@conference {781,
	title = {Hierarchical Correlation Clustering in Multiple 2D Scalar Fields},
	booktitle = {EuroVis 2018, 20th EG/VGTC Conference on Visualization},
	year = {2018},
	month = {07/2018},
	publisher = {Computer Graphics Forum},
	organization = {Computer Graphics Forum},
	abstract = {Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time-dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2-dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field{\textquoteright}s domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.},
	doi = {https://doi.org/10.1111/cgf.13396},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13396},
	author = {Tom Liebmann and Gunther H. Weber and Gerik Scheuermann}
}
@conference {706,
	title = {Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids},
	booktitle = {EuroVis 2016, 18th EG/VGTC Conference on Visualization},
	year = {2016},
	publisher = {Computer Graphics Forum},
	organization = {Computer Graphics Forum},
	abstract = {Simulations and measurements often result in scalar fields with uncertainty due to errors or output sensitivity estimates. Methods for analyzing topological features of such fields usually are not capable of handling all aspects of the data. They either are not deterministic due to using Monte Carlo approaches, approximate the data with confidence intervals, or miss out on incorporating important properties, such as correlation. In this paper, we focus on the analysis of critical points of Gaussian-distributed scalar fields. We introduce methods to deterministically extract critical points, approximate their probability with high precision, and even capture relations between them resulting in an abstract graph representation. Unlike many other methods, we incorporate all information contained in the data including global correlation. Our work therefore is a first step towards a reliable and complete description of topological features of Gaussian-distributed scalar fields.},
	author = {Tom Liebmann and Gerik Scheuermann}
}
@conference {601,
	title = {A Geological Metaphor for Geospatial-temporal Data Analysis},
	booktitle = {IVAPP {\textquoteright}14: Proceedings of the 5th International Conference on Information Visualization Theory and Application},
	year = {2014},
	publisher = {SCITEPRESS},
	organization = {SCITEPRESS},
	author = {Tom Liebmann and Patrick Oesterling and Stefan J{\"a}nicke and Gerik Scheuermann}
}
