The document discusses information visualization and data mapping. It provides examples of early information visualization works from the 1980s to 2000s. It then discusses visual perception principles like pre-attentive features and Gestalt laws that can be applied to design effective visualizations. Next, it covers different types of data like quantitative, ordinal, categorical, and network data. Finally, it discusses the differences between scientific visualization of concrete data versus information visualization of abstract data, which requires visual metaphors. The overall focus is on understanding how to map different data types to appropriate visual representations.
This document summarizes key concepts from a lecture on information visualization. It defines information visualization as "the use of computer-supported, interactive, visual representations of abstract data to amplify cognition." It discusses several principles of information visualization, including overview+detail, focus+context, brushing & linking, and generating insights. It also covers different types of visualization like scientific visualization, data graphics, infographics, and data art.
This document provides a summary of Andrew Vande Moere's presentation on information visualization and design. It includes definitions of information visualization, highlights challenges in visualizing abstract data through metaphors, and discusses different types of information visualization including scientific visualization, data graphics, infographics, and information design. It also covers the roles of interaction, aesthetics, and meaning in information visualization and provides examples of narrative visualization.
Visualisation - techniques, interaction dynamics, big dataJoris Klerkx
Module 3 - cursus Big Data - Visualisation - deel 2
Instituut voor Permanente Vorming
Various visualisation techniques
(adapted from Heer, J., Bostock, M., & Ogievetsjy, V. (2010, May). A Tour through the Visualization Zoo - A survey of powerful visualisation techniques, from the obvious to the obscure. ACM Graphics , 8 (5), https://queue.acm.org/detail.cfm?id=1805128 )
Various interaction techniques
(adapted from Heer, J., & Shneiderman, B. (2012, February). Interactive Dynamics for Visual Analysis. Magazine Queue - Microprocessors , 10 (2), p. 30. http://queue.acm.org/detail.cfm?id=2146416 )
Big data to big to visualize?
This document presents a study comparing 3D walkthroughs created using a game engine to photo stitching techniques for developing virtual tours. The study aims to demonstrate that a 3D walkthrough of a university facility created in Unreal Engine 4 provides advantages over a photo stitching approach. These advantages include improved realism, interactivity, and ease of updating the virtual tour when the real environment changes. Background research on related virtual tour projects and the limitations of photo stitching techniques is also provided.
This document discusses various topics related to visualization and advertising management. It begins by defining visualization and discussing its historical uses. It then covers types of visualization like scientific, educational, information, knowledge and product visualization. It also discusses visualization strategies, elements of advertising execution like creative and media execution, and persuasion techniques used in advertising like pathos, logos and ethos. Finally, it briefly describes sales promotion tools and techniques.
1. Visualizations are a core application of e-science that can help mediate between humans and complex datasets by highlighting patterns and selecting relevant data for analysis.
2. Examples of social science visualizations discussed include History Flow for tracking Wikipedia edits, Evolino simulations of group dynamics, and treemap diagrams of Usenet postings.
3. New "born digital" visualizations like Blog Pulse and TouchGraph provide fast, free online tools to visualize trends in blogs and relationships between websites.
Mapping Invisibles -acquiring GIS for urban planner workshopBeniamino Murgante
Mapping Invisibles -acquiring GIS for urban planner workshop
Małgorzata Hanzl - Institute of Architecture and Town Planning, Technical University of Lodz
Ewa Stankiewicz, Agata Wierzbicka, Tomasz Kujawski, Karol Dzik, Paulina Kowalczyk, Krystian Kwiecinski, Maciek Burdalski, Anna Śliwka, Mateusz Wójcicki, Michał Miszkurka, Semir Poturak, Katarzyna Westrych - Faculty of Architecture, Warsaw University of Technology
This document summarizes key concepts from a lecture on information visualization. It defines information visualization as "the use of computer-supported, interactive, visual representations of abstract data to amplify cognition." It discusses several principles of information visualization, including overview+detail, focus+context, brushing & linking, and generating insights. It also covers different types of visualization like scientific visualization, data graphics, infographics, and data art.
