Beyond Mapping
III
|
Map
Analysis book with companion CD-ROM for hands-on exercises and further reading |
Suitability Models Find the Good, the Bad and
the Hugag — describes
a simple suitability model for characterizing habitat
Mapping Techniques Rate Hugag Habitat
Suitability — expands discussion to Binary Progression and
Rating suitability models
Logic and Extent Elevate Suitability Models to New
Levels — extends
Rating discussion to include additional habitat considerations and model
weighting
Extended
Experience Materials — provides hands-on experience with Suitability
Modeling
Author’s Notes: The figures in this topic use MapCalcTM software. An educational CD with online text, exercises
and databases for “hands-on” experience in these and other grid-based analysis procedures
is available for US$21.95 plus shipping and handling (www.farmgis.com/products/software/mapcalc/
).
<Click
here> right-click to download a printer-friendly version of this topic
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Suitability Models Find
the Good, the Bad and the Hugag
(GeoWorld,
July 2004, pg. 20-21)
A simple
habitat model can be developed using only reclassify and overlay
operations. For example, a Hugag is a
curious mythical beast (see figure 1) with strong preferences for terrain
configuration:
¾ Prefers low elevations (severe nose bleeds at
higher altitudes)
¾ Prefers gentle slopes (fear of falling over and
unable to get up)
¾ Prefers southerly aspects (a place in the sun)
A binary habitat
model of Hugag preferences is the simplest to conceptualize and implement. It is analogous to the manual procedures for
map analysis popularized in the book Design
with Nature, by Ian L. McHarg, first published in 1969. This seminal work was the forbearer of modern
map analysis by describing an overlay procedure involving paper maps,
transparent sheets and pens.
For example, if
avoiding steep slopes was an important decision criterion, a draftsperson would
tape a transparent sheet over a topographic map, delineate areas of steep
slopes (contour lines close together) and fill-in the precipitous areas with an
opaque color. The process is repeated
for other criteria, such as the Hugag’s preference to avoid areas that are northerly-oriented
and at high altitudes. The annotated
transparencies then are aligned on a light-table and the “clear” areas showing
through identify acceptable Hugag habitat.
Figure 1.
The Hugag prefers low elevations, gentle slopes and southerly aspects.
An analogous
procedure can be implemented in a computer by using the value 0 to represent
the unacceptable areas (opaque) and 1 to
represent acceptable habit (clear). As
shown in figure 2, an Elevation map
is used to derive a map of terrain steepness (Slope_map) and orientation (Aspect_map). A value of 0 is assigned to locations Hugags
want to avoid—
Greater than
1800 feet elevation = 0 …too high
Greater than
30% slope = 0 …too
steep
North,
northeast and northwest = 0 …to northerly
—with all other
locations assigned a value of 1 to indicate acceptable areas.
The individual
binary habit maps are shown in 3D and 2D displays on the right side of figure
2. The dark red portions identify
unacceptable areas that are analogous to McHarg’s opaque colored areas
delineated on otherwise clear transparencies.
A Binary
Suitability map of Hugag habitat is generated by multiplying the three
individual binary preference maps (left side of figure 3). If a zero is encountered on any of the map
layers, the solution is sent to zero (bad habitat). For the example location on the right side of
the figure, the preference string of values is 1 * 1 * 0 = 0 (Bad). Only locations with 1 * 1 * 1 = 1 (Good)
identify areas with out any limiting factors—good elevations, good slopes and
good orientation. These areas are
analogous to clear areas showing through the stack of transparencies.
Figure 2.
Binary maps representing Hugag preferences are coded as 1= good and 0=
bad.
While this
procedure mimics manual map processing, it is limited in the information it
generates. The solution is binary and
only differentiates acceptable and unacceptable locations. But isn’t an area that is totally bad (0 * 0
* 0 = 0) different from one that is just limited by one factor (1 * 1 * 0 =
0)? Two factors are acceptable thus making
it “nearly good.”
Figure 3.
The binary habitat maps are multiplied together to create a Binary
Suitability map (good or bad) or added together to create a Ranking Suitability
map (bad, marginal, better or best).
The right side
of figure 3 shows a Ranking Suitability map of Hugag habitat. In this instance the individual binary maps
are simply added together for a count of the number of acceptable
locations. Note that the areas of
perfectly acceptable habitat (light grey) on both the binary and ranking
suitability maps have the same geographic pattern. However, the unacceptable area on the ranking
suitability map contains values 0 through 2 indicating how many acceptable
factors occur at each location. The zero
value for the area in the northeastern portion of the map identifies very bad
conditions (0 + 0 + 0= 0). The example
location, on the other hand, is nearly good (1 + 1 + 0= 2).
