spatial analysis (3d)

CS 128/ES 228 - Lecture 12b 1

Spatial Analysis (3D)

CS 128/ES 228 - Lecture 12b 2

When last we visited…

CS 128/ES 228 - Lecture 12b 3

Buffering – another tool Buffering (building

a neighborhood around a feature) is a common aid in GIS analysis

CS 128/ES 228 - Lecture 12b 4

Using Buffers to Select

•Select the features

•Save the features as a layer

•(Export)

CS 128/ES 228 - Lecture 12b 5

Putting it all together

Siting a nuclear waste dump Build Layer A by selecting only those areas

with “good” geology (good geology layer) Build Layer B by taking a population density

layer and reclassifying it in a boolean (2-valued) way to select only areas with a low population density (low population layer)

Build Layer C by selecting those areas in A that intersect with features in B (good geology AND low population layer)

Build Layer D by selecting “major” roads from a standard roads layer (major roads layer)

CS 128/ES 228 - Lecture 12b 6

Siting the Dump, Part Deux Build Layer E by buffering Layer D at a

suitable distance (major roads buffer layer) Build Layer F by selecting those features from

C that are not in any region of E (good geology, low population and not near major roads layer)

Build Layer G by selecting regions that are “conservation areas” (no development layer)

Build Layer H by selecting those features from F that are not in any region of G (suitable site layer)

See also: Figure 6.9, p. 121

CS 128/ES 228 - Lecture 12b 7

On to 3-D

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Some (More) GIS Queries How steep is the road? Which direction does the hill face? What does the horizon look like? What is that object over there? Where will the waste flow? What’s the fastest route home?

CS 128/ES 228 - Lecture 12b 9

Types of queries Aspatial – make no reference to

spatial data 2-D Spatial – make reference to

spatial data in the plane 3-D Spatial – make reference to

“elevational” data Network – involve analyzing a

network in the GIS (yes, it’s spatial)

CS 128/ES 228 - Lecture 12b 10

3-D Computational Complexity

1984

technology

1997

technology

CS 128/ES 228 - Lecture 12b 11

Approximations In the vector model, each object

represents exactly one feature; it is “linked” to its complete set of attribute data

In the raster model, each cell represents exactly one piece of data; the data is specifically for that cell

THE DATA IS DISCRETE!!!

CS 128/ES 228 - Lecture 12b 12

Surface ApproximationsWith a surface, only a few

points have “true data”

The “values” at other points are only an approximation

The are determined (somehow) by the neighboring points

The surface is CONTINUOUSImage from: http://www.ian-ko.com/resources/triangulated_irregular_network.htm

CS 128/ES 228 - Lecture 12b 13

Types of approximation GLOBAL or LOCAL

Does the approximation function use all points or just “nearby” ones?

EXACT or APPROXIMATE At the points where we do have data, is

the approximation equal to that data?

CS 128/ES 228 - Lecture 12b 14

Types of approximation GRADUAL or ABRUPT

Does the approximation function vary continuously or does it “step” at boundaries?

DETERMINISTIC or STOCHASTIC Is there a randomness component to

the approximation?

CS 128/ES 228 - Lecture 12b 15

Display “by point”

Image from: http://www.csc.noaa.gov/products/nchaz/htm/lidtut.htm

• Notice the (very) large number of data points

•This is not always feasible

•“Draw” the dot

CS 128/ES 228 - Lecture 12b 16

Display “by contour”

Image from: http://www.csc.noaa.gov/products/nchaz/htm/lidtut.htm

• More feasible, but granularity is an issue

• Consider the ocean…

• “Connect” the dots

CS 128/ES 228 - Lecture 12b 17

Display “by surface”

Image from: http://www.csc.noaa.gov/products/nchaz/htm/lidtut.htm

• Involves interpolation of data

• Better picture, but is it more accurate?

• “Paint” the connected dots

CS 128/ES 228 - Lecture 12b 18

Voronoi (Theissen) polygons as a painting tool Points on the

surface are approximated by giving them the value of the nearest data point

Exact, abrupt, deterministic

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Smooth Shading Standard (linear)

interpolation leads to smooth shaded images

Local, exact, gradual, deterministic

X yw

1-

W = *y + (1-)*x

CS 128/ES 228 - Lecture 12b 20

TINs – Triangulated Irregular Networks

Connect “adjacent” data points via lines to form triangles, then interpolate

Local, exact, gradual, possibly stochastic

or

Image from: http://www.ian-ko.com/resources/triangulated_irregular_network.htm

CS 128/ES 228 - Lecture 12b 21

Simple Queries?

The descriptions thus far represent “simple” queries, in the same sense that length, area, etc. did for 2-D.

A more complex query would involve comparing the various data points in some way

CS 128/ES 228 - Lecture 12b 22

Slope and aspect A natural question with elevational

data is to ask how rapidly that data is changing, e.g. “What is the gradient?”

Another natural question is to ask what direction the slope is facing, i.e. “What is the normal?”

slope

aspect

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What is slope? The slope of a curve

(or surface) is represented by a linear approximation to a data set.

Can be solved for using algebra and/or calculus

Image from: http://oregonstate.edu/dept/math/CalculusQuestStudyGuides/vcalc/tangent/tangent.html

CS 128/ES 228 - Lecture 12b 24

Solving for slope In a raster world, we use the

equation for a plane:z = a*x + b*y + c

and we solve for a “best fit”

In a vector world, it is usually computed as the TIN is formed (viz. the way area is pre-computed for polygons)

CS 128/ES 228 - Lecture 12b 25

Our friend calculus Slope is essentially a first derivative

Second derivatives are also useful for…

convexity computations

CS 128/ES 228 - Lecture 12b 26

What is aspect? Aspect is what

mathematicians would call a “normal”

Computed arithmetically from equation of plane

Image from: http://www.friends-of-fpc.org/tutorials/graphics/dlx_ogl/teil12_6.gif

Shows what direction the surface “faces”

CS 128/ES 228 - Lecture 12b 27

Matt Hartloff, ‘2000 Delaunay “Sweep” algorithm uses

Voronoi diagram as first step

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Jackson Hole, WY…then shades result based upon slopes and aspects

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Visibility

What can I see from where? Tough to compute!

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When is an Elevation NOT an Elevation? When it is rainfall, income, or any

other scalar measurement

Bottom Line: It’s one more dimension (any dimension!) on top of the geographic data

CS 128/ES 228 - Lecture 12b 31

Network Analysis Given a network

What is the shortest path from s to t? What is the cheapest route from s to

t? How much “flow” can we get through

the network? What is the shortest route visiting all

points?

Image from: http://www.eli.sdsu.edu/courses/fall96/cs660/notes/NetworkFlow/NetworkFlow.html#RTFToC2

CS 128/ES 228 - Lecture 12b 32

Network complexities

Shortest path Easy

Cheapest path Easy

Network flow Medium

Traveling salesperson

Exact solution is IMPOSSIBLY HARD but can be approximated

All answers learned in CS 232!

CS 128/ES 228 - Lecture 12b 33

Conclusions A GIS without spatial analysis is like a car

without a gas pedal.

It is okay to look at, but you can’t do anything with it.

A GIS without 3-D spatial analysis is like a car without a radio.

It may still be useful, but you wish you had the “luxury”.