This document provides a summary of Andrew Vande Moere's presentation on information visualization and design. It includes definitions of information visualization, highlights challenges in visualizing abstract data through metaphors, and discusses different types of information visualization including scientific visualization, data graphics, infographics, and information design. It also covers the roles of interaction, aesthetics, and meaning in information visualization and provides examples of narrative visualization.
Visualisation - techniques, interaction dynamics, big dataJoris Klerkx
Module 3 - cursus Big Data - Visualisation - deel 2
Instituut voor Permanente Vorming
Various visualisation techniques
(adapted from Heer, J., Bostock, M., & Ogievetsjy, V. (2010, May). A Tour through the Visualization Zoo - A survey of powerful visualisation techniques, from the obvious to the obscure. ACM Graphics , 8 (5), https://queue.acm.org/detail.cfm?id=1805128 )
Various interaction techniques
(adapted from Heer, J., & Shneiderman, B. (2012, February). Interactive Dynamics for Visual Analysis. Magazine Queue - Microprocessors , 10 (2), p. 30. http://queue.acm.org/detail.cfm?id=2146416 )
Big data to big to visualize?
This document presents a study comparing 3D walkthroughs created using a game engine to photo stitching techniques for developing virtual tours. The study aims to demonstrate that a 3D walkthrough of a university facility created in Unreal Engine 4 provides advantages over a photo stitching approach. These advantages include improved realism, interactivity, and ease of updating the virtual tour when the real environment changes. Background research on related virtual tour projects and the limitations of photo stitching techniques is also provided.
This document discusses various topics related to visualization and advertising management. It begins by defining visualization and discussing its historical uses. It then covers types of visualization like scientific, educational, information, knowledge and product visualization. It also discusses visualization strategies, elements of advertising execution like creative and media execution, and persuasion techniques used in advertising like pathos, logos and ethos. Finally, it briefly describes sales promotion tools and techniques.
1. Visualizations are a core application of e-science that can help mediate between humans and complex datasets by highlighting patterns and selecting relevant data for analysis.
2. Examples of social science visualizations discussed include History Flow for tracking Wikipedia edits, Evolino simulations of group dynamics, and treemap diagrams of Usenet postings.
3. New "born digital" visualizations like Blog Pulse and TouchGraph provide fast, free online tools to visualize trends in blogs and relationships between websites.
Mapping Invisibles -acquiring GIS for urban planner workshopBeniamino Murgante
Mapping Invisibles -acquiring GIS for urban planner workshop
Małgorzata Hanzl - Institute of Architecture and Town Planning, Technical University of Lodz
Ewa Stankiewicz, Agata Wierzbicka, Tomasz Kujawski, Karol Dzik, Paulina Kowalczyk, Krystian Kwiecinski, Maciek Burdalski, Anna Śliwka, Mateusz Wójcicki, Michał Miszkurka, Semir Poturak, Katarzyna Westrych - Faculty of Architecture, Warsaw University of Technology
Bring your own idea - Visual learning analyticsJoris Klerkx
Workshop on visual learning analytics that was part of LASI 2014 - http://www.solaresearch.org/events/lasi-2/lasi2014/
Examples of learning dashboards were presented during the workshop by Sven Charleer:
http://www.slideshare.net/svencharleer/learning-dashboard-visual-learning-analytics-workshop-lasi2014-h-harvard
Master Thesis: The Design of a Rich Internet Application for Exploratory Sear...Roman Atachiants
Users who cannot formulate a precise query but know there must be a good answer somewhere, often rely on exploratory search. This requires an interactive and responsive system, or else the user will soon give up. As data bases are becoming larger, more specialized, and more distributed this calls for a Rich Internet Application, fast enough to keep pace with the users explorations. This thesis studies and implements a system, called MultiMap, which computes similarity maps in real-time. This entailed: (1) precomputing every data structure that does not change after the initial query, (2) optimizing algorithms for zooming and map generation (3) and providing a cognitively appropriate visualization of high dimensional space. Applied to a very large movie database, it resulted in a highly responsive, satisfying, usable system.
