Improvement of task-oriented visual interpretation of VGI point data (TOVIP)

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Volunteered Geographic Information (VGI) has already shown great potential for a huge variety of social and commercial applications. With a variety of services and software solutions, both experts and non-expert are able to collect and to display data via the internet.VGI is very often generated as point data, representing points of interest, other qualities or quantities. Typical examples are environmental data (such as measurements of traffic noise, PM10 values, traffic volume), or data about accident or crime spots.VGI data typically shows a very large volume of data as well as semantic and temporal heterogeneity. Both aspects can drastically reduce the usability in visual presentation and exploration, in particular, if the interpretation of high-level (synoptic) patterns is of interest. If the focus is on point data, a decline in rendering performance or the effects of geometric or thematic point clutter are possible.Typically, generalization methods are applied in order to overcome these clutter problems. Instead of looking at a holistic approach or isolated generalization operations only, the focus in this project will be on optimizing generalization workflows designed for specific visual interpretation tasks (such as detecting hot spots or extreme values). When using constraint-based approaches, there are two potentially contradictory aspects to consider: preservation and readability of the spatial patterns. However, constraint-based approaches still have limitations in defining constraints. In addition, research to trigger the generalization process through constraints has been quite limited so far.Especially VGI point data is produced either in multiple scale levels, or over longer periods of time or even in real-time, both requiring non-static displays. However, the improvement in the generalization process of such representations has not been thoroughly investigated. As an example, interactive multi-scale views require consideration of scale transitions, i.e., changes to constraints must be formalized as a function of the task being considered. When multi-temporal representations of static or even moving points are used, the added complexity and limitations of the cognitive workload related to these animations must also be considered.Consequently, the overall goal of this project is to improve the visual interpretability of VGI point data displays – taking into account specific high-level (synoptic) tasks based on static, multi-scale or multi-temporal displays.From a methodological point of view, the project begins with the definition of relevant synoptic tasks. Analytical and empirical investigations define a minimum of constraints. Based on this, agent-based models are developed with the aim of optimizing the entire generalization process. Finally, empirical studies evaluate the assumed progress of the extended set of constraints together with the proposed agent-based optimization method.