Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art

TL;DR

This paper evaluates five home detection algorithms using a large French mobile dataset and introduces a framework to quantify spatial uncertainty, enhancing the reliability of detecting meaningful places without ground truth.

Contribution

It provides a comprehensive performance assessment of popular HDAs and introduces a data-driven method to measure spatial uncertainty in home detection tasks.

Findings

Spatial uncertainty correlates with detection accuracy.

The framework enables validation without ground truth.

Improved population estimation accuracy.

Abstract

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.