Transcript: Decomposition of the main colors How do they use this technique? The authors What is data visualization for them? Data visualization as interdisciplinary approach 2008 The artistic results Luscious Fernanda Viegas and Martin Wattemberg March Pioneers in data visualization and analytic "Flickr flow" from "Artistic Data Visualization: Beyond Visual Analytics" Exaltation of the colors More than ads in fashion magazines Their aim is the identification of a new way to present data to users The colors of every season The flickr flow Data visualization creations are artistic work arts with specific meaning Google art visualization artists Reflections on data visualization "Luscious" The size of the circles represents the circulation of the magazine 2009 January The "Weird" project Critical consume of data visualization 2010 "Wired" Data visualization and fine art: Under the surface February Every circle is a unique combination of colors Pictures to create flickr flow Celebration of "Wired" magazine "artistic visualizations are visualizations of data done by artists with the intent of making art" Each row is a year of the magazine Every slice represents a month Data visualization outcomes represent one- among the others- point of view Winter Fall Summer Spring
Transcript: Data Visualization What is Data Visualization? "The main goal of data visualization is its ability to visualize data, communicating information clearly and effectively. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way." - Vitaly Freidman communicating information clearly and effectively How can Data Visualization help us? Generally, our clients don't have the same analytical background as us. It's important that we keep this in mind when presenting our findings. Communicating results simply and effectively through the use of solid information design will not only help our clients attain a firmer grasp of complex information, but will further the perception of Digitas as a lead digital agency constantly pushing the boundaries of analytic innovation. Wicked cool examples Web Trends Map 4 BuyKP Dashboard Software Tableu Bime Resources informationisbeautiful.net infosthetics.com flowingdata.com newsmap.jp whatdoyousuggest.net edwardtufte.com Thanks ? Analytics Know your audience Ads Evolution we are here
Transcript: Data Collection and Visualization Selection We have created a bar chart visualization that can be viewed on demand by the user which indicated the most popular questions. The bar charts are populated for the questions based on either the views or the votes. Data Collection Visualization for Questions The required data i.e the questions, type, date created, views and votes for the questions, answers, votes & reputation of user are extracted and stored. Although circle packing is not space efficient, it better represents the answers that are more accurate. More the reputation and the votes bigger is the circle, making it easy to identify by zooming in. The dataset we have chosen is the Java questions created in the year 2014. Accepted Answers vs Other answers Reputation vs Votes What are the other answers you should look into if the accepted answer does not satisfy you. What are the most popular questions based on votes and views. Nitin Tummala Ravi Teja Thutari Manojna Kapala Overview Why Circle Packing? Research Questions Answered The project is intended to create a recommender system for the java bounded stack overflow data. The idea is to implement a visualization that recommends the answers to the user based on the votes, reputation of the user who answered the question. Data Visualization StackOverflow Java Recommender
Transcript: From the simplest excel bar graph to the most elaborate infographic, the decisions you make regarding data visualizations will determine the efficacy of your presentation. Make wise choices when preparing presentations by being aware of your options. ELEMENTS 2013 Global Web Traffic by Year Are you representing part of a whole? 2004 2012 Are you relating multiple types of points to each other? Is there even a need for a chart/graph? 2001 Tableau 7 Adobe Suite Excel Statista 1996 http://www.visual-literacy.org/periodic_table/periodic_table.html Let's Practice: Global Web Traffic by Year Data Inspiration BRAINSTORM vizualize.me/gannala Highlight what you want to be seen Fill in knowledge gaps Comparing objects/points to each other? Data has the opportunity to be a narrative story telling device. You have to create the context to make it relevant. Think about your audience, your story, and your conclusion. It's equally imperative to decide what isn't important enough to be put in your infographic. Data Visualization Beyond the Basics This Class: 75% Female 25% Male USA: 51% Female 49% Male World: 45% Female 55% Male Are you describing data or a workflow? 1996 Visual Resumes http://www.tagxedo.com/ - shaped word clouds http://www.statsilk.com/ - mapping software http://timeline.verite.co/ - interactive timelines Basic Graphic Builder Tools: http://piktochart.com/ http://www.easel.ly/ http://create.visual.ly/ http://infogr.am/ http://venngage.com/ http://www.icharts.net/ After you've collected your data, the first important thing to decide is what do you want to say about it? 2012 2010 Telling the right story 2007 Remember: Bad Visualizations DO Exist. Statista: http://libraries.luc.edu/databases/database/1159 Simply Map: http://libraries.luc.edu/databases/database/919 Google Public Data Project: http://www.google.com/publicdata/explore?ds=d5bncppjof8f9_&ctype=b&strail=false&nselm=s&met_x=sp_dyn_le00_in&scale_x=lin&ind_x=false&met_y=sp_dyn_tfrt_in&scale_y=lin&ind_y=false&met_s=sp_pop_totl&scale_s=lin&ind_s=false&dimp_c=country:region&ifdim=country&iconSize=0.5&uniSize=0.035 Proprietary Software at Loyola Periodic Table of Data Visualization Web-Based Graphic Builders
