Constraint-Based Breakpoints for Responsive Visualization Design and Development.

Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods attempt to address the uncertainty issue in choropleth mapping, there are limitations to widely applying these methods due to their strong assumption on the distribution of uncertainty and the way in which similarity or dissimilarity is assessed. This article provides a new classification scheme for choropleth maps when data contain uncertainty. Considering that in a choropleth map, units in the same class are assigned with the same color or pattern, this new approach assumes the existence of a representative value for each class. A maximum likelihood estimation–based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods—including linear programming, dynamic programming, and an interchange heuristic—are developed to solve the new classification problem. The proposed mapping approach has been applied to map the median household income data from the American Community Survey and simulated disease occurrence data. Test results show the effectiveness of the new approach. The linkage between the new approach and the existing methods is also discussed. Key Words: choropleth mapping, map classification, maximum likelihood estimation, uncertainty.

This chapter gives an overview over visualization techniques beyond the node-link diagram. Node-link diagrams are by far the most common visual representation to visualize networks, showing nodes as points, and links as lines or arcs between them. However, with increasing size of a network, as well as data about time and temporal change, geography, or other attributes about node and links, alternative representations are required. Starting from a discussion of the problems of visualizing dense networks through node-link diagrams (i.e. hairballs), this chapter discusses a range of alternatives to node-link diagrams: adjacency matrices to compare networks and visualize multivariate networks; techniques for networks with different node and link types; networks with quantitative attributes on nodes, Ontologies, networks changing over time, and geographic networks. The chapter concludes with a pointer to common network visualization tools and a discussion of how to choose among the wide range of visualization techniques.

Exponential size growth of such graphs as social networks, Internet graphs, etc. requires new approaches to their visualization. Along with node-link diagram representations, adjacency matrices and various hybrid representations are increasingly used for large graphs visualizations. This survey discusses new approaches to the visualization of large graphs using adjacency matrices and gives examples of applications where these approaches are used. We describe various types of patterns arising when adjacency matrices corresponding to modern networks are ordered, and algorithms making it possible to reveal these patterns. In particular, the use of matrix ordering methods in conjunction with algorithms looking for such graph patterns as stars, false stars, chains, near-cliques, full cliques, bipartite cores and near-bipartite cores enable users to create understandable visualizations of graphs with millions of vertices and edges. Examples of hybrid visualizations using node-link diagrams for representing sparse parts of a graph and adjacency matrices for representing dense parts are also given. The hybrid methods are used to visualize co-authorship networks, deep neural networks, to compare networks of the human brain connectivity, etc.

We present and evaluate a framework for constructing sketchy style information visualizations that mimic data graphics drawn by hand. We provide an alternative renderer for the Processing graphics environment that redefines core drawing primitives including line, polygon and ellipse rendering. These primitives allow higher-level graphical features such as bar charts, line charts, treemaps and node-link diagrams to be drawn in a sketchy style with a specified degree of sketchiness. The framework is designed to be easily integrated into existing visualization implementations with minimal programming modification or design effort. We show examples of use for statistical graphics, conveying spatial imprecision and for enhancing aesthetic and narrative qualities of visualization. We evaluate user perception of sketchiness of areal features through a series of stimulus-response tests in order to assess users' ability to place sketchiness on a ratio scale, and to estimate area. Results suggest relative area judgment is compromised by sketchy rendering and that its influence is dependent on the shape being rendered. They show that degree of sketchiness may be judged on an ordinal scale but that its judgement varies strongly between individuals. We evaluate higher-level impacts of sketchiness through user testing of scenarios that encourage user engagement with data visualization and willingness to critique visualization design. Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive. The results of our work have implications for effective information visualization design that go beyond the traditional role of sketching as a tool for prototyping or its use for an indication of general uncertainty.

Static or dynamic graphs are typically visualized by either node-link diagrams, adjacency matrices, adjacency lists, orhybrids thereof. In particular, for the case of a changing graphstructure a viewer wishes to be able to visually compare thegraphs in a sequence. Doing such a comparison task rapidlyand reliably can give support to visually analyze the dynamicgraph for certain dynamic patterns. In this paper we describe a novel dynamic graph visualization that is based on the concept of smooth density fields generated by first splatting the links of a given graph in a certain layout. To further visually enhance the time-varying graph structures we add user-adaptable isolines to the resulting dynamic graph representation. The computed visual encoding of the dynamic graph is aesthetically appealing due to its smooth curves and can additionally be used to do comparisons in a long graph sequence, i.e., from an information visualization perspective it serves as an overview representation supporting to start more detailed analyses processes. To demonstrate theusefulness of the technique we explore real-world dynamic graph data by taking into account visual parameters like node-link layouts, smoothing iterations, number of isolines, and different color codings.

