Assessing VGI Data Quality
Cidália Costa Fonte,Vyron Antoniou,Lucy Bastin,Jacinto Estima,Jamal Jokar Arsanjani,Juan Carlos Laso Bayas,Linda See,Rumiana Vatseva +7 more
- 11 Sep 2017
- pp 137-163
TL;DR: Current data quality indicators for geographic information as part of the ISO 19157 (2013) standard and how these have been used to evaluate the data quality of VGI in the past are reviewed.
read more
Abstract: Uncertainty over the data quality of Volunteered Geographic Information (VGI) is the largest barrier to the use of this data source by National Mapping Agencies (NMAs) and other government bodies. A considerable body of literature exists that has examined the quality of VGI as well as proposed methods for quality assessment. The purpose of this chapter is to review current data quality indicators for geographic information as part of the ISO 19157 (2013) standard and how these have been used to evaluate the data quality of VGI in the past. These indicators include positional, thematic and temporal accuracy, completeness, logical consistency and usability. Additional indicators that have been proposed for VGI are then presented and discussed. In the final section of the chapter, the idea of integrated indicators and workflows of quality assurance that combine many assessment methods into a filtering system is highlighted as one way forward to improve confidence in VGI.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model.
Tingting Ye,Naizhuo Zhao,Xuchao Yang,Xuchao Yang,Zutao Ouyang,Xiaoping Liu,Qian Chen,Kejia Hu,Wenze Yue,Jiaguo Qi,Zhansheng Li,Zhansheng Li,Peng Jia +12 more
TL;DR: A typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids and showed higher accuracy.
239
Estimating the global distribution of field size using crowdsourcing.
Myroslava Lesiv,Juan Carlos Laso Bayas,Linda See,Martina Duerauer,Domian Dahlia,Neal Durando,Rubul Hazarika,Parag Kumar Sahariah,Mar’yana Vakolyuk,V. Blyshchyk,Andrii Bilous,Ana Pérez-Hoyos,Sarah Gengler,Reinhard Prestele,Svitlana Bilous,Ibrar ul Hassan Akhtar,Ibrar ul Hassan Akhtar,Kuleswar Singha,Sochin Boro Choudhury,Tilok Chetri,Žiga Malek,Khangsembou Bungnamei,Anup Saikia,Dhrubajyoti Sahariah,William Narzary,Olha Danylo,Tobias Sturn,Mathias Karner,Ian McCallum,Dmitry Schepaschenko,Dmitry Schepaschenko,Elena Moltchanova,Dilek Fraisl,Inian Moorthy,Steffen Fritz +34 more
TL;DR: A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application, which produced the most accurate global field size map to date.
166
A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints
TL;DR: The results of the comparison show that the positional accuracy of the OSM buildings is at least compatible with the quality of the reference dataset at the scale of 1:5000 since the average deviation, with respect to the authoritative map, is below the expected tolerance of 3 m.
Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions
Feifei Zheng,Ruoling Tao,Holger R. Maier,Holger R. Maier,Holger R. Maier,Linda See,Dragan Savic,Tuqiao Zhang,Qiuwen Chen,Thaine H. Assumpção,Pan Yang,Pan Yang,Bardia Heidari,Jörg Rieckermann,Barbara S. Minsker,Weiwei Bi,Ximing Cai,Dimitri Solomatine,Ioana Popescu +18 more
TL;DR: A review of the state of the art in this field can be found in this article, where the authors present a framework for categorizing the methods used in the seven domains of geophysics considered in this review.
122
Crowdsourced geospatial data quality: challenges and future directions
TL;DR: This editorial highlights how issues stemming from data quality and biases of VGI are discussed and addressed by the articles of this special issue and how the papers highlight emerging technologies, concepts, platforms, debates, and methodologies and techniques within VGI and suggest future research directions.
110
References
How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets:
TL;DR: Analysis of the quality of OpenStreetMap information focuses on London and England, since OSM started in London in August 2004 and therefore the study of these geographies provides the best understanding of the achievements and difficulties of VGI.
1.7K
Assuring the quality of volunteered geographic information
Michael F. Goodchild,Linna Li +1 more
TL;DR: The issues involved in the determination of quality for geospatial data, and the history of research on VGI quality are traced, as well as three approaches to quality assurance, which are described as crowd-sourcing, social, and geographic approaches respectively.
815
Quality Assessment of the French OpenStreetMap Dataset
TL;DR: The quality of French OpenStreetMap data is studied to provide a larger set of spatial data quality element assessments, and raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications.
A review of volunteered geographic information quality assessment methods
TL;DR: Data mining is introduced as an additional approach for quality handling in VGI by reviewing various quality measures and indicators for selected types of VGI and existing quality assessment methods.
The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007-2011
TL;DR: It is shown that the difference between the OSM street network for car navigation in Germany and a comparable proprietary dataset was only 9% in June 2011, and that OSM even exceeds the information provided by the proprietary dataset by 27%.
446