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Geovisualization

Geovisualization. for Geographical Information Science. Jason Dykes Jo Wood, Jonathan Raper, David Mountain. Geographic Information Science Group Department of Information Science City University London. http://www.soi.city.ac.uk/~jad7/. Objectives. A Brief introduction to City GIS Group

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Geovisualization

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  1. Geovisualization for Geographical Information Science • Jason Dykes • Jo Wood, Jonathan Raper, David Mountain Geographic Information Science Group Department of Information Science City University London http://www.soi.city.ac.uk/~jad7/

  2. Objectives • A Brief introduction to City GIS Group • Outline some issues in Visualization and GI • Identify some recent/current/future work & themes • Describe some responses • Demonstrate some software

  3. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  4. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  5. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  6. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  7. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  8. Structure • Part I - Background • Geography, Cartography, Visualization and Information Science • Part II - Examples • cdv • Interactive Graphics and Exploratory Statistics • LandSerf • Visualization and Scale Issues • panoraMap • Visualization and Data Synthesis • New Data Types and Sources • Visualization Time & Space

  9. Part I - Background GISG @ City • Dept. of Information Science, School of Informatics • Mountain, Dykes, Wood, & Raper Academic Collaboration • University of Leicester • Birkbeck College Cartography • ICA Commission on Visualization & Virtual Environments Graphical Statistics • University of Augsburg • Research Institutes (AT&T, Bell Labs) External Organisations • MATRA, National Parks

  10. Geographers - Background in Maps and Mapping Numbers -> Graphics First Law : “Closer things are more alike than distant things” ‘Cartography Unit’ Complex multivariate relationships, restricted dimensions Digital Cartography T1 T2 T3 Real world Real world Raw data Raw data Map Graphics Statistics Map User’s map image T3 T1 T2 User’s Interpretation Cartography Modern Cartography • New goals, symbolism, dynamism, audience, uses...

  11. Cartography for Visualization "Cartography has undergone a profound change over the past decade” (MacEachren, 1997) • Goals of Map Use Information Retrieval Information Exploration • Intended Audience Large Audience Maps for Individuals • Flexibility Low Interactivity High Interactivity

  12. Real world Real world Raw data Raw data ‘Map’ Map Graphics Statistics Digital Cartography Visualization T3 T1 T1 T2 T2 T3 User’s Interpretation User’s Interpretation ‘communication’ ‘visualization’ Cartography for Visualization "Cartography has undergone a profound change over the past decade” (MacEachren, 1997) • Goals of Map Use Information Retrieval Information Exploration • Intended Audience Large Audience Maps for Individuals • Flexibility Low Interactivity High Interactivity • Visualization is about ideation: • “The process of learning by forming or connecting ideas” • Much in common with exploratory statistics (Tukey etc.)

  13. Goals of Map Use Information Retrieval Information Exploration • Intended Audience Large Audience Maps for Individuals • Flexibility Low Interactivity High Interactivity • Goals of Map Use Information Retrieval Information Exploration • Intended Audience Large Audience Maps for Individuals • Flexibility Low Interactivity High Interactivity Geovisualization • Cartography has changed since 1997! • Use of spatial visualization techniques (private, interactive) for... • Exploratory Analysis • Education & Learning • Interface / Interaction / Navigation / Information Retrieval • Applying interactive graphical techniques appropriate as... • New (more) ways of representing information • New (more) ways of interacting with information • New ways of sharing information • New (more) sources and types of data • New types of user and expectation (more users, more experienced)

  14. organisation - representation - communication - analysis Geographic Information Science - Interests 1 - Organisation • Digital Libraries for sharing & synthesis 2 - Representation • New cartographic techniques for representing information • Abstract views, realistic views, new variables & transformations 3 - Communication • Networks for sharing and combining information • Mobile communications for data gathering and distribution 4 - Analysis • Interactive graphics for disparate multivariate spatial data sets

  15. organisation - representation - communication - analysis Part II • cdv • LandSerf • panoraMap • New Data Types and Sources

  16. organisation - representation - communication - analysis Analysis - cdv - Discrete Areal Units • Converts numbers to symbols for ESDA • planar dimensions • retinal variables • Graphical Analysis • multivariate • spatial and statistical • dynamic linking is the key • Abstract Data Views • cartograms • parallel plots • local statistics • local parallel plots

  17. organisation - representation - communication - analysis cdv - Interactive Graphics of Local Statistics

  18. organisation - representation - communication - analysis cdv - Local Graphical Statistics (Parallel Plots)

  19. Real world Raw data ‘Map’ T1 T1 T1 T1 T1 T1 T1 T2 T2 T2 T2 T2 T2 T2 T3 T3 T3 T3 T3 T3 T3 Real world Real world Real world Real world Real world Real world Raw data Raw data Raw data Raw data Raw data Raw data ‘Map’ ‘Map’ ‘Map’ ‘Map’ ‘Map’ ‘Map’ User’s map image User’s map image User’s map image User’s map image User’s map image User’s map image User’s map image organisation - representation - communication - analysis Representation - Virtual Environments • Representations use real world metaphor for navigation and interpretationto various degrees • panoraMap • LandSerf

