Line graphs have been the visualization of choice for temporal data ever since the days of William Playfair (1759-1823), but realistic temporal analysis tasks often include multiple simultaneous time series. In this work, we explore user performance for comparison, slope, and discrimination tasks for different line graph techniques involving multiple time series. Our results show that techniques that create separate charts for each time series--such as small multiples and horizon graphs--are generally more efficient for comparisons across time series with a large visual span. On the other hand, shared-space techniques--like standard line graphs--are typically more efficient for comparisons over smaller visual spans where the impact of overlap and clutter is reduced.
Data Center Interconnect (DCI) extends data centers beyond traditional boundaries using transport options like dark fiber or layer 2/3 services. DCI is necessary for VM migrations, IP renumbering, VLAN scaling, geo-cluster applications, and disaster recovery. Challenges of DCI include broadcast storms, loops, tromboning, security issues, and STP problems. Technology options for DCI include OTV, EVPN+VXLAN, and VPLS, with EVPN+VXLAN being preferred due to support for large VLAN scales, MAC learning in the control plane, and support on both virtual and physical devices. SDN/cloud technologies are changing DCI through orchestration of seamless virtual networks
IPv4 and IPv6 are the two main versions of Internet Protocol (IP) addresses currently in use. IPv4 addresses are 32-bit numbers expressed in dotted decimal notation, while IPv6 addresses are longer 128-bit hexadecimal strings. IP addresses are assigned to identify machines on a network and allow communication and transfer of data between devices.
Cisco Digital Network Architecture - Introducing the Network IntuitiveCisco Canada
The document discusses Cisco's Digital Network Architecture (DNA) and the Cisco DNA Center. It introduces DNA as an open, programmable network architecture powered by automation, analytics, and intent-based policies. It describes how DNA Center allows network administrators to automate network operations, gain deep insights through assurance and analytics features, and translate business objectives into network policies through an intent-based model. Key capabilities of DNA Center discussed include automated network provisioning and deployment, software management, and gathering metrics to identify issues and make data-driven decisions.
This document discusses satellite communication systems. It describes the basic elements which include the satellite in orbit and ground stations. Satellites receive and retransmit signals to allow communication between stations. The document outlines different satellite configurations for point-to-point, point-to-multipoint and multipoint-to-point communication. It also describes common satellite orbits including low, medium and geostationary orbits and how they differ in terms of altitude, coverage, and latency. Frequency bands used for uplinks and downlinks are also identified.
Charles Rennie Mackintosh nació en Glasgow, Escocia en 1868. Estudió en la Escuela de Arte de Glasgow donde conoció a las hermanas Margaret y Frances MacDonald, con quien formó un grupo artístico conocido como los Cuatro de Glasgow. Se casó con Margaret en 1901. Ganó la beca Alexander Thomson que le permitió viajar por Italia y el sur de Francia dedicándose a la pintura. Es conocido principalmente por sus muebles y diseños en el estilo Art Nouveau, incluyendo la Escuela de Artes de Glasgow.
This Case Study demonstrates the value of the ArchiMate® 2.1 modeling language for planning and expressing complex business transformation. The Case Study is about a fictitious manufacturer named ArchiMetal. Through high-level architecture modeling, the ArchiMate language illuminates the coherence between an organization, and its processes, applications, and infrastructure. This Case Study presents examples of ArchiMate models that can be elaborated as necessary for analysis, communication, decision support, and implementation.
Data Center Interconnect (DCI) extends data centers beyond traditional boundaries using transport options like dark fiber or layer 2/3 services. DCI is necessary for VM migrations, IP renumbering, VLAN scaling, geo-cluster applications, and disaster recovery. Challenges of DCI include broadcast storms, loops, tromboning, security issues, and STP problems. Technology options for DCI include OTV, EVPN+VXLAN, and VPLS, with EVPN+VXLAN being preferred due to support for large VLAN scales, MAC learning in the control plane, and support on both virtual and physical devices. SDN/cloud technologies are changing DCI through orchestration of seamless virtual networks
IPv4 and IPv6 are the two main versions of Internet Protocol (IP) addresses currently in use. IPv4 addresses are 32-bit numbers expressed in dotted decimal notation, while IPv6 addresses are longer 128-bit hexadecimal strings. IP addresses are assigned to identify machines on a network and allow communication and transfer of data between devices.
Cisco Digital Network Architecture - Introducing the Network IntuitiveCisco Canada
The document discusses Cisco's Digital Network Architecture (DNA) and the Cisco DNA Center. It introduces DNA as an open, programmable network architecture powered by automation, analytics, and intent-based policies. It describes how DNA Center allows network administrators to automate network operations, gain deep insights through assurance and analytics features, and translate business objectives into network policies through an intent-based model. Key capabilities of DNA Center discussed include automated network provisioning and deployment, software management, and gathering metrics to identify issues and make data-driven decisions.
This document discusses satellite communication systems. It describes the basic elements which include the satellite in orbit and ground stations. Satellites receive and retransmit signals to allow communication between stations. The document outlines different satellite configurations for point-to-point, point-to-multipoint and multipoint-to-point communication. It also describes common satellite orbits including low, medium and geostationary orbits and how they differ in terms of altitude, coverage, and latency. Frequency bands used for uplinks and downlinks are also identified.
Charles Rennie Mackintosh nació en Glasgow, Escocia en 1868. Estudió en la Escuela de Arte de Glasgow donde conoció a las hermanas Margaret y Frances MacDonald, con quien formó un grupo artístico conocido como los Cuatro de Glasgow. Se casó con Margaret en 1901. Ganó la beca Alexander Thomson que le permitió viajar por Italia y el sur de Francia dedicándose a la pintura. Es conocido principalmente por sus muebles y diseños en el estilo Art Nouveau, incluyendo la Escuela de Artes de Glasgow.
