1. CS 128/ES 228 - Lecture 4a1 Spatial Data Models

2. CS 128/ES 228 - Lecture 4a2 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

3. CS 128/ES 228 - Lecture 4a3 What is a spatial model? A simplified representation of part of the real world, referenced to spatial coordinates, and created for a specific purpose

4. CS 128/ES 228 - Lecture 4a4 Two types of features (“entities”) Discrete Continuous

5. CS 128/ES 228 - Lecture 4a5 are What are data? Observations or measurements of the real world Three “modes” (or 3 questions to answer): 1.Spatial mode (where is it?) 2.Thematic mode (what is it?) 3.Temporal mode (when was it observed?)

6. CS 128/ES 228 - Lecture 4a6 Model dimensionality: 2-D X-Y coordinates No elevations Road crossings…

7. CS 128/ES 228 - Lecture 4a7 Model dimensionality: 3-D X-Y-Z coordinates False relief http://earth.esa.int/p ub/INSAR/dem/ves_d em.gif

8. CS 128/ES 228 - Lecture 4a8 More sophisticated 3-D models Wire frame model “draped” with aerial photograph or other surface feature Thematic material can be layered on http://biology.usgs.gov/stt/SNT/noframe/cl111.htm

9. CS 128/ES 228 - Lecture 4a9 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

10. CS 128/ES 228 - Lecture 4a10 Stages of development: 1.Conceptual model: select the features of reality to be modeled and decide what entities will represent them. Driven by purpose of model. 2.Spatial data model: select a format that will represent the model entities. Driven by conceptual model and by data availability. 3.Spatial data structure: decide how to code the entities in the model’s data files. CS concern.

11. CS 128/ES 228 - Lecture 4a11 The modeling process 1.Conceptual model 2.Spatial data model

12. CS 128/ES 228 - Lecture 4a12 Our local “Happy Valley”

13. CS 128/ES 228 - Lecture 4a13 1. Conceptual models  Decide the model’s purpose  Select the features to be modeled

14. CS 128/ES 228 - Lecture 4a14 Spatial entities: 5 types 1.Points 2.Lines 3.Areas (polygons) 4.Networks 5.Surfaces

15. CS 128/ES 228 - Lecture 4a15 Happy Valley spatial entities

16. CS 128/ES 228 - Lecture 4a16 Discrete vs. continuous features Points Lines Areas Networks Discrete features: Continuous features:  Surfaces

17. CS 128/ES 228 - Lecture 4a17 Networks Line entity Used to model features along which material, energy, or information flow Special components: nodes, stops, turns, direction, impedance

18. CS 128/ES 228 - Lecture 4a18 Impedance

19. CS 128/ES 228 - Lecture 4a19 Surfaces Models entity as a continuous feature Every location has a value, even if only interpolated from discrete samples Both: http://snobear.colorado.edu/Mark w/Research/ESRI/ESRI.html

20. CS 128/ES 228 - Lecture 4a20 Digital terrain models

21. CS 128/ES 228 - Lecture 4a21 Precision agriculture Aerial photographSoil pH Crop yield

22. CS 128/ES 228 - Lecture 4a22 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).