CS 128/ES 228 - Lecture 4a1 Spatial Data Models Section 2: lift the lid & look inside a GIS.
-
date post
20-Dec-2015 -
Category
Documents
-
view
215 -
download
1
Transcript of CS 128/ES 228 - Lecture 4a1 Spatial Data Models Section 2: lift the lid & look inside a GIS.
CS 128/ES 228 - Lecture 4a 1
Spatial Data Models
Section 2: lift the lid & look inside a GIS
CS 128/ES 228 - Lecture 4a 2
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 3
Two types of features (“entities”)Discrete
Continuous
CS 128/ES 228 - Lecture 4a 4
What areare 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?)
CS 128/ES 228 - Lecture 4a 5
Model dimensionality: 2-D
X-Y coordinates
No elevations
Road crossings…
CS 128/ES 228 - Lecture 4a 6
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 7
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 8
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 9
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 4a 10
The modeling process
1. Conceptual model
2. Spatial datamodel
Decisions…..
More decisions…
CS 128/ES 228 - Lecture 4a 11
Our local “Happy Valley”
CS 128/ES 228 - Lecture 4a 12
1. Conceptual models
Decide the model’s purpose
Select the features to be modeled
CS 128/ES 228 - Lecture 4a 13
Spatial entities: 5 types
1. Points
2. Lines (= “polylines”)
3. Areas (= “polygons”)
4. Networks
5. Surfaces
CS 128/ES 228 - Lecture 4a 14
Happy Valley spatial entities
CS 128/ES 228 - Lecture 4a 15
Discrete vs. continuous features
Points
Lines
Areas
Networks
Discrete features: Continuous features: Surfaces
CS 128/ES 228 - Lecture 4a 16
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 17
Impedance
CS 128/ES 228 - Lecture 4a 18
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 19
Digital terrain models
CS 128/ES 228 - Lecture 4a 20
Precision agriculture
Aerial photograph Soil pH Crop yield
CS 128/ES 228 - Lecture 4a 21
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).