A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD's boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.

The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node‐link diagrams and adjacency matrices are widespread, they have inherent limitations. Node‐link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data — focusing on three main categories: juxtaposed visualizations, embedded on‐node and on‐edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.

During the past decade, we have witnessed the pervasive penetration of mobile smart devices such as smartphones, tablets, and wearable devices, which significantly enrich Internet applications and improve user experience. In the foreseeable future, mobile smart devices are predicted to take up over 50% of global devices/connections and surpass 4/5 of mobile data traffic by 2021 [1]. Such mobile smart devices as smartphones, phablets, and tablets, undoubtedly reshape the way that users access Internet services, e.g., web browsing. Different from traditional desktop applications, in which users interact via interfaces like large displays, keyboards, and mouses, mobile applications require users to enter the inputs through touch screens and allow them to view the outputs on limited size of displays. This distinct feature introduced by mobile hardware interfaces brings both challenges and opportunities to mobile-based Internet applications. On one hand, mobile service providers should prepare multiple copies of media contents with different resolutions and even multiple versions of application UI layouts to fit various sizes of screens on heterogeneous devices. On the other hand, as media contents are usually organized in certain order in mobile-based Internet applications, it is possible to predict the viewing region (referred as viewport hereafter) given the user inputs and the fixed size of display.

With the increasing prevalence of information networks, research on privacy-preserving network data publishing has received substantial attention recently. There are two streams of relevant research, targeting different privacy requirements. A large body of existing works focus on preventing node re-identification against adversaries with structural background knowledge, while some other studies aim to thwart edge disclosure. In general, the line of research on preventing edge disclosure is less fruitful, largely due to lack of a formal privacy model. The recent emergence of differential privacy has shown great promise for rigorous prevention of edge disclosure. Yet recent research indicates that differential privacy is vulnerable to data correlation, which hinders its application to network data that may be inherently correlated. In this paper, we show that differential privacy could be tuned to provide provable privacy guarantees even in the correlated setting by introducing an extra parameter, which measures the extent of correlation. We subsequently provide a holistic solution for non-interactive network data publication. First, we generate a private vertex labeling for a given network dataset to make the corresponding adjacency matrix form dense clusters. Next, we adaptively identify dense regions of the adjacency matrix by a data-dependent partitioning process. Finally, we reconstruct a noisy adjacency matrix by a novel use of the exponential mechanism. To our best knowledge, this is the first work providing a practical solution for publishing real-life network data via differential privacy. Extensive experiments demonstrate that our approach performs well on different types of real-life network datasets.

Compound graphs are often useful for modeling components of hierarchical systems. While node-link diagrams can be used to visualize these graphs, adjacency matrices have the advantage of eliminating occlusion between edges, even in dense networks. However, matrices do not reveal the hierarchy in a compound graph. To address this issue, we propose a novel hybrid visualization technique that combines an adjacency matrix with treemaps displaying portions of the hierarchy. A prototype is presented that illustrates these ideas and supports visualization of digraphs with weighted edges. Interaction techniques allow the user to reorganize the hierarchical groupings of elements and facilitates the exploration of links within the network. For example, in software engineering, the prototype enables browsing of links between source code modules to help discover the architecture of the software.