Mapping Techniques Rate
Hugag Habitat Suitability
(GeoWorld,
August 2004, pg. 18-19)
The previous
section described a couple of basic techniques for suitability modeling—Binary
and Ranking. Both procedures use “binary
maps” that identify just good (1) and bad (0) conditions on a set of criteria
maps. In the example, three binary
habitat maps (good slopes, aspects and elevations) were multiplied together to
create a Binary Suitability map (bad=
any 0 or good=1*1*1) or added together to create a Ranking Suitability map (bad= 0+0+0= 0, marginal= 1, better= 2 or
best= 1+1+1= 3).
A further
extension of the binary techniques uses a mathematical trick. The criteria maps are reclassified to a
binary progression of numbers (1, 2 and 4) instead of all 1’s for acceptable
habitat (figure 1). When these maps are
summed the result is a unique value for each combination of values. For example, a location with a sum of 3 can
only occur if it is gently sloped (1) plus southerly exposed (2) plus too high
(0). The best habitat is indicated by
the value 7 (1+2+4= 7).
Figure 1. Binary Progression Suitability map with
combinations indicated.
A Binary Progression Suitability map
contains a great deal of information beyond that of a simple binary or ranking
map as it indicates the actual combinations of acceptable and unacceptable
conditions. If there are more than three
criteria layers, the progression is just extended (…8, 16, 32, 64, etc.). In all cases the permutations result in a
unique sum.
However all
binary models suffer the same problem—things are either good or bad with no
degree of goodness. It’s like pass/not
pass grading that doesn’t distinguish exceptional performance (either good or
bad) and forces a sharp boundary instead of a gradient of performance.
Figure 2 depicts
an alternative procedure where each of criteria layers are “graded” on a scale
from 1= very bad to 9= very good. For
this example the calibration was—
Slope Map: >40%= 1 (very bad);
30-40= 3; 20-30= 5; 10-20= 7; 0-10= 9 (very good)
Aspect Map: N, NE, NW= 1 (very bad);
E, Flat= 5; W= 6; SE, S, SW= 9 (very good)
Elevation Map: >1800ft= 1 (very
bad); 1400-1800= 3; 1250-1400= 5; 900-1250= 7; 0-900= 9 (very good)
…then the
individual criteria maps are averaged for an overall score. In addition, lakes are masked as they
represent impossible habitat (drowned Hugags).
Figure 2. Average Suitability map with an average score
for each location.
The result is an
Average Suitability map containing an
overall score for each map location.
Note the results for the example location in both figure 1 and 2. The Binary Progression solution ranks it as
totally acceptable (7= gentle, southerly, low), while the Average Suitability
solution rates it as mediocre habitat (5.3= mid-range on a 1 to 9 scale). The dark green locations, on the other hand,
identify very good habitat (8-9 rating) and the bright red locations indicate
the worst habitat (1-2 rating).
The continuous
gradient solution provides significantly more information than any of the
binary techniques. In practice, the
individual map layers are assigned weights to indicate their relative
importance and a weighted-average is computed.
The areas with high scores can be isolated and designated sensitive habitat
areas for natural resource planning.
Figure 3 shows
the average suitability model applied to a larger area based on freely
available 30m digital elevation data.
When the suitability map is draped on the terrain surface its results
are easily evaluated. The best areas
(dark green) align with the gentle, southerly sloped and relatively low
areas. The worst areas (bright red)
align with steep northerly sloped and relatively high areas.
Figure 3. Draping the Average Suitability map over the
Elevation surface shows good alignment with critical terrain features.
In practical
applications, habitat modeling considers many more factors than simply terrain
configuration. For example, the model
could be extended to evaluate the additional criterion that “Hugags would
prefer to be within or near forested areas” (proximity to
Keep in mind that suitability modeling isn’t restricted to wildlife habitat analysis. The approach is just as valid for identifying “customer habitat” in geo-business, or crop suitability in agriculture, or pipeline suitability for identifying the best route. Like statistics, the suitability modeling cuts a wide swath through many applications as a fundamental analytical tool.
Logic and Extent Elevate Suitability
Models to New Levels
(GeoWorld,
October 2004, pg. 20-21)
The previous sections on suitability modeling used wildlife habitat mapping to illustrate the development of progressively more powerful modeling approaches—binary, ranking, permutation and rating models. All four approaches used the same set of basic criteria—Hugag preference for gentle slopes, southerly aspects and lower elevations—as depicted in figure 1. The difference in how the processing takes place was the focus of discussion.
Figure 1.
Model logic for basic Hugag habitat suitability mapping.
In the case of a binary model each consideration is treated as either good or bad and results in a habitat map that identifies just good and bad habitat areas. A ranking model, on the other hand, uses the same good/bad criteria but identifies the number of good factors for each map location with higher values indicating increasingly higher habitat ranking. A permutation model provides even more information by identifying the unique combination of good and bad factors occurring at each location.
A rating model is the most powerful approach. It breaks the good/bad dichotomy into a gradient of preference most often expressed as 1= very bad to 9= very good. For example, the preference for gentle slopes (S_Pref in figure 1) was assigned as 1 (very bad) = >40%; 3= 30-40; 5= 20-30; 7= 10-20; and 9 (very good) = 0-10%. In a similar manner, categories for aspect and elevation are calibrated then averaged and masked for constrained areas to generate the overall suitability map shown in the figure. This result contains continuous habitat values—considerably more information than simply the spatial coincidence of discrete areas of good/bad classifications.
