Improvement of Task-Oriented Visual Interpretation of VGI Point Data (TOVIP)

Professor Dr.-Ing. Jochen Schiewe
HafenCity University Hamburg, Lab for Geoinformatics and Geovisualization (g2lab)

M.Sc. Martin Knura
HafenCity University Hamburg, Lab for Geoinformatics and Geovisualization (g2lab)

Volunteered Geographic Information (VGI) is very often generated as point data (e.g. Points of Interests, location of a photo taken). As one of the main characteristics, VGI data show an enormous volume as well as semantic and temporal heterogeneity. At a certain map scale and amount of data, this will lead to point clutters, which are not only hiding important information, but also making the map unreadable. Thus, reducing geometric and thematic clutter and improving the interpretability of static, multi-scale or multi-temporal visualizations of VGI points is a task of major relevance. Instead of looking at isolated generalization operations only, the project TOVIP – „Improvement of task-oriented visual interpretation of VGI point data” focuses on optimizing generalization workflows designed for specific high-level visual interpretation tasks, especially focusing on the identification and preservation of spatial patterns.

Normally, generalization methods like aggregation, selection or simplification, are applied in order to overcome the aforementioned clutter problems, merging the user-generated information by reducing the amount of visible point symbols. Nevertheless, under certain conditions, these generation methods disperse spatial patterns, reducing the usability in visual presentation and exploration, especially when the interpretation of high-level patterns (e.g. hot spots, extreme values) is of interest. Therefore, the TOVIP project focuses on the optimization of generalization workflows regarding these specific visual interpretation tasks.

Modelling and optimizing generalization workflows is often done using a constraint-based approach, where constraints are defined as requirements that shall be fulfilled and therefore need the definition of related quantitative measures. When using constraint-based approaches to interpret spatial patterns in a generalized visualization, there are two potentially contradictory aspects to consider: Preservation constraints ensure that the generalized data inherits the existing patterns like clusters or extreme values, while legibility constraints assure that these patterns are still readable by users. Nevertheless, complex measures for evaluating synoptic interpretation tasks based on generalized visualizations are still difficult to define, so the first step of the research project will be a user study to get a better understanding of user behavior during high-level interpretation tasks, which can be used to define constraints and measures for static, multi-scale or multi-temporal visualizations of VGI points. In the second part of the project, the generalization workflow using the previous defined constraints will be processed and controlled through an agent based modelling approach.