Team Members: _____Shelby, Paul and Katie_____
Date: _____01/21/10______
49.5/50= 99%, A+ …awesome
job. Throughout you report the discussion
is thorough, clear and concise (right on the mark) and the presentation is
consistent and well-organized. Use of
enumeration and underline makes the report very easy to follow—thank you.
Part 1.
Understanding Basic Concepts and Terms
Question 1. Based on the lecture and readings briefly define and discuss
the following concepts surrounding “the level of detail” in mapped data—
Map Scale—Map scale refers to the ratio between map distance and ground distance. This is a concept carried over from paper
maps, but in practice, scale is not a useful measure of map detail in the world
of digitized maps. Scale in a
…insert a blank line for a new paragraph as change in
thought
Resolution is a more appropriate way to express map detail. Information in maps can be defined by the
four types of resolution discussed below.
…very good use of highlighting the important point(s)—makes
your report easy to review
Spatial Resolution— Spatial
resolution refers to the smallest addressable unit of
space. Spatial resolution is
limited by the resolution of the software and/or the format used to store and
to analyze the data.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, a vector representation of a river containing 50 points to
define a line segment has a higher spatial resolution than a representation
containing only 25 points. Satellite
imagery with 3 meter resolution (meaning that each pixel “on the ground” is 3
meters by 3 meters) has a higher spatial resolution than imagery with a
resolution of 30 meters.
Thematic Resolution—
Thematic resolution
refers to the number of
classification groupings for a map theme or dataset. A higher thematic resolution would classify
or display more categories (i.e. population subdivided into categories by
ethnicity, income level, and age). A
lower thematic resolution would classify or display fewer categories (i.e.
population subdivided into categories only by age).
Minimum Mapping Resolution— The minimum
mapping resolution refers to the smallest physical grouping of map themes. This means that certain maps will include or
exclude physical features based on a user-defined resolution.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, a map of
housing communities must define the number of houses that constitutes a
community. If a community is defined as
two houses in a square mile section, the resolution will be finer than if the
community were defined as 5 homes in a square mile section.
Temporal Resolution— Temporal
resolution refers to the time lapse between data
acquisitions.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, when observing changes in tree canopy or tracking the effects of
a natural disaster it is necessary to observe changes in the phenomena over
time. Tree canopy studies may require
only seasonal or annual observations, while monitoring a natural disaster may
need hourly or daily data acquisitions.
In this scenario, the temporal resolution for the natural disaster study
is finer than the temporal resolution of the tree canopy study.
Comment on how mismatched
scale/resolution in a set of maps can affect
When using different datasets to perform GIS analysis or create a
map, the mapping resolution of each map must be known and the resulting map’s
resolution can only be as
fine detailed as that of the map with the coarsest resolution.
…for example,
“mixing spatial resolutions” is particularly troublesome when analyzing
coincidence among map layers
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, if a dataset
with a 10m resolution is overlaid on a 30m resolution dataset, the 10m data
will need to be reclassified to the 30m resolution. Differing map resolutions introduces uncertainty into the
analysis …yes, well stated. Statements of uncertainty must be included
with all conclusions regarding resolutions less than the coarsest resolution.
Question 2. Based on the lecture
and readings briefly define and discuss the following map data types. Be sure your definitions include discussion
of which terms refer to the numeric
distribution and which refer to the geographic distribution of mapped data.
Numeric
Distribution …good to
organize as two groups—helps the reader “see” your organization
Nominal — Nominal numbers are qualitative, categorical values. In a given dataset or group of numbers, they do not relate to each other
(i.e. they are independent).
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, nominal numbers may be used to
identify highways in a county or water districts in a region. They cannot be used for mathematical or
statistical analysis.
Ordinal —Ordinal data
is a numerical distribution and defines numbered categories that are ordered or ranked,
but not with a constant step.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, the
categories 1=good, 2=better, and 3=best represent values where 2 is better than
1, but is not twice as good. Limited
math and statistic functions can be performed with this type of data.
