Cartogram representations of self-organizing virtual geographies

Model interpretability is a problem for multivariate data in general and, very specifically, for dimensionality reduction techniques as applied to data visualization. The problem is even bigger for nonlinear dimensionality reduction (NLDR) methods, to which interpretability limitations are consubstantial. Data visualization is a key process for knowledge extraction from data that helps us to gain insights into the observed data structure through graphical representations and metaphors. NLDR techniques provide flexible visual insight, but the locally varying representation distor- tion they generate makes interpretation far from intuitive. For some NLDR models, indirect quantitative measures of this mapping distortion can be calculated explicitly and used as part of an interpretative post-processing of the results. In this Master Thesis, we apply a cartogram method, inspired on techniques of geographic representation, to the purpose of data visualization using NLDR models. In particular, we show how this method allows reintroducing the distortion, measured in the visual maps of several self-organizing clustering methods. The main capabilities and limitations of the cartogram visualization of multivariate data using standard and hierarchical self-organizing models were investigated in some detail with artificial data as well as with real information stemming from a neuro-oncology problem that involves the discrimination of human brain tumor types, a problem for which knowledge dis- covery techniques in general, and data visualization in particular should be useful tools

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

Computing Fast and Scalable Table Cartograms for Large Tables

Given an m x n table T of positive weights and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m x n convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell's weight. In this thesis, we explored different table cartogram algorithms for a large table with thousands of cells and investigated the potential applications of large table cartograms. We implemented Evans et al.'s table cartogram algorithm that guarantees zero area error and adapted a diffusion-based cartographic transformation approach, FastFlow, to produce large table cartograms. We introduced a constraint optimization-based table cartogram generation technique, TCarto, leveraging the concept of force-directed layout. We implemented TCarto with column-based and quadtree-based parallelization to compute table cartograms for table with thousands of cells. We presented several potential applications of large table cartograms to create the diagrammatic representations in various real-life scenarios, e.g., for analyzing spatial correlations between geospatial variables, understanding clusters and densities in scatterplots, and creating visual effects in images (i.e., expanding illumination, mosaic art effect). We presented an empirical comparison among these three table cartogram techniques with two different real-life datasets: a meteorological weather dataset and a US State-to-State migration flow dataset. FastFlow and TCarto both performed well on the weather data table. However, for US State-to-State migration flow data, where the table contained many local optima with high value differences among adjacent cells, FastFlow generated concave quadrilateral faces. We also investigated some potential relationships among different measurement metrics such as cartographic error (accuracy), the average aspect ratio (the readability of the visualization), computational speed, and the grid size of the table. Furthermore, we augmented our proposed TCarto with angle constraint to enhance the readability of the visualization, conceding some cartographic error, and also inspected the potential relationship of the restricted angles with the accuracy and the readability of the visualization. In the output of the angle constrained TCarto algorithm on US State-to-State migration dataset, it was difficult to identify the rows and columns for a cell upto 20 degree angle constraint, but appeared to be identifiable for more than 40 degree angle constraint

Engineering Adaptive Interfaces – Enhancement of Comprehension and Decision-Making

The role of information systems is growing steadily and permeating more and more all levels of our society. Meanwhile, information systems have to support different user groups in various decision situations simultaneously. Hence, the existing design approach to creat- ing a unified user interface is reaching its limits. This work examines adaptive information system design by investigating user-adaptive information visualization and situation-aware nudging. An exploratory eye-tracking study investigates participants’ perception and comprehension of different financial visualizations and shows that none of them can be preferred across the board. Moreover, it reveals expertise knowledge as the research direction for visualization recommendations. Afterward, two empirical studies are conducted to relate different visualizations to participants’ domain-specific knowledge. The first study, conducted with a broad sample of the population, shows that financial and graphical literacy increases participants’ financial decision-making competency with certain visualizations. The second study, conducted with a more specific sample and an additional visualization, underlines a large part of the first study’s results. Additionally, it identifies statistical literacy as an increasing factor in financial decision-making. Both studies are demonstrating that different visualizations cause different cognitive loads despite the same amount of information. After all, the results are used to derive visualization recommendations based on domain-specific knowledge and cognitive load. This work also investigates the situation-aware effectiveness of nudging with the example of decision inertia. In a preliminary study, an experimental task is systematically transferred to different situational contexts by observing situational user characteristics. The identified contexts are examined in a subsequent large-scale empirical study with different nudges to reduce decision inertia. The results show gender-specific differences in decision inertia across the context. Hence, information system design has to adapt to gender and situational user characteristics to support users in their decision-making. Moreover, the study delivers empirical evidence for the contextual effectiveness of nudg- ing. Future nudging research has to incorporate situational user characteristics to provide effective nudges in different situational contexts. Especially, further fundamental research is needed to understand the situational effectiveness of nudging. The study identifies in- dividual situational preferences as one promising research stream