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 –Extent –# rows –# columns –Coordinates –Origin –Orientation –Resolution –Grid cell

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

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 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

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) and OR 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 to a vector shapefile or feature class Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query: multi-layer Examples Multi-layer queries are 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 Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples First we would generate a slope map from our Digital Elevation Model by going to Surface>>Derive Slope Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Query Examples Let’s say our criteria are elevation >800, slope >20% and land use category= coniferous forest (42) Using GIS--

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 factors in 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 solves that equation Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Calculation The darker areas are those with both steep slope and erodable soils. This has the advantage over map query in that we now have 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 Map Calculation and Query We could then, for instance, run a map query to find areas that have high erodability factors and urban land use 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 Lecture Materials by Austin Troy except where noted © 2008 Zonal Statistics Using Zonal Statistics we could summarize the mean, max or sum of the soil index for each of those units, even though one is grid and one is polygon. Here we summarize by mean the subdivision zones by the soil erodability calculation layer Using GIS--

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Zonal Statistics This will create a DBF table that summarizes pixel values by mean, median, max, min, etc., of all pixels falling within a given polygon. Each row represents a polygon and each column represents a different summary statistic Using GIS-- Polygon layer with zones Unique ID for polygons This joins the DBF table to the polygon layer Statistic by which your data will be charted

Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Zonal Statistics It gives us a DBF table with values of mean, max, min, std dev, etc. in the table, plus a chart summarizing the means; 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 Lecture Materials by Austin Troy except where noted © 2008 Raster terrain functions in ArcGIS ArcGIS allows you to take a digital elevation model and derive: Hillshade Aspect Slope Contours

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 Raster terrain functions in ArcGIS This is done by making a hillshade using Spatial analyst, putting the hillshade “under” the DEM in the TOC and making the DEM 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. It requires a grid terrain layer and a point layer and produces a visibility grid layer that tells you where the feature can be seen from, or alternately, what areas someone standing at that feature could see (remember, line of sight is two way). If there are 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. 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.

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 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 This is done in ArcGIS, but can also be done in ArcView. 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