Time-oriented Cartographic Treemaps for Visualization of Public Healthcare Data (original) (raw)

Challenges in Interactive Time-Based Information Visualization

This research paper tries to raise some key questions in the quest for interactive time-based information visualization. Since our ultimate goal is to specify and develop a web-based system for this task that will most likely be a mashup using existent geographical data, we first explain and discuss mashups. We then try to identify a number of use cases for such a system, followed by a state of the art overview. We then try to devise a categorization for the display of and interaction with time-based geospatial data, and close with a discussion of particular challenges identified in the context of the project described in this paper.

Defining visualization operations for temporal cartographic animation design

International journal of applied earth observation and …, 2002

Cartographic animation has emerged as a potentially effective visualization technique that has an intuitive power in representing dynamic geographical phenomena through its ability to show interrelations amongst geospatial data's components, location, attribute and time. Whereas cartographic animation has prominently featured in communicating geospatial information, their use as tools for visual exploration has been hampered by lack of the necessary functionality that is capable of allowing users to interact with the dynamic display. In this paper, we outline an approach that defines visualizations operations or basic visual actions that implement a viewer's task of exploration and characterization of geospatial structures in data or phenomena. The defined operations go along to reinforcing the quest in enabling users to perceive relationships and be able to manipulate geospatial data using more efficient visual tools while keeping low on cognitive demands.

Weighted maps: Treemap visualization of geolocated quantitative data

A wealth of census data relative to hierarchical administrative subdivisions are now available. It is therefore desirable for hierarchical data visualization techniques, to offer a spatially consistent representation of such data. This paper focuses on a widely used technique for hierarchical data, namely treemaps. In particular on a specific family of treemaps, designed to take into account spatial constraints in the layout, called Spatially Dependent Treemap (SDT). The contributions of this paper are threefold. First, present "Weighted Maps", a novel SDT layout algorithm and discuss the algorithmic differences with the other state-of-the-art SDT algorithms. Second, we present the quantitative results and analyses of a number of metrics that where used to assess the quality of the resulting layouts. The analyses are illustrated with figures generated from various data sets. Third, we show that the Weighted Maps algorithm offers a significant advantage for the layout of large flat cartograms and multi-level hierarchies having a large branching factor.

Using Treemaps to represent medical data

Studies in health technology and informatics, 2006

Confronted with the inadequacy of traditional charts, we tested the contribution of Treemaps to the representation of medical data. Treemap charts allow description of large hierarchical collections of quantitative data, on a synthetic way. Treemaps were implemented using PHP5, and were tested in the field of DRG-mining and other medical informations. From now on, this implementation is used in an interactive web-based request tool, and could be used to design interactive piloting tools.

Quantitative visualizations of hierarchically organized data in a geographic context

2009 17th International Conference on Geoinformatics, 2009

Here we introduce a novel quantitative technique for visualizing hierarchically organized data in a geographic context. In contrast to existing techniques, our visualization emphasizes the hierarchical relationships in the data by depicting them in a standard tree format that takes advantage of many fundamental perceptual properties. Our technique allows users to define a geographic axis and visualize how well a tree correlates with the ordering of geographical locations along this axis. This is accomplished by finding the ordering of leaf nodes, subject to the constraints of the tree topology, which minimizes the number of crossings that occur between lines that connect leaf nodes to their associated geographic locations. In this optimal layout, any crossings that occur between these lines indicate discordance between the topology of the tree and the user defined geographic axis. We have developed a branch-and-bound algorithm that allows optimal leaf orderings to be determined quickly enough to support interactive exploration of different geographic axes even for large multifurcating hierarchies. The quantitative nature of our visualization has allowed us to specify a permutation test for determining if the relationship between a tree topology and a geographic axis is statistically significant. In this paper, the utility of our visualization is demonstrated on biological data sets, but our method is applicable to any hierarchical data where geographic structure may be of interest.

Towards exploratory visualization of spatio-temporal data

2000

In the paper we focus on the problem of supporting visual exploration of data having spatial and temporal reference. We suggest some methods and tools based on cartographic visualization of the data. The tools involve a dynamic, highly interactive map display that can change its properties in real time, in particular, perform animation. We seek to advance our tools beyond mere animation towards facilitating exploratory analysis of spatio-temporal data. We diversify our approaches depending on properties of data and the character of their variation in time: changing existence, position, values of thematic attributes etc.

Interactive visual exploration of a large spatio-temporal dataset: reflections on a geovisualization mashup.

… and Computer Graphics, …, 2007

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.

Quantitative Comparison of Time‐Dependent Treemaps

Computer Graphics Forum

Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps that can handle time-dependent data, and (b) corresponding quality criteria that cover both a treemap's visual quality and its stability over time. In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations. We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end, we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data. As part of this evaluation we propose a novel classification scheme for time-dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms. All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies. CCS Concepts • Human-centered computing → Treemaps; • Information systems → Temporal data;

Interactive information visualization to explore and query electronic health records

Information Visualization is mainly concerned with the representation of non-physical data like medical patient records or business data. Time is a very important data dimension in many domains that is different from others. A number of novel Information Visualization methods have been developed in order to facilitate the exploration of data across temporal contexts, but most of these methods are domain specific and only take application specific time aspects into account.

Visualising spatio-temporal health data: the importance of capturing the 4th dimension

arXiv (Cornell University), 2022

Confronted by a rapidly evolving health threat, such as an infectious disease outbreak, it is essential that decision-makers are able to comprehend the complex dynamics not just in space but also in the 4th dimension, time. In this paper this is addressed by a novel visualisation tool, referred to as the Dynamic Health Atlas web app, which is designed specifically for displaying the spatial evolution of data over time while simultaneously acknowledging its uncertainty. It is an interactive and open-source web app, coded predominantly in JavaScript, in which the geospatial and temporal data are displayed side-by-side. The first of two case studies of this visualisation tool relates to an outbreak of canine gastroenteric disease in the United Kingdom, where many veterinary practices experienced an unusually high case incidence. The second study concerns the predicted COVID-19 reproduction number along with incidence and prevalence forecasts in each local authority district in the United Kingdom. These studies demonstrate the effectiveness of the Dynamic Health Atlas web app at conveying geospatial and temporal dynamics along with their corresponding uncertainties.