Visual Analytical Approaches to Evaluating Uncertainty and Bias in Crowdsourced Crisis Information

Concerns about verification mean the humanitarian community are reluctant to use information collected during crisis events, even though such information could potentially enhance the response effort. Consequently, a program of research is presented that aims to evaluate the degree to which uncertainty and bias are found in public collections of incident reports gathered during crisis events. These datasets exemplify a class whose members have spatial and temporal attributes, are gathered from heterogeneous sources, and do not have readily available attribution information. An interactive software prototype, and existing software, are applied to a dataset related to the current armed conflict in Libya to identify ‘intrinsic’ characteristics against which uncertainty and bias can be evaluated. Requirements on the prototype are identified, which in time will be expanded into full research objectives

Crowd-sourced Photographic Content for Urban Recreational Route Planning

Routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) while paying less attention to recreational trips, in particular leisure walks in an urban context. These walks are additionally predefined by time or distance and as their purpose is the process of walking itself, the attractiveness of areas that are passed by can be an important factor in route selection. This factor is hard to be formalised and requires a reliable source of information, covering the entire street network. Previous research shows that crowd-sourced data available from photo-sharing services has a potential for being a measure of space attractiveness, thus becoming a base for a routing system that suggests leisure walks, and ongoing PhD research aims to build such system. This paper demonstrates findings on four investigated data sources (Flickr, Panoramio, Picasa and Geograph) in Central London and discusses the requirements to the algorithm that is going to be implemented in the second half of this PhD research. Visual analytics was chosen as a method for understanding and comparing obtained datasets that contain hundreds of thousands records. Interactive software was developed to find a number of problems, as well as to estimate the suitability of the sources in general. It was concluded that Picasa and Geograph have problems making them less suitable for further research while Panoramio and Flickr require filtering to remove photographs that do not contribute to understanding of local attractiveness. Based on this analysis a number of filtering methods were proposed in order to improve the quality of datasets and thus provide a more reliable measure to support urban recreational routing

Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics

This paper presents the results of an experiment into the relationship between the representation of data and decision-making. Three hundred participants online, were asked to choose between a series of financial investment opportunities using data presented in line charts. A single dependent variable of investment choice was examined over four levels of varying display conditions and randomised data. Three variations to line chart visualisations provided a controlled factor between subjects divided into three groups; -˜standard’ line charts, -˜tall’ line charts, and one dual-series line chart. The final results revealed a consistent main effect and two other interactions between certain display conditions and decision-making. The findings of this paper are significant to the study visualisation and to the field of visual analytics. This experiment was devised as part of a study into Analytical Behaviour, defined as decision-making facilitated by visual analytics - a new topic that encompasses existing research and real-world applications

Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects

Design Spaces in Visual Analytics Based on Goals: Analytical Behaviour, Exploratory Investigation, Information Design & Perceptual Tasks

This paper considers a number of perspectives on design spaces in visual analytics and proposes a new set of four design spaces, based on user goals. Three of the user goals are derived from the literature and are categorised under the terms exploratory investigation, perceptual tasks, and information design. The fourth goal is categorised as analytical behaviour; a recently defined term referring to the study of decision-making facilitated by visual analytics. This paper contributes to the literature on decision-making in visual analytics with a survey of real-world applications within the analytical behaviour design space and by providing a new perspective on design spaces. Central to our analysis is the introduction of decision concepts and theories from economics into a visual analytics context. Given the recent interest in decision-making we wanted to understand the emerging topic of analytical behaviour as a design space and found it necessary to look at more than just decision-making to make a valuable contribution. The result is an initial framework suitable for use in the analysis or design of analytical behaviour applications

Streaming Analytics and Workflow Automation for DFS

Researchers reuse data from past studies to avoid costly re-collection of experimental data. However, large-scale data reuse is challenging due to lack of consensus on metadata representations among research groups and disciplines. Dataset File System (DFS) is a semi-structured data description format that promotes such consensus by standardizing the semantics of data description, storage, and retrieval. In this paper, we present analytic-streams – a specification for streaming data analytics with DFS, and streaming-hub – a visual programming toolkit built on DFS to simplify data analysis work-flows. Analytic-streams facilitate higher-order data analysis with less computational overhead, while streaming-hub enables storage, retrieval, manipulation, and visualization of data and analytics. We discuss how they simplify data pre-processing, aggregation, and visualization, and their implications on data analysis workflows