1. URBDP 422 Urban and Regional Geo-Spatial Analysis
Lecture 8: Raster Spatial Analysis
Lab Exercise 8: Raster Overlay
February 4, 2014

2. Raster Spatial Analysis
Spatial Analyst and ArcGRID are powerful tools for analyzing and understand spatial relationships in your data.
With ArcGIS raster analysis, both spatial analyst and ArcGRID tools you can map the distribution of data such as population density, elevation, and distance.
You can overlay multiple themes and analyze the relationships between them.
You can build complex spatial models using map algebra.

3. Raster Information Coordinates of upper and lower corners Pixel size
Georeferencing system
File information

4. Raster Data

5. Raster Analysis Data Query Modeling Raster Grids Kernel and Filtering
Resampling Raster Grids
resample to produce lower resolution
resample to produce higher resolution
Window or Subset Data
extract a portion of an area in rectangular or other boundary area

6. Database Query Reclassify change the values of the intervals
Overlay two or more layers
add (C=A+B), subtract (C=A-B)
multiply (C= A*B), divide (C=A/B)
Find a subset by codes on one or more layers
Geographic calculations
area, distance, buffer, special allocation based on different algorithms

7. Spatial Queries Neighborhood functions
Create an area (neighborhood) to search
Search for a particular value in this region
Summarize statistics for what is found
e.g.: How much wetland vegetation is in the neighborhood of a major new development?

8. Displaying a histogram of cell values
A histogram is an alternative way of displaying the
data found in a grid theme. It allows you to compare
the number of cells with different values in your grid
theme as a chart.
If your grid theme represents a continuous variable,
you can also use the histogram to check if your
values are normally distributed or if they have some
sort of skew.
Which cells do you want to chart in the histogram?
All cells in a grid theme
Cells that intersect a specified shape
Cells that intersect the features in another theme

9. Spatial Queries Search Identify a source to search from
Set of cells (a land use, road, point, etc.)
Specify the distance you wish to search
Find all of the cells in that region
Summarize findings, e.g., summary of land uses in the vicinity of a new road

10. Spatial Analyst functions
Find Distance
Assign Proximity
Calculate Density
Interpolate Surface
Derive Slope, Aspect
Create Contour
Cell Statistics
Summarize Zones
Tabulate Areas
Map Query, Calculator
Neighborhood Statistics
Reclassify

11. Grid Theme A grid theme represents a geographic layer where
space is partitioned into square cells in a view.
Each cell stores a numeric data value that conveys
information about the geographic layer it
represents.
A grid theme merely points to the geographic
data it represents; it does not contain the data
itself.

12. Raster Data Integration
multiple grid themes share the same X, Y coordinate space
cell values are calculated across multiple grid themes
a single output grid theme is the result of the multiple grids

13. Map calculator Map calculator allows you to perform mathematical
operations across several grid layers.
Map calculator allows you to apply various set
of operators:
- Arithmetic
- Relational
- Boolean
- Logarithmic .

14. A simple example 1 4 3 2 5 4 7 6 3 Input 1 2 4 2 1 2 3 6 + 6 3 3 4 2 1= 7 7 6 6 7 7 13 5
Output 6 10 8 5 2 5 5 10

15. Mathematical operators in the Map Calculator
Arithmetic Operators - The Arithmetic operators (*, /, -, +)
allow for the addition, subtraction, multiplication, and
division of two grid themes.
Boolean Operators - The Boolean operators (And, Not, Or,
and Xor) use Boolean logic (TRUE or FALSE) on the input
values. Output values of TRUE are written as 1 and FALSE
as 0.
Relational Operators - The Relational operators (<, <=, <>,
=, >, and >=) evaluate specific relational conditions. If the
condition is TRUE, the output is assigned 1; if the condition
is FALSE, the output is assigned 0.

16. output_data_set = function (input_data_set(s) {,arguments})
Map algebra
grid themes may be used in arithmetic expressions
output_data_set = input_grid1 operator input_grid
grid themes may be used in algebraic functions
output_data_set = function (input_data_set(s) {,arguments})

17. Modelling in ArcGRID
Four basic categories of functions in map algebra:
local
focal
zonal
global
Operate on user specified input grid(s) to produce an output grid, the cell values in which are a function of a value or values in the input grid(s)

18. Summary of Raster Functions
Local Functions
Process cells on a cell-by-cell basis.
Global Functions
Process data at each pixel on the output grid using a
source GRID.
Focal Functions
Process data for each pixel using the attribute values
of the neighboring cells of that pixel.
Zonal Functions
Process cells on the basis of zones.