This Case Study demonstrates the value of the ArchiMate® 2.1 modeling language for planning and expressing complex business transformation. The Case Study is about a fictitious manufacturer named ArchiMetal. Through high-level architecture modeling, the ArchiMate language illuminates the coherence between an organization, and its processes, applications, and infrastructure. This Case Study presents examples of ArchiMate models that can be elaborated as necessary for analysis, communication, decision support, and implementation.
Stack Zooming for Multi-Focus Interaction in Time-Series Data VisualizationNiklas Elmqvist
In this IEEE PacificVis 2010 presentation, we introduce a method for supporting multi-focus interaction in time-series datasets that we call stack zooming. The approach is based on the user interactively building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the visual exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness among the focus points.
The document discusses the evolution of corporate social responsibility (CSR) practices from CSR 1.0 to CSR 2.0. CSR 1.0 involved one-way communication through printed reports focused on educating stakeholders about a company's impacts. CSR 2.0 integrates CSR into financial reporting and emphasizes collaborative dialogue and engagement with consumers through communication and conversation platforms. It also discusses frameworks like the triple bottom line and B Corporations that integrate social and environmental performance with financial metrics.
GraphDice: A System for Exploring Multivariate Social NetworksNiklas Elmqvist
This document describes GraphDice, a system for exploring multivariate social networks. GraphDice allows users to visualize social networks, with nodes representing individuals and edges representing relationships. It integrates network topology, node and edge attributes, and tabular data views. GraphDice is designed for social network analysts to consistently represent and interact with network data through features like dynamic queries, selection history, and coordinated visualizations and data tables.
Applying Mobile Device Soft Keyboards to Collaborative Multitouch Tabletop Di...Niklas Elmqvist
This document describes two user studies that evaluated different soft keyboard designs for text entry on multitouch tabletop displays. In the first study, participants tested a soft, radial, and pinpoint keyboard under various conditions. Standard QWERTY soft keyboards were found to be the fastest overall. A follow up study directly compared a pinpoint keyboard with shifts to a standard soft keyboard, finding no significant difference. While radial and pinpoint designs did not outperform soft keyboards, future work combining approaches or adding features like speech input could improve tabletop text entry.
Dynamic Insets for Context-Aware Graph NavigationNiklas Elmqvist
This document proposes dynamic insets for context-aware graph navigation. Dynamic insets display nodes just outside the visible screen area in an inset window based on their degree of interest, allowing users to see important surrounding context. An evaluation with 12 participants found dynamic insets significantly outperformed existing bringing-and-going techniques for tasks involving close and distant context in graphs. A follow up study with 6 participants tested dynamic insets on map and social network scenarios, finding they provide useful contextual navigation of large graphs.
This document describes how to create stream graphs, which are stacked area graphs, using Python. It discusses two algorithms for calculating the offset (g0) value for each data point to properly stack the graphs. It then describes the pystreamgraph Python package, which implements these algorithms to generate stream graphs from input data, labels, and colors. The graphs are drawn using SVGFig to output SVG files.
The document discusses the growth of carbon nanotubes on various substrates with and without a catalyst for field emission applications. It describes how carbon nanotubes were grown using chemical vapor deposition on Fe-sputtered silicon, nickel-coated silicon, and copper foil both with and without a catalyst. Various cleaning, deposition, etching, and bonding processes are outlined to prepare and analyze the samples for carbon nanotube growth and potential use in field emission.
Presentation from ACM AVI 2012 in Capri, Italy on gravity navigation. Gravity navigation (GravNav) is a family of multi-scale navigation techniques that use a gravity-inspired model for assisting navigation in large visual 2D spaces based on the interest and
salience of visual objects in the space. GravNav is an instance of topology-aware navigation, which makes use of the structure of the visual space to aid navigation. We have performed a controlled study comparing GravNav to standard zoom and pan navigation, with and without variable-rate zoom control. Our results show a significant improvement for GravNav over standard navigation, particularly when coupled with variable-rate zoom. We also report findings on user behavior in multi-scale navigation.
This document discusses various types of spatial analysis that can be performed using GIS. It describes how GIS allows for visual representation of data, precise vector analysis, integration of spatial and non-spatial data, and updatable analysis. The document then outlines specific spatial analysis techniques including map production, geovisualization, querying and measurement, descriptive summaries, and modeling. It provides examples for each type of analysis like cartograms for geovisualization and buffering and overlay for transformation.
Data visualization is a technique used to communicate data through visual representations such as charts, graphs, and maps. It allows patterns, trends, and correlations in data to be recognized more easily than text-based representations. The history of data visualization dates back to 1160 BC with the Turin Papyrus Map, though it has evolved significantly with modern tools going beyond standard charts. Data visualization has advantages like faster comprehension and understanding connections, but also disadvantages like different interpretations among users and a false sense of understanding without explanations. It has applications in business, science, and many other domains.
Spatial data analysis and visualization are important techniques in geography and GIS. Spatial data analysis involves techniques to analyze geographic data and reveal patterns. Key techniques include spatial data modeling and visualization using maps, charts, and other graphics. Effective visualization communicates data clearly and helps users understand relationships. Common visualization types include bar graphs, pie charts, scatter plots, and box plots, each suited to different data types. Together, spatial analysis and visualization turn raw spatial data into useful information.
