intro. to gis midterm review march 8 th, 2013. reminders lab on next monday try to catch up on...
TRANSCRIPT
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Intro. To GIS
MidtermReview
March 8th, 2013
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Reminders
• Lab on next Monday
• Try to catch up on homework assignments
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Why Georeferencing?
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• 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
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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,…)<<
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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
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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
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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
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A bit of clarification on Optical RS
The end result is surface reflectance/temperature or a thematic map (classified map)
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Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)
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Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)
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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
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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
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Topology
• Concepts – Adjacency– Enclosure– Connectivity
• Terms to be defined– Node– Arc– Polygon
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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?
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Organizing Attribute Data
• Flat Files – Spreadsheets (e.g. excel spreadsheet)
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• Hierarchical
Organizing Attribute Data
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• Relational (What is commonly used in GIS)– Various tables (databases) are “linked”
through unique identifiers
Organizing Attribute Data
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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
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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
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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
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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 OR (population > 40,000,000)] AND (car theft <1)
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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
Population < 20,000,000 OR car theft > 1.5
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GOOD LUCK ON YOUR
MIDTERM!