Interactive spatiotemporal cluster analysis of vast challenge 2008 datasets

We describe a visual analytics method supporting the analysis of two different types of spatio-temporal data, point events and trajectories of moving agents. The method combines clustering with interactive visual displays, in particular, map and space-time cube. We demonstrate the use of the method by applying it to two datasets from the VAST Challenge 2008: evacuation traces (trajectories of people movement) and landings and interdictions of migrant boats (point events)

Spatio-temporal visual analytics: a vision for 2020s

Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. Visual analytics develops methods, analytical workflows, and software tools for analysing data of various types, particularly, spatio-temporal data, which can describe the processes going on in the environment, society, and economy. We briefly overview the achievements of the visual analytics research concerning spatio-temporal data analysis and discuss the major open problems

Visual analytics on eye movement data reveal search patterns on dynamic and interactive maps

In this paper the results of a visual analytics approach on eye movement data are described which allows detecting underlying patterns in the scanpaths of the user’s during a visual search on a map. These patterns give insights in the user his cognitive processes or his mental map while working with interactive maps

From movement tracks through events to places : extracting and characterizing significant places from mobility data

Best VAST 2011 paperInternational audienceWe propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Visual analytics methodology for eye movement studies

Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks

An event-based conceptual model for context-aware movement analysis

Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach

Analysing the spatial dimension of eye movement data using a visual analytic approach

Conventional analyses on eye movement data only take into account eye movement metrics, such as the number or the duration of fixations and length of the scanpaths, on which statistical analysis is performed for detecting significant differences. However, the spatial dimension in the eye movements is neglected, which is an essential element when investigating the design of maps. The study described in this paper uses a visual analytics software package, the Visual Analytics Toolkit, to analyse the eye movement data. Selection, simplification and aggregation functions are applied to filter out meaningful subsets of the data to be able to recognise structures in the movement data. Visualising and analysing these patterns provides essential insights in the user's search strategies while working on a (n interactive) map

A conceptual framework for developing dashboards for big mobility data

Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

Visual analytics approach to user-controlled evacuation scheduling

Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster-affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information