final review final will cover all lectures, book, and class assignments. new lectures since last...
TRANSCRIPT
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.
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.
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
forest fields urban water Total
forest 80 4 0 15 7 106
fields 2 17 0 9 2 30
urban 12 5 9 4 8 38
water 7 8 0 65 0 80
Wetlands 3 2 1 6 38 50
Total 104 36 10 99 55 304
Classification
Reference
The Nominal Data Case• 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)
wetlands
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 tabletall coordinate data from this map is systematically offset
(biased)• Example: the wrong datum is being used
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.
Lecture 21
• Spatial analysis: analysis is considered spatial if the results depend on the locations of the objects being analyzed.
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?
Queries
• Queries – Attribute 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
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
•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
Variable Distance Buffering
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 minutesAreas reachable in 10 minutesOther areas
•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:
Habitat Area(km2) Frequency Density . A 150 4 0.027 nests/km2
B 320 6 0.019 nests/km2
C 350 3 0.009 nests/km2
D 180 3 0.017 nests/km2
Bird’s NestsA B
DC
Habitat TypesA B
DC
Analysis Results
Point Frequency/Density 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
A BExample: Assign land use attributes (polygons) to streams (lines):
Line in Polygon Analysis
David Tenenbaum – GEOG 070 – UNC-CH Spring 2005
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.
0 1 1
0 0 1
1 0 1
0 0 0
1 1 1
0 0 1
AND =
Boolean Operations with Raster Layers
0 1 1
0 0 1
1 0 1
0 0 0
1 1 1
0 0 1
OR =
•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:
•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:
101
100
110
100
111
000
+ =201
211
110
Summation
101
100
110
100
111
000
=100
100
000
Multiplication
101
100
110
100
111
000
+ =301
322
110
100
111
000
+
Summation of more than two layers
Simple Arithmetic Operations
Near the mall Near friend’s houseNear work Good place to live?
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
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.
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.
A 3x3 neighborhood
Neighborhood Operations
An input layer
Cell ofInterest
• Neighborhoods of any size can be used• 3x3 neighborhoods work for all but outer edge cells
Neighborhood operations are called convolution operations.
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
Neighborhood Operation: Majority Filter
24221
13383
13322
72325
33322
24221
13383
13322
72325
33322
24221
13883
13322
72325
33322
InputLayer
ResultLayer
2 3 3
•The majority value (the value that appears most often, also called a mode filter):
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.
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
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
Lecture 25
• Understand the concept of supervised classification
• Understand the concept of unsupervised classification