Spatial Trends in Groundwater Arsenic Concentrations

Arsenic presents complex spatial occurrence trends that can be difficult to identify and understand. This project sought to understand geographic trends in arsenic occurrence using a visualization technique. The approach taken was to link geospatially referenced arsenic concentration information from a water quality database with elevation data contained in Digital Terrain Elevation Data (DTED) files. DTED files are available for all land masses across the world for public download. This allows for the development of three-dimensional plots of arsenic concentration and topography. The plots developed in this manner show that high arsenic is associated with the transition from plains to piedmont on the western side of the Delaware River Valley in New Jersey. In Oklahoma high arsenic is found along the North Canadian River Valley. In New Mexico high concentrations are generally high in the Rio Grande Valley but with an area of low concentration in the southern portion of this valley. In California, arsenic concentrations are high in the middle of the Central Valley but moderate somewhat toward the edges. These results are consistent with mobilization of arsenic by reductive processes in the organic-rich sediments of river valleys, but further statistical analysis is required to confirm the significance of this association. The visualization software used here is broadly applicable and a user guide for this software is available on request

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems

Explorative coastal oceanographic visual analytics : oceans of data

The widely acknowledged challenge to data analysis and understanding, resulting from the exponential increase in volumes of data generated by increasingly complex modelling and sampling systems, is a problem experienced by many researchers, including ocean scientists. The thesis explores a visualization and visual analytics solution for predictive studies of coastal shelf and estuarine modelled, hydrodynamics undertaken to understand sea level rise, as a contribution to wider climate change studies, and to underpin coastal zone planning, flood prevention and extreme event management. But these studies are complex and require numerous simulations of estuarine hydrodynamics, generating extremely large datasets of multi-field data. This type\ud of data is acknowledged as difficult to visualize and analyse, as its numerous attributes present significant computational challenges, and ideally require a wide range of approaches to provide the necessary insight. These challenges are not easily overcome with the current visualization and analysis methodologies employed by coastal shelf hydrodynamic researchers, who use several software systems to generate graphs, each taking considerable time to operate, thus it is difficult to explore different scenarios and explore the data interactively and visually. The thesis, therefore, develops novel visualization and visual analytics techniques to help researchers overcome the limitations of existing methods (for example in understanding key tidal components); analyse data in a timely manner and explore different scenarios. There were a number of challenges to this: the size of the data, resulting in lengthy computing time, also many data values becoming plotted on one pixel (overplotting). The thesis presents: (1) a new visualization framework (VINCA) using caching and hierarchical aggregation techniques to make the data more interactive, plus explorative, coordinated multiple views, to enable the scientists to explore the data. (2) A novel estuarine transect profiler and flux tool, which provides instantaneous flux calculations across an estuary. Measures of flux are of great significance in oceanographic studies, yet are notoriously difficult and time consuming to calculate with the commonly used tools. This derived data is added back into the database for further investigation and analysis. (3) New views, including a novel, dynamic, spatially aggregated Parallel Coordinate Plots (Sa-PCP), are developed to provide different perspectives of the spatial, time dependent data, also methodologies for developing high-quality (journal ready) output from the visualization tool. Finally, (4) the dissertation explored the use of hierarchical data-structures and caching techniques to enable fast analysis on a desktop computer and to overcome the overplotting challenge for this data

Scientific Visualization of Water Quality in the Chesapeake Bay

This paper describes our experience in designing and building a tool for visualizing the results of the CE-QUALICM Three-Dimensional Eutrophication Model, as applied to water quality in the Chesapeake Bay. This model outputs a highly multidimensional dataset over very many timesteps -- outstripping the capabilities of the visualization tools available to the research team. As part of the Army Engineer Research and Development Center (ERDC) Programming Environment and Training (PET) project, a special visualization tool was developed. This paper includes discussions on how the simulation data are handled efficiently, as well as how the issues of usability, flexibility and collaboration are addressed. Introduction The Chesapeake Bay is the largest and most productive estuary in the United States. With a surrounding population of about 15 million people, and a valuable place in the fishing industry, the Chesapeake Bay is a very important natural resource to the region. Population growth..