Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

Abstract The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )

Abstract Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

The advent of modern smart phones and handheld devices has given analysts, decision-makers, and even the general public the ability to rapidly ingest data and translate it into actionable information on-the-go. In this paper, we explore the design and use of a mobile visual analytics toolkit for public safety data that equips law enforcement agencies with effective situation awareness and risk assessment tools. Our system provides users with a suite of interactive tools that allow them to perform analysis and detect trends, patterns and anomalies among criminal, traffic and civil (CTC) incidents. The system also provides interactive risk assessment tools that allow users to identify regions of potential high risk and determine the risk at any user-specified location and time. Our system has been designed for the iPhone/iPad environment and is currently being used and evaluated by a consortium of law enforcement agencies. We report their use of the system and some initial feedback.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.