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.

Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.

The presented work helps users of spatio-temporal uncertainty visualisation methods to select suitable methods according to their data and requirements. For this purpose, an extensive web-based survey has been carried out to assess the usability of selected methods for users in different domains, such as GIS and spatial statistics. The results of the survey are used to incorporate a usability parameter in a categorisation design to characterise the uncertainty visualisation methods. This enables users to determine the uncertainty visualisation method(s) that are most suitable according to their domain of expertise. Finally, the categorisation design has been implemented and incorporated in a web-based tool as the Uncertainty Visualisation Selector. This web application can automatically recommend suitable uncertainty visualisation method(s) from user and data requirements.

With the increased availability of user generated data, assessing the quality and credibility of such data becomes important. In this article, we propose to assess the location correctness of visually generated Volunteered Geographic Information (VGI) as a quality reference measure. The location correctness is determined by checking the visibility of the point of interest from the position of the visually generated VGI (observer point); as an example we utilize Flickr photographs. Therefore we first collect all Flickr photographs that conform to a certain point of interest through their textual labelling. Then we conduct a reverse viewshed analysis for the point of interest to determine if it lies within the area of visibility of the observer points. If the point of interest lies outside the visibility of a given observer point, the respective geotagged image is considered to be incorrectly geotagged. In this way, we analyze sample datasets of photographs and make observations regarding the dependency of certain user/photo metadata and (in)correct geotags and labels. In future the dependency relationship between the location correctness and user/photo metadata can be used to automatically infer user credibility. In other words, attributes such as profile completeness, together with location correctness, can serve as a weighted score to assess credibility.

Urban planning and intelligent transportation management are facing key challenges in today's ever more urbanized world. Providing the right tools to city planners is crucial to cope with these challenges. Data collected from citizens' mobile communication can be used as the foundation for such tools. These kinds of data can facilitate various analysis tasks, such as the extraction of human movement patterns or determining the urban dynamics of a city. City planners can closely monitor such patterns based on which strategic decisions can be taken to improve a city's infrastructure. In this paper, we introduce a novel visual analytics approach for pattern exploration and search in global system for mobile communications mobile networks. We define geospatial and matrix representations of data, which can be interactively navigated. The approach integrates data visualization with suitable data analysis algorithms, allowing to spatially and temporally compare mobile usage, identify regularities, as well as anomalies in daily mobility patterns across regions and user groups. As an extension to our visual analytics approach, we further introduce space-time prisms with uncertain markers to visually analyze the uncertainty of urban mobility patterns.