The effectiveness of using Google Maps Location History data to detect joint activities in social networks

08/15/2022
by   Giancarlos Parady, et al.
0

This study evaluates the effectiveness of using Google Maps Location History data to identify joint activities in social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities ranged from 22 strictest spatiotemporal accuracy criteria to 60 operational criteria. The performance of iPhones was markedly worse than Android devices, irrespective of accuracy criteria. In addition, logit models were estimated to evaluate factors affecting activity detection given different spatiotemporal accuracy thresholds. In terms of effect magnitudes, non-trivial effects on joint activity detection probability were found for floor area ratio (FAR) at location, activity duration, Android device ratio, device model ratio, whether the destination was an open space or not, and group size. Although current activity detection rates are not ideal, these levels must be weighed against the potential of observing travel behavior over long periods of time, and that Google Maps Location History data could potentially be used in conjunction with other data-gathering methodologies to compensate for some of its limitations.

READ FULL TEXT

page 7

page 9

page 13

page 14

page 16

page 18

research
07/07/2018

Nothing But Net: Invading Android User Privacy Using Only Network Access Patterns

We evaluate the power of simple networks side-channels to violate user p...
research
11/06/2019

Attentive Geo-Social Group Recommendation

Social activities play an important role in people's daily life since th...
research
01/25/2023

Location-based Activity Behavior Deviation Detection for Nursing Home using IoT Devices

With the advancement of the Internet of Things(IoT) and pervasive comput...
research
05/23/2017

Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple, Google, and Microsoft

The explosive growth of the location-enabled devices coupled with the in...
research
06/19/2018

Capacitor Based Activity Sensing for Kinetic Powered Wearable IoTs

We propose a novel use of the conventional energy storage component, i.e...
research
01/31/2023

Where You Are Is What You Do: On Inferring Offline Activities From Location Data

Studies have shown that a person's location can reveal to a high degree ...
research
07/17/2017

Modeling travel demand over a period of one week: The mobiTopp model

When mobiTopp was initially designed, more than 10 years ago, it has bee...

Please sign up or login with your details

Forgot password? Click here to reset