Abstract. In 2018, the Swiss Federal Statistical Office celebrated the 125th edition of its most prominent annual publication – the “Statistical Yearbook of Switzerland”. This celebration was also an opportunity of reissuing as a supplement a selection of attractive, but not well-known visualisations from the first Statistical Atlas of Switzerland initially published in 1897 and yearbooks from the 19th century. The original atlas produced issued by the Swiss Federal Statistical Bureau contained 22 coloured “plates”, including 6 maps and 16 diagrams. The techniques of visual display of statistical information were still in their infancy in statistical offices at this time, but this remarkable atlas already shows an expertise and judicious use of these techniques borrowed from the then reference book of Dr Georg von Mayr. The foreword of the original atlas gives a clue of what the authors intended with this visual collection: their main objective was to democratise interest in public statistics and their results using visualisations. In an educational effort, also an explanatory preamble on the methods of graphical representation in general was offered.The data used were relatively recent at the time of publication and the datasets even contain first long-time series, ranging from 1850 to 1898. The themes addressed testify to the development of public statistics at the time and the historical context. We find the traditional demographic themes (birth rate, mortality, marriage, population structure, emigration and health), territorial aspects (surfaces and occupation and land use), political life (elected representatives and voting result), trade aspects (imports and exports) and the development of means of communications (railways, telegraph and telephone). The data visualisation techniques that were used already reflect a certain diversity: choropleth and proportional symbol maps, bar charts, pie charts, line charts and population pyramids, displayed as simple figures or as small multiples.Following a similar first publication of a retro-atlas (The “Graphical-statistical Atlas 1914–2014”), published in 2015, he this latest atlas edition combines the 22 plates from the 1897 atlas as well as 7 older visualisations from the 1891–1899 yearbooks, and enriches this “retro” selection with their counterpart visualisations produced with recent data and modern tools around the year 2017. As the original atlas, the new atlas is fully bilingual and was printed in 2000 copies.It is thus possible for the reader to compare different themes and developments in a broad historical perspective, as well as to appreciate the statistics bureau’s early know-how in visual communication. This anniversary edition is therefore an opportunity to bring the first statistical atlas back to the public's attention, and to show the early interest of official statistics in this excellent “intuitive means of teaching, particularly suitable for popularising the interest in the work of statistics”.

Because the urban geographical basic data is in the data format, coordinate system, data quality can not satisfy with the need of most users. So the urban geographical basic data are processed before using. The purpose is that the data processing is to make the data meet the need of user. The ways of data are processed including the data format is changed, the coordinate is varied, layer is drew, data are edited, the maps are connected and topological relation is built. This article introduced the current condition of the Urban geographical basic data of our country, the data processing technique and method, and search in the information to the urban geographical data, network analysis, and space analysis carried on the discussion.

Choropleth maps are a common and effective way to visualize geographic thematic data. Although cartographers have established many principles about map design, data binning and color usage, less is known about how mapmakers make individual decisions in practice. We interview 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, NGOs, and federal agencies about their choropleth mapmaking decisions and workflows. We categorize our findings and report on how mapmakers follow cartographic guidelines and personal rules of thumb, collaborate with other stakeholders within and outside their organization, and how organizational structures and norms are tied to decision-making during data preparation, data analysis, data binning, map styling, and map post-processing. We find several points of variation as well as regularity across mapmakers and organizations and present takeaways to inform cartographic education and practice, including broader implications and opportunities for CSCW, HCI, and information visualization researchers and practitioners.

Choropleth maps are fundamental tools for geographic data analysis, primarily relying on color to convey information. Consequently, the design of their color schemes is important in choropleth map production. However, the traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly enough for nonprofessionals. These tools provide numerous color schemes, making selection difficult, and cannot also easily fulfill personalized coloring needs, such as requests for “summer-like” map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: Data processing, which provides an overview and classification of the data; Color Concept Design, where color theme and mode are conceptualized based on data characteristics and user intentions; and Color Scheme Design, where specific colors are assigned to classes. Our system incorporates an interactive interface for choropleth map color design and allows users to customize color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting its efficiency and ease of use.

In this paper, we focus on differential privacy preserving spectral graph analysis. Spectral graph analysis deals with the analysis of the spectra (eigenvalues and eigenvector components) of the graph’s adjacency matrix or its variants. We develop two approaches to computing the e-differential eigen decomposition of the graph’s adjacency matrix. The first approach, denoted as LNPP, is based on the Laplace Mechanism that calibrates Laplace noise on the eigenvalues and every entry of the eigenvectors based on their sensitivities. We derive the global sensitivities of both eigenvalues and eigenvectors based on the matrix perturbation theory. Because the output eigenvectors after perturbation are no longer orthogonormal, we postprocess the output eigenvectors by using the state-of-the-art vector orthogonalization technique. The second approach, denoted as SBMF, is based on the exponential mechanism and the properties of the matrix Bingham-von Mises-Fisher density for network data spectral analysis. We prove that the sampling procedure achieves differential privacy. We conduct empirical evaluation on a real social network data and compare the two approaches in terms of utility preservation (the accuracy of spectra and the accuracy of low rank approximation) under the same differential privacy threshold. Our empirical evaluation results show that LNPP generally incurs smaller utility loss.