Spatial data analysis and visualization are important techniques in geography and GIS. Spatial data analysis involves techniques to analyze geographic data and reveal patterns. It transforms raw spatial data into useful information. Data visualization represents data graphically to aid understanding. Common visualization types include maps, bar graphs, pie charts, and scatter plots. Together, spatial analysis and visualization are useful for exploring relationships in geographic data and communicating findings.
This document provides a summary of different types of graphical representations of data. It begins with an introduction to graphical representation, explaining that graphs make data easier to understand versus words alone. Then, it discusses several specific types of graphs:
- Bar graphs and histograms to represent grouped or ungrouped data
- Line graphs to show changes over time
- Pie charts to compare parts of a whole
It also provides examples of each type of graph and discusses their importance and applications. The document aims to explain the basic principles of graphical representation and how different graph types can be used to visualize various types of statistical data.
ICC2017 Washington http://icc2017.org/
6205.2
How hard is it to design maps for beginners, intermediates and experts?
Gáspár Albert
Eötvös Loránd Univerity
Data visualization is the representation of data through use of common graphi...samarpeetnandanwar21
Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization).[4][5][6] When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),[4] it is typically called information graphics.
Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e.g. pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g. scatter plots, distribution plots, box-and-whisker plots), geospatial maps (such as proportional symbol maps, choropleth maps, isopleth maps and heat maps), figures, correlation matrices, percentage gauges, etc., which sometimes can be combined in a dashboard.
This document provides an overview of effective big data visualization. It discusses information visualization and data visualization, including common chart types like histograms, scatter plots, and dashboards. It covers visualization goals, considerations, processes, basics, and guidelines. Examples of good visualization are provided. Tools for creating infographics are listed, as are resources for learning more about data visualization and references. Overall, the document serves as a comprehensive introduction to big data visualization.
AMIA 2015 Visual Analytics in Healthcare Tutorial Part 1David Gotz
A concise introduction to the topic of visualization. Designed for beginners with no prior experience with visualization. These slides were the first part of a half-day tutorial on Visual Analytics held in conjunction with the 2015 AMIA Annual Symposium. It was sponsored by the AMIA Visual Analytics Working Group. For more information, please see www.visualanalyticshealthcare.org or contact the author of the slides: David Gotz @ http://gotz.web.unc.edu
Graph comprehension model talk, Birkbeck and Toulouse Le Mirail, February 2012University of Huddersfield
The document describes a computational model of graph comprehension built within the ACT-R cognitive architecture. The model simulates how experts and novices interpret interaction graphs by encoding spatial relationships between plotted points and applying prior knowledge about graphical representations. It identifies variables, encodes distances between points symbolically, and recognizes patterns to describe effects. While focused on expert-level understanding, the model represents an initial step toward accounting for individual differences and a broader range of graph types.
Enhancing Parallel Coordinates with Curvesmartinjgraham
The document discusses techniques for enhancing parallel coordinate visualizations using curves. It describes how curving lines can help follow objects across axis intersections and resolve clutter. The techniques are best for tracking outliers or small, selected subsets. Spreading techniques are also described to differentiate objects that share values, though they work best on categorical data and with focus+context. Future work is proposed to investigate when curves are most useful and test user understanding further.
This document provides an introduction to Geographic Information Systems (GIS) and the open-source GIS software Quantum GIS (QGIS). It discusses what GIS is, its components and uses, and gives an overview of QGIS's interface and functions. The document then demonstrates how to load shapefiles and join CSV files in QGIS, and provides examples of creating graduated, hot spot and density maps. Finally, it discusses QGIS installation, documentation, file formats and a practical assignment on mapping immunization data.
In information visualization, visual mirages can emerge when the visual representation of data is interpreted or appears to indicate patterns that are not truly present in the data. This can be caused by issues such as incorrect data scaling, the use of improper visualization techniques, or a lack of clear visual signals. Such mirages might be mis-lead and lead to incorrect assumptions. To avoid such blunders, it is critical to extensively evaluate visualizations and verify that they appropriately show data patterns.
Stack Zooming for Multi-Focus Interaction in Time-Series Data VisualizationNiklas Elmqvist
In this IEEE PacificVis 2010 presentation, we introduce a method for supporting multi-focus interaction in time-series datasets that we call stack zooming. The approach is based on the user interactively building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the visual exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness among the focus points.
The document discusses the evolution of corporate social responsibility (CSR) practices from CSR 1.0 to CSR 2.0. CSR 1.0 involved one-way communication through printed reports focused on educating stakeholders about a company's impacts. CSR 2.0 integrates CSR into financial reporting and emphasizes collaborative dialogue and engagement with consumers through communication and conversation platforms. It also discusses frameworks like the triple bottom line and B Corporations that integrate social and environmental performance with financial metrics.
GraphDice: A System for Exploring Multivariate Social NetworksNiklas Elmqvist
This document describes GraphDice, a system for exploring multivariate social networks. GraphDice allows users to visualize social networks, with nodes representing individuals and edges representing relationships. It integrates network topology, node and edge attributes, and tabular data views. GraphDice is designed for social network analysts to consistently represent and interact with network data through features like dynamic queries, selection history, and coordinated visualizations and data tables.
Applying Mobile Device Soft Keyboards to Collaborative Multitouch Tabletop Di...Niklas Elmqvist
This document describes two user studies that evaluated different soft keyboard designs for text entry on multitouch tabletop displays. In the first study, participants tested a soft, radial, and pinpoint keyboard under various conditions. Standard QWERTY soft keyboards were found to be the fastest overall. A follow up study directly compared a pinpoint keyboard with shifts to a standard soft keyboard, finding no significant difference. While radial and pinpoint designs did not outperform soft keyboards, future work combining approaches or adding features like speech input could improve tabletop text entry.
