249 | |
traffic_unique_dev
|
Integer | 265 | |
unique_dwell_g9
|
Integer | 408 | |
unique_traffic_g9
|
Integer | 295 | |
count_d_devices
|
Integer | 703 | |
dwell_total
|
Integer | 2151 | |
traffic_total
|
Integer | 579 | |
observations
|
Integer | 2730 | |
men_enoe
|
Float | 0.66666667 | |
women_enoe
|
Float | 0.33333333 | |
avg_age_possible_enoe
|
Integer | 47.66666667 | |
avg_education_level_enoe
|
Integer | 99 | |
cannot_read_enoe
|
Float | 0.041322314 | |
can_read_enoe
|
Float | 0.958677686 | |
avg_n_children_enoe
|
Float | 2 | |
married_enoe
|
Float | 0.5 | |
single_enoe
|
Float | 0.5 | |
weekend_visits
|
Float | 0.27006905 | |
workday_visits
|
Float | 0.72993095 | |
work_hours_visits
|
Float | 0.47446575 | |
nighttime_visits
|
Float | 0.52553425 | |
avg_hour_visits
|
Float | 13.9988131 | |
avg_day_of_week_visits
|
Float | 3.97556107 | |
avg_distance_work_visits
|
Float | 27.2500084 | |
manufacturing_industry_locations_denue
|
Float | 0.03030303 | |
wholesale_groceries_food_locations_denue
|
Float | 0.333333333 | |
educational_visit_osm
|
Float | 0.320130358 | |
hospital_visit_osm
|
Float | 0.204813236 | |
mall_visit_osm
|
Float | 0.07871647 | |
monthly_income_est_census
|
Float | 115000 | |
total_monthly_income_est_census
|
Float | 1955000 | |
expense_v3_census
|
Float | 0 | |
houses_one_room_census
|
Float | 0 | |
houses_two_rooms_census
|
Float | 0 | |
houses_three_rooms_or_more_census
|
Float | 0 | |
population_c_census
|
Float | 1 | |
population_d_census
|
Float | 0 | |
health_expense_census
|
Float | 11945.5 | |
corporate_visits_denue
|
Float | 0.0000397938199104442 | |
hotels_motels_visits_denue
|
Float | 0.003784458 | |
for_sale_properties_pc
|
Integer | 11 | |
for_rent_properties_pc
|
Integer | 2 |
Description
Country Coverage
History
Volume
1 million | rows |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License | Available |
Yearly License | Available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
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Frequently asked questions
What is PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market?
Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in Mexico (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables.
What is PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market used for?
This product has 5 key use cases. Predik Data-driven recommends using the data for Location Intelligence, Site Visitation, Retail Site Selection, Point of Interest (POI) Mapping, and POI Enrichment. Global businesses and organizations buy Location Data from Predik Data-driven to fuel their analytics and enrichment.
Who can use PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market?
This product is best suited if you’re a 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 I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market go?
This product has 1 years of historical coverage. It can be delivered on a weekly, monthly, quarterly, yearly, and on-demand basis.
Which countries does PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market cover?
This product includes data covering 1 country like Mexico. Predik Data-driven is headquartered in United States of America.
How much does PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market cost?
Pricing information for PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market 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 Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market?
Businesses can buy Location 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 Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market?
Predik Data-driven has reported that this product has the following quality and accuracy assurances: 95% quality rate. 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 Point of Interest (POI) Data + Foot Traffic Data I +200 Data Variables for the Mexican Market?
This product has 3 related products. These alternatives include PREDIK Data-Driven I Point of Interest (POI) Data + Foot Traffic Data I +100 Data Variables for the European Market, The Data Appeal Point of Interest (POI) Data API, Dataset 251M Global POI Data Coverage from 2019, and Xtract.io Polygon Data Map Data Marinas in US and Canada. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.