Volunteered geographic information (VGI) is a phenomenon of recent years, offering an alternative mechanism for the acquisition and compilation of geographic information. As such it offers substantial advantages, but suffers from a general lack of quality assurance. We discuss the issues involved in the determination of quality for geospatial data, and trace the history of research on VGI quality. We describe three approaches to quality assurance, which we term the crowd-sourcing, social, and geographic approaches respectively. We discuss the advantages and limitations of each, and the research that will be needed to operationalize the geographic approach.

Assuring the quality of volunteered geographic information

It is hard to believe that 10 years have passed since we wrote our guest editorial for IJGIS (Sui and Goodchild 2001). Using the nascent evidence that emerged in the late 1990s, we speculated back in 2001 that geographic information systems (GIS) were rapidly becoming part of the mass media. On the basis of the proposition of GIS as media, we were able to link GIScience with theories in media studies such as Marshall McLuhan's law of the media, which considers modern media as modifiable perceptive extensions of human thought (Sui and Goodchild 2003). Remarkable conceptual and technological advances in GIS have been made during the past 10 years. The goal of this review is to provide an update on the ‘GIS as media’ argument we made 10 years ago and to discuss the new challenges for GIScience posed by the growing convergence of GIS and social media.

The convergence of GIS and social media: challenges for GIScience

With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information VGI. During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI.

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A review of volunteered geographic information quality assessment methods

In the past two years, several applications of generating three-dimensional 3D buildings from OpenStreetMap OSM have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS German Authority Topographic–Cartographic Information System as reference data The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis The results show that OSM footprint data in Munich have a high completeness and semantic accuracy There is an offset of about four meters on average in terms of position accuracy With respect to shape, OSM building footprints have a high similarity to those in ATKIS data However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data

Quality assessment for building footprints data on OpenStreetMap

OpenStreetMap (OSM) is one of the most popular examples of a Volunteered Geographic Information (VGI) project. In the past years it has become a serious alternative source for geodata. Since the quality of OSM data can vary strongly, different aspects have been investigated in several scientific studies. In most cases the data is compared with commercial or administrative datasets which, however, are not always accessible due to the lack of availability, contradictory licensing restrictions or high procurement costs. In this investigation a framework containing more than 25 methods and indicators is presented, allowing OSM quality assessments based solely on the data's history. Without the usage of a reference data set, approximate statements on OSM data quality are possible. For this purpose existing methods are taken up, developed further, and integrated into an extensible open source framework. This enables arbitrarily repeatable intrinsic OSM quality analyses for any part of the world.

A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis

In the area of volunteered geographical information (VGI), the issue of spatial data quality is a clear challenge. The data that are contributed to VGI projects do not comply with standard spatial data quality assurance procedures, and the contributors operate without central coordination and strict data collection frameworks. However, similar to the area of open source software development, it is suggested that the data hold an intrinsic quality assurance measure through the analysis of the number of contributors who have worked on a given spatial unit. The assumption that as the number of contributors increases so does the quality is known as 'Linus' Law' within the open source community. This paper describes three studies that were carried out to evaluate this hypothesis for VGI using the OpenStreetMap dataset, showing that this rule indeed applies in the case of positional accuracy.

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Aamer Ather

Papers

How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information