Interval — Interval data is like ordinal data but applies a constant step. Within interval data, none of the values
represent “nothing”.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For example, a temperature of 0° Fahrenheit does not mean there is no
temperature. The constant step means
that the width of each interval is the same and relationships between intervals
are defined. For example, along the
interval of hours in a day, 6 hours is twice as long as 3 hours.
Ratio — Ratio data is ordered numbers tied to an absolute reference.
…insert a blank line for a new paragraph as change in
thought—don’t be afraid of a one sentence paragraph in a report
For
example, a ‘loss ratio’ in a risk assessment would be
[value damage estimate/total value].
This way, it is understood how much of the total value is damaged by the
risk (i.e. flood). A ratio creates an
‘apples to apples’ comparison. Ratio
data can be mapped thematically, and used for mathematical and statistical
calculations.
Binary —Binary data is a special
case within the numerical distribution.
This data type is characterized by presence/absence data often
expressed as 1 (present) or 0 (absent)
…only two states. This simple data structure works well for
many types of map information and can be mathematically manipulated to produce useful
combinations of maps for planning purposes.
Geographic Distribution
…blank
line needed
Choropleth — Choropleth data refers to data grouped into
enumeration units with geographic orientation. The data is assembled into distinct classes
specified by the client, researcher, or expert opinion. Choropleth data is most effective when
applied to phenomena with discrete
boundaries such as states or census blocks. Often a color ramp is used to show the
graduation of value ranges for each class.
Isopleth — Isopleth data refers to a continuous distribution of data
across a geographic space. …forms a predictable gradient. For example, temperature data across a state
or elevation data for a county are isopleth datasets, and can be presented
using a lattice display form.
…excellent
discussion (clear and concise) and very well presented (consistent and
organized)
Access the MapCalc system using the Tutor25.rgs
database.
Screen grab and insert below a
3-D lattice display of the Slope map. (don’t forget to include a Figure
Number, a Caption and to Reference/discuss the figure in the
text of your answer)…
Figure 1-1. Map of Slope.
Data is displayed in the 3-D Lattice view.
…good to include
figure number, title and caption
What is the Data Type of the Slope map? ...what is its Display Type?…what is its Display
Form? —
The data type of the Slope
map is continuous,
the display type is lattice,
and the display form is 3D
(Figure 1-1).
What is the interpretation of a
terrain steepness of 0%? The terrain is perfectly flat. That is, the rise
of the terrain over the run equals zero (i.e. it is a straight line). …all of the surrounding elevation values are
the same
Is a location with a 20% slope
twice as steep as a location with 10% slope?
No.
Although 20% slope is twice 10% slope,
the steepness intervals are not a constant step with the percentage
intervals. For illustration, 10% slope is a 5.7 degree slope
but 20% slope is an 11 degree slope, which is not twice as steep. Steepness is expressed as the tangent of the
rise over run and slope is calculated as a relative percentage of that
steepness.
…excellent discussion
Why are there no negative slope
values? No negative values exist because a slope
calculation is made with absolute
values for rise and run, thus eliminating any chance for negative slope
values …except if direction is specified—uphill, downhill or across.
Effectively all slopes are calculated as if they were located in the
upper right quadrant of Cartesian space.
Screen grab and insert below a
2-D grid display of the Districts map. (don’t forget to include a Figure
Number, a Caption and to Reference/discuss the figure in the
text of your answer)…
Figure
1-2.
Map of Districts. Data is displayed in grid
form with discrete values representing the spatial location of seven districts.
What is the Data Type of the Districts map?
...what is its Display Type?…what is its Display
Form? —The data type of the districts map is discrete, the display type is grid,
and the display form is 2D
(Figure 1-2).
Could you calculate a slope map
from the Districts map? Technically
yes. Slope could be calculated because the grid
cells house numerical values. However,
the slope would not mean anything mathematically. The
map only contains nominal values linked to district boundaries. Actual physical characteristics of this area
cannot be determined from this map alone.
