CS 128/ES 228 - Lecture 4a 1

Spatial Data Models

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What is a spatial model?

A simplified representation of part of the real world, referenced to spatial coordinates, and created for a specific purpose

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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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Two types of features

Discrete Continuous

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What is data?

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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?)

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Model dimensionality: 2-D

X-Y coordinates

No elevations

Road crossings…

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Model dimensionality: 3-D

X-Y-Z coordinates

False relief

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More sophisticated 3-D models

Wire frame model“draped” withaerial photographor other surfacefeature

Thematic material can be layered on

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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

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Stages of development:

1. Conceptual model: select the features of reality to be modeled and decide what entities will represent them

2. Spatial data model: select a format that will represent the model entities

3. Spatial data structure: decide how to code the entities in the model’s data files

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The modeling process

1. Conceptual model

2. Spatial datamodel

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Our local “Happy Valley”

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1. Conceptual models

Decide the model’s purpose

Select the features to be modeled

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Spatial entities: 5 types

1. Points

2. Lines

3. Areas (polygons)

4. Networks

5. Surfaces

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Happy Valley spatial entities

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Discrete vs. continuous features

Points

Lines

Areas

Networks

Discrete features: Continuous features: Surfaces

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Networks

Line entity

Used to model features along which material, energy, or information flow

Special components: nodes, stops, turns, direction, impedance

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Impedance

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Surfaces

Continuous feature

Every location has a value, even if only interpolated from discrete samples

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Digital terrain models

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Precision agriculture

Aerial photograph Soil pH Crop yield

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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).