Seva socio-ecological visualization analytics: a new visual analytics environment for interdisciplinary decision-making linking human biometrics and environmental data

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Aly Etman, Mohamed, Ahmed
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Electronic thesis
Architectural sciences
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It is becoming increasingly clear that contributions from many disciplines are needed in order to understand the complex problems posed by climate change and resource degradation. Therefore, novel interdisciplinary approaches are increasingly sought from designers, scientists, researchers, and policy-makers. However, the conventional methods for sharing data do not provide sufficient support for collaboration across disparate fields, fostering uncertainty in the discourse due to an array of factors including discrepancies in the quantity and quality of data sets and models. Although an interdisciplinary approach is key to nurturing better solutions by permitting more stakeholders with diverse approaches to participate in the process of developing novel solutions for global health and environments, impediments remain to the comprehensive presentation and exchange of the pertinent data and models. This dissertation presents a novel approach to to determine the limitations and challenges of the visualization, sharing and analysis of a more extensive array of data types and models, from structured data and knowledge management to live streaming. Critically, the contextualization and semantic annotations of data quality and significance are automatically integrated into the uploading of information to the visualization environment, so that vital contributions to the analysis of meaning and the inference of knowledge are preserved with the provenance of the data. The Socio-Ecological Visualization Analytics (SEVA) environment was designed to incorporate multiple techniques with a focus that allows the user to explore, analyze, and share the data and knowledge findings. This research uses two use cases that incorporate both human factors and environmental data in order to demonstrate how SEVA accommodates a far greater range of users, in comparison to existing visualization and modeling environments with a significantly extended array of interdisciplinary datasets and methods that can be combined. The research aims to investigate the potential for SEVA to foster interdisciplinary research by enabling greater access to extensive and complex data sets that were not previously comprehensible. This environment is tackling some of the main challenges to interdisciplinary research: (i) the use of heterogeneous data types; (ii) the scalability of information; (iii) the lack of characterization of error and uncertainty; and (iv) the necessity for building adaptable environments that allow for the characterization of confidence in the data sharing and visualization process. Through two use cases, the SEVA environment is proposed and tested concerning the limitations and challenges of current interdisciplinary approaches towards visualizing and sharing information.
School of Architecture
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Rensselaer Polytechnic Institute, Troy, NY
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