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Interactive selection of multivariate features in large spatiotemporal data

Jingyuan Wang, Robert Sisneros, Jian Huang
2013 2013 IEEE Pacific Visualization Symposium (PacificVis)  
Selecting meaningful features is central in the analysis of scientific data.  ...  Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications.  ...  Traditional feature extraction techniques are commonly utilized in many data analysis applications that involve large-scale multivariate spatiotemporal datasets.  ... 
doi:10.1109/pacificvis.2013.6596139 dblp:conf/apvis/WangSH13 fatcat:kszkamkbcnhc3jjpfz7ohopxpe

A survey on visual analysis of ocean data

Cui Xie, Mingkui Li, Haoying wang, Junyu Dong
2019 Visual Informatics  
A major challenge in analysis of huge amounts of ocean data is the complexity of the data and the inherent complexity of ocean dynamic processes.  ...  Based on the main analysis tasks in the field of oceanography, the survey emphasizes related interactive visualization techniques and tools from four aspects: visualization of multiple ocean environmental  ...  Science Foundation of Shandong Province,China (No.ZR2018ZB0852).  ... 
doi:10.1016/j.visinf.2019.08.001 fatcat:lnf2ixaegjhzjcz3tumnwhgjam

The parallel coordinate plot in action: design and use for geographic visualization

Robert M Edsall
2003 Computational Statistics & Data Analysis  
MacEachren, director of the GeoVISTA Center at Penn State, for his support and supervision of this research. In addition, Dr. Linda J.  ...  Pickle at the National Cancer Institute provided key ÿnancial and intellectual assistance in the development of the HealthVisPCP system. Mr.  ...  The search was for space-time-attribute features in this large data set. A key step in the process, which includes initial data selection, preprocessing, and transfor-39 mation, is data mining.  ... 
doi:10.1016/s0167-9473(02)00295-5 fatcat:odlychhoqndrvaz34jesulhqs4

Interactive visual exploration and analysis of origin-destination data

Linfang Ding, Liqiu Meng, Jian Yang, Jukka M. Krisp
2018 Proceedings of the ICA  
In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data.  ...  The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data.  ...  We implement the proposed approach in a web-based interactive system and carry out experiments on a large amount of real-world floating car data from Shanghai.  ... 
doi:10.5194/ica-proc-1-29-2018 fatcat:fzw5rg5hijd7hjt3oprddnzyhi

Scalable Lagrangian-Based Attribute Space Projection for Multivariate Unsteady Flow Data

Hanqi Guo, Fan Hong, Qingya Shu, Jiang Zhang, Jian Huang, Xiaoru Yuan
2014 2014 IEEE Pacific Visualization Symposium  
Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection.  ...  In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP).  ...  Academy of Sciences Grant No.  ... 
doi:10.1109/pacificvis.2014.15 dblp:conf/apvis/GuoHSZHY14 fatcat:3dq2v6owkfbllpcjochf3fdj6q

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

Feng Wang, Wenwen Li, Sizhe Wang, Chris Johnson
2018 ISPRS International Journal of Geo-Information  
Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data.  ...  To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi7070266 fatcat:nf72gpftnvafbao6h7uxnq2o3e

A Visual Analytics Approach to Understanding Spatiotemporal Hotspots

R. Maciejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani, W.S. Cleveland, S.J. Grannis, D.S. Ebert
2010 IEEE Transactions on Visualization and Computer Graphics  
In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal datasets.  ...  To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data  ...  data.  ... 
doi:10.1109/tvcg.2009.100 pmid:20075482 fatcat:w43rmb3blfbu3eqwftxj5fl7ee

Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Zichao He, Chunna Zhao, Yaqun Huang
2022 Applied Sciences  
Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data  ...  Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications.  ...  Features of large-scale data can be obtained through deep learning techniques.  ... 
doi:10.3390/app12115731 fatcat:ftmtdgcknbfgfkf6oziwiglxri

Visualizing Patterns in a Global Terrorism Incident Database

Diansheng Guo, Ke Liao, Michael Morgan
2007 Environment and Planning, B: Planning and Design  
However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect the views of the US Department of Homeland Security.  ...  This research was partially supported by a grant from the Walker Institute of International and Area Studies, University of South Carolina.  ...  This feature is particularly important in visualizing the terrorist incident data.  ... 
doi:10.1068/b3305 fatcat:a2bdbnk7anfu7msxw6cly6vczq

RISeer: Inspecting the Status and Dynamics of Regional Industrial Structure via Visual Analytics [article]

Longfei Chen, Yang Ouyang, Haipeng Zhang, Suting Hong, Quan Li
2022 arXiv   pre-print
industrial sector and its composition, the dynamic nature, and the large number of multivariant features of RIS records have obscured a deep and fine-grained understanding of RIS.  ...  Understanding the current status and dynamics of RIS will greatly assist in studying and evaluating the current industrial structure.  ...  This work is partially supported by the Key Projects of Shanghai Soft Science Research Program from the Science and Technology Commission of Shanghai Municipality (No. 22692112900, 21692108700).  ... 
arXiv:2208.00625v1 fatcat:a26dl2peqzf4vba6sr6yozf56u

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

Huijie Zhang, Ke Ren, Yiming Lin, Dezhan Qu, Zhenxin Li
2019 Sustainability  
data patterns in the context of attributes.  ...  In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city.  ...  Acknowledgments: Thanks to the experts who provided requirements and user feedback for our work, as well as the participants who actively participated in the evaluation of the system.  ... 
doi:10.3390/su11102944 fatcat:22vawj6v2jgdvbczqx7d2dwzre

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Jing He, Haonan Chen, Yijin Chen, Xinming Tang, Yebin Zou
2019 ISPRS International Journal of Geo-Information  
) with large data amounts and high dimensions.  ...  study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi8020063 fatcat:fjd5jjfrhncfpowutovxxiyhpe

Brain decoding: Opportunities and challenges for pattern recognition

Dimitri Van De Ville, Seong-Whan Lee
2012 Pattern Recognition  
To cope with the curse of dimensionality, appropriate feature selection and regularization are necessary.  ...  Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data.  ... 
doi:10.1016/j.patcog.2011.06.001 fatcat:ykrkdjom4ranjpsiufb6v2elwq

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

Verena Bessenbacher, Sonia Isabelle Seneviratne, Lukas Gudmundsson
2022 Geoscientific Model Development  
Correlation between original ERA-5 data and gap-filled ERA-5 data is high in many regions, although it shows artefacts of the interpolation procedure in large gaps in high-latitude regions during winter  ...  Thus, the framework can be a tool for gap filling a large range of remote sensing observations commonly used in climate and environmental research.  ...  5 data.  ... 
doi:10.5194/gmd-15-4569-2022 fatcat:2kxw3quiczhdviobnmshmu3dgq

Visual Analytics of Air Pollution Propagation through Dynamic Network Analysis

Ke Ren, Yiyao Wu, Huijie Zhang, Jia Fu, Dezhan Qu, Xiaoli Lin
2020 IEEE Access  
Therefore, the air quality data are featured with spatiotemporal, highdimensional and large-scale properties.  ...  There is currently a lack of visual analytics methods for exploring the air pollution propagation network with spatiotemporal multivariate features.  ...  KE REN received her B.Sc. degree in 2016 from Northeast Normal University. Now she is a PhD candidate in School of Information Science and Technology, Northeast Normal University.  ... 
doi:10.1109/access.2020.3036354 fatcat:yfiteqiuazgthdjn4gi45v3kkq
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