  20. organisation - representation - communication - analysis panoraMap • Synthesizes geo-referenced data types: • linked panoramic imagery • raster images • GPS data • multimedia data • Virtual Environment • Visualization of discrete spatial data • point & area • qualitative and quantitative • New views • geographic parallel plots • Close to Real Time • GPS 2-way

  21. organisation - representation - communication - analysis Hub Architecture - Jo Wood • Organisation - Digital Geo-Library • Database of suitable data types • spatial data, multimedia, text, numeric • Metadatabase • context, rights, currency • geo-reference, gazetteer, key words • Communication - Java protocols • Java Classes, ActiveX, DOS, Java Bean • Standard • methods of communication • output formats

  22. organisation - representation - communication - analysis Analysis - LandSerf - Jo Wood • Representation - Virtual Environment • Analysis - Measures underlying phenomena • Continuous surfaces • Multi-scale measurement • Multi-scale classification • Communication - Internetworked • Uses Hub communications mechanism • panoraMap • Uses Hub communications mechanism • Networked Virtual Environment for Visualization!

  23. organisation - representation - communication - analysis Challenges and Opportunities • New Data Types : Passive creation of data sets that are … • Personalised • “who?” - generally relate to individuals • Location sensitive • “where?” - network triangulation or satellite positioning • Temporally sensitive • “when?” - and “how often?” • High resolution • Organisation / Representation / Communication / Analysis

  24. organisation - representation - communication - analysis Summarising Spatio-Temporal Data Sets • Location, bearing • Absolute/Relative • Rate of change • Rate of rate of change • Proximity to nodes • Shape • Periods • Variation over time • Sinuosity • Density • Temporal • Spatial • Scale

  25. organisation - representation - communication - analysis Graphical Techniques for Data Analysis • Exogenous information • background maps • transport networks • local data • previous routes • personal details

  26. organisation - representation - communication - analysis Graphical Techniques for Data Analysis • Exogenous information • background maps • transport networks • local data • previous routes • personal details • New representations • spotlights • multi-scale (time and space) • Visualization • interactive selection • cdv-TS : Groups / Attributes

  27. organisation - representation - communication - analysis HyperGeo : An Application

  28. organisation - representation - communication - analysis HyperGeo : Location-Dependent Information Provision

  29. organisation - representation - communication - analysis HyperGeo : Collaborative Project • Technology providing new opportunities for mobile information • More data collected • More data transferred • User and location sensitive particularly useful • Where am I now • Where is the nearest X? • What is that? • Spatial Context-Dependent Information for Tourists

  30. organisation - representation - communication - analysis City & HyperGeo • Technology • Component based approach • Modules access common (central) user profiles (cf Hub) • Analysis of spatio-temporal data • Creating and summarising spatio-temporal elements of profiles • Envelopes of behaviour • Predictive models? • Highlighting privacy issues • Removing data but retaining higher level information

  31. Summary and Conclusion

  32. organisation - representation - communication - analysis Summary and Conclusion - More Work Required 1 - Organisation: Data Sources • Opportunities for new, detailed and mobile information • More information - data types & volumes 2 - Representation: Modern Cartography • New possibilities for representation • Help us to interpret large, complex, dynamic spatial data sets 3 - Communication: Networks • Data synthesis and sharing • Information on the move 4 - Anaysis: Spatio-TemporalVisualization • Exploratory analysis • Derive higher level information and knowledge • Integration into research process & new techniques

  33. T1 T2 T3 Real world Raw data Map User’s map image Data Transformations & Research Challenges Cartography: Challenges: T1 T2 T3 Real world Raw data ‘Map’ User’s Interpretation User’s map image

  34. T1 T2 T3 Real world Raw data Map User’s map image T1 T2 T3 Real world Raw data ‘Map’ User’s Interpretation User’s map image Higher levels of interaction options & control New data sources and collection devices New data transformations Data Transformations & Research Challenges Cartography: Challenges:

  35. T1 T2 T3 Real world Raw data Map User’s map image T1 T2 T3 Real world Raw data ‘Map’ User’s Interpretation User’s map image Data Transformations & Research Challenges Cartography: Challenges:

  36. T1 T2 T3 Real world Raw data Map User’s map image Additional & external data sources, transformations on demand Cognitive & usability issues relating to new representation and interfaces Real time collection. Augmented Reality Data Transformations & Research Challenges Cartography: Challenges: T1 T2 T3 Real world Raw data ‘Map’ User’s Interpretation User’s map image

  37. More Information • Dykes and Wood (2001) Geoinformatics 3(8) • Dykes et al.(1999) IJGIS 13(4) • Dykes (1998) The Statistician 47(3) • Dykes (2000) Computers Environment & Urban Systems 24(2) • www.geog.le.ac.uk/jad7/CEUS/ • Mountain & Raper (2000) AGI Paper • http://www.soi.city.ac.uk/~dmm/hypergeo/pubs/W2.4.pdf • Wood et al. (1999) E&PB (26) • Raper (2001) GeoPlace • http://www.geoplace.com/ge/2001/0101/0101tt.asp http://www.soi.city.ac.uk/mgi/ http://www.soi.city.ac.uk/~jad7/

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