Text-based Spatial and Temporal Visualizations and their Applications in Visual Analytics

Textual labels are an essential part of most visualizations used in practice. However, these textual labels are mainly used to annotate other visualizations rather than being a central part of the visualization. Visualization researchers in areas like cartography and geovisualization have studied the combination of graphical features and textual labels to generate map based visualizations, but textual labels alone are not the primary focus in these representations. The idea of using symbols in visual representations and their interpretation as a quantity is gaining more traction. These types of representations are not only aesthetically appealing but also present new possibilities of encoding data. Such scenarios regularly arise while designing visual representations, where designers have to investigate feasibility of encoding information using symbols alone especially textual labels but the lack of readily available automated tools, and design guidelines makes it prohibitively expensive to experiment with such visualization designs. In order to address such challenges, this thesis presents the design and development of visual representations consisting entirely of text. These visual representations open up the possibility of encoding different types of spatial and temporal datasets. We report our results through two novel visualizations: typographic maps and text-based TextRiver visualization. Typographic maps merge text and spatial data into a visual representation where text alone forms the graphical features, mimicking the practices of human map makers. We also introduce methods to combine our automatic typographic maps technique with spatial datasets to generate thema-typographic maps where the properties of individual characters in the map are modified based on the underlying spatial data. Our TextRiver visualization is composed of collection of stream-like shapes consisting entirely of text where each stream represents thematic strength variations over time within a corpus. Such visualization enables additional ways to encode information contained in temporal datasets by modifying text attributes. We also conducted a usability evaluation to assess the potential value of our text-based TextRiver design

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.

Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies

Text in Visualization: Extending the Visualization Design Space

This thesis is a systematic exploration and expansion of the design space of data visualization specifically with regards to text. A critical analysis of text in data visualizations reveals gaps in existing frameworks and the use of text in practice. A cross-disciplinary review across fields such as typography, cartography and technical applications yields typographic techniques to encode data into text and provides the scope for the expanded design space. Mapping new attributes, techniques and considerations back to well understood visualization principles organizes the design space of text in visualization. This design space includes: 1) text as a primary data type literally encoded into alphanumeric glyphs, 2) typographic attributes, such as bold and italic, capable of encoding additional data onto literal text, 3) scope of mark, ranging from individual glyphs, syllables and words; to sentences, paragraphs and documents, and 4) layout of these text elements applicable most known visualization techniques and text specific techniques such as tables. This is the primary contribution of this thesis (Part A and B). Then, this design space is used to facilitate the design, implementation and evaluation of new types of visualization techniques, ranging from enhancements of existing techniques, such as, extending scatterplots and graphs with literal marks, stem & leaf plots with multivariate glyphs and broader scope, and microtext line charts; to new visualization techniques, such as, multivariate typographic thematic maps; text formatted to facilitate skimming; and proportionally encoding quantitative values in running text – all of which are new contributions to the field (Part C). Finally, a broad evaluation across the framework and the sample visualizations with cross-discipline expert critiques and a metrics based approach reveals some concerns and many opportunities pointing towards a breadth of future research work now possible with this new framework. (Part D and E)

MOG 2007:Workshop on Multimodal Output Generation: CTIT Proceedings

This volume brings together presents a wide variety of work offering different perspectives on multimodal generation. Two different strands of work can be distinguished: half of the gathered papers present current work on embodied conversational agents (ECA’s), while the other half presents current work on multimedia applications. Two general research questions are shared by all: what output modalities are most suitable in which situation, and how should different output modalities be combined

Using font attributes in knowledge maps and information retrieval

Font specific attributes, such as bold, italic and case can be used in knowledge mapping and information retrieval to encode additional data in texts, lists and labels to increase data density of visualizations; encode data quantitative data into search lists; and facilitate text skimming and refinement by visually promoting of words of interest

Adaptive Layout for Interactive Documents

This thesis presents a novel approach to create automated layouts for rich illustrative material that could adapt according to the screen size and contextual requirements. The adaption not only considers global layout but also deals with the content and layout adaptation of individual illustrations in the layout. An unique solution has been developed that integrates constraint-based and force-directed techniques to create adaptive grid-based and non-grid layouts. A set of annotation layouts are developed which adapt the annotated illustrations to match the contextual requirements over time

Ways of Visualizing Curves

International audienceThis paper reviews the many ways curves are used to encode data in information visualization. As part of our review, we introduce a curve-based visualization framework where data can be encoded in two major ways: i) through a curve’s shape (a process we call embedding) and ii) through a curve’s local visual attributes (a process we call enrichment). Our framework helps describing and organizing the rich design space of curve-based data visualizations, and offer inspiration for novel data visualizations

Shape, Space and Typeface: Mapping Black Subjectivity through Caribbean Aesthetics

The Caribbean is frequently imagined and aestheticized by the image of the basin, which limits the way the region is confined in geographic and historic terms. By conceptualizing the poets as mapmakers, the collections by Kei Miller, Olive Senior, and M. NourbeSe Phillip reference the container of the basin but remediate it in poetic terms. The movement towards a distinctive lack of containment illustrates the dynamic literary and geographical operations of the Caribbean, linking typography and topography. Reading with a new lens, including digital resources that re-spatialize these poems, demonstrates the complexities that characterize the formation of these texts and how they resist neat containers and containment, thereby charting new ways to redraw and reimagine places and spaces