This special issue presents research work on the subject of Volunteered Geographic Information (VGI) and Geovisualisation. The relevance of this research field is illustrated by the fact that the German Research Foundation funds a wide-ranging priority program with the acronym VGIscienceFootnote 1 conducted from 2016 to 2022, which deals with the topics “Information retrieval and analysis of VGI”, “Geovisualisation and user interactions related to VGI” and “Active participation, social context and privacy awareness”. VGI refers to spatial information that is voluntarily collected by laypeople and made available to the public so that others can further use and process these data.
Volunteers have expertise in local events and phenomena as well as in-depth knowledge of relevant contextual factors. In this sense, people do act beyond a simple sensor, which records information from the environment—people additionally interpret and enrich them. A specific type of VGI are data from social media, such as microblogs (Twitter, Weibo, Facebook), photo or video sharing platforms (Flickr, Instagram, Youtube) or points of interest (OpenStreetMap, Foursquare, Yelp). If these contain a geographical reference, one also speaks of geosocial media data. It is specific to this kind of data that typically no further processing of the data is intended by the users submitting the social media content. VGI and geosocial media data can be used to discover, localise and react on special social events and developments at an early stage. This includes information e.g., relating to the environment, transport, health, politics, disasters, as well as tourism and leisure activities.
With regards to geovisualisation, certain visualisation methods are particularly suitable for displaying VGI and are therefore frequently used, e.g. methods to visualise point data, heat maps based on interpolated point data, grid based visualisations, usage of text to show georeferenced tags in maps, graph and trajectory visualisations or methods of uncertainty representation. The question “What were the methods used to visualise LBSMD and to communicate findings?” is answered within a review paper by McKitrick et al. (2021) with distinction of basic visualization outputs, point mapping, raster surfaces, thematic maps, qualitative content diagrams, transmission diagrams and web mapping.
This special issue summarises five articles that present new approaches to the analysis and visualization of VGI and geosocial media as well as empirical studies on the interpretation of point-based and landmark visualisations.
The first paper “Visualizing point density on geometry objects: application in an urban area using social media VGI” proposes two new variants of point density visualisations. Urban planning tasks are considered as a use case to analyse the bicycle parking infrastructure. For this purpose, bicycles were detected in photos of geosocial media data, represented as point data and assigned to streets, squares or pedestrian zones with the help of sub-geometries. The subdivision and classification of geometry features were carried out by utilising a grid-based approach and heatmap contours. In the second paper “Visualizing the Off-Screen Evolution of Trajectories” a visualization approach is presented that supports the analysis of trajectories, e.g., animal trajectories or GPS tracks of a hiker. For this purpose, glyphs are designed in the form of disk sectors, which describe how trajectories evolve outside the visible screen. The third paper “Analyzing the EU Migration Crisis as reflected on Twitter” shows how geosocial media data can be processed and visually analysed. A facet model is used that aims on the interpretation of spatial, temporal, topical and social information. This is combined with a typicality measure, which enables the normalisation of inhomogeneous hashtag distributions.
The last two papers present empirical studies related to the visualisation of VGI, which is often available as cluttered point data and might contain spatial inaccuracies. The paper “Analysis of user behaviour while interpreting high-level patterns in point data sets” aims on studying perception of point pattern such as extreme values, clusters or hot spots, which should be preserved during generalisation. Therefore, a method was developed based on think aloud interviews and presentation of this data as attention respective flow maps, typically used to visualise trajectories from eye tracking analysis. The last paper “Uncertainties in Spatial Orientation: Critical Limits for Landmark Inaccuracies in Maps in the Context of Map Matching” studies the effects of metric and semantic spatial inaccuracies of landmark representations. Users had to carry out map matching trials between presentation of landmarks within a 3D scene and the corresponding 2D map. The findings confirm that inaccurately georeferenced landmarks will affect peoples’ ability to match maps to the represented 3D environments.
It can be concluded for the analysis and use of VGI and geosocial media, new approaches to processing and visualisation can be developed that take into account the specific characteristics of these data. Ideally, different visualisation variants are made available to the user and options for interaction are offered in order to influence the process of abstraction, visual analysis and cartographic presentation.