A VGI Quality Assessment Method for VGI based on Trustworthiness

Forati, Amir Masoud; Karimipour, Farid

doi:10.1553/giscience2016_01_s3

Cited by 2 publications

(3 citation statements)

References 11 publications

(11 reference statements)

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“…Over the past decade, numerous methods and algorithms have been proposed to determine VGI quality based on the concepts of trustworthiness and reputation [31][32][33][34][35][36]. These methods assume that high degree of participation leads to high quality of data.…”

Section: Discussionmentioning

confidence: 99%

“…Through a VGI case study on semantics used in landscape description, Forati et al [37] illustrated that there is a correlation between VGI contributors' demographic characteristics (age, gender) and the quality of their VGI. Expanding on their results, Forati and Karimipour [32] presented an algorithm that can more efficiently accredit users and their edits based on factors affecting users' performances and evaluate the quality of VGI. Their findings show that VGI users' behaviors can be classified based on their demographic and social characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Existing methods have not been sufficiently responsive toward qualitative analysis of VGI with a low level of participation, or with information generated by new users. Few researchers have attempted to include the demographic and social characteristics of users in assessing and predicting the behavior of users in the VGI system [32]. Yet, the demographic and social characteristics of users often shape the VGI quality and data content.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Volunteered Geographic Information Users Contributions Pattern and its Impact on Information Quality

Forati¹,

Ghose²

2020

Preprint

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Recent advancements in web-based geospatial software and smartphone technology have popularized the process of voluntary production and sharing of geospatial data by individual citizens. Through such Volunteered Geographic Information (VGI) activities, people across the world participate in online mapping projects (such as OpenStreetMap) to insert their spatial information. The quality of data generated by such VGI activities has profound impacts on online mapping projects and their spatial database. In this study, we examine the VGI contribution pattern in OpenStreetMap through three case study neighborhoods located in three major cities: Tehran, London, and Los Angeles, and investigate how it might affect the process of quality assessment of VGI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Volunteered Geographic Information Users Contributions Pattern and its Impact on Information Quality

Forati¹,

Ghose²

2020

Preprint

Self Cite

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Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

Alghanim

Jilani²,

Bertolotto

et al. 2021

IJGI

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Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

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A VGI Quality Assessment Method for VGI based on Trustworthiness

Cited by 2 publications

References 11 publications

Volunteered Geographic Information Users Contributions Pattern and its Impact on Information Quality

Volunteered Geographic Information Users Contributions Pattern and its Impact on Information Quality

Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

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