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Detecting Home Locations from CDR Data: Introducing Spatial Uncertainty to the State-of-the-Art

Lookup NU author(s): Maarten Vanhoof



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


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.

Publication metadata

Author(s): Vanhoof M, Reis F, Smoreda Z, Plötz T

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Mobile Tartu

Year of Conference: 2016

Print publication date: 29/01/2016

Acceptance date: 02/04/2016

Date deposited: 16/02/2018