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. The phenomena is known as Volunteered Geographic Information (VGI). During the last 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 paper, we review various quality measures and indicators for selected types of VGI, and existing quality assessment methods. As an outcome, the paper 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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As transport systems are pushed to the limits in many cities, governments have tried to resolve problems of traffic and congestion by increasing capacity. Miller (2013) contends the need to identify new capabilities (instead of capacity) of the transport infrastructure in order to increase efficiency without extending the physical infrastructure. Kenyon and Lyons (2003) identified integrated traveller information as a facilitator for better transport decisions. Today, with further developments in the use of geographic information systems (GIS) and a greater disposition by the public to provide volunteered geographic information (VGI), the potential of information is not only integrated across modes but also user-generated, real-time and available on smartphones anywhere. This geographic information plays today an important role in sectors such as politics, businesses and entertainment, and presumably this would extend to transport in revealing people's preferences for mobility and therefore be useful for decision-making. The widespread availability of networks and smartphones offer new opportunities supported by apps and crowdsourcing through social media such as the successful traffic and navigation app Waze, car sharing programmes such as Zipcar, and ride sharing systems such as Uber. This study aims to develop insights into the potential of governments to use voluntary (crowdsourced) geographic information effectively to achieve sustainable mobility. A review of the literature and existing technology informs this article. Further research into this area is identified and presented at the end of the paper.

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Drawing on John Agnew's (1987) theoretical framework for the analysis of place (location, locale and sense of place) and on Doreen Massey's (1991) interpretation of Kilburn High Road (London), the contribution develops an analysis of the notion of place in the case study of Kilburn High Road by comparing the semantics emerging from Doreen Massey's interpretation of Kilburn High Road in the late Nineties with those from a selection of noisy and unstructured volunteered geographic information collected from Flickr photos and Tweets harvested in 2014-2015. The comparison shows how sense of place is dynamic and changing over time and explores Kilburn High Road through the categories of location, locale and sense of place derived from the qualitative analysis of VGI content and annotations. The contribution shows how VGI can contribute to discovering the unique relationship between people and place which takes the form given by Doreen Massey to Kilburn High Road and then moves on to the many forms given by people experiencing Kilburn High Road through a photo, a Tweet or a simple narrative. Finally, the paper suggests that the analysis of VGI content can contribute to the detection of the relevant features of street life, from infrastructure to citizens' perceptions, which should be taken into account for a more human-centered approach in planning or service management.