Research

Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions

Monitoring and predicting crack propagation in rock using Acoustic Emission (AE) technology is integral to a variety of sub-disciplines in the geosciences. The utility of existing AE data, however, is severely limited by prevailing visualization techniques, which suffer from problems of occlusion. We introduce a novel approach to visualize 3D data through time (4D data) using individual (AE) event data collected from a granite boulder for a period over three years.  We implement a 3D extension of Ripley’s K function to evaluate the magnitude of clustering, and use the scale at which clustering is the greatest as an input for three dimensional kernel density estimation (3DKDE) of AE events. We develop a parallel approach with load balancing to decrease the computational effort for 3DKDE and hence, reduce execution time. The results from the 3DKDE allow for comprehensible visualization of high AE density areas and their changes through time, which is a substantial improvement over traditional methods. Our framework is scalable and portable to a variety of other disciplines such as epidemiology, ecology, and any point data by extension. Assumptions and limitations are identified as well as possible future research directions.

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Fine-scale visualization of pollen concentrations across the Eastern United States

Allergic rhinitis (hay fever) resulting from seasonal pollen affects 15-30% of the population in the United States, and can exacerbate several related conditions including asthma, atopic eczema, and allergic conjunctivitis.  Frequent monitoring and accurate prediction of pollen counts may help physicians treat sensitive patients.  We reconstruct the dynamics of pollen concentrations across the Eastern United States at a very fine scale by interpolating daily pollen counts in both space and time for the Eastern United States for the year 2016. We conducted a space-time cross-correlation and inferred the optimal spatial and temporal range at which correlation vanishes.  Given the sheer volume of the computation requirement, we follow a parallel computational approach facilitated by a spatiotemporal domain decomposition algorithm. We visualize the results of the spatiotemporal interpolation in a 3D environment to identify the seasonal dynamics of high and low pollen concentrations. We demonstrate the capabilities of HPC to reduce the computational burden of spatiotemporal models, facilitating analysis and decision-making by producing results in a timely matter. Our framework improves the understanding of large-scale seasonal pollen patterns that may aid physicians with treatment plans for sensitive patients, such as limiting outdoor exposure or physical activity. Our approach is also portable to analyze other large spatiotemporal explicit datasets, such as air pollution and precipitation.

Desjardins, M. R., Hohl, A., Griffith, A., & Delmelle, E. Fine-scale visualization of pollen concentrations across the Eastern United States: A space-time parallel approach.

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Discovering Space-Time Patterns of Infectious Disease

Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. I develop an adaptive space-time domain decomposition approach for parallel computation of the space-time kernel density. I apply this methodology to individual reported dengue cases from 2010 to 2011 in the city of Cali, Colombia. The parallel implementation reaches significant speedup compared to sequential counterparts. Density values are visualized in an interactive 3D environment, which facilitates the identification and communication of uneven space-time distribution of disease events. My framework has the potential to enhance the timely monitoring of infectious diseases.

Hohl, A., Delmelle, E., Tang, W., & Casas, I. (2016). Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. Spatial and Spatio-temporal Epidemiology, 19, 10-20.

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Spatiotemporal Domain Decomposition

Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data.

In this project, I perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. Then, I quantify computational intensity of each subdomain to balance workloads among processors. Preliminary results show that the parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. This approach is portable of other space-time analytical tests.

Saule, E., Panchananam, D., Hohl, A., Tang, W., & Delmelle, E. (2017). Parallel Space-Time Kernel Density Estimation. arXiv preprint arXiv:1705.09366.

Hohl, A., Casas, I., Delmelle, E., & Tang, W. (2016, January). Hybrid Indexing for Parallel Analysis of Spatiotemporal Point Patterns. In International Conference on GIScience Short Paper Proceedings (Vol. 1, No. 1).

Hohl, A., Delmelle, E. M., & Tang, W. (2015). Spatiotemporal Domain Decomposition for Massive Parallel Computation of Space-Time Kernel Density. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 7-11.

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Quantifying Hydrological Connectivity for Passively Dispersed Microorganisms

Understanding the diverse ways that landscape connectivity influences the distribution of microbial species is central to managing the spread and persistence of numerous biological invasions. In what became my Master’s thesis research, I used geospatial analytics to examine the degree to which the hydrologic connectivity of landscapes influences the transport of passively dispersed microbes, using the invasive plant pathogen Phytophthora ramorumas a case study.

I modeled the probability of pathogen occurrence at stream baiting stations based on nine environmental variables, developing a novel geospatial approach to quantify the hydrologic connectivity of host vegetation and inoculum pressure derived from least cost distance analyses in each watershed. I also examined the influence of local environmental conditions within the immediate neighborhood of a baiting station, therefore performing this analysis on multiple spatial scales.

Hydrologic landscape connectivity was a key predictor of pathogen occurrence in streams after accounting for variation in climate and exposure to inoculum. This study illustrates a geospatial approach to modeling the degree to which hydrologic systems play a role in shaping landscape structures conducive for the transport of passively dispersed microbes in heterogeneous watersheds.

Hohl, A., Václavík, T., & Meentemeyer, R. K. (2014). Go with the flow: geospatial analytics to quantify hydrologic landscape connectivity for passively dispersed microorganisms. International Journal of Geographical Information Science, 28(8), 1626-1641.

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