Representations Models Why Representations or Models How do

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Representations / Models

Representations / Models

Why Representations or Models? • How do we know what we know? • Human

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

Surface of the Earth? • 500, 000 sq km – on average 100 sq

Surface of the Earth? • 500, 000 sq km – on average 100 sq m is sensed directly p = 100/500, 000, 000 m p = 0. 0000002 or 2 x 10 -13 spatially • 5 billion years – we live through ~70 p = 70/5, 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!

Knowing the World • Everything else via communication – Speech – Text – Photographs

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

Jonathan Raper’s Week in 2 -D 1 km Each color= 1 day Courtesy Jonathan

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

Jonathan Raper’s Month in 3 -D X & y axes are spatial and z

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 autocompleted. Model produced by Earthvision (http: //www. dgi. com/) Courtesy Jonathan Raper of City University London, GISci 2002 Keynote

More Representations in Space/Time

More Representations in Space/Time

Representation in Space/Time • What would more detail show? • Infinite complexity Simplification –

Representation in Space/Time • What would more detail show? • Infinite complexity Simplification – must reduce to manageable volume

Geographic Representation • “Location, location!” – to map, to link based on the same

Geographic Representation • “Location, location!” – to map, to link based on the same place, – to measure distances and areas • Time – height above sea level (slow? ) – Sea surface temperature (fast) • Attributes – physical or environmental – soci-economic (e. g. , population or income)

Geographic Representation The “atom” of geographic information < location, time, attribute > “It’s chilly

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”

“Atoms” of Geographic Information • an infinite number • two-word description of every sq

“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?

Limiting Detail • aggregate, generalize, approximate • ignore the water? ! – 2/3 of

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

The Problem of Infinite Complexity • many ways of limiting detail • a GIS

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

Objects and Fields How many students at OSU? Clouds in sky? Fish in the

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

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

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

Fields what varies continuously and how smoothly measurable at every point on a surface

Fields what varies continuously and how smoothly measurable at every point on a surface • Radiation captured by satellite • Elevation • Temperature • Soil type • Soil p. H • 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.

Field/Raster Worldview Tessellated Ground Plane Orange County, CA Courtesy of Russ Michel, Pictometry Intl.

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

Object/Vector Worldview Projected with flat ground plane Courtesy of Russ Michel, Pictometry Intl. Inc.

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

Fields • each variable has one value everywhere • variable is a function of

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)

Fields and Objects • Objects are intuitive, part of everyday life – May overlap

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

One Variable as Pt (grid or sample), TIN Raster, Poly, Contours What changes? Representation

One Variable as Pt (grid or sample), TIN Raster, Poly, Contours What changes? Representation or phenomenon?

Ontology • Ontology: the study of the basic elements of description • "what we

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

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

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

Describing LOCATION

Describing LOCATION

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

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

ICAN Interoperability Prototype ican. ucc. ie Starts with metadata interoperability “Mapping” of Terms: MIDA:

ICAN Interoperability Prototype ican. 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

Gateway to the Literature • Goodchild, M. F. , M. Yuan, Cova, T. Towards

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. 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.