Background The problem of visualizing a hierarchical dataset is an important and useful technique in many real-life situations.Folder systems,stock markets,and other hierarchical-related datasets can use this tech-niq...Background The problem of visualizing a hierarchical dataset is an important and useful technique in many real-life situations.Folder systems,stock markets,and other hierarchical-related datasets can use this tech-nique to better understand the structure and dynamic variation of the dataset.Traditional space-filling(square)-based methods have the advantages of compact space usage and node size as opposed to diagram-based methods.Space-filling-based methods have two main research directions:static and dynamic performance.Methods This study presented a treemapping method based on balanced partitioning that enables excellent aspect ratios in one variant,good temporal coherence for dynamic data in another,and in the third,a satisfactory compromise between these two aspects.To layout a treemap,all the children of a node were divided into two groups,which were then further divided until groups of single elements were reached.After this,these groups were combined to form a rectangle representing the parent node.This process was performed for each layer of the hierarchical dataset.For the first variant from the partitioning,the child elements were sorted and two groups,sized as equally as possible,were built from both big and small elements(size-balanced partition).This achieved satisfactory aspect ratios for the rec-tangles but less so temporal coherence(dynamic).For the second variant,the sequence of children was taken and from this,groups,sized as equally as possible,were created without the need for sorting(sequence-based,good compromise between aspect ratio and temporal coherency).For the third variant,the children were split into two groups of equal cardinalities,regardless of their size(number-balanced,worse aspect ratios but good temporal coherence).Results This study evaluated the aspect ratios and dynamic stability of the employed methods and proposed a new metric that measures the visual difference between rectangles during their movement to represent temporally changing inputs.Conclusion This study demonstrated that the proposed method of treemapping via balanced partitioning outperformed the state-of-the-art methods for several real-world datasets.展开更多
Stochastic simulation is an essential method for modeling complex geological structures in geosciences.Evaluating the uncertainty of the realizations of stochastic simulations can better describe real phenomena.Howeve...Stochastic simulation is an essential method for modeling complex geological structures in geosciences.Evaluating the uncertainty of the realizations of stochastic simulations can better describe real phenomena.However,uncertainty evaluation of stochastic simulation methods remains a challenge due to the limited data from geological surveys and the uncertainty in reliability estimation with stochastic simulation models.In addition,understanding the sensitivity of the parameters in stochastic simulation models is invaluable when exploring the parameters with a higher influence on the uncertainty associated with predictions generated from stochastic simulation.To facilitate uncertainty evaluation in stochastic simulation methods,we use the circular treemap as an interactive workflow to explore prediction uncertainty in and the parameter sensitivity of multiple-point geostatistical(MPS)stochastic simulation methods.In this work,we present a novel visualization framework for assessing the uncertainty in MPS stochastic simulation methods and exploring the parameter sensitivity of the MPS methods.We present a new indicator to integrate multiple metrics that characterize geospatial features and visualize these metrics to assist domain experts in making decisions.Parallel coordinates-scatter matrix plots and multi-dimensional scaling(MDS)plots are used to analyze the parametric sensitivity of MPS stochastic simulation methods.The realizations and parameters of two MPS stochastic simulation methods are used to test the applicability of the proposed visualization workflow and the visualization methods.The results demonstrate that our workflow and the visualization methods can assist experts infinding the model with less uncertainty and improve the efficiency of parameter adjustment using different MPS stochastic simulation methods.展开更多
文摘Background The problem of visualizing a hierarchical dataset is an important and useful technique in many real-life situations.Folder systems,stock markets,and other hierarchical-related datasets can use this tech-nique to better understand the structure and dynamic variation of the dataset.Traditional space-filling(square)-based methods have the advantages of compact space usage and node size as opposed to diagram-based methods.Space-filling-based methods have two main research directions:static and dynamic performance.Methods This study presented a treemapping method based on balanced partitioning that enables excellent aspect ratios in one variant,good temporal coherence for dynamic data in another,and in the third,a satisfactory compromise between these two aspects.To layout a treemap,all the children of a node were divided into two groups,which were then further divided until groups of single elements were reached.After this,these groups were combined to form a rectangle representing the parent node.This process was performed for each layer of the hierarchical dataset.For the first variant from the partitioning,the child elements were sorted and two groups,sized as equally as possible,were built from both big and small elements(size-balanced partition).This achieved satisfactory aspect ratios for the rec-tangles but less so temporal coherence(dynamic).For the second variant,the sequence of children was taken and from this,groups,sized as equally as possible,were created without the need for sorting(sequence-based,good compromise between aspect ratio and temporal coherency).For the third variant,the children were split into two groups of equal cardinalities,regardless of their size(number-balanced,worse aspect ratios but good temporal coherence).Results This study evaluated the aspect ratios and dynamic stability of the employed methods and proposed a new metric that measures the visual difference between rectangles during their movement to represent temporally changing inputs.Conclusion This study demonstrated that the proposed method of treemapping via balanced partitioning outperformed the state-of-the-art methods for several real-world datasets.
基金supported by the National Natural Science Foundation of China(Nos.42172333,41902304,U1711267)the Knowledge Innovation Program of Wuhan-Shuguang Project(No.2022010801020206).
文摘Stochastic simulation is an essential method for modeling complex geological structures in geosciences.Evaluating the uncertainty of the realizations of stochastic simulations can better describe real phenomena.However,uncertainty evaluation of stochastic simulation methods remains a challenge due to the limited data from geological surveys and the uncertainty in reliability estimation with stochastic simulation models.In addition,understanding the sensitivity of the parameters in stochastic simulation models is invaluable when exploring the parameters with a higher influence on the uncertainty associated with predictions generated from stochastic simulation.To facilitate uncertainty evaluation in stochastic simulation methods,we use the circular treemap as an interactive workflow to explore prediction uncertainty in and the parameter sensitivity of multiple-point geostatistical(MPS)stochastic simulation methods.In this work,we present a novel visualization framework for assessing the uncertainty in MPS stochastic simulation methods and exploring the parameter sensitivity of the MPS methods.We present a new indicator to integrate multiple metrics that characterize geospatial features and visualize these metrics to assist domain experts in making decisions.Parallel coordinates-scatter matrix plots and multi-dimensional scaling(MDS)plots are used to analyze the parametric sensitivity of MPS stochastic simulation methods.The realizations and parameters of two MPS stochastic simulation methods are used to test the applicability of the proposed visualization workflow and the visualization methods.The results demonstrate that our workflow and the visualization methods can assist experts infinding the model with less uncertainty and improve the efficiency of parameter adjustment using different MPS stochastic simulation methods.
