Statistical Enrichment Models for Activity Inference from Imprecise Location Data

Abstract

Inference on location data has generated a great amount of interest and there are two data sources. GPS location data from GPS-based endpoint devices have high precision, but suffer from intermittent availability, small coverage of users and extra demand on devices' battery. Telecom mobility data, on the other hand, have the complementary advantages of continuous availability and complete coverage but generally with coarse location accuracy. 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. 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.

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