Lecture by Austin Troy © 2005 Lecture 13: Introduction to Raster Spatial Analysis ------Using GIS-- Introduction to GIS Lecture Notes by Austin Troy, University of Vermont
Lecture by Austin Troy © 2005
Lecture 13:Introduction to Raster Spatial
Analysis
------Using GIS--Introduction to GIS
Lecture Notes by Austin Troy, University of Vermont
Lecture by Austin Troy © 2005
Raster data-A RefresherGrid Elements
Extent
# rows
# columns
Coordinates
Origin
Orientation
Resolution
Grid cell
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
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--Introduction to GIS
Lecture by Austin Troy © 2005
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 2000
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query ExamplesSingle layer numeric example: elevation > 2000 ft
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query ExamplesMulti-criteria, single layer, categorical map query: looking for all developed land use types, using attribute codes (11, 12, 13) and OR
------Using GIS--Introduction to GIS
Vertical lines mean OR
Lecture by Austin Troy © 2005
Map Query ExamplesResults in a 1/0 binary layer, showing urbanized areas
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query ExamplesOne can then convert this to a vector shapefile or feature class
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query: 2 layer ExamplesMulti-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--Introduction to GIS
Let’s say we want to find identify potential habitat for a rare plant that grows at higher elevation, on steeper slopes and in coniferous forest
Lecture by Austin Troy © 2005
Map Query ExamplesFirst we would generate a slope map from out Digital Elevation Model by going to Surface>>Derive Slope
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query ExamplesLet’s say our criteria are elevation >800, slope >20% and land use category= coniferous forest (42)
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map Query ExamplesAgain we end up with a 1/0 binomial query layer
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map CalculationWe 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--Introduction to GIS
Lecture by Austin Troy © 2005
Map CalculationNow we simply type in the equation and a new grid is created that solves that equation
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Map CalculationThe 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--Introduction to GIS
Lecture by Austin Troy © 2005
Map Calculation and QueryWe could then, for instance, run a map query to find areas that have high erodability factors and urban land use.
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Zonal StatisticsNow, 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--Introduction to GIS
Lecture by Austin Troy © 2005
Zonal StatisticsUsing 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--Introduction to GIS
Lecture by Austin Troy © 2005
Summary by ZoneThis will create a DBF table that summarizes the pixel values by mean, median, max, min, etc., of all the pixels falling within a given polygon. Each row represent a polygon and each column represents a different summary statistic
------Using GIS--Introduction to 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
Lecture by Austin Troy © 2005
Summary by ZoneIt gives us a DBF table with values of mean, max, min, stdv, etc. in the table, plus a chart summarizing the means;
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Summary by ZoneNow 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 underlaying that polygon
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Raster terrain functions in Arc GISArc GIS allows you to take a digital elevation model and derive:
•Hillshade
•Aspect
•Slope
•Contours
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Raster terrain functions in AVDEM + Hillshade = Hillshaded DEM
------Using GIS--Introduction to GIS
+ =
Lecture by Austin Troy © 2005
Raster terrain functions in AVThis 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--Introduction to GIS
Lecture by Austin Troy © 2005
Raster terrain functions in Arc GISSlope: Contours: Aspect:
------Using GIS--Introduction to GIS
Lecture by Austin Troy © 2005
Viewshed analysisThis 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 can see a particular grid cell.
Introduction to GIS
Lecture by Austin Troy © 2005
Viewshed analysisLet’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.
So we geocode the house locations, overlay them on a high-resolution digital elevation model and run a viewshed analysis
The lower the resolution, the more likely we’ll be wrong
This generates a grid with three values, representing how many houses can see a given pixel
Introduction to GIS
Lecture by Austin Troy © 2005
Viewshed analysisThis is done in ArcGIS 8, but can also be done in ArcView.
Red represents areas that can be seen by 1 house, blue by 2 or more
Introduction to GIS
Lecture by Austin Troy © 2005
Viewshed analysisIn 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.
Introduction to GIS