While processing approach is an important consideration, the model logic and extent can be even more important in determining model accuracy. In practical applications, the habitat model would likely consider many more factors than simply terrain configuration. Figure 2 shows a flowchart of the extended model logic to evaluate the additional criteria that “Hugags would prefer to be in forested areas” (Forest map), that “Hugags would prefer to be near water” (proximity to Water map) and that “Hugags would prefer to be far from roads” (proximity to Roads map).
Figure 2.
Extended model logic for considering Hugag preference for being in
forests, near water and far from roads.
In suitability modeling, these considerations are treated as separate sub-models to derive the necessary criteria, then calibrated on the 1 to 9 preference scale and averaged with the basic set of terrain considerations for an overall habitat map shown in the figure.
Note that a large part of the model’s strength or weakness is established in Step 1—calibrate criteria maps. As much as possible, the identification of map criteria needs to reflect good science and/or expert opinion to capture factors that are both important and easily measurable. Similarly, the calibration of the maps into the 1-9 preference range needs to capture realistic relative values, not whimsical or biased assignments.
Step2— combine
calibrated maps is another area requiring considerable understanding of the
system being modeled. A simple average
of the calibrated map layers assumes that all of the criteria are equally
important. The right inset in Figure 3
shows the habitat results for expert thinking that Hugags are “10 times more concerned about slope, forest and water
considerations than they are about aspect, elevation and roads
considerations.”
The procedure for determining relative importance involves computing the weighted-average of the six map layers. It is analogous to a professor’s grading some exams more important than others in determining a class grade. In this case, the map values correspond to student grades on each exam; each student is represented as a grid cell on the map, kind of like their desk seats in the classroom floor plan.
Figure 3.
Habitat rating maps for progressively more powerful model logic and
processing.
Note the
similarities and differences in the maps induced by the additional criteria
(Extended) and relative weighting of map layers (Weighted). Provided expert opinion is sound, the
weighted map on the right would be considered the most accurate representation
of Hugag habitat.
Keep in mind that calibrating and weighting are extremely critical steps in suitability modeling. Procedures, such as Delphi and AHP, can be used to derive these factors in a quantitative, objective, consistent and comprehensive manner (see Author’s Notes). In addition, purposeful changing these factors can reflect different assumption scenarios analogous to “what if” questions applied to traditional spreadsheet analysis.
From this perspective, it is how the suitability maps change
that becomes information about the sensitivity of a project area to the
interplay of criteria, calibrations and weights. This takes us well beyond mapping to
assessing the spatial relationships within a system and their logical
expression within a GIS. As GIS technology
matures, the focus is shifting from simply access of static map products
depicting physical features for navigation and inventory to a dynamic
environment that enables “thinking with a stack maps” within decision-making
contexts.
_____________________
Extended
Experience Materials: see www.innovativegis.com/basis/,
select “Column Supplements” for a PowerPoint slide set, instructions and free
evaluation software for classroom or individual “hands-on” experience in
suitability modeling. If you are viewing
this topic online, click on the links below:
- Suitability Modeling Slide Set – a series
of PowerPoint slides describing Binary, Ranking and Rating approaches to suitability
modeling described in GeoWorld, July and August, 2004 Beyond Mapping
columns. (627KB).
- For hands-on experience in
Suitability Modeling—
- <click here> to download
and install a free 14-day evaluation copy of MapCalc Learner software (US$21.95 full purchase price).
- <click here> to download
the Hugag database and place
the file in the folder C:\Program Files\Red Hen
Systems\MapCalc\MapCalc Data\.
- <click here> to download
the Hugag script and place the
file in the folder …\MapCalc Data\ Scripts\.
- Click Startà Programsà MapCalc Learnerà MapCalc Learner to access the MapCalc Software.
Select the Hugags.rgs database you downloaded.
Click on the Map Analysis
button to pop-up the script editor.
Select Scriptà Open and
select the Hugag.scr file you downloaded. A
series of commands comprising the model will appear.- Double-click
on the first command line Slope Elevation Fitted for Slopemap note the dialog box specifications, and press OK to submit the command.
Minimize the script window and use the map
display/navigation buttons in the lower toolbar to explore the output
map.
See http://www.innovativegis.com/basis/Senarios/Movies/MC_Basics.exe (click “Open”) for a short online video of demonstrating several of the MapCalc display functions.- Repeat
the process in sequence using the other command lines while relating the
results to the discussion in this topic and the slide set identified
above.
…direct questions and comments to jberry@innovativegis.com.
…an additional set of tutorials using MapCalc software is available
online at…
http://www.innovativegis.com/basis/Senarios/Tutorials/Default.htm
…example techniques and
applications using map analysis are available online at…
http://www.innovativegis.com/basis/Senarios/Default.html