This document provides an introduction to data visualization. It discusses what data visualization is, why it is used, and the stages involved in creating visualizations from data. Key points include:
- Data visualization involves using visual representations of data to help people analyze and communicate information more effectively.
- Visualizations are used for tasks like recording information, analyzing data to support reasoning, and communicating information.
- The process of creating visualizations involves understanding the properties of the data, properties of images and perception, and rules for mapping data to visual encodings.
- Important considerations include which visual variables to use to encode different data properties, principles of visual perception, and enabling interaction with the data. Validation of the effectiveness of
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This document describes the SAX-VSM (Symbolic Aggregate approXimation - Vector Space Model) method for interpretable time series classification. SAX-VSM transforms time series data into symbolic representations called "words", then applies TF-IDF (Term Frequency - Inverse Document Frequency) to select discriminative words and create feature vectors, allowing classification using techniques like k-NN. The method is shown to achieve high accuracy on benchmark datasets like Gun/Point and Coffee Spectrograms, outperforming Euclidean and DTW distance measures. Open questions remain around efficient parameter searching and evaluation methodology.
The last of a 3 part series given in 2014 on the basics of user interface design, this session focuses on the aspects of user-experience that are often unconscious and overlooked.
This document summarizes machine learning techniques used at NASA's Jet Propulsion Laboratory. It discusses how machine learning can be used to analyze large datasets that are too complex for humans to fully examine alone. Examples include identifying features in hyperspectral images and discovering patterns in genetic and meteorological data. Both supervised and unsupervised machine learning algorithms are covered.
This document contains the course syllabus for a data warehousing, filtering, and mining lecture at Temple University. The key points are:
- The course will cover data warehousing, data mining techniques like classification, clustering, association rule mining.
- Grading will be based on homework assignments, quizzes, a class presentation, individual project, and final exam.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- The goal is to discuss efficient data analysis techniques for strategic decision making from large databases.
This document provides a summary of a course syllabus for a data warehousing and mining course. The key details include:
- The course meets on Tuesdays from 4:40-7:10pm and is taught by Professor Slobodan Vucetic.
- The objective is to discuss data management techniques like data warehouses, data marts, and online analytical processing (OLAP) for efficient data analysis.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- Grading will be based on homework, quizzes, a class presentation, individual project, and a final exam.
Information Visualisation – an introductionAlan Dix
Slides for the Information Visualisation unit of my 2013 online course on HCI
https://hcibook.com/hcicourse/2013/unit/08-infovis
Contents:
* What is Information Visualisation? – making data easier to understand using direct sensory experience – especially visual! ... but can have aural, tactile ‘visualisation’
* Why Information Visualisation? – for the data analyst: scientist, statistician, possibly you; and for the data consumer: audience, client, reader, end-user
* A Brief History of Visualisation – from 2500 BC to 2012
Visualisation in Context Section – how visualisation fits into the decision making process
* Designing Visualisation – choosing representations, managing trade-offs and making them flexible through interaction
* Classic Visualisation – the visualisations that have shaped the area
Predicting Facial Expression using Neural Network Santanu Paul
The document is a final project report on facial expression recognition using machine learning models. It explores a facial expression dataset, performs dimensionality reduction using PCA and LDA, and builds classification models including SVM and a neural network. The models aim to classify images as happy or sad, with the neural network achieving 65.8% accuracy. Further improvements could involve tuning model parameters and using a convolutional neural network.
Knowledge Graphs - The Power of Graph-Based SearchNeo4j
1) Knowledge graphs are graphs that are enriched with data over time, resulting in graphs that capture more detail and context about real world entities and their relationships. This allows the information in the graph to be meaningfully searched.
2) In Neo4j, knowledge graphs are built by connecting diverse data across an enterprise using nodes, relationships, and properties. Tools like natural language processing and graph algorithms further enrich the data.