Dynamic Insets for Context-Aware Graph NavigationNiklas Elmqvist
This document proposes dynamic insets for context-aware graph navigation. Dynamic insets display nodes just outside the visible screen area in an inset window based on their degree of interest, allowing users to see important surrounding context. An evaluation with 12 participants found dynamic insets significantly outperformed existing bringing-and-going techniques for tasks involving close and distant context in graphs. A follow up study with 6 participants tested dynamic insets on map and social network scenarios, finding they provide useful contextual navigation of large graphs.
This document describes how to create stream graphs, which are stacked area graphs, using Python. It discusses two algorithms for calculating the offset (g0) value for each data point to properly stack the graphs. It then describes the pystreamgraph Python package, which implements these algorithms to generate stream graphs from input data, labels, and colors. The graphs are drawn using SVGFig to output SVG files.
The document discusses the growth of carbon nanotubes on various substrates with and without a catalyst for field emission applications. It describes how carbon nanotubes were grown using chemical vapor deposition on Fe-sputtered silicon, nickel-coated silicon, and copper foil both with and without a catalyst. Various cleaning, deposition, etching, and bonding processes are outlined to prepare and analyze the samples for carbon nanotube growth and potential use in field emission.
Presentation from ACM AVI 2012 in Capri, Italy on gravity navigation. Gravity navigation (GravNav) is a family of multi-scale navigation techniques that use a gravity-inspired model for assisting navigation in large visual 2D spaces based on the interest and
salience of visual objects in the space. GravNav is an instance of topology-aware navigation, which makes use of the structure of the visual space to aid navigation. We have performed a controlled study comparing GravNav to standard zoom and pan navigation, with and without variable-rate zoom control. Our results show a significant improvement for GravNav over standard navigation, particularly when coupled with variable-rate zoom. We also report findings on user behavior in multi-scale navigation.
This document discusses various types of spatial analysis that can be performed using GIS. It describes how GIS allows for visual representation of data, precise vector analysis, integration of spatial and non-spatial data, and updatable analysis. The document then outlines specific spatial analysis techniques including map production, geovisualization, querying and measurement, descriptive summaries, and modeling. It provides examples for each type of analysis like cartograms for geovisualization and buffering and overlay for transformation.
Data visualization is a technique used to communicate data through visual representations such as charts, graphs, and maps. It allows patterns, trends, and correlations in data to be recognized more easily than text-based representations. The history of data visualization dates back to 1160 BC with the Turin Papyrus Map, though it has evolved significantly with modern tools going beyond standard charts. Data visualization has advantages like faster comprehension and understanding connections, but also disadvantages like different interpretations among users and a false sense of understanding without explanations. It has applications in business, science, and many other domains.
Spatial data analysis and visualization are important techniques in geography and GIS. Spatial data analysis involves techniques to analyze geographic data and reveal patterns. Key techniques include spatial data modeling and visualization using maps, charts, and other graphics. Effective visualization communicates data clearly and helps users understand relationships. Common visualization types include bar graphs, pie charts, scatter plots, and box plots, each suited to different data types. Together, spatial analysis and visualization turn raw spatial data into useful information.
Spatial data analysis and visualization are important techniques in geography and GIS. Spatial data analysis involves techniques to analyze geographic data and reveal patterns. It transforms raw spatial data into useful information. Data visualization represents data graphically to aid understanding. Common visualization types include maps, bar graphs, pie charts, and scatter plots. Together, spatial analysis and visualization are useful for exploring relationships in geographic data and communicating findings.
This document provides a summary of different types of graphical representations of data. It begins with an introduction to graphical representation, explaining that graphs make data easier to understand versus words alone. Then, it discusses several specific types of graphs:
- Bar graphs and histograms to represent grouped or ungrouped data
- Line graphs to show changes over time
- Pie charts to compare parts of a whole
It also provides examples of each type of graph and discusses their importance and applications. The document aims to explain the basic principles of graphical representation and how different graph types can be used to visualize various types of statistical data.
ICC2017 Washington http://icc2017.org/
6205.2
How hard is it to design maps for beginners, intermediates and experts?
Gáspár Albert
Eötvös Loránd Univerity
Data visualization is the representation of data through use of common graphi...samarpeetnandanwar21
Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization).[4][5][6] When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),[4] it is typically called information graphics.
Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e.g. pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g. scatter plots, distribution plots, box-and-whisker plots), geospatial maps (such as proportional symbol maps, choropleth maps, isopleth maps and heat maps), figures, correlation matrices, percentage gauges, etc., which sometimes can be combined in a dashboard.
This document provides an overview of effective big data visualization. It discusses information visualization and data visualization, including common chart types like histograms, scatter plots, and dashboards. It covers visualization goals, considerations, processes, basics, and guidelines. Examples of good visualization are provided. Tools for creating infographics are listed, as are resources for learning more about data visualization and references. Overall, the document serves as a comprehensive introduction to big data visualization.
AMIA 2015 Visual Analytics in Healthcare Tutorial Part 1David Gotz
A concise introduction to the topic of visualization. Designed for beginners with no prior experience with visualization. These slides were the first part of a half-day tutorial on Visual Analytics held in conjunction with the 2015 AMIA Annual Symposium. It was sponsored by the AMIA Visual Analytics Working Group. For more information, please see www.visualanalyticshealthcare.org or contact the author of the slides: David Gotz @ http://gotz.web.unc.edu
Graph comprehension model talk, Birkbeck and Toulouse Le Mirail, February 2012University of Huddersfield
The document describes a computational model of graph comprehension built within the ACT-R cognitive architecture. The model simulates how experts and novices interpret interaction graphs by encoding spatial relationships between plotted points and applying prior knowledge about graphical representations. It identifies variables, encodes distances between points symbolically, and recognizes patterns to describe effects. While focused on expert-level understanding, the model represents an initial step toward accounting for individual differences and a broader range of graph types.
