Foot Traffic & Mobility Data
Using foot traffic to understand human mobility patterns is a key component of spatial analysis for use cases in Retail, Tourism, Events, Out of Home Media & many more areas. Using POIs to estimate people movement & crowd dynamics is no longer enough to provide accurate insights. Discover why using CARTO.

Human Mobility Datasets
Spain · Human Mobility
Vodafone
Footfall - Spain (Grid 250m)
Licence
Premium dataPlacetype
GridTemporal aggr.
MonthlySpain · Human Mobility
Vodafone
Origin Destination Matrix - Spain (Grid 250m)
Licence
Premium dataPlacetype
GridTemporal aggr.
MonthlyUnited States of America · Human Mobility
Unacast
Activity - United States of America (Census Block Group)
Licence
Premium dataPlacetype
Census RegionTemporal aggr.
MonthlyBrazil · Human Mobility
Unacast
Activity - Brazil (Quadgrid 17)
Licence
Premium dataPlacetype
Spatial IndexTemporal aggr.
MonthlyMexico · Human Mobility
Unacast
Activity - Mexico (Quadgrid 17)
Licence
Premium dataPlacetype
Spatial IndexTemporal aggr.
MonthlyUnited Kingdom · Human Mobility
CKDelta
Catchment Areas - United Kingdom (Quadgrid 15)
Licence
Premium dataPlacetype
Spatial IndexTemporal aggr.
Monthly
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Example of Foot Traffic Analysis
Local governments
Local governments (such as New York City) rely on GPS data to gain insights that measure the risk of overcrowding by station. In this example, NYC was able to blend MTA turnstile data with Safegraph’s human mobility data to identify which subway stations were at higher risk during the COVID-19 outbreak.
Category
Data Set


Example of Foot Traffic Analysis
QSR chains & retailers
QSR (quick service restaurant) chains & retailers use mobility data to understand catchment areas to their locations. In this case, we can see an OD (origin destination) matrix around a McDonald’s location in Brooklyn, enabling expansion planners to identify opportunities for growth & consolidation.
Category
Data Set


Example of Foot Traffic Analysis
Tourism organizations & local governments
Tourism organizations & local governments rely on anonymized & aggregated human mobility data to understand tourism patterns, both for national & international travellers. Through Vodafone’s mobility data, tourism decision-makers can understand where tourists come from, how long and where they stay, as well as which leisure destinations they choose.
Category
Data Set
Discover other Spatial Data Categories
Real Estate
Property statistics, prices, and history to drive decisions and investment.Demographics
The most recent census data including: age, income, household types, and more.Financial
Merchant and ATM transaction data from leading banks and credit card companies.Road Traffic
Data from routing apps and GPS to analyze traffic patterns and commuters.Human Mobility
Mobile device and GPS data provide insight into human movement patterns.POI
Location data for business establisments, restaurants, schools, attractions, …Environmental
Climate and weather data, including exposure to weather-based hazards.Behavioral
Browsing habits, app usage, feelings and experiences shared over social media platforms,…Boundary
Digital boundaries for data aggregation and display on a map.COVID-19
Data especially curated to analyse the impact of the COVID-19 pandemic.