Ordered Treemap Layouts (2001)

Treemaps, a space-filling method of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, and fail to preserve an ordering of the underlying data. This paper introduces the ordered treemap, which addresses these two shortcomings. The ordered treemap algorithm ensures that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials, we show that compared to other layout algorithms ordered treemaps are more stable while maintaining relatively low aspect ratios of the constituent rectangles. A second test set uses stock market data

Spatially Ordered Treemaps

Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described

HierarchyMap: A Novel Approach to Treemap Visualization of Hierarchical Data

The HierarchyMap describes a novel approach for Treemap Visualization method for representing large volume of hierarchical information on a 2-dimensional space. HierarchyMap algorithm is a new ordered treemap algorithm. Results of the implementation of HierarchyMap treemap algorithm show that it is capable of representing several thousands of hierarchical data on 2-dimensional space on a computer and Portable Device Application (PDA) screens while still maintaining the qualities found in existing treemap algorithms such as readability, low aspect ratio, reduced run time, and reduced number of thin rectangles. The HierarchyMap treemap algorithm is implemented in Java programming language and tested with dataset of Departmental and Faculty systems of Universities, Family trees, Plant and Animal taxonomy structure

Using treemaps for variable selection in spatio-temporal visualisation

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar

Monitoring the Health of Computer Networks with Visualization - VAST 2012 Mini Challenge 1 Award: "Efficient Use of Visualization"

The complex computer networks of large organisations contain many machines of many types, used in many geographic locations. Although system administrators should monitor the health of each machine, they need to do so within the context of the whole computer network. Our visualization presents the health of a fictitious financial institution's computer network at a snapshot in time and over a time range, and preserves the important aspects of each facility's administrative and geographic context. Using the "Bank of Money" VAST Challenge dataset, our visualization allowed us to correctly identify several areas of concern, as well as hypothesise about their causes

Comparative Analysis between Split and HierarchyMap Treemap Algorithms for Visualizing Hierarchical Data

We carried out comparative analysis between Split treemap algorithm and a more recently introduced treemap algorithm called HierarchyMap. HierrachyMap and Split are Treemap Visualization methods for representing large volume of hierarchical information on a 2-dimensional space. Split layout algorithm has been developed much earlier as an ordered layout algorithm with capability to preserve order and reduce aspect ratio. HierarchyMap is a newer ordered treemap algorithm developed to overcome certain deficiencies of the Split layout algorithm. The two algorithms were analyzed to compare their rate of complexity. They were also implemented using object-oriented programming tool and compared using a number of standard metrics for measuring treemap algorithms. Their implementation shows that HierarchyMap and Split although maintain the same level of data ordering and usability but HierarchyMap algorithm has better aspect ratio, better readability, low run-time, and less number of thin rectangles compared to Split treemap algorithm. Since aspect ratio is an important metric for determining the efficiency of treemaps on 2-D and small screens, and the result of the analysis shows that HierarchyMap is better efficient than Split treemap alagorithm, we conlude that HierarchyMap is more efficient than Split treemap algorithm

Space Partitioning Schemes and Algorithms for Generating Regular and Spiral Treemaps

Treemaps have been widely applied to the visualization of hierarchical data. A treemap takes a weighted tree and visualizes its leaves in a nested planar geometric shape, with sub-regions partitioned such that each sub-region has an area proportional to the weight of its associated leaf nodes. Efficiently generating visually appealing treemaps that also satisfy other quality criteria is an interesting problem that has been tackled from many directions. We present an optimization model and five new algorithms for this problem, including two divide and conquer approaches and three spiral treemap algorithms. Our optimization model is able to generate superior treemaps that could serve as a benchmark for comparing the quality of more computationally efficient algorithms. Our divide and conquer and spiral algorithms either improve the performance of their existing counterparts with respect to aspect ratio and stability or perform competitively. Our spiral algorithms also expand their applicability to a wider range of input scenarios. Four of these algorithms are computationally efficient as well with quasilinear running times and the last algorithm achieves a cubic running time. A full version of this paper with all appendices, data, and source codes is available at \anonymizeOSF{\OSFSupplementText}

A Stable Greedy Insertion Treemap Algorithm for Software Evolution Visualization

Computing treemap layouts for time-dependent (dynamic) trees is an open problem in information visualization. In particular, the constraints of spatial quality (cell aspect ratio) and stability (small treemap changes mandated by given tree-data changes) are hard to satisfy simultaneously. Most existing treemap methods focus on spatial quality, but are not inherently designed to address stability. We propose here a new treemapping method that aims to jointly optimize both these constraints. Our method is simple to implement, generic (handles any types of dynamic hierarchies), and fast. We compare our method with 14 state of the art treemaping algorithms using four quality metrics, over 28 dynamic hierarchies extracted from evolving software codebases. The comparison shows that our proposal jointly optimizes spatial quality and stability better than existing methods