Transcript Introduction to GIS - University of Vermont

------Using GIS-Introduction to GIS
Lecture 14:
More Raster and Surface Analysis in
Spatial Analyst
By Weiqi Zhou, University of Vermont
Thanks are due to Prof. Troy, upon whose lecture much of this material is based.
------Using GIS-Introduction to GIS
Converting vector to raster
©2007 Austin Troy
------Using GIS-Introduction to GIS
Converting vector to raster
©2007 Austin Troy
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Distance Analysis
• Used to answer questions related to distance
– Proximity
– Straight Line Distance Measurement
– Cost Weighted Distance Measurement
– Shortest Path
©2007 Austin Troy
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Proximity
• Create zones based on proximity to features.
©2007 Austin Troy
------Using GIS-Introduction to GIS
Distance Measurement
• Calculate distance from each cell in the raster to the
closest source (feature)
©2007 Austin Troy
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Cost Weighted Distance Measurement
• Specify a cost raster to calculate cost weighted distance
©2007 Austin Troy
Introduction to GIS
Density Functions
©2007 Austin Troy
Introduction to GIS
Density Functions
• A raster density surface, based just on the abundance of points
within a “kernel” or data frame.
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
• A “local” method of summarizing raster data within a
neighborhood by a statistical measure, like mean, stdv.
– Statistic types
– Neighborhood shape
– Neighborhood settings
• Window size
• Units
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
• Statistic type: Mean
• 3x3 cell squared neighborhood.
Neighborhood
Processing cell
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
• Neighborhood statistics creates a new grid
layer with the neighborhood values
• This can be used to:
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Simplify or “filter down” the features represented
Emphasize areas of sudden change in values
Look at rates of change
Look at these at different spatial scales
©2007 Austin Troy
Introduction to GIS
Neighborhood Filters
• Improve the quality of raster grids by
eliminating spurious data or enhancing
features.
• Filter types
– Low pass filters
– High pass filters
©2007 Austin Troy
Introduction to GIS
Low Pass filtering
• Functionality: averaging filter
– Emphasize overall, general trends at the expense of local
variability and detail.
– Smooth the data and remove statistical “noise” or extreme
values.
• Summarizing a neighborhood by mean
– The larger the neighborhood, the more you smooth, but the
more processing power it requires.
– A circular neighborhood: rounding the edges of features.
– Resolution of cells stays the same.
– Using median instead of mean, but the concept is similar.
©2007 Austin Troy
Introduction to GIS
High Pass Filter
• Functionality: edge enhancement filter
– Emphasize and highlight areas of tonal roughness, or
locations where values change abruptly from cell to cell.
– Emphasize local detail at the expense of regional,
generalized trends.
• Perform a high pass filter
– Subtracting a low pass filtered layer from the original.
– Summarizing a neighborhood by standard deviation
– Using weighted kernel neighborhood
©2007 Austin Troy
Introduction to GIS
Why do we care about this?
• Low pass filtering: filtering out anomalies
Bathymetry mass points:
sunken structures
©2007 Austin Troy
Introduction to GIS
Why do we care about this?
• Low pass filtering: filtering out anomalies
We see sudden
anomaly in grid
Say we wanted to “average”
that anomaly out
©2007 Austin Troy
Introduction to GIS
Why do we care about this?
• Try a low-pass filter of 5 cells
We can still see those anomalies but
they look more “natural” now
©2007 Austin Troy
Introduction to GIS
Why do we care about this?
• Try a low-pass filter of 25 cells
The anomalies have been “smoothed
out” but at a cost
©2007 Austin Troy
Introduction to GIS
What about high pass filters?
• Find the wrecks
All areas of sudden change, including
our wrecks, have been isolated
©2007 Austin Troy
Introduction to GIS
Applying a high pass filter
• Subtracting the mean grid from the original one.
Applied a low pass filter:
Summarizing the mean with
a 20x 20 cell neighborhood
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
We do this using the map calculator
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
Using standard deviation is a form of high-pass filter.
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
• Here is the same function with 8x8 cell neighborhood.
©2007 Austin Troy
Introduction to GIS
Neighborhood Statistics
Applying filters on remote sensing imagery.
©2007 Austin Troy