At PREDIK Data Driven we process massive datasets with a number of complex, computer intensive calculations to make them easier to use in different data science and machine learning applications, related to understanding customer behaviors. 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 in turn obtain the data through partnerships with location aware apps. All the process is compliant with applicable privacy laws. We 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 behaviors.This dataset is available for all countries. 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. Allowing to differentiate between car traffic, foot traffic, and stationery observations. Night base of the device: we calculate the approximated location of where the device spends the night hours. Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work neighborhood. Income level: we use the neighborhood of the devices, and intersect it whith available socioeconomic data, to infer the deviceโs 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 include a standardized location category (park, hospital, among others). Delivery schemas We can deliver the dataset in three different formats: โข Full dataset: one record per mobile ping. These datasets are very large and should only be consumed by experienced teams with large computing budgets. โข 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, by helping to understand who visited a specific POI, characterize, and understand the consumers behavior. โข 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.