Enhancing Parallel Coordinates with Curvesmartinjgraham
The document discusses techniques for enhancing parallel coordinate visualizations using curves. It describes how curving lines can help follow objects across axis intersections and resolve clutter. The techniques are best for tracking outliers or small, selected subsets. Spreading techniques are also described to differentiate objects that share values, though they work best on categorical data and with focus+context. Future work is proposed to investigate when curves are most useful and test user understanding further.
This document provides an introduction to Geographic Information Systems (GIS) and the open-source GIS software Quantum GIS (QGIS). It discusses what GIS is, its components and uses, and gives an overview of QGIS's interface and functions. The document then demonstrates how to load shapefiles and join CSV files in QGIS, and provides examples of creating graduated, hot spot and density maps. Finally, it discusses QGIS installation, documentation, file formats and a practical assignment on mapping immunization data.
In information visualization, visual mirages can emerge when the visual representation of data is interpreted or appears to indicate patterns that are not truly present in the data. This can be caused by issues such as incorrect data scaling, the use of improper visualization techniques, or a lack of clear visual signals. Such mirages might be mis-lead and lead to incorrect assumptions. To avoid such blunders, it is critical to extensively evaluate visualizations and verify that they appropriately show data patterns.
Unit III covers data visualization. It discusses how data visualization tools are needed to analyze and understand large amounts of data. Effective data visualization presents conclusions, chooses appropriate graph types, and ensures visuals accurately reflect numbers to prevent misinterpretations. History of data visualization is discussed using Napoleon's 1812 march as an example. Advantages of data visualization include easily sharing information and exploring opportunities, while disadvantages can include biased information and losing core messages.
This document provides an overview of an introductory GIS review session at DUSP. It introduces the GIS resources and computing environment at DUSP. It then reviews key GIS concepts like the basic components of a GIS, how planners use GIS, making maps, working with relational databases, descriptive statistics, geoprocessing tools like buffering and clipping data, and methods of extracting data. The session aims to help students prepare for the GIS test out and provides information on where to get help with GIS at DUSP and upcoming workshops through the DUSPVIZ initiative.
This ppt comprises information about graphical representation of data. It includes description about pie chart bar digram histogram and others. It also comprises the advantages and disadvantages of graphical representation of data.
This document summarizes a survey on graph kernels. It begins with an introduction to graph kernels and why they are useful for domains with non-vectorizable structured data like bioinformatics and social networks. It then outlines the survey's topics which include related work, graph representation fundamentals, kernel methods, divisions of graph kernels based on their design, expressivity of graph kernels, applications, experimental studies and results, and a practitioner's guide. The survey categorizes graph kernels based on their design paradigm, graph features used, and computation method. It also discusses theoretical approaches to measure graph kernel expressivity and experimental evaluations of graph kernels for classification.
Evaluating the Perceptual Impact of Rendering Techniques on Thematic Color Ma...Matthias Trapp
Rendering techniques can improve spatial perception and orientation in 3D virtual environments but may reduce accuracy of point-wise value estimation of thematic data. An experiment evaluated different rendering techniques on tasks of mental mapping, distance estimation, and value estimation using color mappings in a 3D city model. Results showed that edge enhancement and abstracted façade textures improved mental mapping the most, while rendering techniques reduced accuracy for point estimates but increased it for area estimates. The impact of rendering techniques depends on the visualization task.
2017 GIS in Education Track: Sharing Historical Maps and Atlases in Web AppsGIS in the Rockies
This document discusses sharing historical maps and atlases through interactive web applications. It outlines a workflow for digitizing map collections that includes scanning maps, georeferencing them, creating mosaic datasets and image services, and developing a web app for users to view, compare and download maps. The goal is to provide rich online access to collections while archiving physical maps. Recommendations cover project planning, metadata, quality scanning, precise georeferencing, and sharing resources to enable others to digitize their own map archives.
This document provides an overview of an introductory presentation for a course on information graphics. It introduces various types of visual tools that can be used to compare data, show processes, relationships, and concepts. These include graphs, charts, flow charts, network maps, and more. It discusses how information graphics can visually communicate context, hierarchical relationships, and explanations through visual differences. The document also outlines the course structure, including topics to be covered each week, assignments, grading criteria, and a call for volunteers to lead case studies.
Similar to Graphical Perception of Multiple Time Series (20)
Tracing and Sketching Performance using Blunt-tipped Styli on Direct-Touch ...Niklas Elmqvist
This study examined tracing and sketching performance using blunt-tipped styli on direct-touch tablets compared to using fingers. 14 participants performed tracing and sketching tasks with 3 different input methods. Tracing was faster, had fewer failures, and participants felt more comfortable using a blunt stylus compared to fingers. Sketches drawn with a blunt stylus were rated higher quality by crowdsourced voters. A follow up study with 6 participants directly compared blunt and sharp styli, finding sketches were selected 3 times more often when using a blunt stylus. The findings provide evidence that blunt styli can better support tracing and sketching tasks on direct-touch tablets compared to fingers or sharp styli.
Munin: A Peer-to-Peer Middleware forUbiquitous Analytics and Visualization S...Niklas Elmqvist
Presentation from IEEE VIS 2014 on Munin, our Java toolkit for peer-to-peer visualization systems for ubiquitous analytics. Published in IEEE TVCG and presented by Sriram Karthik Badam.
