Information about visualizing geographic data is available on this GIS guide.
Books on Data Visualization
Visual Knowledge Discovery and Machine Learning by Boris KovalerchukThis book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.
Publication Date: 2018-01-26
User-Centered Evaluation of Visual Analytics by Jean Scholtz; David Ebert (Editor); Niklas Elmqvist (Editor)Visual analytics has come a long way since its inception in 2005. The amount of data in the world today has increased significantly and experts in many domains are struggling to make sense of their data. Visual analytics is helping them conduct their analyses. While software developers have worked for many years to develop software that helps users do their tasks, this task is becoming more and more onerous, as understanding the needs and data used by expert users requires more than some simple usability testing during the development process. The need for a user centered evaluation process was envisioned in Illuminating the Path, the seminal work on visual analytics by James Thomas and Kristin Cook in 2005. We have learned over the intervening years that not only will user-centered evaluation help software developers to turn out products that have more utility, the evaluation efforts can also help point out the direction for future research efforts. This book describes the efforts that go into analysis, including critical thinking, sensemaking, and various analytics techniques learned from the intelligence community. Support for these components is needed in order to provide the most utility for the expert users. There are a good number of techniques for evaluating software that has been developed within the human-computer interaction (HCI) community. While some of these techniques can be used as is, others require modifications. These too are described in the book. An essential point to stress is that the users of the domains for which visual analytics tools are being designed need to be involved in the process. The work they do and the obstacles in their current processes need to be understood in order to determine both the types of evaluations needed and the metrics to use in these evaluations. At this point in time, very few published efforts describe more than informal evaluations. The purpose of this book is to help readers understand the need for more user-centered evaluations to drive both better-designed products and to define areas for future research. Hopefully readers will view this work as an exciting and creative effort and will join the community involved in these efforts.
Publication Date: 2017-10-06
Data Visualization by Amar SahayThis book discusses data and information visualization techniques-the decision-making tools with applications in health care, finance, manufacturing engineering, process improvement, product design, and others. These tools are an excellent means of viewing the current state of the process and improving them. The initial chapters discuss data analysis, the current trends in visualization, the concepts of systems and processes from which data are collected. The second part is devoted to quality tools-a set of graphical and information visualization tools in data analysis, decision-making, and Lean Six-Sigma quality. The eight basic tools of quality discussed are the Process Maps, Check Sheets, Histograms, Scatter Diagrams, Run Charts, Control Charts, Cause-and-Effect Diagrams, and Pareto Charts. The new quality tools presented are the Affinity, Tree, and Matrix Diagrams, Interrelationship Digraph, Prioritizing Matrices, Process Decision Program Chart, and Activity Network Diagram along with Quality Function Deployment (QFD) and Multivari Charts.