M259 Visualizing Information Jan 14: DATA SOURCE THUR
George LegradyM259 Visualizing Information2014 WinterGeorge LegradyM259 Visualizing Information2014 WinterThis Week:TUES: Jan 14Discuss Data SourceM259 Visualizing InformationJan 14: DATA SOURCETHUR: Jan 16a) Karl Yerkes Presentation on databasestructureGeorge Legrady, legrady@mat.ucsb.eduYoon Chung Han hanyoonjung@gmail.com George LegradyM259 Visualizing Informationb) Yoon will follow with SQL examples2014 Winter2 Directions for Data Analysis:1. The study of the data organization, itsanomalies, history, etc.2. The exploration of content (culturalinformation)George LegradyM259 Visualizing Information2014 WinterData Visualization Focus Areas:DATA SYSTEMS§ Data retrieval and analysis§ System anomaly analysisCULTURE§ Cultural patterns:§ Historical Context: How has the library transformed itselfVISUALIZATIONS§ What are ways to visualize data (content focus)§ Invent new visualizations (exploration of the visuallanguage)ALGORITHMS§ Apply Algorithms: What are interesting algorithms by whichto process data§ Create new algorithms1
George LegradyM259 Visualizing Information2014 WinterM259 Visualizing InformationGeorge Legrady2014 WinterM259 Visualizing InformationGeorge Legrady2014 WinterData Source§ Patrons check out books, cds, dvds from theSeattle Public Library§ Each time someone checks out a movie,book, cd, data is received by the hour§ Appx 30000 per day; 10 million annual;§ Over 67 million datasets since September2005George LegradyM259 Visualizing Information2014 WinterRem Koolhaas Architect§ Patrons check out books, cds, dvds from theSeattle Public LibraryRem Koolhaas Visionary Architecture§ Seattle Public Library maincollection of books,government publications,periodicals, audio visualmaterials and thetechnology to access anddistribute information online.§ The building is divided intoeight horizontal layers, eachvarying in size to fit itsfunction.2
M259 VisualizingMAKINGVISIBLEInformationTHE INVISIBLELegradyWinter“Library Unbound” GeorgeSeattle PublicLibrary2014CommissionGeorge LegradyM259 Visualizing Information2014 Winter“Making Visible the Invisible” ArtWork§ Seattle Public Library artwork opened Sept.7, 2005 extended to 2019§ Provides raw data for the study of culturallibrary patron practices over time§ Possibly the longest running public dynamicdata visualization projectData Flow for the Networked CommunityM259 Visualizing InformationGeorge Legrady2014 WinterProject Concept§ Conceptualize the library as a “Data ExchangeCenter”§ Correlation is made between the flow of data(books, DVD, etc.) leaving the library and§ What patrons considers interesting informationat any specific time§ This data is information that can be calculatedmathematically and represented visuallyM259 Visualizing InformationGeorge Legrady2014 WinterRem Koolhaas Concept§ Collect hourly circulation of checkouts,analyze the data, and representthrough visualizations§ Installation in the Mixing Chamber, alarge open 19,500 sq ft spacededicated to information retrieval andpublic accessible computer research.§ Analysis featured as animations on 6large LCD panels located on a glasswall behind the librarians‘ maininformation desk3
M259 Visualizing InformationGeorge Legrady2014 WinterData SourceGeorge Legrady2014 WinterDewey Classification System§ Patrons check out books, cds, dvds from theSeattle Public Library§ Each time someone checks out a movie,book, cd, data is received by the hour§ Appx 30000 per day; 10 million annual;§ Over 67 million datasets since September2005M259 Visualizing InformationM259 Visualizing InformationGeorge Legrady2014 WinterTen topics each subdivided into 100subclasses:000 - Generalities100 - Philosophy & Psychology200 - Religion300 - Social Science400 - Language500 - Natural Science & Mathematics600 - Technology & Applied Sciences700 - Arts800 - Literature900 - Geography & HistoryM259 Visualizing InformationGeorge Legrady2014 WinterMySQL: Select itemNumber, cout, collcode, itemtype, barcode, title,callNumber, deweyClass, subj from inraw where year(cout) 2007 andmonth(cout) 1 and month(cout) 4 limit 50;Daily Volume Activity (see at outlog/inlog)4
