Browsing by Author "Hoteit, Ibrahim"

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    RedSeaAtlas: A Visual Analytics Tool for Spatio-temporal Multivariate Data of the Red Sea
    (The Eurographics Association, 2019) Afzal, Shehzad; Ghani, Sohaib; Tissington, Garth; Langodan, Sabique; Dasari, Hari Prasad; Raitsos, Dionysios; Gittings, John; Jamil, Tahira; Srinivasan, Madhusudhanan; Hoteit, Ibrahim; Bujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Interactive visualizations play an essential role in supporting the analysis tasks of ocean and atmospheric scientists working on a variety of simulation models and observational datasets. Designing visual analytics systems intended for addressing problems in the ocean and atmospheric domain require careful task analysis of the requirements of domain experts and scientists, and understanding their analysis workflows. This paper explores the design of a visual analytics tool (RedSeaAtlas) based on meetings and interviews with domain experts working on diverse research problems that involve analyzing spatio-temporal multivariate datasets of the Red Sea region, to understand their task requirements. This kind of visual analytics tool has widespread applications in areas, such as navigational guidance of marine vessels, fisheries operations, environmental impact assessments, coastal development, renewable energy, risk management, policy making, etc. We provide expert evaluation of this tool based on different case studies targeting some of these application areas. We also discuss the challenges associated with the use of varying visualization tools in the ocean and atmospheric community, focusing on aspects related to visualization research.
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    The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Afzal, Shehzad; Hittawe, Mohamad Mazen; Ghani, Sohaib; Jamil, Tahira; Knio, Omar; Hadwiger, Markus; Hoteit, Ibrahim; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
    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.
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    A Visual Analytics Framework for Renewable Energy Profiling and Resource Planning
    (The Eurographics Association, 2023) Pammi, Ramakrishna P.; Afzal, Shehzad; Dasari, Hari Prasad; Yousaf, Muhammad; Ghani, Sohaib; Venkatraman, Murali Sankar; Hoteit, Ibrahim; Angelini, Marco; El-Assady, Mennatallah
    Renewable energy growth is one of the focus areas globally against the backdrop of the global energy crisis and climate change. Energy planners are looking into clean, safe, affordable, and reliable energy generation sources for a net zero future. Countries are setting energy targets and policies prioritizing renewable energy, shifting the dependence on fossil fuels. The selection of renewable energy sources depends on the suitability of the region under consideration and requires analyzing relevant environmental datasets. In this work, we present a visual analytics framework that facilitates users to explore solar and wind energy datasets consisting of Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffusive Horizontal Irradiance (DHI), and Wind Power (WP) spanning across a 40 year period. This framework provides a suite of interactive decision support tools to analyze spatiotemporal patterns, variability in the variables across space and time at different temporal resolutions, Typical Meteorological Year (TMY) data with varying percentiles, and provides the capability to interactively explore and evaluate potential solar and wind energy equipment installation locations and study different energy acquisition scenarios. This work is conducted in collaboration with domain experts involved in sustainable energy planning. Different use case scenarios are also explained in detail, along with domain experts feedback and future directions.
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    Visualization Environment for Analyzing Extreme Rainfall Events: A Case Study
    (The Eurographics Association, 2023) Kress, James; Afzal, Shehzad; Dasari, Hari Prasad; Ghani, Sohaib; Zamreeq, Arjan; Ghulam, Ayman; Hoteit, Ibrahim; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk
    Extreme rainfall events can devastate infrastructure and public life and potentially induce substantial financial and life losses. Although weather alert systems generate early rainfall warnings, predicting the impact areas, duration, magnitude, occurrence, and characterization as an extreme event is challenging. Scientists analyze previous extreme rainfall events to examine the factors such as meteorological conditions, large-scale features, relationships and interactions between large-scale features and mesoscale features, and the success of simulation models in capturing these conditions at different resolutions and their parameterizations. In addition, they may also be interested in understanding the sources of anomalous amounts of moisture that may fuel such events. Many factors play a role in the development of these events, which vary depending on the locations. In this work, we implement a visualization environment that supports domain scientists in analyzing simulation model outputs configured to predict and analyze extreme precipitation events. This environment enables visualization of important local features and facilitates understanding the mechanisms contributing to such events. We present a case study of the Jeddah extreme precipitation event on November 24, 2022, which caused great flooding and infrastructure damage. We also present a detailed discussion about the study's results, feedback from the domain experts, and future extensions.