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More Raster and Surface Analysis in Spatial Analyst
Lecture 10 ------Using GIS-- Introduction to GIS Lecture 14: More Raster and Surface Analysis in Spatial Analyst Lecture notes by Austin Troy, University of Vermont
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Converting vector to raster
------Using GIS-- Introduction to GIS Converting vector to raster Can convert raster to vector or vice versa. When converting vector to raster, must specify an attribute field upon which raster z values will be based. When just yes/no, must often create a new field. Example: protected areas ©2005 Austin Troy
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Converting vector to raster
------Using GIS-- Introduction to GIS Converting vector to raster Or you may be converting based on a variable, like land use ©2005 Austin Troy
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------Using GIS-- Introduction to GIS Proximity Can use raster distance functions to create zones based on proximity to features; here, each zone is defined by the highway that is closest ©2005 Austin Troy
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Lecture 10 ------Using GIS-- Introduction to GIS Distance Measurement Can create distance grids from any vector feature based on straight line ©2005 Austin Troy
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Lecture 10 ------Using GIS-- Introduction to GIS Distance Measurement Can also weight distance based on friction factors, like slope ©2005 Austin Troy
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Introduction to GIS Density Functions We can also use sample points to map out density raster surfaces. This need to require a z value in each, it can simply be based on the abundance and distribution of points. ©2005 Austin Troy
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Lecture 10 Introduction to GIS Density Functions These settings would give us a raster density surface, based just on the abundance of points within a “kernel” or data frame. In this case, a z value for each point is not necessary. ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics From last lecture: this is a “local” method of summarizing raster data within a neighborhood by a statistical measure, like mean, stdv, min ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics In Arc GIS, neighborhood statistics command allows you to specify statistic: Min, max, mean, standard deviation, range, sum, variety ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Neighborhood statistics creates a new grid layer with the neighborhood values This can be used to: 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 ©2005 Austin Troy
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Introduction to GIS Neighborhood Filters Generating neighborhood means is similar to RS technique called low pass filtering: Low pass filtering: takes “tonally rough” surfaces, with abrupt changes in cell values, and makes those values vary more smoothly. The opposite is called a high-pass filter. High pass filtering: emphasizes detailed, abrupt changes in cell values, deemphasizes areas of gradual change. ©2005 Austin Troy
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Introduction to GIS Low Pass filtering Usually in low-pass filtering, the median is used instead, but the concept is similar. Low-pass filters emphasize overall, general trends at the expense of local variability and detail. It serves to smooth the data and remove statistical “noise” or extreme values that occur in isolation or small patches. While lose feature detail, different from changing resolution; Resolution of cells stays the same. The larger the neighborhood, the more you smooth, but the more processing power it requires. A circular neighborhood has the effect of rounding the edges of features a little more. ©2005 Austin Troy
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Introduction to GIS High Pass filtering One way of obtaining this is by subtracting a low pass filtered layer from the original. This serves to emphasize and highlight areas of tonal roughness, or locations where values change abruptly from cell to cell. The result is to emphasize local detail at the expense of regional, generalized trends. Summarizing a neighborhood by standard deviation is another form of high pass filter. ©2005 Austin Troy
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Why do we care about this?
Introduction to GIS Why do we care about this? Low pass filtering: filtering out anomalies Bathymetry mass points: sunken structures ©2005 Austin Troy
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Why do we care about this?
Introduction to GIS Why do we care about this? After turning into raster grid We see sudden anomaly in grid Say we wanted to “average” that anomaly out ©2005 Austin Troy
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Why do we care about this?
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 ©2005 Austin Troy
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Why do we care about this?
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 ©2005 Austin Troy
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What about high pass filters?
Introduction to GIS What about high pass filters? Say we wanted to isolate where the wreck was All areas of sudden change, including our wrecks, have been isolated ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Example, using a DEM showing elevation ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics A low pass filter of the DEM done by taking the mean values for a 3x3 cell neighborhood: notice it’s hardly different Low pass DEM ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics But if we take the mean for a 10 unit square neighborhood… Notice how much smoother it is; note also how much less detail there is in this low pass filter ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Now, here’s one with a 20 unit square neighborhood ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Here’s one with a 10 unit radius circular neighborhood The only difference from 20 unit square is that edges are more rounded ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Here’s one with a 20 wide x 5 tall unit rectangular neighborhood Note how there is more detail in the vertical axis (features facing left and right) than in the horizontal axis (features facing down and up); so horizontal feature detail is resampled to a lower resolution than vertical feature detail ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Here’s what it looks like the other way: 20 tall x 5 wide Here note better feature definition for features along the horizontal axis, with more detail to features facing down or up ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics In this high-pass filter the mean is subtracted from the original It represents all the local variance that is left over after taking the means for a 3 meter square neighborhood ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics We do this using the map calculator ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics If we do a high-pass filter by subtracting from the original the means of a 20x 20 cell neighborhood, it looks different because more local variance was “thrown away” when taking a mean with a larger neighborhood Dark areas represent things like cliffs and steep canyons ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Using standard deviation is a form of high-pass filter because it is looking at local variation, rather than regional trends. Here we use 3x3 square neighborhood ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Note how similar it looks to a slope map. This is because it is showing standard deviation, or normalized variance, in spot heights, which is similar to a rate of change. Hence it is emphasizing local variability over regional trends. The resolution of the slope is quite high because it is sampling only every nine cells. When we go to a larger neighborhood, by definition, the resulting map is much less detailed because the standard deviation of a large neighborhood changes little from cell to cell, since so many of the same cells are shared in the neighborhood of cell x,y and cell x,y+1. Look at the following as an example. ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Here is the same function with 8x8 cell neighborhood. Here, the coarser resolution due to the larger neighborhood makes it so that slope rates seem to vary more gradually over space ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Here’s what it looks like with a circular 4 unit radius neighborhood You can see that an 8 unit diameter circle gives slightly more detail and fine resolution than an 8 unit square (if you look closely) ©2005 Austin Troy
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Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Later on we’ll look at filters and remote sensing imagery, but here is a brief example of a low-pass filter on an image that has been converted to a grid. This can help in classifying land use types ©2005 Austin Troy
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