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dc.contributor.advisorMondal, Debajyoti
dc.creatorHasan, Mohammad Rakib
dc.date.accessioned2021-11-18T21:26:57Z
dc.date.available2021-11-18T21:26:57Z
dc.date.created2021-11
dc.date.issued2021-11-18
dc.date.submittedNovember 2021
dc.identifier.urihttps://hdl.handle.net/10388/13681
dc.description.abstractGiven 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.
dc.format.mimetypeapplication/pdf
dc.subjectTable Cartogram
dc.subjectData Visualization
dc.subjectAlgorithm
dc.subjectImage Processing
dc.subject
dc.titleComputing Fast and Scalable Table Cartograms for Large Tables
dc.typeThesis
dc.date.updated2021-11-18T21:26:57Z
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Saskatchewan
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.Sc.)
dc.type.materialtext
dc.contributor.committeeMemberKeil, Mark
dc.contributor.committeeMemberStakhanova, Natalia
dc.contributor.committeeMemberKhan, Shahedul
dc.creator.orcidhttps://orcid.org/0000-0003-2378-7507


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