This Mobility & Foot traffic dataset includes enriched 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 during night hours & day hours?  

-What's the frequency of the visits by day of the week and hour of the day?

Extra insights

-Visitors´ relative Income Level. 

-Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. 

- Footfall measurement in all types of establishments (shopping malls, stand-alone stores, etc).

-Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. 

- Origin/Destiny matrix.

- Vehicular traffic, measurement of speed, types of vehicles, among other insights.

Overview & Key Concepts 

Each record corresponds to a ping from a mobile device, at a particular moment in time, and at a particular lat and long. 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, process and enrich these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different tailor-made solutions for companies and also 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 approximate location of where the device spends the night, which is usually its 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). 

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, and 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.