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------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 ------Using GIS-Introduction to GIS Distance Analysis • Used to answer questions related to distance – Proximity – Straight Line Distance Measurement – Cost Weighted Distance Measurement – Shortest Path ©2007 Austin Troy ------Using GIS-Introduction to GIS 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 ------Using GIS-Introduction to GIS 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: – – – – 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