VASA: Visual Analytics for Simulation-based ActionNiklas Elmqvist
Slides from our IEEE VAST 2014 talk at IEEE VIS on VASA, a visual analytics system for interactive computational steering of pipelines of asynchronous simulation models.
Slides from T.J. Jankun-Kelly's IEEE VisWeek 2012 presentation on visualization for games. Electronic games are starting to incorporate in-game telemetry that collects data about player, team, and community
performance on a massive scale, and as data begins to accumulate, so does the demand for effectively analyzing this data. We use examples from both old and new games of different genres to explore the theory and design space of visualization for games. Drawing on these examples, we define a design space for this novel research topic and use it to formulate design patterns for how to best apply visualization technology to games. We then discuss the implications that this new framework will
potentially have on the design and development of game and visualization technology in the future.
PolyZoom: Multiscale and Multifocus Exploration in 2D Visual SpacesNiklas Elmqvist
Slides from ACM CHI 2012 presentation given by Sohaib Ghani.
Abstract: The most common techniques for navigating in multiscale visual spaces are pan, zoom, and bird’s eye views. However, these techniques are often tedious and cumbersome to use, especially when objects of interest are located far apart. We present the PolyZoom technique where users progressively build hierarchies of focus regions, stacked on each other such that each subsequent level shows a higher magnification. Correlation graphics show the relation between parent and child viewports in the hierarchy. To validate the new technique, we compare it to standard navigation techniques in two user studies, one on multiscale visual search and the other on multifocus interaction. Results show that PolyZoom performs better than current standard techniques.
Animated transitions are popular in many visual applications but they can be difficult to follow, especially when many objects
move at the same time. One informal design guideline for creating effective animated transitions has long been the use of slow-in/slow-out pacing, but no empirical data exist to support this practice. We remedy this by studying object tracking performance under different conditions of temporal distortion, i.e., constant speed transitions, slow-in/slow-out, fast-in/fast-out, and an adaptive technique that slows down the visually complex parts of the animation. Slow-in/slow-out outperformed other techniques, but we saw technique differences depending on the type of visual transition.
Hugin: A Framework for Awareness and Coordination in Mixed-Presence Collabora...Niklas Elmqvist
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
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Power Grid Model
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Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
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10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
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12. Jupyter Notebooks with Code Examples
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3. 3
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
4. 4
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
5. Graphical Perception
The ability of users to comprehend the visual
encoding and thereby decode the information
presented in the graph.
The ability of users to comprehend the visual encoding and thereby decode
the information presented in the graph, representing multiple time series.
5
6. 6
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
7. Motivation
• Line graphs: common type of statistical data graphics
• Used to visualize temporal data in various domains
• Example: finance, politics, science, engineering, and medicine
• Comparison is a common task for time series data
• Within same time series, across different time series
• Example: Stock analyst, Cardiologist
• Graphical perception of multiple series plays an important
role in the success of temporal visualizations
• Effective guidelines are required for designers
• Many visualization applications show multiple time series
• Find a suitable line graph technique for comparison task
7
8. 8
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
9. Contributions
• We evaluate graphical perception of multiple time
series as a function of different visualization types,
under different conditions
• Evaluating the effect of the following conditions
• Visualization type
• Number of series
• Available space
• Task type
9
10. 10
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
11. Related Work
• Graphical perception is not a new research topic
• Croxton et al. (1927) compared bar charts with circle
diagrams and pie charts
• Cleveland and McGill (1984) formalize the use of
graphical perception for measuring the effectiveness of
various graph techniques
• Simkin and Hastie (1987) compared the accuracy of
judgment based on comparison and estimation
• Lam et al. (2007) study the differences between low and
high-resolution visual representations of line graphs
• Heer et al. (2008) measure the effect of chart size and
layering on user performance (horizon graphs)
11
12. 12
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
13. Line Style Line Color
• We identify five different factors to classify
the line graph visualization techniques
• Space management
• Space per series
• Identity
• Baseline
• Visual clutter
Shared Space Split Space
Classification Criteria
13
Available Space = S Available Space = S/N
Common Baseline Individual Baseline
14. Simple Line Graphs (SG)
Space
management
Space per
series
Identity Baseline Visual Clutter
Shared S Line Common Medium
14
21. 21
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
22. Study Hypotheses
H1 Shared-space techniques will perform
better for tasks with local visual span
H2 Split-space techniques will perform better
for tasks with dispersed visual span
H3 Many concurrent time series will cause
decreased performance
H4 Small display space will cause decreased
performance
22
23. Tasks
• Maximum: local comparison across all time
series
• Discrimination: dispersed comparison of time
series
• Slope: dispersed rate estimation across all
time series
23
24. Study Design
• Visualization type (V)
• SG, BG, SM, HG
• Tasks (T)
• Maximum, Slope, Discrimination
• Number of time series (N)
• 2, 4, 8
• Total Chart Size (S)
• 48 px (small), 96 px (medium), 192 px (large)
24
25. 25
Overview
• Graphical Perception
• Motivation
• Contributions
• Related Work
• Visualization of Multiple Time Series
• User Study
• Study Result
29. Completion time vs. Visualization type
29
Discrimination task Maximum task Slope task
30. Summary of Findings
• Shared-space techniques (SG and BG) were faster than
splits-space techniques for Maximum (H1 )
• Split-space techniques (SM and HG) were faster than
shared-space techniques for Discrimination (H2 )
• The Slope task, with dispersed visual span, was
special—SM and SG were fastest here
• Higher numbers of concurrent time series caused
decreased correctness and increased completion time
(H3 )
• Decreased display space allocation had a negative
impact on correctness, but had little effect on time
(partially confirming H4)
30
31. Conclusion
• I have presented results from a user study on
the graphical perception of multiple
simultaneous time series
• Results from our experiment indicate that
• Superimposed/shared space line graph techniques
work best for local tasks
• Juxtaposed/split space techniques work best for
dispersed ones
31
32. GraphicalPerception
O F M U LT I P L E T I M E S E R I E S
Waqas Javed
E-mail: wjaved@purdue.edu
Website: http://web.ics.purdue.edu/~wjaved/
Thanks!
