In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly an...In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.展开更多
The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimension...The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model.Setting each index as a one-dimensional attribute,the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory.The Multi-dimensional cloud generator can calculate the certainty of each grade,and then determine the stability levels of the surrounding rock according to the principle of maximum certainty.Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models,we find that the Multi-dimensional Cloud Model can provide a more accurate solution.Since the classification results of the Multidimensional Cloud Model are difficult to be presented intuitively and visually,we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model.This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability,and it can also be applied to other types of classification problems.展开更多
Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline...By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.展开更多
With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously i...With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously increase rapidly.Features of these data include massive volume,widespread distribution,multiple-sources,heterogeneous,multi-dimensional and dynamic in structure and time.The present study recommends an integrative visualization solution for these data,to enhance the visual display of data and data archives,and to develop a joint use of these data distributed among different organizations or communities.This study also analyses the web services technologies and defines the concept of the marine information gird,then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method.We discuss how marine environmental data can be organized based on the spatiotemporal visualization method,and how organized data are represented for use with web services and stored in a reusable fashion.In addition,we provide an original visualization architecture that is integrative and based on the explored technologies.In the end,we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats,sea surface temperature fields,sea current fields,salinity,in-situ investigation data,and ocean stations.An integration visualization architecture is illustrated on the prototype system,which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.展开更多
Protecting and preserving our environmental systems require the ability to understand the spatio-temporal distri- bution of soils, parent material, topography, and land cover as well as the effects of human activities...Protecting and preserving our environmental systems require the ability to understand the spatio-temporal distri- bution of soils, parent material, topography, and land cover as well as the effects of human activities on ecosystems. Space-time modelling of ecosystems in an environmental digital library is essential for visualizing past, present, and future impacts of changes occurring within such landscapes (e.g., shift in land use practices). In this paper, we describe three novel features, spa- tio-temporal indexing, visualization, and geostatistical genre, for the environmental digital library, Environmental Visualization and Geographic Enterprise System (ENVISAGE), currently in progress at the University of Florida.展开更多
Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works w...Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.展开更多
Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters.Nevertheless,few software tools can...Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters.Nevertheless,few software tools can comprehensively visualize hydrometeorological data in different scales,dimensions,and time.In this paper,an interactive 4D spatio-temporal visualization system based on a virtual globe is proposed.Voxel-based data model and multi-level index are adopted to organize the field data in a unified data structure.Meanwhile,it is resampled in both spatial and temporal dimensions in memory to prepare smooth data stream for rendering.Ten field models,including large-scale volume rendering and adaptive streamline rendering,are accelerated and integrated to display the field data collaboratively.The profile analysis and eddy tracking improve user experience in interactively exploring specific scenes.The system is tested against both large-scale meteorological data in the atmosphere and small-scale hydrological data at the surface,using typhoon landfall and riverine flood,respectively.The results demonstrate the applicability and efficiency of the system to dynamically visualize hydrometeorological data.展开更多
Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimen...Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.展开更多
The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally charac...The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.).展开更多
Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,whi...Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.展开更多
This paper reviews the literature on the visualization of individual mobility data,with a focus on thematic integration.It emphasizes the importance of visualization in understanding mobility patterns within a populat...This paper reviews the literature on the visualization of individual mobility data,with a focus on thematic integration.It emphasizes the importance of visualization in understanding mobility patterns within a population and how it helps mobility experts address domain-specific questions.We analyze 38 papers published between 2010 and 2024 in GIS and VIS venues that describe visualizations of multidimensional data related to individual movements in urban environments,concentrating on individual mobility rather than traffic data.Our primary aim is to report advances in interactive visualization for individual mobility analysis,particularly regarding the representation of thematic information about people’s motivations for mobility.Our findings indicate that the thematic dimension is only partially represented in the literature,despite its critical significance in transportation.This gap often stems from the challenge of identifying data sources that inherently provide this information,necessitating visualization designers and developers to navigate multiple,heterogeneous data sources.We identify the strengths and limitations of existing visualizations and suggest potential research directions for the field.展开更多
We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it sati...We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it satisfies.The approach can filter out pattern-irrelevant attributes,leading to significant visual structures of samples satisfying the same rules in the projection.In addition,analysts can understand a visual structure based on the rules that the involved samples satisfy,which improves the projection’s pattern interpretability.Our research involves two methods for achieving and applying the approach.First,we give a method to learn rule-based embedding for each sample.Second,we integrate the method into a system to achieve an analytical workflow.Cases on real-world dataset and quantitative experiment results show the usability and effectiveness of our approach.展开更多
