LEADER 01771oam 2200481zu 450 001 996211774903316 005 20210807003112.0 010 $a1-5090-7623-9 035 $a(CKB)1000000000710786 035 $a(SSID)ssj0000453251 035 $a(PQKBManifestationID)12139244 035 $a(PQKBTitleCode)TC0000453251 035 $a(PQKBWorkID)10480989 035 $a(PQKB)11500214 035 $a(EXLCZ)991000000000710786 100 $a20160829d2008 uy 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$a2008 IEEE International Symposium on Computer-Aided Control System Design : September 3-5, 2008, San Antonio, Texas 210 31$a[Place of publication not identified]$cIEEE$d2008 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4244-2221-3 606 $aAutomatic control$xData processing$vCongresses 606 $aComputer-aided design$vCongresses 606 $aMechanical Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 606 $aMechanical Engineering - General$2HILCC 615 0$aAutomatic control$xData processing 615 0$aComputer-aided design 615 7$aMechanical Engineering 615 7$aEngineering & Applied Sciences 615 7$aMechanical Engineering - General 676 $a620/.00420285 712 02$aIEEE Control Systems Society 712 02$aInstitute of Electrical and Electronics Engineers 712 12$aIEEE International Symposium on Computer-Aided Control System Design 801 0$bPQKB 906 $aPROCEEDING 912 $a996211774903316 996 $a2008 IEEE International Symposium on Computer-Aided Control System Design : September 3-5, 2008, San Antonio, Texas$92540897 997 $aUNISA LEADER 03322nam 2200481 450 001 9910554243903321 005 20230412040256.0 010 $a0-231-55015-4 024 7 $a10.7312/schw19310 035 $a(OCoLC)1164823539 035 $a(CKB)4100000011726890 035 $a(MiAaPQ)EBC6181760 035 $a(DE-B1597)566437 035 $a(DE-B1597)9780231550154 035 $a(PPN)255829523 035 $a(EXLCZ)994100000011726890 100 $a20210209d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBetter data visualizations $ea guide for scholars, researchers, and wonks /$fJonathan Schwabish 210 1$aNew York :$cColumbia University Press,$d[2021] 215 $a1 online resource (368 pages) $cillustrations 311 08$a0-231-19310-6 320 $aIncludes bibliographical references and index. 327 $tFrontmatter --$tCONTENTS --$tINTRODUCTION --$tPART ONE: PRINCIPLES OF DATA VISUALIZATION --$t1. VISUAL PROCESSING AND PERCEPTUAL RANKINGS --$t2. FIVE GUIDELINES FOR BETTER DATA VISUALIZATIONS --$t3. FORM AND FUNCTION: LET YOUR AUDIENCE'S NEEDS DRIVE YOUR DATA VISUALIZATION CHOICES --$tPART TWO: CHART TYPES --$t4. COMPARING CATEGORIES --$t5. TIME --$t6. DISTRIBUTION --$t7. GEOSPATIAL --$t8. RELATIONSHIP --$t9. PART-TO-HOLE --$t10. QUALITATIVE --$t11. TABLES --$tPART THREE: DESIGNING AND REDESIGNING YOUR VISUAL --$t12. DEVELOPING A DATA VISUALIZATION STYLE GUIDE --$t13. REDESIGNS --$tCONCLUSION --$tAPPENDIX 1: DATA VISUALIZATION TOOLS --$tAPPENDIX 2: FURTHER READING AND RESOURCES --$tAcknowledgments --$tReferences --$tIndex 330 $aNow more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message. 606 $aInformation visualization 606 $aVisual analytics 615 0$aInformation visualization. 615 0$aVisual analytics. 676 $a001.4226 686 $aST 320$2rvk 700 $aSchwabish$b Jonathan A.$0808076 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554243903321 996 $aBetter data visualizations$92820155 997 $aUNINA