Estimating the Biasing Effect of Behavioural Patterns on Mobile Fitness App Data by Density-Based Clustering

Bergman, Cecilia; Oksanen, Juha

doi:10.1007/978-3-319-33783-8_12

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Similarly, users who contribute SVI data may unintentionally compromise personal information by repeatedly collecting data along routine commuting routes (e.g., [83]). Other issues of concern include the quality of VSVI data from different sources, the availability of relevant assessment methods [84], location spoofing [85], the uploading of fake data [86] and issues with participation inequality [87,88]; however, these could be overcome as the number and diversity of contributors and data sources increase. In order to advance our understanding of VSVI in light of these issues, and in particular with respect to data trustworthiness and quality, we suggest that additional research that focuses specifically on spatial and temporal user contribution patterns is needed.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir

Schuchard

Crooks

et al. 2020

IJGI

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Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.

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Section: Discussionmentioning

confidence: 99%

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir

Schuchard

Crooks

et al. 2020

IJGI

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“…As they are present in both the datasets, superusers influencing the results and acting as the differentiating factor between the two cities seems highly unlikely. While user bias can produce misleading results [4], it is important to note the context of the study, which in this case, is heuristic choice popularity distribution, and not popularity of any specific route or street segment. In relevance to this study, there could be cases where super-users, by recording their weekday walking trips using the same route (and thus the same heuristic), influence one heuristic greatly than others.…”

Section: Discussionmentioning

confidence: 99%

The impact of urban road network morphology on pedestrian wayfinding behaviour

Bhowmick¹,

Winter²,

Stevenson³

et al. 2020

JOSIS

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Pedestrians do not always choose the shortest available route during the process of wayfinding. Instead, their route choices are influenced by strategies, also known as wayfinding heuristics. These heuristics aim to minimize cognitive effort of the pedestrian and their application usually leads to satisfactory route choices. Our previous study evaluated and analyzed resultant routes from the application of four well-known pedestrian wayfinding heuristics across nine distinct network morphologies via simulation. It was observed that the variation in the cost (difference in route length between a heuristic route and the shortest route, expressed as a percentage of the shortest route length) across the four heuristics increased with an increase in the irregularity of the network. Based on these results, we claimed that, people may opt for more diverse heuristics while walking through relatively regular networks, as route cost across heuristics are more similar in magnitude and thus applying any one of them would not result in a substantial difference in the travelled distance. Likewise, they may prefer specific heuristics in the relatively irregular networks, as some heuristics are significantly costlier than others, thus creating greater variation in cost across heuristics and hence would result in significantly greater travelled distances. In this study, we investigated this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum, as established in the literature. Using statistical tests, we claim with confidence that on an average, heuristic choice distribution is uniform in Melbourne, a city having a regular network morphology. On the contrary, heuristic choice distribution was skewed in Beijing, a city with more irregularities in its street network morphology. This novel finding could help urban planners and future researchers in producing more accurate patterns of aggregate pedestrian movement in outdoor urban spaces.

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“…Smartphone mapping applications such as Google Maps, Waze, Uber, and related technologies create tremendous privacy challenges with personal data behind proprietary algorithms, and also contribute great prospects for understanding travel at high resolution over broad geographies (Weidemann et al 2018). Planners use fitness app data to understand cycling routes, but studies have shown that big data sources provide only a segment of the population, varying significantly from survey data using traditional sampling methods (Bergman and Oksanen 2016a). Similarly, comparisons of several big data sources on the same routes show significant differences, representing a fraction of total travel (Griffin and Jiao 2015a).…”

Section: Introductionmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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Estimating the Biasing Effect of Behavioural Patterns on Mobile Fitness App Data by Density-Based Clustering

Cited by 5 publications

References 29 publications

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

The impact of urban road network morphology on pedestrian wayfinding behaviour

Mitigating Bias in Big Data for Transportation

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