With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal referen...With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.展开更多
Song Ci is treasured in traditional Chinese culture,which indicates social and cultural evolution in ancient times.Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci,it ...Song Ci is treasured in traditional Chinese culture,which indicates social and cultural evolution in ancient times.Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci,it is still unclear how to effectively distribute and promote Song Ci in the public sphere.The complexity and abstraction of Song Ci hamper the general public from closely reading,analyzing,and appreciating these excellent works.By means of a set of visual analysis methods,e.g.the spatiotemporal visualization,we exploit visual storytelling to explicitly present the latent and abstractive features of Song Ci.We apply straightway visual charts and lighten the burden of understanding the stories,in order to achieve an effective public distribution.The effectiveness and aesthetics of our work are demonstrated by a user study of three participants with different backgrounds.The result reveals that our story is effective in the distribution,understanding,and promotion of Song Ci。展开更多
Visualizing 2-D vector fields using streamlines is one popular flow visualization technique. Standard streamline generation algorithms compute the density of streamlines across the domain, detect features, and employ ...Visualizing 2-D vector fields using streamlines is one popular flow visualization technique. Standard streamline generation algorithms compute the density of streamlines across the domain, detect features, and employ customized rules to emphasize features. In this process, feature characterization and visual clarity are heavily considered. Simultaneously preserving the temporal coherence for time-varying vector fields, however remains a challenge. In this paper, we present a coherent and feature-aware streamline generation algorithm by employing a feature-guided streamline seeding technique and a coherent streamline placing scheme. For each frame, a feature map is first computed with critical points or the Finite-Time Lyapunov Exponent （FTLE） approach, and is used to initialize a set of seeds by leveraging the Poisson Disk distribution. These seeds are further optimized by using a deformation-driven moving mesh method. To preserve the temporal coherence, the streamlines generated from the seeds are individually checked subject to their correspondences to the ones in the previous frame. Subsequently, additional streamlines are sequentially inserted in low-density regions. We demonstrate our algorithm on both Computation Fluid Dynamics （CFD） and non-CFD datasets, and compare it with the recent literature展开更多
基金supported by the National Natural Science Foundation of China(U14352186140340161273189)
文摘In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.
基金supported by the National Natural Science Foundation of China(No.52074296).
文摘The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model.Setting each index as a one-dimensional attribute,the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory.The Multi-dimensional cloud generator can calculate the certainty of each grade,and then determine the stability levels of the surrounding rock according to the principle of maximum certainty.Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models,we find that the Multi-dimensional Cloud Model can provide a more accurate solution.Since the classification results of the Multidimensional Cloud Model are difficult to be presented intuitively and visually,we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model.This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability,and it can also be applied to other types of classification problems.
基金National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
文摘By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.
基金Supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX1-YW-12-04)the National High Technology Research and Development Program of China (863 Program) (Nos.2009AA12Z148,2007AA092202)Support for this study was provided by the Institute of Geographical Sciences and the Natural Resources Research,Chinese Academy of Science (IGSNRR,CAS) and the Institute of Oceanology, CAS
文摘With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously increase rapidly.Features of these data include massive volume,widespread distribution,multiple-sources,heterogeneous,multi-dimensional and dynamic in structure and time.The present study recommends an integrative visualization solution for these data,to enhance the visual display of data and data archives,and to develop a joint use of these data distributed among different organizations or communities.This study also analyses the web services technologies and defines the concept of the marine information gird,then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method.We discuss how marine environmental data can be organized based on the spatiotemporal visualization method,and how organized data are represented for use with web services and stored in a reusable fashion.In addition,we provide an original visualization architecture that is integrative and based on the explored technologies.In the end,we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats,sea surface temperature fields,sea current fields,salinity,in-situ investigation data,and ocean stations.An integration visualization architecture is illustrated on the prototype system,which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.
文摘Protecting and preserving our environmental systems require the ability to understand the spatio-temporal distri- bution of soils, parent material, topography, and land cover as well as the effects of human activities on ecosystems. Space-time modelling of ecosystems in an environmental digital library is essential for visualizing past, present, and future impacts of changes occurring within such landscapes (e.g., shift in land use practices). In this paper, we describe three novel features, spa- tio-temporal indexing, visualization, and geostatistical genre, for the environmental digital library, Environmental Visualization and Geographic Enterprise System (ENVISAGE), currently in progress at the University of Florida.
文摘Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.
基金National Natural Science Foundation of China:[Grant Number No.41722109,61825103,91738302]Major State Research Development Program of China:[Grant Number No.2017YFB0504103]+2 种基金Hubei Provincial Natural Science Foundation of China:[Grant Number No.2018CFA053]Natural Science Foundation Innovation Group Project of Hubei Province,China:[Grant Number No.2016CFA003]Wuhan Yellow Crane Talents(Science)Program:[Grant Number 2016].
