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

Abstract

Despite their increasing popularity in human mobility studies, few studies have investigated the geo‐spatial quality of GPS‐enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter “active mobile phone data”). We focus on two key issues in active mobile phone data—systematic gaps in tracking records and positioning uncertainty—and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants’ online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals’ frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

“…We filled these gaps using a set of imputation strategies with a 30-min imputation interval, while taking into account self-reported place information provided by study participants and positional uncertainty of the data. Detailed information on the data and the imputation strategies are available in Yoo et al [ 51 ].…”

A Review of Recent Spatial Accessibility Studies That Benefitted from Advanced Geospatial Information: Multimodal Transportation and Spatiotemporal Disaggregation

“…The advent of GPS-enabled devices (e.g., taxi trajectories or cell phones) has significantly facilitated the estimation of time-dependent mobility. It is possible to provide anonymized individual movements [22] and predict the mobility of a particular space and time based on historical travel time data [67]. Particularly, temporal dynamics in mobility are the most important variable for measuring temporal changes in spatial accessibility [45,63].…”

“…Specifically, the advent of sophisticated transportation databases, such as the general transit feed specification (GTFS) and Uber Movement (https://movement.uber.com/ (accessed on 1 August 2021)), enables the estimation of various travel times per different transportation modes (e.g., public transit, private car) and dynamic travel times under time-variant traffic conditions. In addition, the advent of GPS-equipped devices (e.g., smartphones) facilitates the tracing of anonymized movement of individuals [22] and enhances the space and temporal granularity of data [23]. With improved granularity, it is possible to further investigate the nonhomogeneous distribution of people within conventionally coarser geographical units (e.g., neighborhoods, census tracts) and to systematically estimate the time-variant distribution of floating populations.…”

A note on the propagation of positional uncertainty in environmental models

“…VGI may be used for a range of applications, including disaster management, where positional accuracy is of high importance (Vahidnia, Hosseinali, & Shafiei, 2020). While Zandbergen and Barbeau (2011) found that the positional uncertainty in mobile phone GPS was less than 30 m, a study by Yoo, Roberts, Eum, and Shi (2020) showed that this could increase to more than 1 km depending on the time of day and the type of location. At present, mobile wireless sensor networks are becoming more popular for data acquisition.…”