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Spatial Data Models. What do you see out the window?. Naïve view : a blur of colors on my retina Topological view : a collection of points, lines & areas in geometric relation to each other Object-oriented view : sidewalks, buildings, trees, people … Western NY view : a lot of snow.
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Spatial Data Models CS 128/ES 228 - Lecture 4a
What do you see out the window? • Naïve view: a blur of colors on my retina • Topological view: a collection of points, lines & areas in geometric relation to each other • Object-oriented view: sidewalks, buildings, trees, people … • Western NY view: a lot of snow CS 128/ES 228 - Lecture 4a
What is a spatial model? A simplified representation of part of the real world, referenced to spatial coordinates, and created for a specific purpose CS 128/ES 228 - Lecture 4a
Two types of features (“entities”) Discrete Continuous CS 128/ES 228 - Lecture 4a
What are data? • Observations or measurements of the real world • Three “modes” (or 3 questions to answer): • Spatial mode (where is it?) • Thematic mode (what is it?) • Temporal mode (when was it observed?) CS 128/ES 228 - Lecture 4a
Model dimensionality: 2-D • X-Y coordinates • No elevations • Road crossings… CS 128/ES 228 - Lecture 4a
Model dimensionality: 3-D • X-Y-Z coordinates • False relief http://earth.esa.int/pub/INSAR/dem/ves_dem.gif CS 128/ES 228 - Lecture 4a
More sophisticated 3-D models • Wire frame model“draped” withaerial photographor other surfacefeature • Thematic material can be layered on http://biology.usgs.gov/stt/SNT/noframe/cl111.htm CS 128/ES 228 - Lecture 4a
Model dimensionality: 4-D • X-Y-Z coordinates + temporal dimension Fig. 7. A geographic information system representation of glacier shrinkage from 1850 to 1993 in Glacier National Park. The Blackfeet Jackson glaciers are in the center. The yellow areas reflect the current area of each glacier; other colors represent the extent of the glaciers at various times in the past.Courtesy C. Key, USGS and R. Menicke, National Park Service http://biology.usgs.gov/stt/SNT/noframe/cl111.htm CS 128/ES 228 - Lecture 4a
Stages of development: • Conceptual model: select the features of reality to be modeled and decide what entities will represent them. Driven by purpose of model. • Spatial data model: select a format that will represent the model entities. Driven by conceptual model and by data availability. • Spatial data structure: decide how to code the entities in the model’s data files. CS concern. CS 128/ES 228 - Lecture 4a
The modeling process • Conceptual model • Spatial datamodel CS 128/ES 228 - Lecture 4a
Our local “Happy Valley” CS 128/ES 228 - Lecture 4a
1. Conceptual models • Decide the model’s purpose • Select the features to be modeled CS 128/ES 228 - Lecture 4a
Spatial entities: 5 types • Points • Lines • Areas (polygons) • Networks • Surfaces CS 128/ES 228 - Lecture 4a
Happy Valley spatial entities CS 128/ES 228 - Lecture 4a
Points Lines Areas Networks Discrete features: Continuous features: Discrete vs. continuous features • Surfaces CS 128/ES 228 - Lecture 4a
Networks • Line entity • Used to model features along which material, energy, or information flow • Special components: nodes, stops, turns, direction, impedance CS 128/ES 228 - Lecture 4a
Impedance CS 128/ES 228 - Lecture 4a
Surfaces • Models entity as a continuous feature • Every location has a value, even if only interpolated from discrete samples Both: http://snobear.colorado.edu/Markw/Research/ESRI/ESRI.html CS 128/ES 228 - Lecture 4a
Digital terrain models CS 128/ES 228 - Lecture 4a
Precision agriculture Aerial photograph Soil pH Crop yield CS 128/ES 228 - Lecture 4a
Oceanography Estimate of phytoplankton distribution in the surface ocean: global composite image of surface chlorophyll a concentration (mg m-3) estimated from SeaWiFS data (Source: NASA Goddard Space Flight Center, Maryland, USA and ORBIMAGE, Virginia, USA). CS 128/ES 228 - Lecture 4a