Exercise #1 — Dissecting a suitability model (MapCalc)

  Introduction to GIS Modeling, GEOG 3160, University of Denver, Spring 2000

 

Name                 Anonymous

Date        January 12, 2001

 

 

Q1

8

Q2

8

Q3

7

Q4

10

Q5

8

Q6

8

 

49

 

49/50… excellent!!!

 

Part 1.  Review MapCalc Manual, Tutorials and Example Applications

 

The MapCalc Manual, Tutorials and Example Applications are available on the MapCalc Learner CD…

ü       Manual is at \MapCalc\User’s Guide\MapCalc User’s Guide.pdf

ü       Tutorials are at \Educational Resources\MapCalc Tutorial\MapCalc Tutorial.pdf

ü       Example Applications are at \Educational Resources\Applications\Default.html

Note: a continually updated set of application examples are online at…

      http://dev.pacificmeridian.com/basis2/Senarios/Default.htm

 

You are encouraged to review the manual and work through the six tutorials to become familiar with the basic MapCalc operations (you will complete the seventh tutorial in Part 3 below).

 

(Optional 1-1… 5points).  Choose one of the Application Examples and briefly describe its basic approach, input requirements, processing operations and results.

Comment on the strengths and weaknesses ingrained in the model.

Suggest how you might improve and/or expand the model.

 

(Optional 1-2… 15points).  Choose one of the five analytical classes of map analysis operations and briefly describe the algorithm for the individual operation in the class.

Identify and briefly discuss any limitations in the algorithms.

Explain in what function each of the command options performs in implementing each of the operations.

When possible, suggest any modifications that would extend the capabilities of an operation.

 

Part 2.  Downloading Exercise Documents

 

Download the Exercise #1 homework template by…

ü       Access the class web site at http://dev.pacificmeridian.com/basis/GMcourse_01

ü       Under the “Links to Homework Assignments” section, right-click on “Download”…

[#1- Dissecting a suitability model (MapCalc) Download]

            …then check “Save Target As”

ü       Specify C:\Temp\Exer1.doc as the and location for the file (or a specially designated “exercise” folder on your own computer)

ü       Open the document in Word and enter your answers after each question

 

Part 3.  Dissecting a Suitability Model

 

8/8  Question 1.  Based on class discussion list the five criteria used to determine campground suitability. 

Suggest and briefly outline two additional spatial criteria that might be added to extend the model.

 

The five criteria for the campground suitability model are 1) gentle slope, 2) proximity to roads, 3) proximity to water, 4) good view of water, and 5) sites facing towards the west. 

 

Two additional criteria that could be added to the model are 1) proximity to hiking trails and 2) proximity to wildlife.  Along with other outdoor activities, campers often enjoy hiking.  Nearness to hiking trails, appropriate for either day trips or longer excursions could be a factor in considering suitable campground sites.  The presence or absence of specific species of wildlife may also be a consideration in locating campground sites.  Several animal species have become popular for wildlife viewing.  For example, the cranes that migrate through Southern Colorado attract many tourists each fall and spring.  The presence of other species may be unappealing to the majority of campers.  Examples could include large predators, such as bears and wolves, or other unpleasant creatures such as the ticks suspected of carrying Lyme Disease or Rocky Mountain Spotted Fever.

…good discussion.

 

8/8  Question 2.  Based on class discussion identify and briefly discuss the four sub-models comprising the Campground Suitability model.

 

The four sub-models or analysis levels are 1) base, 2) derived, 3) interpreted, and 4) modeled.  At the base level are the maps that contain the raw information required to define the criteria for the model.  These maps are either acquired or encoded (fact; physical things).  Maps at the second level are “derived” from the information on these base maps by performing some mathematical operation or algorithm on the data.  At the third level, the data is rated according to how well it fits the criteria of the model.  The data is ranked based on a subjective scale defined by the user.  At this interpretation level, the user attempts to determine or judge just what values of the measured criteria are deemed desirable or not.  These interpreted maps are combined through a weighted process in the fourth level to create a modeled map representing the final results. 

 

Access MapCalc using the TUTOR25 database. 

Display each of the Base Maps by…

ü       From the Main Menu select Window then choose the name of a base map

ü       Right-click on a map to pop-up its complete legend in the Shading Manger and note the relative proportion of each map category

 

7/8  Question 3.  Identify which of the base maps contain “discrete” and which contain “continuous” data. 

Briefly discuss the difference between the two types of data.

 

In this model, the data in the Elevation base map is continuous, and the data in both the Roads and Water base maps is discrete.  Both discrete and continuous data contain sets of numbers, but the difference is in how the numbers relate to each other.  With discrete data the numbers are used to describe different categories of data (nominal and ordinal numbers).  For example, the Road base map assigns a value of 1 to represent a poor road, and a value of 4 to represent a heavy duty road.  The heavy duty road is not 4 times as busy as a poor road or 4 times as wide as a poor road.  These categories are arbitrarily assigned a numerical value solely as an identifier, and there is no relationship between the numbers.  With continuous data a relationship exists between the numbers (interval and ratio numbers), so that each number represents a value.  For example, an elevation of 1,000 feet is 2 times as high as an elevation of 500 feet.  Continuous data represents variables that can be measured and can have an infinite number of potential values, unlike discrete data which is limited to integers only. Spatial distribution perspective—continuous dat forms a gradient; can be interpolated; isopleth…discrete forms abrupt boundaries; cannot interpolate; cloropleth.

