1. Final Review
Final will cover all lectures, book, and class assignments.
New lectures since last test are 18 – 26, summarized here. Over half the test will come from this last portion of the course.

2. Lecture 18 Review the following satellite products: Landsat MSS
Landsat TM
SPOT
IKONOS
QUICKBIRD
Terra
MODIS
GOES
For each: know basic applications, spatial resolution, approximate temporal resolution.

3. Lecture 19
How does Differential Correction aid in GPS accuracy?

4. Lecture 20
Error:
Difference between the real world and the geographic data representation of it.
Location errors
Attribute errors
Accuracy: (another way of describing error)
Extent to which map data values match true values

5. The Nominal Data Case Reference Classification
An example is when you determine the accuracy of a landcover classification.
We can build something called a confusion matrix:
This compares your classification with your ground-truth sample (the very accurate sample data, as mentioned)
Reference
forest
fields
urban
water
wetlands
Total
forest 80 4 15 7
106
fields 2 17 9 2 30
Classification
urban 12 5 9 4 8 38
water 7 8 65 80
Wetlands 3 2 1 6 38 50
Total
104 36 10 99 55
304

6. Bias Error is unbiased when the error is in ‘random’ directions.
GPS data
Human error in surveying points
Error is biased when there is systematic variation in accuracy within a geographic data set
Example: GIS tech mistypes coordinate values when entering control points to register map to digitizing tablet
all coordinate data from this map is systematically offset (biased)
Example: the wrong datum is being used

7. Fuzzy Approaches to Uncertainty
Consider a landcover classification with these classes:
Forest
Field
Urban
water
We don’t assign a single class to each landcover pixel.
Instead, we create a probability of membership to each class.
We create 4 layers:
Layer 1:
The attribute data for each pixel is the probability that pixel is in forest.
Layer 2:
The attribute data for each pixel is the probability that pixel is a field.
Layer 3:
The attribute data for each pixel is the probability that pixel is urban.
Layer 4:
The attribute data for each pixel is the probability that pixel is water.

8. Lecture 21
Spatial analysis: analysis is considered spatial if the results depend on the locations of the objects being analyzed.

9. Topology Most spatial analyses are based on topological questions:
How near is Feature A to Feature B
What features contain other features?
What features are adjacent to other features?
What features are connected to other features?

10. Queries Queries Attribute based Location based
Example: show me all pixels in a raster image with BV > 80.
Location based
Find all block groups in Orange County with an average of > 1 child per household

11. Measurement of Length Types of length measurements
Euclidean distance: straight-line distance between two points on a flat plane (as the crow flies)
Manhattan Distance limits movement to orthogonal directions
Great Circle distance: the shortest distance between two points on the globe
Network Distance:
Along roads
Along pipe network
Along electric grid
Along phone grid
By river channels

12. Variable Distance Buffering
The buffer zone constructed around each feature can be based on a variable distance according to some feature attribute(s)
Suppose we have a point pollution source, such as a power plant. We want to zone residential areas some distance away from each plant, based on the amount of pollution that power plant produces
For smaller power plants, the distance might be shorter.
For larger power plants that generate a lot of pollutant, we choose longer distances

13. Raster Buffering
Buffering operations also can be performed using the raster data model
In the raster model, we can perform a simple distance buffer, or in this case, a distance buffered according to values in a friction layer (e.g. travel time for a bear through different landcover):
lake
Areas reachable in 5 minutes
Areas reachable in 10 minutes
Other areas

14. Point Frequency/Density Analysis
We can use point in polygon results to calculate frequencies or densities of points per area
For example, given a point layer of bird’s nests and polygon layer of habitats, we can calculate densities:
Bird’s Nests A B D C
Habitat Types A B D C
Analysis Results
Habitat Area(km2) Frequency Density
A nests/km2
B nests/km2
C nests/km2
D nests/km2

15. Line in Polygon Analysis
Overlay line layer (A) with polygon layer (B)
In which B polygons are A lines located?
» Assign polygon attributes from B to lines in A
Example: Assign land use attributes (polygons) to streams (lines): A B
David Tenenbaum – GEOG 070 – UNC-CH Spring 2005

16. Lecture 22
Questions from this section are likely to be ‘problems’ – I may show you a small raster image (with numbers in each cell), and have you calculate the intersection/‘and’ or the union/‘or’ image.

17. Boolean Operations with Raster Layers
The AND operation requires that the value of cells in both input layers be equal to 1 for the output to have a value of 1: 1 1
AND =
The OR operation requires that the value of a cells in either input layer be equal to 1 for the output to have a value of 1: 1 1 OR =

18. Simple Arithmetic Operations1 + = 2
Summation 1  =
Multiplication 1 + = 3 2
Summation of more than two layers
Near the mall
Near work
Near friend’s house
Good place to live?

19. Spatial Interpolation
You have point data (temp or air pollution levels).
You want the values across your full study site.
Spatial interpolation estimates values in areas with no data.
creates a contour map by drawing isolines between the data points, or
creates a raster digital elevation model which has a value for every cell

20. Spatial Interpolation: Inverse Distance Weighting (IDW)
One method of interpolation is inverse distance weighting:
The unknown value at a point is estimated by taking a weighted average of known values
Those known points closer to the unknown point have higher weights.
Those known points farther from the unknown point have lower weights.

21. Lecture 23

22. Neighborhood Operations
In neighborhood operations, we look at a neighborhood of cells around the cell of interest to arrive at a new value.
We create a new raster layer with these new values.
An input layer
A 3x3 neighborhood
Cell of
Interest
Neighborhoods of any size can be used
3x3 neighborhoods work for all but outer edge cells
Neighborhood operations are called convolution operations.

23. Neighborhood Operations
The neighborhood is often called:
A window
A filter
A kernel
They can be applied to:
raw data (BV’s)
classified data (nominal landcover classes)
A 3x3 neighborhood

24. Neighborhood Operation: Majority Filter
The majority value (the value that appears most often, also called a mode filter): 2 4 1 3 8 7 5
Input
Layer
Result

25. Edge Enhancement Edge enhancement filters sharpen images. Normal Image
Sharpening Filter
Edge enhancement filters sharpen images.

26. The Centroid
The centroid is the spatial mean. The ‘average’ location of all points.
The centroid can also be thought of as the balance point of a set of points.

27. USA Population Centroid
Population centroid change over time in USA

28. Lecture 24

29. Landcover Pattern Metrics
Landcover pattern metrics describe the pattern of landcover in a landscape.
Landcover fragmentation
Average patch size
Distance between patches of the same landcover
Patch shape
Long and thin vs. round or square
Jagged edges vs. clean edges

30. Location-Allocation Problems
This class of problems in known as location-allocation problems, and solving them usually involves choosing locations for services, and allocating demand to them to achieve specified goals
Those goals might include:
minimizing total distance traveled
minimizing the largest distance traveled by any customer
maximizing profits
minimizing a combination of travel distance and facility operating cost

31. Lecture 25 Understand the concept of supervised classification
Understand the concept of unsupervised classification

32. Population Environment
Be prepared to comment on population-environment studies that I give you on the test.