3) Cypher is Neo4j's graph query language that allows users to search for graph patterns and return relevant data and paths. This reveals why certain information was returned based on the context and structure of the knowledge graph.
The document outlines the agenda for a class on data visualization. It includes a review of data representations, visual variables, and best practices. Students will characterize a visualization and discuss what makes good information design. They will also begin working on a class project by deciding on their data, type of representation, visual variables and making initial sketches.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
Data Science and Machine learning-Lect01.pdfRAJVEERKUMAR41
The document provides an overview of a course on data science and machine learning. It discusses the goals of allowing computers to learn patterns from data and make decisions based on those patterns. The course aims to provide strong foundations in data science concepts and emerging technologies. It covers topics like linear algebra, data representation, statistics, probability, machine learning algorithms for classification and clustering.
Data Science in Industry - Applying Machine Learning to Real-world ChallengesYuchen Zhao
This slide deck gives an introduction on data science focusing on three most common tasks including regression, classification and clustering. Each task comes with a real world data science project to illustrate the concepts. This presentation was initially created for a one-hour guest lecture at Utah State University for teaching and education purposes.
Multimedia content based retrieval slideshare.pptgovintech1
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
Bring your own idea - Visual learning analyticsJoris Klerkx
Workshop on visual learning analytics that was part of LASI 2014 - http://www.solaresearch.org/events/lasi-2/lasi2014/
Examples of learning dashboards were presented during the workshop by Sven Charleer:
http://www.slideshare.net/svencharleer/learning-dashboard-visual-learning-analytics-workshop-lasi2014-h-harvard
Master Thesis: The Design of a Rich Internet Application for Exploratory Sear...Roman Atachiants
Users who cannot formulate a precise query but know there must be a good answer somewhere, often rely on exploratory search. This requires an interactive and responsive system, or else the user will soon give up. As data bases are becoming larger, more specialized, and more distributed this calls for a Rich Internet Application, fast enough to keep pace with the users explorations. This thesis studies and implements a system, called MultiMap, which computes similarity maps in real-time. This entailed: (1) precomputing every data structure that does not change after the initial query, (2) optimizing algorithms for zooming and map generation (3) and providing a cognitively appropriate visualization of high dimensional space. Applied to a very large movie database, it resulted in a highly responsive, satisfying, usable system.
This document provides an introduction to data visualization. It discusses what data visualization is, why it is used, and the stages involved in creating visualizations from data. Key points include:
- Data visualization involves using visual representations of data to help people analyze and communicate information more effectively.
- Visualizations are used for tasks like recording information, analyzing data to support reasoning, and communicating information.
- The process of creating visualizations involves understanding the properties of the data, properties of images and perception, and rules for mapping data to visual encodings.
- Important considerations include which visual variables to use to encode different data properties, principles of visual perception, and enabling interaction with the data. Validation of the effectiveness of
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This document describes the SAX-VSM (Symbolic Aggregate approXimation - Vector Space Model) method for interpretable time series classification. SAX-VSM transforms time series data into symbolic representations called "words", then applies TF-IDF (Term Frequency - Inverse Document Frequency) to select discriminative words and create feature vectors, allowing classification using techniques like k-NN. The method is shown to achieve high accuracy on benchmark datasets like Gun/Point and Coffee Spectrograms, outperforming Euclidean and DTW distance measures. Open questions remain around efficient parameter searching and evaluation methodology.
The last of a 3 part series given in 2014 on the basics of user interface design, this session focuses on the aspects of user-experience that are often unconscious and overlooked.
This document summarizes machine learning techniques used at NASA's Jet Propulsion Laboratory. It discusses how machine learning can be used to analyze large datasets that are too complex for humans to fully examine alone. Examples include identifying features in hyperspectral images and discovering patterns in genetic and meteorological data. Both supervised and unsupervised machine learning algorithms are covered.
This document contains the course syllabus for a data warehousing, filtering, and mining lecture at Temple University. The key points are:
- The course will cover data warehousing, data mining techniques like classification, clustering, association rule mining.
