1. Raster Data Analysis

2. Raster Data The other GIS data …
Spatial data analysis using a raster data format is one of the fastest growing areas of GIS.
Undervalued due to its aesthetics

3. GIS data models
Raster model
Vector model

4. Georeferenced to earth’s surface
Mastering ArcGIS
Chapter 1
The raster data model
X, Y location
X, Y location
Columns
Rows
Raster data file
Georeferenced to earth’s surface

5. Discrete data: values only at points, lines or within polygons
Continuous data: implied values everywhere – may be from discrete data

6. Raster GIS Raster applications Images data: raster required
Colour values
Continuous data: raster required
Elevation: DEMs (or TINs)
Discrete data: vector
- can be converted to raster

7. Types of raster data Continuous raster: DEM Discrete raster: land use
Mastering ArcGIS
Chapter 1
Types of raster data
Continuous raster: DEM
Discrete raster: land use
Continuous raster: image
Discrete raster: roads

8. Characteristics of Raster Data

10. A different raster convention

11. ESRI raster processing Arc/Info (pre-2000): GRID ArcGIS: Spatial Analyst Default format: ESRI GRID Contents of DEM folder (below) + Info files Spatial Analyst pdf:

12. ESRI GRID formats

13. Other raster formats (easier for Transfer and data exchange)
X, Y

14. Converting points to raster
Rasterised point layers are not ‘compact’

15. Lines to raster
Lines are contiguous pixels

16. Polygons to raster Similar areas have adjacent pixels
Attribute table shows number of pixels in each value, graphed in a histogram

17. Vector to Raster conversion
Similar to scanning – specify cell size and attribute used

18. Advantages
A simple data structure
Overlay operations are straight forward
High spatial variability is efficiently represented (e.g. relief).
Only raster can easily store image data (e.g. photos)
Disadvantages
Data structure is not compact
Limited in attribute management: each pixel has one data value
output can appear 'blocky'
Raster formats: (file + header files)
tif, gif, jpg, bmp (Graphics formats)
img (Imagine image)
grd (ESRI GRID) – more complex file

19. GIS overlay analysis
Raster: analysis, modelling Vector: inventory, output

20. GIS overlay analysis

21. (for a ‘stack’ of grids)
Algebra examples
(for a ‘stack’ of grids)
Add, subtract, divide
Maximum, minimum
Mean, standard deviation
scale

22. TINs versus raster DEMs
TIN: elevations at triangle vertices of uniform facets
DEM: elevation values, one for each grid square

23. Layers created from DEM Hill shading Slope Aspect Curvature Viewshed
DEMs: analysis per pixel
Layers created from DEM
Hill shading
Slope
Aspect
Curvature
Viewshed
Water flow
Watershed

24. Slope Models Hydrology Route Planning Slope Stability
Avalanche Prediction
Habitat Use
Fire Behavior
many more

25. Slope Models Calculate rate of change in X and Y Slope in Degrees is:
[dz/dx] = ((c + 2f + i) - (a + 2d + g) / (8 * x_cell_size)
[dz/dy] = ((g + 2h + i) - (a + 2b + c)) / (8 * y_cell_size)
Slope in Degrees is:
slope_degrees = ATAN ( √ ( [dz/dx]2 + [dz/dy]2 ) ) *
This is the calculation performed in ArcGIS
there are other ways of calculating slope from a grid

26. Hillshade Models: solar radiation
Arcgis solar radiation

27. Watershed Analysis
Assigns each area of a dataset to the point to which it will flow, creating watersheds
BC and Canada watersheds available to download and in Lab

28. Viewshed analysis: line of sight Other applications: forest lookouts, viewpoints, cutblocks ?

29. Raster Analysis
Make sure students review!

30. 3 types of Raster operations
Local: Operations performed on a cell by cell basis
Neighborhood: Operations performed using a moving group of cells
Zonal: Operations performed using zones (groups of cells having the same value)

31. Local operations - overview
Cell by cell operations
Computes output cell values as a function of the input cell values
Can be done using single or multiple rasters
“No data" cells not included in calculations
Common uses: reclassification and overlays

32. Local Operations – Reclassification(single raster)
One to one
change – input raster cell value is replaced with new
value in the output raster (integer rasters only)

33. Local Operations – Reclassification(single raster)
Range of values – a new value is given to a range of values
in the input raster (integer and floating point rasters)

34. Reclassification Applications
Simplification
(creating groups for analysis)
Replace
values based on new information
Create
common scales for ranking data
values (ex: creating suitability classes)

35. Local Operations – Multiple Rasters
Operation: add raster 1 and raster 2 cell values to produce an output
raster with the summed cell values

36. Local Operations – Multiple Rasters
Examples of operations that can be done using multiple rasters:
Mathematical functions
Summary statistics
Combine operation (Combines rasters by
assigning a unique output value to each unique combination of input values).
Applications: change detection studies; predicting habitats favorable for wildlife species

37. Neighborhood (focal) Operations
l Uses values for the cells within the neighborhood to
calculate the value for the focal cell
l Focal cell moves from cell to cell
l Applies to single rasters
l Can produce summary statistics
l “No data” cells not included in analysis
l Common shapes used for neighborhood analysis:

38. Neighborhood Operations
Operation: Summation (including value of focal cell)
Neighborhood size: 3 x 3 rectangle; red circle = focal cell
Gray square = no data for that cell’s value

40. Neighborhood Operations
Operation: Summation (including value of focal cell)
Neighborhood size: 3 x 3 rectangle; red circle = focal cell
Gray squares = no data for that cell’s value

41. Neighborhood Operations -Common Applications
Data simplification
Terrain analysis
Image processing
Site selection

42. Zonal Operations
Involves groups of cells with the same values or similar features (zones)
l Cells do not need to be contiguous to be in a zone
l Can be used with a single raster or with two rasters

43. Zonal Operations Single raster zonal operations
– Measures the geometry of each zone
(area, perimeter, centroid, thickness, etc.)
l Two raster zonal operations
Involves
an input raster and a zonal raster
to produce a new raster that summarizes cell
values in the input raster by zone

44. Zonal Thickness – Single Raster Example
Answers the question:
”How far you can run into a forest at its deepest point before you are running out of it?”

45. Zonal Operation – Two Raster Example
Example application
Zone layer – soil type
Value layer – vegetation type
Output table – Number of
vegetation types associated with
each soil type

46. Zonal Operations Applications
Landscape ecology analyses
* Comparisons of data sets using
descriptive statistics

47. Managing Raster data
Typical management of raster data would occur with the folder structure
Talked about managing vector data in a geodatabase, raster can be managed in the same way

48. Things to consider when managing raster data
Is your data continuous?
Does it matter?
What is the scale of your data.
Resolution/Grid size
How will the product be displayed.
Mixture of raster vector (slopes)