Statistical Enrichment Models for Activity Inference from Imprecise Location Data

The focus of this paper is on the development of a location insight system for activity labeling and user segment inference, based only on Telecom mobility data enriched with point-of-interest (POI) data. There is a proliferation of GPS-based endpoint devices in deployment. However location data from such devices suffered from intermittent availability, small coverage of users and extra demand on battery. Telecom mobility data have the complementary advantages of continuous availability and complete coverage but generally with coarse location accuracy. Specifically, we estimate activity patterns based on Bayesian techniques while infer user segments via a tree-based classification algorithm. We test the performance of our inference system via simulated mobility data and investigate the impact of important factors such as granularity of data. We conclude that imprecise location data, enriched with other source of information, can be used for the development of new generation activity inference models.