Spatial Data Models Section 2 lift the lid

Spatial Data Models Section 2: lift the lid & look inside a GIS CS 128/ES 228 - Lecture 4 a 1

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 4 a 2

Two types of features (“entities”) Discrete Continuous CS 128/ES 228 - Lecture 4 a 3

What are data? n Observations or measurements of the real world n 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? ) CS 128/ES 228 - Lecture 4 a 4

Model dimensionality: 2 -D n X-Y coordinates n No elevations n Road crossings… CS 128/ES 228 - Lecture 4 a 5

Model dimensionality: 3 -D n X-Y-Z coordinates http: //earth. esa. int/pu b/INSAR/dem/ves_de m. gif n False relief CS 128/ES 228 - Lecture 4 a 6

More sophisticated 3 -D models n Wire frame model “draped” with aerial photograph or other surface feature n Thematic material can be layered on http: //biology. usgs. gov/stt/SNT/noframe/cl 111. htm CS 128/ES 228 - Lecture 4 a 7

Model dimensionality: 4 -D n 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/cl 111. htm CS 128/ES 228 - Lecture 4 a 8

Stages of development: 1. Conceptual model: select the features of reality to be modeled and decide what entities will represent them. Driven by the purpose of the model. 2. Spatial data model: select a format that will represent the model entities. Driven by the conceptual model and by data availability. 3. Spatial data structure: decide how to code the entities in the model’s data files. CS concern. CS 128/ES 228 - Lecture 4 a 9

The modeling process 1. Conceptual model Decisions…. . 2. Spatial data model More decisions… CS 128/ES 228 - Lecture 4 a 10

Our local “Happy Valley” CS 128/ES 228 - Lecture 4 a 11

1. Conceptual models § Decide the model’s purpose § Select the features to be modeled CS 128/ES 228 - Lecture 4 a 12

Spatial entities: 5 types 1. Points 2. Lines (= “polylines”) 3. Areas (= “polygons”) 4. Networks 5. Surfaces CS 128/ES 228 - Lecture 4 a 13

Happy Valley spatial entities CS 128/ES 228 - Lecture 4 a 14

Discrete vs. continuous features Discrete features: n Points n Lines n Areas n Networks Continuous features: § Surfaces CS 128/ES 228 - Lecture 4 a 15

Networks n Line entity n Used to model features along which material, energy, or information flow n Special components: nodes, stops, turns, direction, impedance CS 128/ES 228 - Lecture 4 a 16

Impedance CS 128/ES 228 - Lecture 4 a 17

Surfaces n Models entity as a continuous feature n Every location has a value, even if only interpolated from discrete samples Both: http: //snobear. colorado. edu/Mark w/Research/ESRI. html CS 128/ES 228 - Lecture 4 a 18

Digital terrain models CS 128/ES 228 - Lecture 4 a 19

Precision agriculture Aerial photograph Soil p. H CS 128/ES 228 - Lecture 4 a Crop yield 20

Oceanography Estimate of phytoplankton distribution in the surface ocean: global composite image of surface chlorophyll a concentration (mg m-3) estimated from Sea. Wi. FS data (Source: NASA Goddard Space Flight Center, Maryland, USA and ORBIMAGE, Virginia, USA). CS 128/ES 228 - Lecture 4 a 21