Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Lecture 13: Introduction to Raster Spatial Analysis Using GIS-- By Austin Troy and Weiqi Zhou, University of Vermont

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster data-A Refresher Raster Elements –Grid cell (pixel) –Resolution –Coordinate system –Coordinates –Origin –Extent –# rows –# columns Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster data-A Refresher Raster Elements –Grid cell (pixel) –Resolution –Coordinate system –Coordinates –Origin –Extent –# rows –# columns Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster data-A Refresher Raster Elements –Grid cell (pixel) –Resolution –Coordinate system –Coordinates –Origin –Extent –# rows –# columns Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster data-A Refresher Raster Elements –Grid cell (pixel) –Resolution –Coordinate system –Coordinates –Origin –Extent –# rows –# columns Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster data-A Refresher Raster Elements –Grid cell (pixel) –Resolution –Coordinate system –Coordinates –Origin –Extent –# rows –# columns Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Analysis Tools Operators – Map algebra and Raster Calculator Areal functions (local, focal, zonal, global) Surface generation Topographic analyses (hillshade, slope, aspect, contours) Applying transparency (drape) Density Spatial Interpolation Geometric correction (georeferencing) Other Specialized functions Hydrology, Image Analyst, 3D Analyst Require use of ArcGIS Spatial Analyst extension Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Analysis Overview Practical considerations (formats) Raster overlay queries – Example: [elevation > 2500] AND [slope > 20] Raster overlay calculations – Example: [soil_depth_1990] – [soil_depth_2000] Zonal Statistics Raster terrain functions intro (hillshade, slope, aspect, contours) Viewshed Analysis Neighborhood Statistics (lecture 14) Distance Functions (lecture 14) Spatial Interpolation (lecture 14) Using GIS-- Local Focal

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Image Format Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 ESRI’s Raster Grid Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Integer vs. Floating Point Grids Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 One raster has many GRID files Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 GRID warning Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster File Formats Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Areal Functions 4 general types: 1.Local – single cell locations 2.Focal – locations within a neighborhood of cells 3.Zonal – locations within zones 4.Global – all locations/cells Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Areal Functions Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Overlay Queries The raster data model performs overlay operations more efficiently than the vector model. Raster cells have a one-to- one relationship between layers Using GIS-- Source: ESRI

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Overlay Queries Raster overlay queries involve the combining of two or more separate thematic layers to identify relationships between them such as: –Areas that are common to all layers –Areas that meet criteria from each layer Query example: [elevation > 2500] AND [Slope > 20] Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Overlay Calculations Map algebra can be performed to identify relationships between layers, or to derive indices that describe phenomena Map calculations create a new layer Calculation example: (Soil_depth_1990) – (Soil_depth_2000) = loss in soil between 1990 and Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © Using GIS-- Source: ESRI Map Algebra Warning: NODATA in (anywhere) means NODATA out!

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Calculator Warning Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster Calculator Warning Using GIS-- Slide courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © Using GIS-- Source: ESRI Map Algebra More advanced functions are also available (power, logarithmic, etc.)

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Single layer numeric example: elevation > 2000 ft Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Results in a binary True/False layer

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Multi-criteria, single layer, categorical map query: looking for all developed land use types, using attribute codes (11, 12, 13) with OR operator Using GIS-- Vertical lines mean OR

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Results in a 1/0 binary layer, showing urbanized areas Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples One can then convert this output to feature class or shapefile Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query: multi-layer Examples Multi-layer queries use criteria across two or more layers; in this case we’ll query land use (categorical), elevation (number) and slope (number) Using GIS-- Let’s say we want to identify potential habitat for a rare plant that grows at higher elevations, on steeper slopes and in coniferous forest

Fundamentals of GIS Raster Query: Slope Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

Fundamentals of GIS Raster Query: Multi-layer examples Multiple criteria, multiple layers Land Cover = Coniferous Forest (42) Elevation > 800 Slope > 20% Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Again we end up with a 1/0 binomial query layer Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Calculation We can also run calculations between layers: here we’ll multiply the k factor (soil erodability factor) by slope; let’s just imagine this will yield a more accurate and spatially explicit index of erodability that takes into account slope at each pixel Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Calculation Now we simply type in the equation and a new grid is created that contains the result of that equation Using GIS--

Fundamentals of GIS Map Calculation and Query We could then run a map query to find areas that have high erodibility factors and urban land use. Lecture Materials by Austin Troy except where noted © Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Calculation Darker areas feature both steep slopes and erodible soils. Advantage over map query approach: result is a continuous index of values, rather than just a “true” / “false” dichotomy Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Zonal Statistics Now, say we had a proposed subdivision map (this one is made up). We could overlay it on our new index layer and figure out which proposed subdivisions are problematic Using GIS--

Fundamentals of GIS Zonal Statistics Summarize the mean, max or sum for some value within each of the bounding units Polygon and Raster Raster and Raster Here we summarize by subdivision zones the mean soil erodibility value (from our calculation) Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

Fundamentals of GIS Zonal Statistics Produces a DBF table with the specified summary statistics Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Zonal Statistics Now we can plot out the subdivision boundaries (zones) by a soil erosion statistic. In this case, clearly the most meaningful one is the mean of the soil erosion statistic. This represent the mean value, by polygon, of all the soil erosion pixels underlying that polygon Using GIS--

Fundamentals of GIS Raster Surface Tools Arc GIS allows you to use a digital elevation model (DEM) to derive: Hillshade Slope Contours Aspect Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster terrain functions in ArcGIS DEM + Hillshade = Hillshaded DEM Using GIS-- +=

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Display Options 1.Place the hillshade “under” the DEM in the TOC 2.Make the DEM partially transparent Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Raster terrain functions in ArcGIS Slope: Contours:Aspect: Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis This is a multi-layer function that analyzes visibility based on terrain (elevation). It requires a grid terrain layer and a point layer and produces a visibility grid layer that tells you visibility of every cell from the point feature(s). Graphic courtesy of Leslie Morrissey

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis If there is more than one point feature, then each grid cell tells you how many of the point features can be seen from a given point. However in that case, you lose information about the other direction; You don’t know which features (points) can see a particular grid cell. Iterate for each point, then overlay.

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis Viewshed analysis can use “offsets” to define the height of the viewer or of the object being viewed; designated using a new field in the input layer’s attribute table. offset A offset B

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis Let’s say we’re local planners who are considering putting in a new waste treatment facility in a valley where the vacation homes of five rich and powerful Hollywood executives are located. We want it in a place that won’t ruin anyone’s views, since they comprise 95% of the local tax base. This generates a grid with three values, representing how many houses can see a given pixel

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis Red represents areas that can be seen by 1 house, blue by 2 or more

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis In order to compare the viewability of several facilities, separate viewshed analyses need to be done for each feature. In the next example we will look at three candidate sites for a communications tower. Each will produce a viewability grid. This grid can then be superimposed on a layer showing residential areas. Since each grid will belong to a different tower, we can tell which tower will be most viewable from the residential areas through simple overlay analysis.

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Viewshed analysis In this case, red is for tower 1, blue for 2 and green for 3