Identifying stops from mobile phone location data by introducing uncertain segments

Abstract

Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.

Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter?

“…Specifically, a hybrid positioning system adopted in recent mobile phones yields the most accurate location information via Assisted GPS (A‐GPS) when a reliable A‐GPS position fix is available, but it is switched into Wi‐Fi and then a cellular network in the absence of a reliable A‐GPS. Zandbergen () also drew a similar conclusion that the positional accuracy in both Wi‐Fi and cellular positioning systems may have erratic spatial patterns, such as indoor jitter (Hwang et al., ) or ping‐pong effects (Zhao et al, ), and may fail to meet the published accuracy specification with median error of 600 m.…”

Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data

“…The spatial resolutions range from hundreds of meters in downtown areas ( Xu et al, 2016 ) to kilometers in suburban areas ( Ahas et al, 2015 ). From a temporal resolution perspective, the average temporal intervals between adjacent records in datasets in existing studies could range from several minutes (e.g., Zhao et al, 2018 ) to several hours ( Xu et al, 2016 ).…”

“…As a result, there are some location oscillation and drift patterns in raw mobile phone location data. Detecting and removing these noisy records from a dataset is critical for improving the outcomes of the inferred activity chains ( Horn et al, 2014 ; Zhao et al, 2018 ).…”

Editorial: GIScience for human dynamics research in a changing world

“…Three articles in this special issue are technical and methodological in nature, and they develop methods to improve data quality, protect privacy with data sharing, and compress data volume while minimizing data loss, respectively. Zhao et al () develop a method that applies specific rules to evaluate uncertain segments along a trajectory to minimize misidentification of stops and moves. It is critical to correctly segment a trajectory into stops and moves in order to study human activity patterns.…”