Howmuch Do We Knowabout the Contributors to Volunteered Geographic Information and Citizen Science Projects?

Mooney, Peter; Morgan, Lorraine

doi:10.5194/isprsannals-ii-3-w5-339-2015

Cited by 4 publications

(4 citation statements)

References 23 publications

(42 reference statements)

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“…Since the task of creating geographic information is no longer exclusively performed by trained professionals, data quality can be a concern and has been cited as a hindrance to its use [12,13]. OSM data quality has often been assessed extrinsically by comparing it to other reference datasets (see Section 2.2).…”

Section: Introductionmentioning

confidence: 99%

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Madubedube

Coetzee

Rautenbach

2021

IJGI

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Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of experience or skills, which has raised concerns about quality. When reference data is not available to assess the quality of OSM data, intrinsic methods that assess the data and its metadata can be used. In this study, we applied unsupervised machine learning for analysing OSM history data to get a better understanding of who contributed when and how in Mozambique. Even though no absolute statements can be made about the quality of the data, the results provide valuable insight into the quality. Most of the data in Mozambique (93%) was contributed by a small group of active contributors (25%). However, these were less active than the OSM Foundation’s definition of active contributorship and the Humanitarian OpenStreetMap Team (HOT) definition for intermediate mappers. Compared to other contributor classifications, our results revealed a new class: contributors who were new in the area and most likely attracted by HOT mapping events during disaster relief operations in Mozambique in 2019. More studies in different parts of the world would establish whether the patterns observed here are typical for developing countries. Intrinsic methods cannot replace ground truthing or extrinsic methods, but provide alternative ways for gaining insight about quality, and they can also be used to inform efforts to further improve the quality. We provide suggestions for how contributor-focused intrinsic quality assessments could be further refined.

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Section: Introductionmentioning

confidence: 99%

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Madubedube

Coetzee

Rautenbach

2021

IJGI

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“…The cooperative mechanisms can partly explain the situation, including "citizen science" and "patch work" [9][10][11], the Linus law in the VGI context [12] and social collaborations between contributors [13]. However, the characteristics of individual contributors, such as expertise [14], education and income [15], still significantly matter [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, the characteristics of individual contributors, such as expertise [14], education and income [15], still significantly matter [11]. This leads to a natural question: Do most contributions actually come from amateurs?…”

Section: Introductionmentioning

confidence: 99%

Amateur or Professional: Assessing the Expertise of Major Contributors in OpenStreetMap Based on Contributing Behaviors

Yang

Fan

Jing

2016

IJGI

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Volunteered geographic information (VGI) projects, such as OpenStreetMap (OSM), provide an alternative way to produce geographic data. Research has proven that the resulting data in some areas are of decent quality, which guarantees their usability in various applications. Though these achievements are normally attributed to the huge heterogeneous community mainly consisting of amateurs, it is in fact a small percentage of major contributors who make nearly all contributions. In this paper, we investigate the contributing behaviors of these contributors to deduce whether they are actually professionals. Various indicators are used to depict the behaviors on three themes: practice, skill and motivation, aiming to identify solid evidence for expertise. Our case studies show that most major contributors in Germany, France and the United Kingdom are hardly amateurs, but are professionals instead. These contributors have rich experiences on geographical data editing, have a decent grasp of professional software and work on the project with enthusiasm and concentration. It is less unexpected that they can create geographic data of high quality.

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“…Some “less-active” mappers can quickly disengage from the OSM project after only completing a small number of contributions. This can contribute to the large number of features in the OSM database which have little or no attributes or tags attached to them [ 13 , 17 , 18 ]. In this regard, the online user-interface for viewing the OSM map and editing the OSM database can often lead to confusing many contributors even more [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Using Crowdsourced Trajectories for Automated OSM Data Entry Approach

Basiri

Amirian

Mooney

2016

Sensors

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The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database.

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Howmuch Do We Knowabout the Contributors to Volunteered Geographic Information and Citizen Science Projects?

Cited by 4 publications

References 23 publications

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Amateur or Professional: Assessing the Expertise of Major Contributors in OpenStreetMap Based on Contributing Behaviors

Using Crowdsourced Trajectories for Automated OSM Data Entry Approach

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