George LegradyM259 Visualizing Information2014 WinterGeorge LegradyM259 Visualizing Information2014 WinterDaily Volume Activity (see at outlog/inlog)17George LegradyM259 Visualizing Information2014 WinterData is Multivariate§ Data is multivariate. Each transactionincludes numeric, ordinal, interval scale(time, date), string, and other classificationdata.§ Include:§ § § § § § § ItemType (bks, cds)Collection dateCheck-out/check-in hour/dayTitleDewey ClassificationKeywordsUnique IDs (barcode, etc.)George LegradyM259 Visualizing Information2014 Winter1st Project Development Process§ Data search: Knowledge discovery§ Data analysis: What patterns emerge§ Data formulation through algorithmicprocessing§ Translation into visualization§ Correlation with External Data§ Publication5
George LegradyM259 Visualizing Information2014 WinterReview Course Links:George LegradyM259 Visualizing Information2014 Winter1st Assignment: A Compelling Data Query§ State a Question: Can be cultural, systembased, compare data, etc. Should beexploratory or compelling .§ Dewey Classification System§ Library itemtypes§ Translate into a Query:§ Seattle Library Item Search§ Report Result: (a Data printout to .csv file)§ Legrady Project: Top 20 Dewey§ Processing Time: How long the search§ Previous Course Projects§ Comment & Analysis: Report what youfound through the data§ MySQL Queries§ Assignment is due Tues Jan 21George LegradyM259 Visualizing Information2014 WinterHow to Proceed§ Ask a general question§ Change syntax if necessaryGeorge LegradyM259 Visualizing Information2014 WinterVisualizations from M259 Course 2012 Projects: Anis Haron,Yoon Chung Han, RJ Duran§ Review Results§ Ask question differently§ Review Results§ Go into more detail 2013 Projects:Jay Byungkyu Kang, [2d], [3d],Saeed MahaniBegin with Inraw – by the week-end switch tomore specific tables with MySQL innerjoinfunction6
George LegradyM259 Visualizing Information2014 WinterData Analysis Directions2014 WinterData Analysis Directions Statistical Narrative Visual Explorations Statistical Narrative Play of the Imagination Visual ExplorationsHot Topics 2005 – 2010, Patrick Rudolph!Superhero Popularity, Domagoj Baricevic!George LegradyM259 Visualizing InformationGeorge LegradyM259 Visualizing Information2014 Winter“Catcher in the Rye” – Anis HaronM259 Visualizing InformationGeorge Legrady2014 Winter1stMap ofcheckout of New Item (SystemExploration by Karl Yerkes)§ What to map to show “Change Over time”§ Search for patterns: What exactly to lookfor?§ External correlation (news events)?relevant?§ Feedback: How does the visualization impacton circulation?§ Look to the future: Technology changesevery 3 years. How will the project live on for10 years?7
George LegradyM259 Visualizing Information2014 WinterM259 Visualizing InformationGeorge Legrady2014 WinterM259 Visualizing InformationGeorge Legrady2014 WinterIssues & Challenges§ What to map to show “Change Over time”§ Search for patterns: What exactly to lookfor?§ External correlation (news events)?relevant?§ Feedback: How does the visualization impacton circulation?§ Look to the future: Technology changesevery 3 years. How will the project live on for10 years?George LegradyM259 Visualizing Information2014 WinterRJ Duran Frequency Pattern Tree Algorithm for theword “Water” in relation to other Words Over 75 million datasets Focus on the syntax visual language MySQL for data query Processing: Computational design 4 projects in 10 weeks:§ SQL Data Query§ 1D linear frequency§ 2D spatial mapping§ Correlation with external dataset§ 3D interactiveIntegrate Your ExpertiseComputer Science: Integrate complexalgorithms to visualizationStatistics: Implement statistical probabilityproblems to data analysis and visualizationSound/Signal processing: Consider data assignal and explore translation between sonic,signal and visual patternsSocial Science: Identify cultural patterns,changes, transformationsGeography: Explore spatial mappingCinematic/Literary: Explore data pattern asnarrative development8
