Visualization of 40 Years of Tropical Cyclone Positions and Their Rainfall

Correos de investigadores: [email protected] || [email protected] || [email protected] || [email protected] article focuses on a visualization of tropical cyclone track data occurring over a 40- year period (1970–2010) and their relationship with (extremely) heavy rainfall reported by 88 Central American weather stations. The purpose of the visualization is to associate the paths of tropical cyclones in oceanic areas with heavy rainfall inland. Thus, the potential for producing a set of rainfall patterns might somehow help in predicting where different impacts like flooding might occur when tropical cyclones develop in specific oceanic regions. The visualization will serve as a key tool for CIGEFI scientists to apply in their work to determine critical positions of the tropical cyclones associated with extremely heavy rainfall events at daily timescales.Universidad de Costa Rica/[805-B9-454]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-610]/UCR/Costa RicaUniversidad de Costa Rica/[EC-497]/UCR/Costa RicaUniversidad de Costa Rica/[805-A4-906]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-074]/UCR/Costa RicaUniversidad de Costa Rica/[805-A1-715]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de FísicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Abstract visualization of large-scale time-varying data

The explosion of large-scale time-varying datasets has created critical challenges for scientists to study and digest. One core problem for visualization is to develop effective approaches that can be used to study various data features and temporal relationships among large-scale time-varying datasets. In this dissertation, we first present two abstract visualization approaches to visualizing and analyzing time-varying datasets. The first approach visualizes time-varying datasets with succinct lines to represent temporal relationships of the datasets. A time line visualizes time steps as points and temporal sequence as a line. They are generated by sampling the distributions of virtual words across time to study temporal features. The key idea of time line is to encode various data properties with virtual words. We apply virtual words to characterize feature points and use their distribution statistics to measure temporal relationships. The second approach is ensemble visualization, which provides a highly abstract platform for visualizing an ensemble of datasets. Both approaches can be used for exploration, analysis, and demonstration purposes. The second component of this dissertation is an animated visualization approach to study dramatic temporal changes. Animation has been widely used to show trends, dynamic features and transitions in scientific simulations, while animated visualization is new. We present an automatic animation generation approach that simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. We also extend the concept of animated visualization to non-traditional time-varying datasets--network protocols--for visualizing key information in abstract sequences. We have evaluated the effectiveness of our animated visualization with a formal user study and demonstrated the advantages of animated visualization for studying time-varying datasets

Global Triggers for Attacking and Analyzing Ranking Models

Text ranking models based on BERT are now well established for a wide range of pas- sage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial attack is under-explored. In this work, we argue that BERT- rankers are vulnerable to adversarial attacks targeting retrieved documents given a query. We propose algorithms for generating adversarial perturbation of documents locally to individual queries or globally across the dataset using gradient-based optimization methods. The aim of our algorithms is to add a small number of tokens to a highly relevant or non-relevant document to cause a significant rank demotion or promotion. Our experiments show that a few number of tokens can already change the document rank by a large margin. Besides, we find that BERT-rankers heavily rely on the docu- ment start/head for relevance prediction, making the initial part of the document more susceptible to adversarial attacks. More interestingly, our statistical analysis finds a small set of recurring adversar- ial tokens that when concatenated to documents result in successful rank demo- tion/promotion of any relevant/non-relevant document respectively. Finally, our ad- versarial tokens also show particular topic preferences within and across datasets, exposing potential biases from BERT pre-training or downstream datasets

Contrasting Climate Ensembles: A Model-based Visualization Approach for Analyzing Extreme Events

AbstractThe use of increasingly sophisticated means to simulate and observe natural phenomena has led to the production of larger and more complex data. As the size and complexity of this data increases, the task of data analysis becomes more challeng- ing. Determining complex relationships among variables requires new algorithm development. Addressing the challenge of handling large data necessitates that algorithm implementations target high performance computing platforms. In this work we present a technique that allows a user to study the interactions among multiple variables in the same spatial extents as the underlying data. The technique is implemented in an existing parallel analysis and visualization framework in order that it be applicable to the largest datasets. The foundation of our approach is to classify data points via inclusion in, or distance to, multivariate representations of relationships among a subset of the variables of a dataset. We abstract the space in which inclusion is calculated and through various space transformations we alleviate the necessity to consider variables’ scales and distributions when making comparisons. We apply this approach to the problem of highlighting variations in climate model ensembles