Special Issue "Volunteered Geographic Information and Citizen Science"

Special Issue Editors

Dr. Gavin McArdle
E-Mail Website
Guest Editor
School of Computer Science and CeADAR (Ireland’s National Centre for Applied Data Analytics & AI), University College Dublin (UCD) Dublin, Belfield, Dublin 4, Ireland
Interests: spatial data analysis, urban dynamics; geovisualisation; location-based services
Dr. Bianca Schoen-Phelan
E-Mail Website
Guest Editor
School of Computer Science, Technological University Dublin (TU Dublin), Dublin, Ireland
Interests: navigational support in VR; spatial databases; LiDAR; citizen science; crowd-sourced spatial data; machine learning

Special Issue Information

Dear Colleagues,

Traditionally, Citizen Science has referred to the collection and analysis of data about the natural world by laypeople. Over the past 10 years, that definition has broadened and there has been an increase in the number of Citizen Science projects which now encompasses a wider range of activities to empower communities to act on gathered data. While Citizen Science is not new, technology such as the internet has enabled citizens to share their data with others and increase the visibility of projects. Furthermore, technologies such as mobile phones, GPS devices, home weather stations and a plethora of other sensors permit interested members of the public to collect and analyse data about their environment and location. Given the importance of location in many Citizen Science projects, such projects can be described as a form of Volunteered Geographic Information (VGI), a term formally introduced by Goodchild in 2007 to describe the collection and sharing of geographic data by private individuals.

The term VGI has been expanded in recent years to encompass other forms of spatial data such as that generated by individuals in location-based social networks. While this can be a valuable source of geographic data, it does not necessarily represent a Citizen Science project as individuals are not sharing data with a specific scientific output in mind. In these cases, the geographical content is a by-product of sharing the location for social purposes.

True Citizen Science and VGI projects encompass the complete data life cycle including collection, storage, processing, dissemination, use in applications and archiving. This Special Issue seeks articles which describe the state-of-the-art in these areas with special focus on the spatial aspects of the data. Another key aspect of VGI which we seek to further understand is the motivation for individuals to participate and freely share their time and data to Citizen Science initiatives. Finally, given that VGI is collected by untrained volunteers using consumer-grade equipment and sensors, the veracity of the data and resulting applications are continuously questioned. Several interesting projects which seek to validate and improve VGI quality have been proposed recently, and we also seek articles in this important area.

Dr. Gavin McArdle
Dr. Bianca Schoen-Phelan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • volunteered geographic information
  • citizen science
  • data quality
  • motivations
  • data collection

Published Papers (6 papers)

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Research

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Article
Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM
ISPRS Int. J. Geo-Inf. 2021, 10(7), 436; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070436 - 25 Jun 2021
Viewed by 526
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
Article
An Approach to Improve the Quality of User-Generated Content of Citizen Science Platforms
ISPRS Int. J. Geo-Inf. 2021, 10(7), 434; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070434 - 25 Jun 2021
Viewed by 413
Abstract
The quality of the user-generated content of citizen science platforms has been discussed widely among researchers. Content is categorized into data and information: data is content stored in a database of a citizen science platform, while information is context-dependent content generated by users. [...] Read more.
The quality of the user-generated content of citizen science platforms has been discussed widely among researchers. Content is categorized into data and information: data is content stored in a database of a citizen science platform, while information is context-dependent content generated by users. Understanding data and information quality characteristics and utilizing them during design improves citizen science platforms’ overall quality. This research investigates the integration of data and information quality characteristics into a citizen science platform for collecting information from the general public with no scientific training in the area where content is collected. The primary goal is to provide a framework for selecting and integrating data and information quality characteristics into the design for improving the content quality on platforms. The design and implementation of a citizen science platform that collects walking path conditions are presented, and the resulting implication is evaluated. The results show that the platform’s content quality can be improved by introducing quality characteristics during the design stage of the citizen science platform. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
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Article
Assessment and Visualization of OSM Consistency for European Cities
ISPRS Int. J. Geo-Inf. 2021, 10(6), 361; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060361 - 25 May 2021
Viewed by 861
Abstract
Volunteered Geographic Information (VGI) is a widely used data source in various fields and services, such as environmental monitoring, disaster and crisis management, SDI, and mapping. Quality is a critical factor for the usability of VGI. This study focuses on evaluating logical consistency [...] Read more.
Volunteered Geographic Information (VGI) is a widely used data source in various fields and services, such as environmental monitoring, disaster and crisis management, SDI, and mapping. Quality is a critical factor for the usability of VGI. This study focuses on evaluating logical consistency based on the topological relationships between geographic features while considering semantics. It addresses internal (i.e., between thematic layers) and external (i.e., between specific features from different thematic layers) logical consistency. Attribute completeness is computed to support the use of semantics. A tool for assessing the consistency and attribute completeness is designed and implemented in the ArcGIS environment. An open-source web mapping application informs users about VGI consistency with multiscale visualization and indices. Data from OpenStreetMap (OSM), one of the most popular collaborative projects, are evaluated for six European cities: Athens, Berlin, Paris, Utrecht, Vienna, and Zurich. The case study uses OSM-derived data, downloaded from Geofabrik and organized into thematic layers. OSM’s consistency is evaluated and visualized at the regional, city, and feature levels. The results are discussed and conclusions on attribute completeness and consistency are derived. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
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Article
Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach
ISPRS Int. J. Geo-Inf. 2020, 9(11), 652; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110652 - 30 Oct 2020
Viewed by 730
Abstract
Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In [...] Read more.
Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
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Article
Privacy-Aware Visualization of Volunteered Geographic Information (VGI) to Analyze Spatial Activity: A Benchmark Implementation
ISPRS Int. J. Geo-Inf. 2020, 9(10), 607; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100607 - 20 Oct 2020
Cited by 3 | Viewed by 906
Abstract
Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resource, for example, supporting visitation pattern analysis to evaluate collective values and improve [...] Read more.
Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resource, for example, supporting visitation pattern analysis to evaluate collective values and improve natural well-being. In recent years, however, user privacy has become an increasingly important consideration. Potential conflicts often emerge from the fact that VGI can be re-used in contexts not originally considered by volunteers. Addressing these privacy conflicts is particularly problematic in natural resource management, where visualizations are often explorative, with multifaceted and sometimes initially unknown sets of analysis outcomes. In this paper, we present an integrated and component-based approach to privacy-aware visualization of VGI, specifically suited for application to natural resource management. As a key component, HyperLogLog (HLL)—a data abstraction format—is used to allow estimation of results, instead of more accurate measurements. While HLL alone cannot preserve privacy, it can be combined with existing approaches to improve privacy while, at the same time, maintaining some flexibility of analysis. Together, these components make it possible to gradually reduce privacy risks for volunteers at various steps of the analytical process. A specific use case demonstration is provided, based on a global, publicly-available dataset that contains 100 million photos shared by 581,099 users under Creative Commons licenses. Both the data processing pipeline and resulting dataset are made available, allowing transparent benchmarking of the privacy–utility tradeoffs. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
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Review

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Review
A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects
ISPRS Int. J. Geo-Inf. 2020, 9(12), 704; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120704 - 25 Nov 2020
Cited by 4 | Viewed by 1019
Abstract
Citizen science, the participation of the public in scientific projects, is growing significantly, especially with technological developments in recent years. Volunteers are the heart of citizen science projects; therefore, understanding their motivation and how to sustain their participation is the key to success [...] Read more.
Citizen science, the participation of the public in scientific projects, is growing significantly, especially with technological developments in recent years. Volunteers are the heart of citizen science projects; therefore, understanding their motivation and how to sustain their participation is the key to success in any citizen science project. Studies on participants of citizen science projects illustrate that there is an association between participant motivation and the type of contribution to projects. Thus, in this paper, we define a motivational framework, which classifies participant motivation taking into account the typologies of citizen science projects. Within this framework, we also take into account the importance of motivation in initiating and sustaining participation. This framework helps citizen science practitioners to have comprehensive knowledge about potential motivational factors that can be used to recruit participants, as well as sustaining participation in their projects. Full article
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
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