An individual-level ground truth dataset for home location detection
Luca Pappalardo, Leo Ferres, Manuel Sacasa, Ciro Cattuto, Loreto Bravo

TL;DR
This study evaluates the accuracy of various home detection algorithms across different mobile phone data streams using a ground truth dataset of 65 participants, revealing how data type and algorithm choice impact detection success.
Contribution
Provides the first comprehensive ground truth dataset for home detection, analyzing multiple data streams and their influence on algorithm performance.
Findings
Hour-of-day algorithm performs best on XDR data.
CPRs require less data for accurate home detection.
Data stream type significantly affects detection accuracy.
Abstract
Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data. Despite its widespread use, home detection relies on a few assumptions that are difficult to check without ground truth, i.e., where the individual that owns the device resides. In this paper, we provide an unprecedented evaluation of the accuracy of home detection algorithms on a group of sixty-five participants for whom we know their exact home address and the antennas that might serve them. Besides, we analyze not only Call Detail Records (CDRs) but also two other mobile phone streams: eXtended Detail Records (XDRs, the ``data'' channel) and Control Plane Records (CPRs, the network stream). These data streams vary not only in their temporal granularity but also they differ in the data generation mechanism', e.g., CDRs are purely human-triggered…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Data-Driven Disease Surveillance · Mobile Crowdsensing and Crowdsourcing