19. Local functions 5 4 7
input 25 49 16
output = sqr(input)

20. Focal functions 5 4 7
input 11 16
output = focalsum(input)

21. Focal functions 5 4 7
input 11 16
output = focalsum(input)

22. Zonal functions 5 4 7 input Zone 2 zone Zone 1 9 7 7 7 9 7 7 7 9 9 9 7
output = zonalsum(zone, input) 9 9 9 7

23. Global functions 5 4 7 input 6 7 8 9 5 6 7 8 4 5 6 7
output = trend(input) 4 5 6 6

24. Neighbourhood filters
Type of focal function
used for processing of remotely sensed images
change value of target cell based on values of a set of neighbouring pixels within the filter
size, shape and characteristics of filter
filtering of raster data
filtering using established classes
filtering based on values of other pixels within specified filter and using certain rules (diversity, frequency, average, minimum, maximum, etc.)

25. Neighborhood statistics
Neighborhood statistics are performed through focal functions.
The neighborhood is defined as the group of cells for which statistics will be calculated.
The neighborhood (a.k.a. kernel or focus) can be shaped as a circle, rectangle, ring, or wedge. We can calculate the following statistics
Minimum
Maximum
Mean
Median
Sum
Range
Standard Deviation
Majority
Minority
Variety

26. Filtering
The neighborhood function calculates a single statistic for that neighborhood and places it in the output grid in the center cell of the neighborhood. The process is performed for every input cell location in the analysis window.
Neighborhood statistics is used for filtering a data set:
A "low pass" filter is a 3 by 3 cell focal mean performed for an entire grid to smooth out anomalies and peaks in surfaces.
A "high pass" filter is a 3 by 3 focal function that performs a focal sum of the kernel cells. The neighborhhod function uses a set of coefficients to apply to the high pass. The objective is to sharpen edges by multiplying the cells by a set of coefficients. ArcView's default high-pass coefficients are the following:

27. Lecture Example1
Problem Objective: finding areas in King County with a high degree of suitable habitat for a rare plant species
Using Science and Spatial Data to ID areas:
Condition 1. Only grows in large forest patches.
Condition 2. Grows on east facing slopes
Condition 4. Grows on slopes between 15 and 60 degrees
Condition 3. Low population density
1. modified ‘Story Problem’ from QGIS material

28. Large patches – dealing with issues
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Condition 1.
What does large mean?
Required data. Forest lands (from land cover)
Spatial Model.
Land cover -> reclassify to get forest patches -> calculate area of forest patches -> reclassify to forest patch threshold
frequency per plot
patch size (hectares)

29. Large patches – dealing with issues
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
1. Spatial Model hiccups. Fragstats can’t process large data sets-> (Extract by Mask [three county region])
Modified Model Process:
Land cover -> reclassify to get forest patches -> extract by study region ->
-> calculate area of forest patches -> reclassify patch output based on size threshold

30. Large patches – dealing with issues
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
2. Spatial Model hiccups. ArcMap can’t reclassify -> subset spatial extent -> Look up (Spatial Analyst) and
Modified Model Process:
Land cover -> reclassify to get forest patches -> extract by study region ->
-> calculate area of forest patches -> extract by study region -> Look up (Spatial Analyst) [area] -> reclassify patch output based on size threshold
 In my example, lrg_ptch == 1 not good. Lrg_ptch == 2 is good.

31. East facing slopes
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Condition 1.
East facing slopes (aspect)
Required data. Digital elevation model
Spatial Model.
DEM-> calculate aspect -> reclassify to get east facing slopes

32. Moderately sloped regions
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Condition 1.
Grows on slopes between 15 and 60 degrees
Required data. Digital elevation model
Spatial Model.
DEM-> calculate slopes -> reclassify to get east desired slopes

33. Population density Version 1
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Condition 1.
Only grows in areas with low population density (<3 people per acre)
Required data. Census Data
Spatial Model.
Census data-> convert total population to density estimate -> convert feature to raster -> reclassify

34. Population density Version 2
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Condition 1.
Only grows in areas with low population density (<3 people per acre)
Only grows in areas with low population density (? units per acre)
Required data. Parcel Data
Spatial Model.
Select developed residential parcels -> convert feature to point -> calculate density -> ?focal statistics? -> reclassify

35. Suitable Habitat
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Integrating Conditions.
Condition 1. Only grows in large forest patches.
Condition 2. Grows on east facing slopes
Condition 4. Grows on slopes between 15 and 60 degrees
Condition 3. Low population density
Spatial Model.
Raster calculator -> multiply rasters together

36. Estimate Suitable Habitat Area by Watershed
Operationalizing Science to ID areas: map habitat by translating relationships into spatial conditions
Integrating Conditions.
Condition 1. Only grows in large forest patches.
Condition 2. Grows on east facing slopes
Condition 4. Grows on slopes between 15 and 60 degrees
Condition 3. Low population density
Spatial Model.
Zonal Statistics (sum)