- Grading will be based on homework assignments, quizzes, a class presentation, individual project, and final exam.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- The goal is to discuss efficient data analysis techniques for strategic decision making from large databases.
This document provides a summary of a course syllabus for a data warehousing and mining course. The key details include:
- The course meets on Tuesdays from 4:40-7:10pm and is taught by Professor Slobodan Vucetic.
- The objective is to discuss data management techniques like data warehouses, data marts, and online analytical processing (OLAP) for efficient data analysis.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- Grading will be based on homework, quizzes, a class presentation, individual project, and a final exam.
Information Visualisation – an introductionAlan Dix
Slides for the Information Visualisation unit of my 2013 online course on HCI
https://hcibook.com/hcicourse/2013/unit/08-infovis
Contents:
* What is Information Visualisation? – making data easier to understand using direct sensory experience – especially visual! ... but can have aural, tactile ‘visualisation’
* Why Information Visualisation? – for the data analyst: scientist, statistician, possibly you; and for the data consumer: audience, client, reader, end-user
* A Brief History of Visualisation – from 2500 BC to 2012
Visualisation in Context Section – how visualisation fits into the decision making process
* Designing Visualisation – choosing representations, managing trade-offs and making them flexible through interaction
* Classic Visualisation – the visualisations that have shaped the area
Predicting Facial Expression using Neural Network Santanu Paul
The document is a final project report on facial expression recognition using machine learning models. It explores a facial expression dataset, performs dimensionality reduction using PCA and LDA, and builds classification models including SVM and a neural network. The models aim to classify images as happy or sad, with the neural network achieving 65.8% accuracy. Further improvements could involve tuning model parameters and using a convolutional neural network.
Knowledge Graphs - The Power of Graph-Based SearchNeo4j
1) Knowledge graphs are graphs that are enriched with data over time, resulting in graphs that capture more detail and context about real world entities and their relationships. This allows the information in the graph to be meaningfully searched.
2) In Neo4j, knowledge graphs are built by connecting diverse data across an enterprise using nodes, relationships, and properties. Tools like natural language processing and graph algorithms further enrich the data.
3) Cypher is Neo4j's graph query language that allows users to search for graph patterns and return relevant data and paths. This reveals why certain information was returned based on the context and structure of the knowledge graph.
The document outlines the agenda for a class on data visualization. It includes a review of data representations, visual variables, and best practices. Students will characterize a visualization and discuss what makes good information design. They will also begin working on a class project by deciding on their data, type of representation, visual variables and making initial sketches.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
Data Science and Machine learning-Lect01.pdfRAJVEERKUMAR41
The document provides an overview of a course on data science and machine learning. It discusses the goals of allowing computers to learn patterns from data and make decisions based on those patterns. The course aims to provide strong foundations in data science concepts and emerging technologies. It covers topics like linear algebra, data representation, statistics, probability, machine learning algorithms for classification and clustering.
Data Science in Industry - Applying Machine Learning to Real-world ChallengesYuchen Zhao
This slide deck gives an introduction on data science focusing on three most common tasks including regression, classification and clustering. Each task comes with a real world data science project to illustrate the concepts. This presentation was initially created for a one-hour guest lecture at Utah State University for teaching and education purposes.
Multimedia content based retrieval slideshare.pptgovintech1
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
Similar to Introduction to Information Visualization (Part 1) (20)
Practical eLearning Makeovers for EveryoneBianca Woods
Welcome to Practical eLearning Makeovers for Everyone. In this presentation, we’ll take a look at a bunch of easy-to-use visual design tips and tricks. And we’ll do this by using them to spruce up some eLearning screens that are in dire need of a new look.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
16. [Proposed] Time Plan
29 october 2012
- Data + Perception + Data Mapping
- Bad/Good Infographic Guidelines
5 november 2012
- Infovis, Storytelling,Research Results, ...
- Compelling Dataviz Examples
18. Six Degrees of Mohamed Atta
http://business2.com/articles/mag/0,1640,35253,FF.html
19. US Terrorism Response Org Chart
http://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart
20.
21.