 

Open the script “…\MapCalc\MapCalc data\Scripts\Campground.scr” by…

ü       From the Main Menu click on the Map Analysis button

ü       From the Map Analysis Menu select ScriptàOpen and specify “campground.scr”

 

10/10  Question 4.  Identify the base map, analytical operation used and the information contained in each of the five "derived" maps.

 

Derived Map

Base Map

Analytical Operation

Information Contained

Slopemap

Elevation

Slope

Percent slope, continuous data, ranging from 0 to 65 %

Proximity_roads

Roads

Spread

Distance from each cell to the nearest road, continuous data, ranging from 0 to 10.7 measured in cell units

Proximity_water

Water

Spread

Distance from each cell to the nearest water feature, continuous data, ranging from 1 to 10.1 measured in cell units

Exposure_water

Water and Elevation

(Water is the feature viewed over the Elevation surface)

Radiate

Number of cells with water visible from each cell, continuous data, ranging from 0 to 121, measured in cell units

Aspectmap

Elevation

Orient

Direction each cell faces, discrete data, in compass point octants assigned a value of 0 to 8, with 9 representing a flat surface

…excellent organization and discussion!

 

8/8  Question 5.  What percent of the project area is classified as "Excellent (9)" for each of the "Interpreted Maps" (S_pref, R_pref, W_pref, V_pref and A_pref)? 

Which map layer is least spatially limiting? 

Which map layer is most spatially limiting?

 

The area classified as excellent for each of the interpreted maps is as follows:  S_pref = 16%, R_pref = 18%, W_pref = 39%, V_pref = 20%, and A_pref = 86%. 

 

I used both of the methods to determine the areas described in the e-mails sent to the class  Using the shading manager with the data first classified as discrete and then as continuous.  Both methods resulted in the same area measurements.…whew, you never know about first release software

 

The map with the largest area rated as excellent would be the least limiting, and that would be A_pref.  So, Aspectmap is the layer that is the least spatially limiting. Conversely, the map with the smallest area rated as excellent would be the most limiting, and that would be S_pref.  So, Slopemap is the layer that is most spatially limiting.  Although, with R_pref at just 18% and V_pref at 20%, both Proximity_roads and Exposure_water come close to the limiting the data as much as Slopemap. 

 

8/8  Question 6.  What portion of the Potential_average map is ranked Excellent (9), Very Good (8), Good (7), Acceptable (6), Marginal (5), or Poor (<5)? 

…excellent discussion and extension.

 

To get the area measurements for this table, I set the number of categories in shading manager to 6 with the limits defined as 9-10, 8-9, 7-8, 6-7, 5-6, and 0-5. 

 

After reading the e-mail regarding masking the areas containing water, I did create the binary map for water, and computed the resulting map as instructed.  The results are reported in the following table under NO_Water_Pot_avg.  The results using the binary masking file NO_prox found in the script sequence, are reported under the column labeled P_avg_mask_NO_prox.  It is interesting to note the different results.  If the two maps masking water had been the same, the results of the model would also have been the same, since the maps were multiplied together.  The difference is due to the different masking techniques.  The NO_prox map masked all cells within 1.4 cells of all water features.  My method masked only the pond and lake cells, with the lower number of masked cells resulting in larger acceptable areas. 

 

I also created a map taking into consideration water coverage and areas of extreme slope.  Using the binary masking files from the script, NO_prox and NO_slope, a final map Potential_masked was created that eliminates both areas within 1.4 cells of water features and areas with slopes greater than 50%.  The results of this map are reported in the table below.  Again since more constraints were applied to the data, there is less acceptable area suitable for the campground.  The percent of acceptable areas (sum of ratings from 9 to 5) dropped significantly, from a range of 85-75% to only 62-45%. 

 

I also considered doing a similar masking process for the Roads data, since it would be unsuitable to build a campground right on a road .  But after reviewing the cell size, 328 feet, I concluded that there would most likely be adequate room to construct a campground within a cell containing a road. …yes, keeping in mind that the road cell is 328x328ft or 164 feet on either side of the centerline (not a line but a connected set of cells that contain a road).    

 

Suitability Rating

POTENTIAL_

average

P_AVG_mask

_NO_prox

NO_Water_Pot_avg

Potential

_masked

Excellent (9)

0

0

0

0

Very Good (8)

5

2.2

5

2.2

Good (7)

32

12

28

12

Acceptable (6)

34

24

27

19

Marginal (5)

14

14

14

12

Poor (<5)

15

48

25

55

 

 (Optional 1-3… 5points).  Create another campground suitability map (POTENTIAL2) that changes some or all of the preference calibrations. 

Summarize the changes you made and describe how you implemented them. 

Complete the following table…

 

Suitability Rating

POTENTIAL_

average

POTENTIAL2_

average

Excellent (9)

 

 

Very Good (8)

 

 

Good (7)

 

 

Acceptable (6)

 

 

Marginal (5)

 

 

Poor (<5)

 

 

 

…briefly discuss how significant the changes are and which map layer(s) you think are having the greatest impact.

 

(Optional 1-4…5points).  Choose another suitability modeling application and create a flowchart of its processing similar to the Campground Suitability model.  Identify and briefly describe the "model criteria," sub-models and initial thoughts on calibration (classify).