Acknowledgments:
This research was partially funded by Google, Inc., under the
project “Multi-Focus Interaction for Time-Series Visualization”.
33. More Information
• Online version
https://engineering.purdue.edu/~elm/projects/gvis/
• Pivot website:
https://engineering.purdue.edu/pivot/
• Pivot on Facebook
http://www.facebook.com/#!/pages/PivotLab/1315
05430222567
33
Editor's Notes
GraphicalPerception of Multiple Time Series
Presented by
Waqas Javed
Bryan McDonnel
And Niklas Elmqvist
@ Purdue University
In this talk
I will first discuss what we mean by the graphical perception
Then will highlight the motivation for this work
Next will talk about an overview of our work
And some of the prior work in this field
Then I will discuss commonly used visualization techniques for multiple time series
Followed by the design of user study to measure the effectiveness of these techniques
And last but not the least discussion about the results we obtained from the user study
Graphical perception is defined as
The ability of users to comprehend the visual encoding and thereby decode the information presented in the graph.
[click] When the graph have multiple time series, we talk about the graphical perception of multiple time series
/*
Graphical perception of multiple time series can be defined as
The ability of users to comprehend the visual encoding and thereby decode the information presented in the graph, representing multiple time series.
*/
Motivation:
[Click]Line graphs are today one of the most common types of statistical data graphics. They are used
to visualize temporal data in a wide array of domains such as finance, politics, science, engineering, and medicine.
// verbal bridge here
[Click]Common tasks involving time series data often involve many concurrent series.
Consider a stock analyst surveying the history of a set of stocks in an effort to find the next investment.
This comparison will have to be conducted across each of the time series representing each individual stock.
[Click]Graphical perception of multiple series plays an important role in the success of temporal visualizations used for such tasks.
[Click]Effective guidelines are required for designers who need to find a suitable method when building a visualization application that support comparison across multiple time series.
We evaluate graphical perception of multiple time series as a function of different visualization types, under different conditions
[Click] In particular, we evaluate the effect of following conditions
[Click] Visualization type
[Click] Number of series
[Click] Available space
[Click] Task type
I will discuss each of these in detail later
Evaluation of graphical perception for statistical data graphics has a long history, originating from even before there were computers and graphics to turn charts into interactive visualizations.
To our knowledge their exist no prior work that consider the graphical perception of multiple time series as a function of the parameters presented in the previous slide.
[Click] Croxton et al. compared bar charts with circle diagrams and pie charts, and discussed the relative merits of these visualization techniques to perform the comparison tasks.
[Click] Cleveland and McGill (1984) formalize the use of graphical perception for measuring the effectiveness of various graph techniques such as bar charts and pie charts
[Click]Simkin and Hastie compared the accuracy of judgment while using simple bar charts, divided bar
charts, and pie charts. Their findings are based on the comparison and estimation tasks, involving only two charts at a time.
[Click]Lam et al. did investigate graphical perception of multiple line series, but their study focuses more on differences between low-resolution and high-resolution visual representations than on comparing the performance of line graph techniques.
[Click]Heer et al. performed two controlled experiments to measure the effect of chart size and layering on user performance while performing discrimination and estimation tasks on data.
To classify the line graph visualization techniques we identify five different factors, suited for our experiment.
These include
[Click] Space management: that describes whether space is “shared” or “split” between time series. Shared space is typically more amenable to comparison between series (because they are overlaid in the same space), while data in split space may be easier to perceive (due to less clutter).
[Click] Space per series: This factor defines the amount of vertical display space allocated to each individual time series.
[Click] Identity: We often have to use graphical attributes such as color, or line style to convey identity. This factor is often more important for “line” techniques than for “area” techniques.
[Click] Baseline: Comparison between time series is made easier with a “common” baseline than for an “individual” baseline, or one based on the “previous” time series displayed.
[Click] Visual clutter: The clutter associated with the visualization technique plays an important role for large values of time series.
Now I will briefly discuss five different line graph time series visualization techniques based on the factors highlighted in the previous slide
Simple line graphs
Most commonly used line graph visualization where time is mapped to the horizontal-axis, and the value is mapped to the vertical-axis.
Adding multiple time series is easy—just assign each series a unique graphical property, such as a color or a line style,
and then add them to the shared space.
As seen in Table, simple line graphs use a common baseline in shared space, making comparisons across series simple.
However, because each series is represented by a line, distinguishing the series is challenging.
Small multiples applied to line graph visualization means that instead of adding all time series to the same graph space, we split the space into individual graphs, one for each time series.
It is important that all charts use the same axis-scaling to allow easy comparison across the charts.
Although such a technique decreases visual clutter but this allocates less vertical resolution to each individual time series.
A stacked graph is a shared space technique where one time series uses the value of the previous series as a baseline (the first series will use the origin of the graph as a baseline).
Figure shows an example stacked graph for four time series.
Because of the curious use of variable baselines, stacked graphs can use filled areas instead of lines to ease identification.
space allocation for each graph is proportional to the sum of values of all time series.
Horizon graph. This visualization technique was originally presented by Saito et al. under the name “two-tone pseudo coloring”.
