CyberGIS-enabled reproducible agent-based modeling for scalable emergency evacuation

Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Component-wise Interpolation of Solenoidal Vector Fields: A Comparative Numerical Study

Vector-field interpolation is a fundamental task in flow simulation and visualization. The common practice is to interpolate the vector field in a component-wise fashion. When the vector field of interest is solenoidal (divergencefree), such an approach is not conservative and gives rise to artificial divergence. In this work, we numerically compare some recently proposed scalar interpolation methods on the Cartesian and body-centered cubic lattices, and investigate their ability to conserve the solenoidal nature of the vector field. We start with a sampled version of a synthetic solenoidal vector field and use an interpolative component-wise reconstruction method to approximate the vector field and its divergence at arbitrary locations. Our results show that an improved scalar interpolation method does not necessarily lead to a more conservative vector field approximation.N

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

With the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses