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2. geography
3. cartography

GEOGRAPHIC INFORMATION SYSTEMS
Lecture 19: Spatial Modeling Continued
Spatial Modeling (cont’d)
- spatial models are often developed as a decision support tool in a site suitability analysis
- in a site suitability analysis, we can use map algebra to develop a Boolean, weighted or combined model
- map algebra allows us to add or multiple layers of spatial information
- in the last class we worked through an example of a Boolean model (siting marmot habitat of public land)
- for the final project in this class we will develop a weighted model
2) Weighted Model
- in a weighted model variables (layers) are analyzed to determine which location is the most suitable
- features in each layer are classified and ranked on a scale of say 1 to 5, with 5 being the most suitable
1 = very low suitability
2 = low suitability
3 = medium suitability
4 = high suitability
5 = very high suitability
- layers are then added to determine the most suitable areas (highest total)
Suitability rank = (ranked layer 1) + (ranked layer 2) + (ranked layer 3)
example feature or grid cell value = 4 + 2 + 5 = 11/3 – 3.67
- in a raster analysis, this would be the grid cells in layer one + grid cells in layer two, etc
- in a vector analysis, this would be the features in layer one + features in layer two, etc
Buffalo Commons Suitability Model
To illustrate how a weighted analysis works, we will develop a model to find the best location for a Buffalo
Commons (wildlife preserve) on the western Great Plains. The original idea for a Buffalo Commons came from
Frank and Debora Popper - who proposed the idea as a way to spur economic development through ecotourism.
The original paper was published in Planning Magazine in 1987.
The paper is titled the Great Plains: From Dust to Dust (link provided).
To develop the model we will use five selected variables:
- population density
- population change,
- land value,
- percent federal land,
- and adjacency to Indian reservations.
In each of the first four layers, the data for each county is classified and then ranked on a scale of 1-5 (with 1 =
lowest suitability, 5 = highest suitability). For the last layer, counties will be assigned a value of 1 if they were
adjacent to an Indian reservation and 0 if they were not adjacent. When the layers are added, the maximum
score for a county will be 5+5+5+5+1 = 21. This value can then be divided by 4.2 to normalize the final ranking
(with a maximum score of 5).
The results from the analysis will show the best location(s) for a Buffalo Commons on the Great Plains. It is
important to recognize that these results are based on the criteria (variables) we selected. If we selected different
variables, the results might be somewhat different. It is therefore critical to include all of the important variables
that are relevant to the analysis. At the same time, it is important not to include variables that are strongly related.
For example, if we included a variable (layer) for crop production and a variable (layer) for land value - these two
variables are strongly related. If we included both layers, the analysis would double-weight this variable and skew
the final results.
Also note that the Buffalo Commons example will be analyzed using vector data (counties). The same general
approach can be used with raster data.
Copyright© 2015, Kevin Mulligan, Texas Tech University
Steps to Building a Spatial Model
- to build a spatial model, the process can be subdivided into four basic steps
1) Clearly define the problem
a) Where is the best location to … ?
b) define the regional (spatial) extent of the project
c) define the resolution necessary for the data (the resolution of the analysis)
- e.g. does the analysis use data at the state level, county level or census tracks
2) Select the variables to be input to the model
a) select all of the important variables (layers)
- if you do not incorporate an important variable in the model, your analysis will be
skewed and your results will be misleading
b) eliminate variables that measure the same quantity
- if two variables are strongly related, and both variables are used in the analysis,
the influence of that variable will be double-weighted in the final results
3) Apply weights to the variables
a) this is often a subjective decision
- if you believe that certain variables are more important that others, they can be weighted
- for example, the ranking of a variable can be multiplied by 1.5 in the analysis to give
that variable more influence on the final result
b) you can also use Boolean operators (0,1)
- if we use a Boolean variable and multiply features by 0, they will drop out of the analysis
- in this case the spatial model has become a “combined model”
4) Apply the model
a) run the processing steps necessary to combine the variables
- the final result of your analysis will not likely be an absolute location
- rather, the result will provide you with a guide to making an informed decision
Copyright© 2015, Kevin Mulligan, Texas Tech University
GIST 3300 / 5300
Geographic Information Systems
Spatial Modeling (cont’d)
Spatial Modeling (Site Suitability Analysis)
Boolean Model
Weighted Model
Final Project
Geographic Information Systems
Spatial Modeling
1) Boolean Model
- features in each layer are assigned values of 0 or 1
0 = not acceptable
1 = acceptable
- layers are multiplied
Suitability = (Boolean layer 1) x (Boolean layer 2) x (Boolean layer 3)
possible feature or grid cell values: 0 x 0 x 1 = 0
0x1x0=0
1x0x0=0
1x1x0=0
0x1x1=0
1x0x1=0
1 x 1 x 1 = 1 (only acceptable features)
Geographic Information Systems
Spatial Modeling
2) Weighted Model
- features in each layer are ranked (e.g. scale of 1 to 5)
1 – very low suitability
2 – low suitability
3 – medium suitability
4 – high suitability
5 – very high suitability
- layers are added
Suitability = (ranked layer 1) + (ranked layer 2) + (ranked layer 3)
example feature or grid cell value: 4 + 2 + 5 = 11/3 layers = 3.67
Geographic Information Systems
Spatial Modeling
2) Weighted Model Example
- features in each layer are ranked (e.g. scale of 1 to 5)
1 – very low suitability
2 – low suitability
3 – medium suitability
4 – high suitability
5 – very high suitability
- layers are added
The Buffalo Commons
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Weighted Model Example
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Weighted Model Example
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Weighted Model Example
- Frank and Deborah Popper
- recognized the problem on the western Great Plains
- proposed the idea of a Buffalo Commons
- never stated explicitly where it should go
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Weighted Model Example
The problem:
Where is the best location for a Buffalo Commons on the western Great Plains?
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North American Bison
Bison bison
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Asian Water Buffalo - NO
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African Cape Buffalo – NO
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North American Bison
Bison bison
YES
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Spatial Modeling (Site Suitability Analysis)
Steps to Building a Spatial Model
1) Clearly define the problem
a) “Where is the best location to …
b) define the regional (spatial) extent of the analysis
c) define the resolution necessary for the data
2) Select the variables to be input to the model
a) select all of the important variables (layers)
b) eliminate variables that indirectly measure the same quantity
3) Apply weights to the variables (optional)
a) often a subjective decision
b) can use Boolean operators (0 or 1)
4) Apply the model
a) run the processing steps
Geographic Information Systems
Weighted Model Example
Variables:
Population Density
+ Population Change
+ Percent Federal land
+ Farm Land Value
+ Adjacency to Reservations
= best location (suitability)
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Population Density
Ranking
Very High (1)
…
Very Low (5)
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Population Change
Ranking
Large Increase (1)
…
Large Decline (5)
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Percent Federal
Land Ranking
Very Low (1)
…
Very High (5)
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Farm Land Value
Ranking
Very High (1)
…
Very Low (5)
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Adjacent to
Indian Land
Not Adjacent (0)
Adjacent (1)
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Weighted Model Example
Population Density (1-5)
+ Population Change (1-5)
+ Percent Federal Land (1-5)
+ Farm Land Value (1-5)
+ Adjacent to Reservation (0 or 1)
= 21 max . . . Divide by 4.2
Suitability Scale ~1.0-5.0
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Final Suitability
Ranking
Very Low
…
Very High
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