22. Space Shuttle Launch
. O-ring damage data
. launch or not launch?
. risk of human lives versus loosing reputation
. ambient temperature at launch: 25-30 degrees F
36. Choice of “Visual” / “Data”
. anything can be ‘translated’ in anything
. can be ineffective (wrong answers)
. can be inefficient (takes too much effort)
. can be disengaging (no users, giving up, ...)
37. How to Design Visualization?
. 1. understanding properties of the image
. 2. understanding properties of the data
. 3. understanding how to map data to an image
42. Pre-Attentive Features
. time taken to make a decision is constant
. and is less than 200-250ms (< eye movement)
. independent of number of added detractors
. primitive features, low-level visual processing
. salience depends on: ‘strength’, and ‘context’
43. ‘Pop-out’ Features
. form: line orientation, length, width, visual marks,...
. color: hue, intensity, ...
. motion: flicker, direction of motion, ...
. spatial position: depth, convex/concave shape,...
44. orienta(on size
length,
width closure
curvature density,
contrast number,
es(ma(on colour
(hue)
Some already known pre-attentive visual features
45. Viewer can rapidly & accurately determine
whether the target (red circle) is present or absent:
difference detected in color
Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
46. Viewer can rapidly & accurately determine
whether the target (red circle) is present or absent
difference detected in form (curvature)
Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
47. Viewer cannot rapidly & accurately determine whether target is present or
absent when target has combined two or more features, also present in the
distracters. Viewer must search sequentially
Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
48. Hue-on-form feature hierarchy: (a) a horizontal hue boundary is pre-
attentive identified when form is held constant; (b) a vertical hue boundary
is pre-attentively identified when form varies randomly in the background
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
49. Hue-on-form feature hierarchy: (c) a vertical form boundary is preattentively
identified when hue is held constant; (d) horizontal form boundary cannot be pre-
attentively identified when hue varies randomly
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
50. Viewer cannot rapidly & accurately determine border by a conjunction of
features (red circles & blue squares on the left, blue circles and red squares on
the right)
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
51. Target has a unique feature with respect to distractors (i.e. open sides).
The group can be detected pre-attentively.
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
52. Target does not have a unique feature with respect to distractors.
The group cannot be detected pre-attentively.
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
53. A sloped line among vertical lines is pre-attentive.
A sloped line among other sloped ones is not.
Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
68. Law of Symmetry
We assume the horizontal / vertical part is identical. Good for before/after comparisons...
69. •pattern
recognition
•we
are
constantly
grouping
objects
based
on
color,
shape,
direction,
proximity,
closure/enclosure,
...
•so:
where
should
a
chart
legend
be
located?
•
keep
it
‘simple’
•we
like
simple,
close,
smooth,
symmetrical,
easy-‐to-‐
process
shapes...
•design
accordingly
•if
we
are
aware
of
these
laws
we
can
take
advantage
of
them
to
design
better
charts
or
dashboards.
•be
careful
•be
aware
of
their
negative
impact:
we
shouldn’t
force
the
reader
to
see
groups
that
aren’t
really
there
http://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
80. Information Visualization (Scientific) Visualization
Examples Examples
stock market, friend clouds, microscopic events,
network, DNA functions, ... human organs, ...
Data Characteristics Data Characteristics
Abstract Concrete
Multi-dimensional 2 or 3 dimensional
Mostly time-dependent
Requirements. Requirements
Visual metaphor 3D and fast rendering
User interaction User interaction
Focus: Exploration Focus: Analysis
then Analysis then Exploration
then Presentation then Presentation
81. Kinds of data...
S Stevens “On the theory of scales and measurements” (1946)
84. Categorical / Hierarchical Data
. can be categorized
. e.g. alphabetically, thematically, functionality
. e.g. desktop folder hierarchy, work hierarchy, ...
85
85. Network / Relational Data
. one-to-many, many-to-one relationships
. often these are ‘weighted’
. e.g. social networks, people working on projects, ...
