Implications for Public Policy

Aguiléra, Anne; Boutueil, Virginie

doi:10.1016/b978-0-12-812647-9.00004-4

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…Ideally if the data has no bias, P safegraph g should equal to P census g , indicating their ratio is 1. By computing the difference between the actual ratio and 1 for each population group, we can estimate whether the sampled device is evenly distributed among block groups or not as illustrated in Eq (3). A bias value far from 0 indicates high sampling bias, with positive values indicating over-representing (over-sampling) a specific population group and negative values indicating underrepresenting (under-sampling) a specific population group.…”

Section: Plos Onementioning

confidence: 99%

“…Modern smartphones are equipped with highly sensitive Global Positioning System (GPS) receivers that can provide accurate location data to installed applications, such as Google Maps [1] and social media platforms including Twitter, Facebook, and Instagram [2]. Such location data has become a vital source of geospatial big data for human mobility studies, allowing researchers to gain insights into travel trajectories, activity patterns, and behavior across large geographic areas with a high level of granularity [3,4]. Several commercial data companies started to provide mobile location data including SafeGraph, Cuebiq, X-mode, and Foursquare, to name a few.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

Li,

Ning,

Jing

et al. 2024

PLoS ONE

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Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

Li,

Ning,

Jing

et al. 2024

PLoS ONE

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“…Many mobility services are now available on smartphones [87]. Some can be used to obtain real-time information on transport systems (roads and public transport) and recommendations on the best choice of routes and transport modes.…”

Section: Carpooling Platforms and Appsmentioning

confidence: 99%

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

Aguiléra

Pigalle

2021

Sustainability

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This article proposes several new research directions regarding the future and sustainability of carpooling practices. The reflection is based on a systematic review of the literature (2010–2021) and a consideration of some of the main recent changes in carpooling, such as carpooling platforms and apps, and changes in lifestyles that may affect carpooling practices, such as telework. Carpooling is defined here as the sharing of short- or long-distance car rides between people who are not members of the same household, for a trip (or part of a trip) already scheduled by the driver, free of charge or expense sharing. After a description of the corpus selection method used, followed by a brief review of existing literature, we propose three new avenues for research. The first avenue is a call for greater consideration of forms of transport, other than carpooling, to work (or to the place of study), which remain poorly studied. The second avenue proposes the investigation of the role that the current digitalisation of carpooling, namely online carpooling platforms and their integration into mobility platforms, and also the use of social media by carpoolers, could play in the coming years. The third avenue argues that the consequences of the rise of collaborative consumption and the current pandemic, namely teleworking practices and the perception of shared mobility, could have an effect on carpooling, which merits specific investigation.

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Mobility as a Service and the Avoid-Shift-Improve Approach

Ringenson

Kramers

2021

Progress in IS

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Implications for Public Policy

Cited by 4 publications

References 65 publications

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

Mobility as a Service and the Avoid-Shift-Improve Approach

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