文摘Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters.Nevertheless,few software tools can comprehensively visualize hydrometeorological data in different scales,dimensions,and time.In this paper,an interactive 4D spatio-temporal visualization system based on a virtual globe is proposed.Voxel-based data model and multi-level index are adopted to organize the field data in a unified data structure.Meanwhile,it is resampled in both spatial and temporal dimensions in memory to prepare smooth data stream for rendering.Ten field models,including large-scale volume rendering and adaptive streamline rendering,are accelerated and integrated to display the field data collaboratively.The profile analysis and eddy tracking improve user experience in interactively exploring specific scenes.The system is tested against both large-scale meteorological data in the atmosphere and small-scale hydrological data at the surface,using typhoon landfall and riverine flood,respectively.The results demonstrate the applicability and efficiency of the system to dynamically visualize hydrometeorological data.
基金This work was supported by the Malaysian Ministry of Education(SLAI)and Universiti Teknologi Malaysia(UTM).
文摘Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.
基金supported by the National Natural Science Foundation of China(NSFC)Grant Nos.61702271,61702270.
文摘The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.).
基金This work was supported by the Science and Technology Project of China Southern Power Grid Corporation(ZBKJXM20180157)the National Natural Science Foundation of China(Grant Nos.61772456,61761136020).
文摘Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.
基金supported by the Auvergne Rhône-Alpes region in France through doctoral funding from 2016 to 2019funded by CAPES(Finance Code 001)CNPq,Brazilian federal research funding agencies.
文摘This paper reviews the literature on the visualization of individual mobility data,with a focus on thematic integration.It emphasizes the importance of visualization in understanding mobility patterns within a population and how it helps mobility experts address domain-specific questions.We analyze 38 papers published between 2010 and 2024 in GIS and VIS venues that describe visualizations of multidimensional data related to individual movements in urban environments,concentrating on individual mobility rather than traffic data.Our primary aim is to report advances in interactive visualization for individual mobility analysis,particularly regarding the representation of thematic information about people’s motivations for mobility.Our findings indicate that the thematic dimension is only partially represented in the literature,despite its critical significance in transportation.This gap often stems from the challenge of identifying data sources that inherently provide this information,necessitating visualization designers and developers to navigate multiple,heterogeneous data sources.We identify the strengths and limitations of existing visualizations and suggest potential research directions for the field.
文摘We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it satisfies.The approach can filter out pattern-irrelevant attributes,leading to significant visual structures of samples satisfying the same rules in the projection.In addition,analysts can understand a visual structure based on the rules that the involved samples satisfy,which improves the projection’s pattern interpretability.Our research involves two methods for achieving and applying the approach.First,we give a method to learn rule-based embedding for each sample.Second,we integrate the method into a system to achieve an analytical workflow.Cases on real-world dataset and quantitative experiment results show the usability and effectiveness of our approach.
文摘With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.
基金supported by the National Natural Science Foundation of China(61772456,61972122)the Fundamental Research Funds for the Central Universities(2-2050205-21-688).
文摘Song Ci is treasured in traditional Chinese culture,which indicates social and cultural evolution in ancient times.Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci,it is still unclear how to effectively distribute and promote Song Ci in the public sphere.The complexity and abstraction of Song Ci hamper the general public from closely reading,analyzing,and appreciating these excellent works.By means of a set of visual analysis methods,e.g.the spatiotemporal visualization,we exploit visual storytelling to explicitly present the latent and abstractive features of Song Ci.We apply straightway visual charts and lighten the burden of understanding the stories,in order to achieve an effective public distribution.The effectiveness and aesthetics of our work are demonstrated by a user study of three participants with different backgrounds.The result reveals that our story is effective in the distribution,understanding,and promotion of Song Ci。
基金Supported by the National High-Tech Research and Development (863) Program of China (No. 2012AA120903)the National Natural Science Foundation of China (No.61003193)the Commonweal Project of Science and Technology Department of Zhejiang Province (No.2011C21058)
文摘Visualizing 2-D vector fields using streamlines is one popular flow visualization technique. Standard streamline generation algorithms compute the density of streamlines across the domain, detect features, and employ customized rules to emphasize features. In this process, feature characterization and visual clarity are heavily considered. Simultaneously preserving the temporal coherence for time-varying vector fields, however remains a challenge. In this paper, we present a coherent and feature-aware streamline generation algorithm by employing a feature-guided streamline seeding technique and a coherent streamline placing scheme. For each frame, a feature map is first computed with critical points or the Finite-Time Lyapunov Exponent （FTLE） approach, and is used to initialize a set of seeds by leveraging the Poisson Disk distribution. These seeds are further optimized by using a deformation-driven moving mesh method. To preserve the temporal coherence, the streamlines generated from the seeds are individually checked subject to their correspondences to the ones in the previous frame. Subsequently, additional streamlines are sequentially inserted in low-density regions. We demonstrate our algorithm on both Computation Fluid Dynamics （CFD） and non-CFD datasets, and compare it with the recent literature
