Intro. To GIS Midterm Review March 8 th , 2013

Intro. To GIS

MidtermReview

March 8th, 2013

Reminders

• Lab on next Monday

• Try to catch up on homework assignments

Why Georeferencing?

• Georeferencing – The process of converting a map or an

image (or scanned map) from one coordinate system to another by using a set of control points and a transformation equation.

• Two steps– Coordinate transformation (scaling,

rotating, skew)– Resamping

Georeferencing

Coordinate Transformation

• Methods– First-order polynomial (Affine)– 2nd Order polynomial– 3rd order polynomial

– Spline

2nd order

1st order

>>Control points are used to estimate the coefficients (a0,b0,…)<<

Transformation types: Affine

The affine transformation function is:x’ = Ax + By + Cy’ = Dx + Ey + F

• where x and y are coordinates of the input layer and x’ and y’ are the transformed coordinates.

• The C and F parameters control shift in origin (translation)

• A, B, D, E control scale and rotation • their values are determined by comparing the

location of source and destination control points.

•Scales, skews, rotates, and translates •6 unknowns( A,B,C,D,E,F) so a minimum of three “displacement links” required•Little benefit from more than 18-30 links•The most common choice

Example: Transformation

• Let’s do a simple example– We would like to calculate new coordinates for point A(x=1,

y=1), i.e., we want to convert coordinate system (x,y) to (x’,y’).– We assume a 1st order (affine) transformation works fine– All the six coefficients (for affine transformation) are given

(a0=1, a1=1.1, a2=0.4 and b0=0.2,b1=1.8,b2=0.8)

– x’ and y’ are the new coordinates for (x,y) in the new coordinate system

– Continue on next Slide >>>>

1

.5 , 8

Resampling

• Let’s continue on… After the transformation, the question is:– What is the pixel value for .5 , 8 ???? (That’s what we call

resampling)• The new coordinate system is, in fact, a new raster dataset (right),

which is slightly rotated, scaled, skewed, or distorted depending on the order of polynomial.

• We need to estimate pixel values from the original raster data (left/yellow dot), i.e., resampling, for the new dataset (right/green)

coordinate 6865

70 80

Pixel valuex

x’

78 73 78

74

69

y

1

1

2

32

31

2

3

12 3

y’

e.g., Average of 80 and 68 would be the pixel’s new value

A bit of clarification on Optical RS

The end result is surface reflectance/temperature or a thematic map (classified map)

Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)

Midterm Overview

• Based primarily on lecture and homework/book• Good knowledge of lab exercises helps! • Closed notes, closed book, no computers• Basic calculators • Question types will include:– Multiple choice– True-False– Short answer – Few long answer

Vector Data and Topology

• Topology– The arrangement for how point, line, and polygon

features share geometry– Or knowledge about relative spatial positioning

• Two types of vector models exist in a GIS– Geo-relational Vector Model

• Arc Coverage (has topology) >>> format: binay• Shape files (no topology) >>>> format: *.shp,

*.shx, *dbf, etc.– Object-based Vector Model

• Includes classes and geodatabases >>> format: *.mdb

Topology

• Concepts – Adjacency– Enclosure– Connectivity

• Terms to be defined– Node– Arc– Polygon

Query

• A query is a “question” posed to a database (attribute data)

• Examples:– Mouse click on a map symbol (e.g. road) may mean

• What is the name of road pointed to by mouse cursor ?

– Typing a keyword in a search engine (e.g. google, yahoo) means• Which documents on web contain given keywords?

– SELECT ‘FROM Senator S’ WHERE S.gender = ‘F’ means• Which senators are female?

Organizing Attribute Data

• Flat Files – Spreadsheets (e.g. excel spreadsheet)

• Hierarchical


• Relational (What is commonly used in GIS)– Various tables (databases) are “linked”

through unique identifiers


Query: Making Selections

• Usually interested in some subset of the data

• Selections can be made in two primary ways:– Select by Attribute – specify matching

criteria– Select by Location – based on spatial

proximity

Select by Attribute Tips

• Be careful with case sensitivity and spaces• Use parentheses to carefully construct a query• Use “Boolean” Operators (AND, OR, NOT, LIKE)– AND means both criteria, OR means either– NOT allows you to exclude some criteria– LIKE lets you be more flexible, use wildcard

characters (_ for one character, % for many)– Verify your expression to make sure it works

Selection Criteria (#8.8)

Country Population(millions)

Energy Use(barrels of oil

per capita)

Infant Mortality(per 1000)

Life Expectancy(per 1000)

Car Theft(%)

Australia 19.9 5,668 4 79.2 2.2

England 59.3 5,945 5 77.5 2.6

Finland 5.2 6,456 4 78.0 0.5

France 59.7 4,350 4 79.2 1.8

Japan 127.2 4,071 3 81.6 0.1

Netherlands

16.2 5,993 5 78.3 0.5

Norway 4.6 6,019 4 78.9 1.5

South Africa

45.3 3,703 52 46.5 2.4

Spain 41.1 2,945 5 78.3 0.5

United States

291.0 8,066 7 77.3 0.5

Per capita energy use > 4,000 AND population < 20,000,000




per capita)



Car Theft(%)

Australia 19.9 5,668 4 79.2 2.2

England 59.3 5,945 5 77.5 2.6

Finland 5.2 6,456 4 78.0 0.5

France 59.7 4,350 4 79.2 1.8

Japan 127.2 4,071 3 81.6 0.1

Netherlands

16.2 5,993 5 78.3 0.5

Norway 4.6 6,019 4 78.9 1.5

South Africa

45.3 3,703 52 46.5 2.4

Spain 41.1 2,945 5 78.3 0.5

United States

291.0 8,066 7 77.3 0.5

[Per capita energy use < 4,000 OR (population > 40,000,000)] AND (car theft <1)




per capita)



Car Theft(%)

Australia 19.9 5,668 4 79.2 2.2

England 59.3 5,945 5 77.5 2.6

Finland 5.2 6,456 4 78.0 0.5

France 59.7 4,350 4 79.2 1.8

Japan 127.2 4,071 3 81.6 0.1

Netherlands

16.2 5,993 5 78.3 0.5

Norway 4.6 6,019 4 78.9 1.5

South Africa

45.3 3,703 52 46.5 2.4

Spain 41.1 2,945 5 78.3 0.5

United States

291.0 8,066 7 77.3 0.5

Population < 20,000,000 OR car theft > 1.5

GOOD LUCK ON YOUR

MIDTERM!

Intro. To GIS Midterm Review March 8 th , 2013

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