scholarly journals Test Data Sets for Evaluating Data Visualization Techniques

1995 ◽  
pp. 9-21 ◽  
Author(s):  
R. Daniel Bergeron ◽  
Daniel A. Keim ◽  
Ronald M. Pickett
Keyword(s):  
Data Visualization ◽  
Test Data ◽  
Data Sets ◽  
Visualization Techniques

Visualization of Big Data Sets Using Computer Graphics

2018 ◽  
pp. 578-603
Author(s):  
Anna Ursyn ◽  
Edoardo L'Astorina
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Visual Analysis ◽  
Data Sets ◽  
Arrival Times ◽  
Learning And Teaching ◽  
Knowledge Domains ◽  
Visualization Of Data ◽  
And Mathematics ◽  
Visualization Techniques

This chapter discusses some possible ways of how professionals, researchers and users representing various knowledge domains are collecting and visualizing big data sets. First it describes communication through senses as a basis for visualization techniques, computational solutions for enhancing senses and ways of enhancing senses by technology. The next part discusses ideas behind visualization of data sets and ponders what is and what not visualization is. Further discussion relates to data visualization through art as visual solutions of science and mathematics related problems, documentation objects and events, and a testimony to thoughts, knowledge and meaning. Learning and teaching through data visualization is the concluding theme of the chapter. Edoardo L'Astorina provides visual analysis of best practices in visualization: An overlay of Google Maps that showed all the arrival times - in real time - of all the buses in your area based on your location and visual representation of all the Tweets in the world about TfL (Transport for London) tube lines to predict disruptions.

Data Visualization

2020 ◽  
pp. 306-323
Author(s):  
Evan F. Sinar
Keyword(s):  
Survey Data ◽  
Data Visualization ◽  
Repeated Administration ◽  
Quantitative Information ◽  
Evidence Based ◽  
Data Sets ◽  
High Quality ◽  
Text Information ◽  
Visualization Techniques ◽  
The Impact

Data visualization—a set of approaches for applying graphical principles to represent quantitative information—is extremely well matched to the nature of survey data but often underleveraged for this purpose. Surveys produce data sets that are highly structured and comparative across groups and geographies, that often blend numerical and open-text information, and that are designed for repeated administration and analysis. Each of these characteristics aligns well with specific visualization types, use of which has the potential to—when paired with foundational, evidence-based tenets of high-quality graphical representations—substantially increase the impact and influence of data presentations given by survey researchers. This chapter recommends and provides guidance on data visualization techniques fit to purpose for survey researchers, while also describing key risks and missteps associated with these approaches.

scholarly journals Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

2021 ◽  
Author(s):  
Jacob Hale ◽  
Suzanna Long ◽  
Vinayaka Gude ◽  
Steven Corns
Keyword(s):  
Data Visualization ◽  
Short Term Memory ◽  
Decision Makers ◽  
Data Sets ◽  
Modeling And Control ◽  
Trend Extraction ◽  
Univariate Time Series ◽  
Lstm Network ◽  
Visualization Techniques ◽  
And Control

Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life.

scholarly journals Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries

2019 ◽  
Vol 8 (8) ◽  
pp. 348 ◽  
Author(s):  
Netek ◽  
Brus ◽  
Tomecka
Keyword(s):  
Data Visualization ◽  
Dynamic Environment ◽  
Performance Testing ◽  
Large Data ◽  
Test Point ◽  
Limiting Factor ◽  
Data Sets ◽  
Internet Platform ◽  
Point Data ◽  
Visualization Techniques

We are now generating exponentially more data from more sources than a few years ago. Big data, an already familiar term, has been generally defined as a massive volume of structured, semi-structured, and/or unstructured data, which may not be effectively managed and processed using traditional databases and software techniques. It could be problematic to visualize easily and quickly a large amount of data via an Internet platform. From this perspective, the main aim of the paper is to test point data visualization possibilities of selected JavaScript Mapping Libraries to measure their performance and ability to cope with a big amount of data. Nine datasets containing 10,000 to 3,000,000 points were generated from the Nature Conservation Database. Five libraries for marker clustering and two libraries for heatmap visualization were analyzed. Loading time and the ability to visualize large data sets were compared for each dataset and each library. The best-evaluated library was a Mapbox GL JS (Graphics Library JavaScript) with the highest overall performance. Some of the tested libraries were not able to handle the desired amount of data. In general, an amount of less than 100,000 points was indicated as the threshold for implementation without a noticeable slowdown in performance. Their usage can be a limiting factor for point data visualization in such a dynamic environment as we live nowadays.

scholarly journals Overlapping issues and solutions in data visualization techniques

2019 ◽  
Vol 16 (3) ◽  
pp. 1600
Author(s):  
Nur Diana Izzati Husin ◽  
Nur Atiqah Sia Abdullah
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Network Data ◽  
Data Sets ◽  
Complex Data ◽  
Advantages And Disadvantages ◽  
Data Analyst ◽  
Visualization Process ◽  
New Knowledge ◽  
Visualization Techniques

<span>The tremendous growth of big data has caused the data visualization process becomes more complex and challenging, and yet, data is expected to be increased from time to time. With these massive and complex data, it is getting harder for the data analyst to interpret or read the data in order to gain new knowledge or information. Therefore, it is important to visualize these data using different techniques. However, there are many remaining issues in data visualization techniques. These issues make the data visualization a big challenge to the data analyst. The most common issue in data visualization techniques is the overlapping issue. This paper reviews the overlapping issues in multidimensional and network data visualization techniques. The existing solutions are also reviewed and discussed in term of advantages and disadvantages. This paper concludes the advantages of the overlapping issues and solutions, before discussing their drawbacks. This paper suggests the color-based approach, relocation, and reduction of data sets to solve the overlapping issues.</span>

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

Analytical stress-strain relation for soils

2021 ◽  
Author(s):  
Kristian Krabbenhoft ◽  
J. Wang
Keyword(s):  
Test Data ◽  
Narrow Range ◽  
Strain Range ◽  
Data Sets ◽  
Soil Tests ◽  
Stress Strain ◽  
Strain Relation ◽  
Stress Strain Relation ◽  
Typical Test ◽  
Typical Soil

A new stress-strain relation capable of reproducing the entire stress-strain range of typical soil tests is presented. The new relation involves a total of five parameters, four of which can be inferred directly from typical test data. The fifth parameter is a fitting parameter with a relatively narrow range. The capabilities of the new relation is demonstrated by the application to various clay and sand data sets.

Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

2021 ◽  
Author(s):  
David Cotton ◽  
Keyword(s):  
Coastal Zone ◽  
Test Data ◽  
River Discharge ◽  
Altimeter Data ◽  
Inland Waters ◽  
Data Sets ◽  
Data Set ◽  
Discharge Data ◽  
Processing Algorithms ◽  
The Impact

&lt;p&gt;&lt;strong&gt;Introduction&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.&lt;/p&gt;&lt;p&gt;New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.&lt;/p&gt;&lt;p&gt;A series of case studies will assess these products in terms of their scientific impacts.&lt;/p&gt;&lt;p&gt;All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The scientific objectives of HYDROCOASTAL are to enhance our understanding&amp;#160; of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes&lt;/p&gt;&lt;p&gt;The technical objectives are to develop and evaluate&amp;#160; new SAR&amp;#160; and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Project&amp;#160; Outline&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;There are four tasks to the project&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters&lt;/li&gt; &lt;li&gt;Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets&lt;/li&gt; &lt;li&gt;Impacts Assessment: The impact of these global products will be assess in a series of Case Studies&lt;/li&gt; &lt;li&gt;Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Presentation&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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