[Click]
Starting with a simple line graph, fill the area beneath the curve with a blue color for values above baseline, and a red
color for values below baseline.
Split the value range into discrete ranges, or bands,
[Click]
and mirror the negative values above the baseline.
[Click]
Final step introduces the notion of virtual resolution by wrapping the graph space using the bands, as discussed by Heer et al..
This virtual resolution and wrapping of negative values means that more space can be allocated for each individual time series despite the fact that horizon graphs use split space—instead of S/N, the space allocation for small multiples (other split space technique), horizon graphs achieve S/N times twice the number of bands.
Just like small multiples, the split space layout means that the visual clutter is low.
The main reason why time series in simple line graphs can be difficult to identify is that the identifying graphical properties are restricted to a single (often thin) line.
If we could somehow fill the whole area beneath the line, it will help the viewer distinguish between different time series.
However, turning the lines into filled areas means that one curve might hide the other.
[Click] We introduce Braided graphs to solve the problem by identifying the intersection points in time where two series change value ordering.
[Click] Each filled area representing a series is cut into segments at these intersection points, and the individual segments are then depth-sorted and drawn with the highest value segment first.
Braided graph technique maintains common baseline.
However, the resulting graph has a potentially high visual clutter for large numbers of series.
We performed a user study with 16 graduate students to measure the performance of these techniques.
In particular validating the following hypothesis
[H1] Strength of the shared space techniques is that they permit easier direct comparison across series for a small visual span. Therefore, we predict that shared-space techniques (braided and simple graphs) will have better completion time for this kind of tasks.
[H2] Our pilot study indicated that for larger visual spans, overlap and visual clutter will become a major factor for shared-space techniques. Split-space techniques, on the other hand, avoid occlusion, and we thus predict that they will have a better time performance for this kind of tasks.
[H3]Many concurrent time series will cause decreased performance.
[H4]Small display space will cause decreased performance. We also predict that the amount of vertical display allocated to each visualization will have a direct effect on user performance.
For the experiment we wanted to include tasks that are representative for common uses of temporal visualization. For this we included three different tasks in the experiment.
[Click]
Maximum task (local task)
This task required the participants to find the time series with the highest value at a specific point in time.
[Click]
//Discrimination (global task)
The discrimination task consisted of having the user determine which time series had the highest value at a point specific to each series.
[Click]
Slope (global task)
Assessing the global slope requires users to find the time series with the highest increase during the whole displayed time period.
We designed the study as a within-subjects full factorial analysis on
[Click]
Visualization type: we included all the visualization types discussed except stacked graphs as our pilot study show that stacked graphs were mostly unsuitable for the tasks studied in this experiment.
[Click]
Tasks (T)
[Click]
Number of Series
For the experiment we used 2, 4 and 8 time series
[Click]
Total chart size (based on pilot) we used three different chart sizes
During the experiment we recorded time and correctness measures for each trial
//
For the experiment we used two repetitions per each condition generating 216 trials per participant and a total of 3,456 trials for the complete experiment
In the experiment we used two repetitions per each condition . For the analysis we used the average of the two repetitions while analyzing the measured data.
This figure shows the correctness measure for different tasks as a function of the visualization type.
[Click] We found no significant effect of visualization type for correctness measure of the tasks, it indicates that the participants were equally careful, regardless of line graph type.
However we did observe a significant effect of number of time series, total chart size and task type on the correctness measure, confirming H3 and 4.
[Click] We can see the effect of task type in this figure as well.
[Click] Participants performed best for the Max tasks, identifying them as the easiest one. While slope tasks turned out to be the most difficult ones.
//
The time to complete a trial was measured from when the charts were first displayed to when the user clicked
the Okay button on the answer dialog.
We found that the time samples violated the normality assumptions of the analysis of variance, so we
analyzed the logarithm of the times through repeated measures analysis of variance RM-ANOVA.
//
This figure shows the effect of number of time series on completion time for different task types. [Click]
Our analysis confirmed that the number of time series has a significant effect on the completion time, as predicted in H3.
Effect of chart size on completion time for different task types, is shown in this figure.
[Click]
We observed no significant effect of chart size on the completion time of different tasks. Partially invalidating H4
It appeared that since participants were asked to complete each task as quickly as possible and thus they tended to use the same amount of time regardless of chart size. But this behavior of their was manifested in decreased correctness, on which chart size did have a significant effect;
In other words, a classic time/accuracy trade-off.
These graphs show completion times for each task as a function of the visualization type.
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We found that visualization type had a significant effect on the completion time for different tasks.
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We analyzed this further using a post hock Tukey HSD test; the figure at the bottom shows pair-wise relations for all tasks having a significant time difference.
[Click] We observed for the discrimination tasks split space techniques performed significantly better than split space techniques.
[Click] While for the Max tasks split space techniques outshined shared space techniques.
[Click] There was a mix trend for the slope tasks.
We can summarize the findings from our experiment as follows:
1- Shared-space techniques (SG and BG) were faster than split space techniques for the local Maximum task (confirming H1)
2- On the otherhand Split-space techniques (SM and HG) were faster than shared space techniques for the dispersed Discrimination task (confirming H2)
3- The Slope task, with dispersed visual span, had a mix trend —SM and SG were fastest here
4- Higher numbers of concurrent time series caused decreased correctness and increased completion time (confirming H3)
5- Decreased display space allocation had a negative impact on correctness, but had a little effect on time (partially confirming H4).
[click] I have presented results from a user study on the graphical perception of multiple time series
[click] We found that superimposed/shared space line graph techniques work best for local tasks, whereas juxtaposed/split space techniques work best for dispersed ones