Situating GIS Services: New Neighbours and Spaces

Between Spring of 2017 and Fall of 2018 Trent Library underwent extensive building renovations. This gave the Maps, Data & Government Information Centre an opportunity to look at our space footprint and ideal layout.  This article discusses some key factors we considered in context of trends for GIS support services

An Exploration of The Application of Spatial Network Screening Methods On Iowa Rural Road Crashes

Safety on the roadway system is important due to its usage on mobility and accessibility, especially on rural roads in the state of Iowa. Single vehicle run off road crashes have been increasing in the United States and studies and research has increased due to the concern with those. For this effort, a spatial-temporal method of traffic safety network screening is utilized in order to evaluate the concerning type of crashes in particular locations. The study of single vehicle run off road crashes using the proposed method is important since distributions and clusters of crashes along roadways can be observed and further evaluations can be performed

Scalable Visualization of Multivariate Spatiotemporal Distributions from Scientific Simulation Data

As computational power increases, scientists simulate complex physical systems at a larger scale. The resulting data is valuable but cumbersome due to its size and complexity. This dissertation is driven primarily by exasperating data visualization challenges in two domains: gyrokinetic particle-in-cell plasma physics simulations devoted to solving problems in tokamak fusion energy production and turbulent combustion simulations devoted to improving fuel efficiency and the formulation of new environmentally friendly fuels.Challenges in these domains stem from data size and complexity due to high dimensionality, large numbers of simulation grid points, large numbers of particles, and the complicated joint physical and statistical interpretations of particles in particle-in-cell plasma simulations. In both cases, due to chaos and turbulence, the systems can evade predictability and exhibit emergent properties and pattern formations that are not yet well understood through causal analysis starting from earlier states and parameters. Ultimately, the simulations produce large amounts of spatially distributed and multivariate data elements that jointly model the states of the simulations at each time step. The data is precious to researchers; however, since large-scale and complex data distributions characterize the state spaces, it is non-trivial and time-consuming for scientists to comb through and absorb the data. Furthermore, since the raw data is too large to manage with current I/O limitations, data storage systems, and networks, researchers are forced to make uncertain compromises as they reduce the data. Our contributions ameliorate these problems through data summarization and interactive visualization. First, we develop methods and systems for visualizing particle data and phase-space particle distribution functions from tokamak fusion simulations. To accomplish this, we introduce a novel approach for the interactive visualization of large sets of spatially organized histograms. The approach is leveraged into a visualization system tailored for the study of phase-space particle distribution functions and the evolution of the statistical weights of the simulation superparticles. We then develop tools for visualizing large sets of multivariate trajectories, such as the phase-space particle trajectories in fusion simulations, as well as trajectories of particles from linear accelerator simulations. Finally, we develop an approach for spatial statistical visualization of multivariate volume data from turbulent combustion simulations. The approach is based on a novel dynamic, nested, hierarchical spatial decomposition method based on isobands, connected components, and the restricted centroidal Voronoi tessellation. Our tessellation is restricted between level sets so that the Voronoi tessellation conforms to the boundaries of surface-based features. We leverage the tessellation in a custom visualization system for interactive local spatial statistical analysis. The system is designed in collaboration with expert combustion scientists