Transcript: using computers and mobile devices to drill down into charts and graphs for more details, and Dipity WHAT IS DATA VISUALIZATION ? presentation of data in a pictorial or graphical format saves time and energy. enables decision makers to see analytical results presented visually traditional electronic spreadsheet cannot visually represent the information due to data presentation limitations. iCharts data visualization makes it easier for decision makers across all organizations to: patterns can be spotted quickly and easily help people see things that were not obvious to them before. interactively (and immediately) changing what data you see and how it is processed. it is faster for people to grasp the meaning of many data points when they are displayed in charts and graph WHY IS IT IMPORTANT? INTERACTIVE VISUALIZATION presents the data in a way that the director (top management) can easily interpret Identify areas that need attention or improvement. Understand what factors influence your customers’ behavior. Know which products to place where. Predict sales volumes. Discover how to increase revenues or reduce expenses. Able to convey information in a universal manner and make it simple to share ideas with others. DATA VISUALIZATION
Transcript: Visualization tools go beyond the standard charts and graphs used in Excel spreadsheets, displaying data in more sophisticated ways such as dials and gauges, geographic maps, time-series charts, spark lines, heat maps, tree maps and detailed bar, pie and fever charts. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. Visualized data is frequently displayed in business intelligence (BI) dashboards and performance scorecards that provide users with high-level views of corporate information,metrics and key performance indicators (KPIs). The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur, can also be included. This would work great, but there’s a secondary problem: since UNC is a good 40 minute drive away, our grad assistants tend to have very rigid schedules, which are fixed well in advance — so we can’t just alter our grad assistants’ schedules on short notice to have them cover a class. Meanwhile, instruction scheduling is very haphazard, due to wide variation in how course slots are configured in the weekly calendar, so it can be hard to predict when instruction requests are likely to be scheduled. What we need is a technique to maximize the likelihood that a grad student’s standing schedule will overlap with the timing of instruction requests that we do get — before the requests come in. Luckily, we had some accrued data on our instructional activity from previous semesters. This seemed like the obvious starting point: look at when we taught previously and see what days and times of day were most popular. The data consisted of about 80 instruction sessions given over the course of the prior two semesters; data included date, day of week, session start time, and a few other tidbits. The data was basically scraped by hand from the instruction records we maintain for annual reports; my colleague Anne Burke did the dirty work of collecting and cleaning the data, as well as the initial analysis. The chart above gets the fundamentals right — since we’re designing weekly schedules for our grad assistants, it’s clear that the relevant dimensions are days of week and times of day. However, there are basically two problems with the stacked bar chart approach: (1) The resolution of the stacked bars — morning, afternoon and evening — is too coarse. We need to get more granular if we’re really going to see the times that are popular for instruction; (2) The stacked bar chart slices just don’t fit our mental model of a week. If we’re going to solve a calendaring problem, doesn’t it make a lot of sense to create a visualization that looks like a calendar? When I thought about analyzing the data in these terms, the concept of a heatmap immediately came to mind. A heatmap is a tool commonly used to look for areas of density in spatial data. It’s often used for mapping click or eye-tracking data on websites, to develop an understanding of the areas of interest on the website. A heatmap’s density modeling works like this: each data point is mapped in two dimensions and displayed graphically as a circular “blob” with a small halo effect; in closely-packed data, the blobs overlap. Areas of overlap are drawn with more intense color, and the intensity effect is cumulative, so the regions with the most intense color correspond to the areas of highest density of points. In this graphic, days of the week are represented by the horizontal rows of blobs, with Monday as the first row and Friday as the last. The leftmost