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Representations / Models

Representations / Models. Why Representations or Models?. How do we know what we know? Human sight Visible spectrum, horizon at ~10km visibility 100 km Human sound 50Hz to 15KHz up to 100 m Taste, Touch, Smell. Surface of the Earth?. 500,000,000 sq km

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Representations / Models

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  1. Representations / Models

  2. Why Representations or Models? • How do we know what we know? • Human sight • Visible spectrum, horizon at ~10km visibility 100 km • Human sound • 50Hz to 15KHz up to 100 m • Taste, Touch, Smell

  3. Surface of the Earth? • 500,000,000 sq km • on average 100 sq m is sensed directly p = 100/500,000,000,000,000 m p = 0.0000000000002 or 2 x 10 -13 spatially • 5 billion years • we live through ~70 p = 70/5,000,000,000 p = 0.000000014 or 1.4 x 10 -8 temporally \ we know almost nothing of the surface of the Earth via our senses!

  4. Knowing the World • Everything else via communication • Speech • Text • Photographs • Radio, TV • Maps • Internet • Databases

  5. Jonathan Raper’s Week in 2-D 1km Each color= 1 day Darker= later in the day Courtesy Jonathan Raper of City University London, GISci 2002 Keynote

  6. Jonathan Raper’s Month in 3-D X & y axes are spatial and z is seconds from midnight. Points are from GPS carried on all journeys with static time auto-completed. Model produced by Earthvision (http://www.dgi.com/) Courtesy Jonathan Raper of City University London, GISci 2002 Keynote

  7. Geographic Representation • Spatial (location, where) • to map, to link based on the same place, • to measure distances and areas • Temporal (time) • height above sea level (slow?) • sea surface temperature (fast) • Attributes • physical or environmental • soci-economic (e.g., population or income)

  8. Geographic Representation The “atom” of geographic information < location, time, attribute > “It’s chilly today in Corvallis” < Corvallis, today, chilly > “at 44° N, 123° E at 12 noon PST the temperature was 60°F”

  9. Describing LOCATION

  10. “Atoms” of Geographic Information • an infinite number • two-word description of every sq km on the planet, 10 Gb • store one number for every sq m, 1 Pb (trillion bytes) • Too much for any system • How to limit?

  11. Limiting Detail • aggregate, generalize, approximate • ignore the water?! • 2/3 of planet! • one temperature for all of Corvallis • one number for an entire polygon • sample the space • only measure at weather stations, temp. varies slowly • all geographic data miss detail • all are uncertain to some degree

  12. The Problem of Infinite Complexity • many ways of limiting detail • a GIS user must make choices • GIS developers must allow for many options • Most important option is how we choose to think about the world

  13. Terminology

  14. Features and Attributes Features (Objects) • Well-defined boundaries in empty space • “Desktop littered w/ objects” • World littered w/ cars, houses, etc. • Counts • 49 houses in a subdivision How many students at OSU? Clouds in sky? Fish in the sea? Atmospheric highs in N. hemisphere today?

  15. Objects and Properties • Objects are intuitive, part of everyday life • May overlap • Properties worth measuring at every point • Often associated with scientific measurements • surfaces, fronts, highs, lows • Both objects and properties can be represented either in raster or in vector form

  16. One Variable as Pt (grid or sample), TINRaster, Poly, ContoursWhat changes? Representation or phenomenon?

  17. What constitutes a “mountain?” • 1000 ft was magic number but how?

  18. Additional Slides

  19. Fields:care to count every peak, valley, ridge, slope???

  20. Fieldswhat varies continuously and how smoothlymeasurable at every point on a surface • Radiation captured by satellite • Elevation • Temperature • Soil type • Soil pH • Rainfall • Land cover type • Ownership An image of part of the lower Colorado River in the southwestern USA. The lightness of the image at any point measures the amount of radiation captured by the satellite's imaging system. Image derived from a public domain SPOT image, courtesy of Alexandria Digital Library, University of California, Santa Barbara.

  21. Field/Raster WorldviewTessellated Ground Plane Orange County, CA Courtesy of Russ Michel, Pictometry Intl. Inc.

  22. Object/Vector Worldview Projected with flat ground plane Projected with tessellated ground plane Orange County Street Centerlines Courtesy of Russ Michel, Pictometry Intl. Inc.

  23. Fields • each variable has one value everywhere • variable is a function of location • field = a way of conceiving of geography as a set of variables, each having one value at every location on the planet • zf = f (x,y,z,t)

  24. Ontology • Ontology: the study of the basic elements of description • "what we tell about" • semantics, “semantic interoperability” • discrete objects and fields are two different ontologies www.ucgis.org Research Challenge in Ontology

  25. A Coastal “Geo-Ontology” Courtesy Jonathan Raper of City University London, GISci 2002 Keynote

  26. ICAN Interoperability Prototypeican.ucc.ie Starts with metadata interoperability “Mapping” of Terms: MIDA: “Coastline” is similar to OCA: “Shoreline” “Coastline” “Shoreline” Atlas X ISO Metadata & MIDA terminology FGDC Metadata & OCA terminology X Standard & X terminology …

  27. Gateway to the Literature • Goodchild, M. F., M. Yuan, Cova, T. Towards a general theory of geographic representation in GIS. Int. J. Geog. Inf. Sci. 21(3-4): 239-260, 2007. • Comber, A., P.R. Fisher, J., and R. Wadsworth, Integrating land-cover data with different ontologies: Identifying change from inconsistency, Int. J. Geog. Inf. Sci., 18 (7), 691-708, 2004. • Golledge, R., The Nature of Geographic Knowledge, Annals of the AAG, 92(1): 1-14, 2002. • Kavouras, M., M. Kokla, and E. Tomai, Comparing categories among geographic ontologies, Comp. Geosci, 31 (2), 145-154, 2005. • Kuhn, W., Semantic reference systems, Int. J. Geog. Inf. Sci., 17 (5), 405-409, 2003.

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