Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art
This work addresses the challenge of validating and improving home detection algorithms for population-scale mobile phone data, with incremental contributions to spatial uncertainty assessment.
The paper tackled the problem of detecting home locations from Call Detail Records (CDR) by evaluating five popular criteria on a large dataset (~18 million users) and introducing a data-driven framework to assess spatial uncertainty, showing how this uncertainty can improve results like nation-wide population estimation.
Non-continuous location traces inferred from Call Detail Records (CDR) at population scale are increasingly becoming available for research and show great potential for automated detection of meaningful places. Yet, a majority of Home Detection Algorithms (HDAs) suffer from "blind" deployment of criteria to define homes and from limited possibilities for validation. In this paper, we investigate the performance and capabilities of five popular criteria for home detection based on a very large mobile phone dataset from France (~18 million users, 6 months). Furthermore, we construct a data-driven framework to assess the spatial uncertainty related to the application of HDAs. Our findings appropriate spatial uncertainty in HDA and, in extension, for detection of meaningful places. We show how spatial uncertainties on the individuals' level can be assessed in absence of ground truth annotation, how they relate to traditional, high-level validation practices and how they can be used to improve results for, e.g., nation-wide population estimation.