TITLE: An individual-level ground truth dataset for home location detection 

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 while CPR is purely machine-triggered
events. Finally, we quantify the amount of data that is needed for each stream
to carry out successful home detection for each stream. We find that the choice
of stream and the algorithm heavily influences home detection, with an
hour-of-day algorithm for the XDRs performing the best, and with CPRs
performing best for the amount of data needed to perform home detection. Our
work is useful for researchers and practitioners in order to minimize data
requests and to maximize the accuracy of home antenna location.
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 while CPR is purely machine-triggered
events. Finally, we quantify the amount of data that is needed for each stream
to carry out successful home detection for each stream. We find that the choice
of stream and the algorithm heavily influences home detection, with an
hour-of-day algorithm for the XDRs performing the best, and with CPRs
performing best for the amount of data needed to perform home detection. Our
work is useful for researchers and practitioners in order to minimize data
requests and to maximize the accuracy of home antenna location.
An individual-level ground truth dataset for home location detection
http://arxiv.org/abs/2010.08814v1