George LegradyM259 Visualizing Information2014 WinterData Processing Functions§ Validation: Ensuring that supplied data is"clean, correct and useful”§ Sorting: Arranging items in some sequenceand/or in different sets§ Summarization: Reducing detail data to itsmain points§ Aggregation: Combining multiple pieces ofdata (from various sources)§ Analysis: Collection, organization, analysis,interpretation and presentation of data§ Reporting: List detail or summary data orcomputed informationGeorge LegradyM259 Visualizing Information2014 WinterSome References§ Atlas of Science, Katy Borner§ Graphics of Large Datasets, Unwin, Theus,Hofmann§ Visualizing Data, Ben Fry§ Robert Kosara, social analysisPrevious student projects:http://vislab.mat.ucsb.edu/courses.html9
M259 Visualizing Information George Legrady 2014 Winter M259 Visualizing Information Jan 14: DATA SOURCE George Legrady, legrady@mat.ucsb.edu Yoon Chung Han hanyoonjung@gmail.com M259 Visualizing Information George Legrady 2014 Winter This
QRP Fox Hunt 0200Z-0330Z, Jan 6 NCCC Sprint 0230Z-0300Z, Jan 6 PODXS 070 Club PSKFest 0000Z-2400Z, Jan 7 WW PMC Contest 1200Z, Jan 7 to 1200Z, Jan 8 SKCC Weekend Sprintathon 1200Z, Jan 7 to 2400Z, Jan 8 Original QRP Contest 1500Z, Jan 7 to 1500Z, Jan 8 Kid's Day Contest 1800Z-2359Z, Jan 7 ARRL RTTY Roundup 1800Z, Jan 7 to 2400Z, Jan 8
historically elevated (and is expected to remain so). 150 250 350 450 550 650 750 850 950 Jan-07 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17 Jan-19 Jan-21 /MT High-Analysis Phosphate Global Net Price Calculated from Published Weekly Spot Prices 0 100 200 300 400 500 600 700 800 900 1,000 Jan-20 Jul-20 Jan-21 Jul-21 Jan-22 /MT Global DAP/MAP Benchmark .
Lake Erie ‐ Niagara River Monthly Hydrology: 2004‐2013 relative to 1926‐2013 0 50 100 150 200 250 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 Jan 2009 Jan 2010 Jan 2011 Jan 2012 Jan 2013 Jan Basin Precipitation [% of LTA] ‐0.30 ‐0.20 ‐0.10 0.00 0.10 0.20 0.30 0.40 0.50 Water Level [m from LTA] ‐1,000 ‐500 0 500 1,000 1,500
Domestic and regional forces shaping Asia Asian consumer demand to support future growth Note: (1) HSBC Global Research Industrial output, 2000-20091 Asia recovery1 90 100 110 120 130 140 150 160 170 180 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 90 95 100 105 110 115 EM Asia G3 USA Helping lift personal incomes and .
Written Comprehensives for Double Major Seniors Only AS NEEDED JAN 6-7 JAN 5-6 JAN 4-5 JAN 9-10 Written Comprehensives for Seniors JAN 10-11 JAN 9-10 JAN 8-9 JAN 13-14 Oral Comprehensive Exams for Seniors JAN 12-14
Thu, Jan 21 Chapter 4 Simple Soccer Anatomy Fri, Jan 22 Chapter 4 Simple Soccer Anatomy Sun, Jan 24 3 - Tank States [5%] 3Mon, Jan 25 Chapter 4 Simple Soccer Anatomy Tue, Jan 26 Futures: TBD Wed, Jan 27 4 - My Team [3%] Thu, Jan 28 Brainstorming: Simple Soccer Strategy Fri, Jan 29 Chapter 6 LUA Scripting Sun, Jan 31 5 - Team Design [3%]
QRP Fox Hunt 0200Z-0330Z, Jan 1 Phone Fray 0230Z-0300Z, Jan 1 SARTG New Year RTTY Contest 0800Z-1100Z, Jan 1 AGCW Happy New Year Contest 0900Z-1200Z, Jan 1 CWops Mini-CWT Test 1300Z-1400Z, Jan 1 and 1900Z-2000Z, Jan 1 and 0300Z-0400Z, Jan 2 AGCW VHF/UHF Contest 1400Z-1700Z, Jan 1 (144) and .
AMERICAN BOARD OF RADIOLOGY, ) ) CLASS ACTION ) Trial by Jury Demanded Defendant. ) CLASS ACTION COMPLAINT Plaintiff Sadhish K. Siva, (“Plaintiff”), for his Complaint against Defendant American Board of Radiology (“ABR” or “Defendant”) hereby alleges as follows: INTRODUCTION 1. This case is about ABR’s illegal and anti-competitive conduct in the market for initial board .