extent of each row corresponds to approximately 8am, while the rightmost extent is about 7:30pm. The key in the upper left indicates (more or less) the number of overlapping data points in a given location. A bit of labeling helps to clarify things: Heatmap of instruction data, labeled with days of week and approximate indications of time of day. Right away, we get a good sense of the shape of the instruction week. This presentation reinforces the findings of the earlier chart: that Monday, Tuesday, and Thursday are busiest, and that Friday afternoon is basically dead. But we do see a few other interesting tidbits, which are visible to us specifically through the use of the heatmap: Monday, Tuesday and Thursday aren’t just busy, they’re consistently well-trafficked throughout the day. Friday is really quite slow throughout. There are a few interesting hotspots scattered here and there, notably first thing in the morning on Tuesday. Wednesday is quite sparse overall, except for two or three prominent afternoon/evening times. There is a block of late afternoon-early evening time-slots that are consistently busy in the first half of the week. Using this information, we can take a much more informed approach to scheduling our graduate students, and
Transcript: Understanding the interpretation of signs and symbols Questions? Presenters Data visualization is the study of the visual representation of data Edward Tufte Examples "The whole is greater than the sum of its parts”. ”Gestalt” means “pattern” in German, and these laws tell us how we group things we see. Yes, it’s grouping, but grouping with the purpose. That purpose is pragnanz or good form: we like ordered, simple, symmetrical, closed forms that are easier to process. External Cognition Gestalts Laws "Information design makes complex information easier to understand and to use.” – AIGA from aiga.org The use of the external world to assist with memory and computational problem solving. Semiotics What is Data Visualization? Thank You Data Visualization "A picture is worth a thousand words" "Information design addresses the organization and presentation of data: its transformation into valuable, meaningful information." – Nathan Shedroff
Transcript: http://www.bbc.co.uk/programmes/b00wgq0l Design: http://www.cs171.org/lectures/02-Design.pdf Keith Andrews - Information Visualisation lectures: http://courses.iicm.tugraz.at/ivis/ivis.pdf Pen & Paper Classics Cognition: http://www.cs171.org/lectures/08-Cognition.pdf References Data Visualization Chaomei Chen - Overview: https://crawford.anu.edu.au/public_policy_community/content/doc/2010_Chen_Information_visualization.pdf Process: http://www.cs171.org/lectures/03-Process.pdf Maps: http://www.cs171.org/lectures/12-Maps.pdf http://www.thehumanfaceofbigdata.com/ http://www.inf.ethz.ch/personal/peikert/SciVis/Literature/keim-Tutorial2000.pdf Categories/Techniques http://paginas.fe.up.pt/~tavares/downloads/publications/artigos/IJI_Manuscript_DA_JT.pdf The beauty of data http://pear.ly/chW8v http://cacm.acm.org/magazines/2010/6/92482-a-tour-through-the-visualization-zoo/fulltext Introduction to Information Visualization: http://www.cad.zju.edu.cn/home/chenwei/visclass/ Interaction: http://www.cs171.org/lectures/10-Interaction.pdf Perception: http://www.cs171.org/lectures/05-Perception.pdf Analytics Facts of Perception: http://www.cs171.org/lectures/PerceptionAndColorRules.pdf Time series data Trees and Networks: http://www.cs171.org/lectures/13-TreesAndNetworks.pdf REF How to Become a Data Scientist Big Data Visual Analytics Digital Library: http://vadl.cc.gatech.edu/taxonomy/ http://nirvacana.com/thoughts/becoming-a-data-scientist/ Video Infographics Data is Beautiful to Explore Index Charts Stacked Graphs Small Multiples Horizon Graphs Statistical Distributions Stem-and-Leaf Plots Q-Q Plots Scatter Plot Matrix Parallel Coordinates Information Visualization wiki: http://www.infovis-wiki.net/ CS171 lectures: http://www.cs171.org/lectures/ Introduction: http://www.cs171.org/lectures/01-Introduction.pdf Force-directed Layouts Arc Diagrams Matrix Views Introduction http://www.ted.com/talks/aaron_koblin.html http://pinterest.com/mashable/infographics/ http://xkcd.com/1080/ Crowd-sourced data High Dimentional: http://www.cs171.org/lectures/14-HighDimensional.pdf The Joy of Statistics Techniques: http://www.infovis-wiki.net/index.php?title=Category:Techniques Maps Flow maps Choroplath Maps Graduated Symbol Maps Cartograms Hierarchies Node-link Diagrams Adjacency Diagrams Enclosure Diagrams Perception The Art https://www.youtube.com/playlist?p=PL800D5CBC2E5E1AE4 http://fundersandfounders.com/ http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/articles/03_Perception.pdf Colour: http://www.cs171.org/lectures/07-Color.pdf Patterns: http://www.cs171.org/lectures/06-Patterns.pdf REFERENCES Static Infographics http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html Perception & Design Statistical: http://www.cs171.org/lectures/11-StatisticalGraphs.pdf Interactive Infographics http://www.visual-analytics.eu/ http://www.infovis-wiki.net/index.php?title=Prefuse Techniques for Large Data http://www.bu.edu/ceit/files/2012/12/Teaching-Talk-Visual-Literacy.pdf Networks REFERENCES
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