86
86. Nominal / Unstructured Data
. no order, no units, only “equal or different”
. e.g. Australia, Belgium, Mexico
87
87. Temporal / Dynamic Data
. time dependent
. related to progress of time, history, ...
. e.g. stock market, news stories, sensor readings,...
88
89. § dimensionality
§ # of attributes
§ scale / size
§ # of items
§ value range
§ bits/value, min/max, etc.
§ time
dependency?
90. “The current complexity of data is
staggering, and our ability to collect
data is increasing at a faster rate that
our ability to analyse it.”
Data Complexity
91. How is data ‘complex’?
. size: number of records
. dimensionality: number of attributes
. time-dependency: data changes over time
92. Data is more complex now?
. complexity of human society always increasing
. quantity always increasing, never decreasing!
. speed of data creation always increasing
99. because the data is abstract...
“the challenge is to invent
new metaphors for
presenting information &
developing ways to
manipulate these
metaphors to make sense
out of the information...”
Information Visualization ‘Design’ Challenge
100. Data + data mapping = visual representation that can be (relatively fairly) interpreted
101. data insight
10010110 knowledge
transfer
data mapping
mapping
inversion
visualisation comprehension
!
visual transfer
Data Mapping Methodology
102. data §data mapping requirements?
§ computable
10010110 § no user interaction required
§ algorithm: data -> value
data § comprehensible
mapping § user understands
§ intuitively, within short time
visualisation § invertible
§ mapping backwards from
§ form to exact data value
103
Data Mapping Methodology
106. 1 items
data
ê
???
2 attributes
data
ê
???
Data Characteristics
From abstract data to visual form
107. 1 data items
ê
visual objects
2 data attributes
ê
visual object properties
Data Mapping
From abstract data to visual form
108. objects
point, line, area, volume, ...
properties
position
size, length, area, volume
orientation, angle, slope
color, texture, transparency
shape
animation, time, blink
Visualization “Language”
From abstract data to visual form
109. Example scatterplot of movie dataset
Year → X
Length → Y
Popularity → size
Subject → color
Award? → shape
110. Data Mapping Limitations
. data scale: one object for each tuple?
. data dimensionality: visual cue for each attribute?
. value range: metaphorical range?
. time dependency: can metaphor change?
120. “‘Graphical Excellence’ is that which
gives to the viewer a great number of
ideas in the shortest time, with the
least ink, in the smallest space”
. “show data variation”, not “design variation”
. communication: clarity, precision, efficiency
. simplicity of design - complexity of data
121. French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
124. 1. Show comparisons, contrasts, differences
2. Show causality, mechanism, explanation,
systematic structure
3. Show multivariate data; that is, show more
than 1 or 2 variables
4. Completely integrate words, numbers, images,
diagrams
5.Thoroughly describe the evidence: title, authors
and sponsors, data sources, add measurement
scales, highlight relevant issues
6.Analytical presentations ultimately stand or fall
depending on the quality, relevance and
integrity of their content
Principles for the Analysis and Presentation of Data - Tufte
126. 2. Show causality, mechanism,
explanation, systematic structure
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
127. 3. Show multivariate data; that is,
show more than 1 or 2 variables
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
128. 4. Completely integrate words,
numbers, images, diagrams
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
129. 5.Thoroughly describe the
evidence: title, authors and
sponsors, data sources, add
measurement scales, highlight
relevant issues
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
130. 6.Analytical presentations
ultimately stand or fall depending
on the quality, relevance and
integrity of their content
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
139. 2. Data-Ink Ratio
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
140. 2. Data-Ink Ratio
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
141. Five Laws of “Data-Ink”
• Above all else show (only) the data
• Maximize the data-ink ratio
• Erase non-data ink
• Erase redundant data-ink
• Revise and edit
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
172. 5. Expressiveness
Avoid size as quantitative
junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
173. 5. Expressiveness
Avoid size as quantitative
junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
174. 5. Expressiveness
Maps only useful for spatial distribution. They do not take into account population density
junkcharts.typepad.com
(which explains trends), physical size of districts (which are visually more prominent),...