What, if any, would be the
interpretation of its slope values? Any slope values generated for this map would be senseless
combinations of categorical values and could not support any useful
interpretation. …0% slope would be district interiors; any
calculated slope value would indicate a district boundary zone
…comparison of Districts and
District_slope maps show a strong relationship between district boundaries and
non-zero slope values—
…discussion discussion throughout; excellent
presentation throughout
Part 2.
Characterizing Geographic Space …Discrete
versus Continuous
Access the MapCalc system using the Tutor25.rgs
database and display a 2-D lattice contour plot of the Elevation map.
Click on the Layer Mesh icon
(a)
to superimpose the 25x25 analysis grid.
Click on the Toggle 3-D View
icon (b) to get a lattice (wireframe) plot of the map surface.
Click on the Shading Manager
icon (c) to pop-up the thematic mapping window.
Note that the current settings for “Calculation Mode for Ranges is Equal Ranges” and the “Number of
Ranges is 10”
Assign the Calculation tab settings
to Mode=
Click on the Rotate icon (d)
then click and drag the plot to rotate the display.
Click on the Zoom In/Out
buttons (e) to rescale the plot.
Click on the Reset View to
Defaults icon (f) to return the plot to its standard form.
Click on the Use Cells icon (g)
to view the surface as an extruded plot.
Change the calculation tab settings
to Mode= Equal Count and Ranges= 7. Note the changes in pattern of the colored
zones, then press OK.
Change the calculation tab settings
to Mode= +/- 1 Standard Deviation and Ranges= 7. Note the changes in pattern of the colored
zones, then press OK.
Question 3. Prepare a brief
write-up (including screen grabs you deem important to your discussion) of the
“Thematic Mapping” processing you just completed. Include discussion of the
The
‘Thematic Mapping’ modes (
The
Figure 2-1. 3D Lattice Display of Elevation classified using
The
Equal Count
Calculation Mode attempts to maintain a uniform number of grid cells in
each of its classes. Using the
above example, when set to 7 ranges, the elevation classes each have roughly 90
cells (Figure 2-2).
Figure 2-2. 3D Lattice Display of Elevation classified using
Equal Count Calculation Mode.
The
+/- Standard Deviation
Calculation Mode defines classes according to the spread of the data along a standard normal curve …well stated.
The first and last
classes represent the data values in the tails of the curve ...“unusually” low and high values, respectively. The rest of the data is dispersed among the
remainder of classes within the set standard deviation values (Figure
2-3). …the
data is divided into equal ranges within + and – 1 SD (about 66% of the data
provided it is normally distributed) and the upper and lower tails of the
normal distribution (about 100-66= 34= 17% of the data)
Figure 2-3. 3D Lattice Display of Elevation classified using +/-
Standard Deviation Calculation Mode.
Lastly,
the User Defined
Calculation Mode allows
the user to control the number of classes…so
do the other techniques, as well as the minimum and maximum values of each class range
(Figure 2-4).
Figure 2-4. 3D Lattice
Display of Elevation classified using User Defined Calculation Mode.
Which of the four different
thematic displays do you think “best represents” the map surface? Explain your reasoning.
The
…very good discussion and
presentation throughout
Question 4. Repeat the process
for your favorite method (Equal ranges, Equal Count or +/- 1 Standard
Deviation) using Ranges= 5 for the
number of intervals. Insert screen captures
below as side-by-side displays for both the Ranges= 7 map (from above question) and the Ranges= 5 map you just generated.
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Figure 4-1. |
Figure 4-2. |
Describe the differences in the
two plots and discuss their impacts on the thematic display of the continuous
surface data.
The
…good discussion and presentation
Part 3.
Click on the Slope icon
(under the Neighbors button) and specify…
Map Analysisà Neighbors à
Slope
Note that the slope values form a
gradient from 0% (flat) to 65% (steep) that is automatically “categorized” into
seven intervals with colors “ramped” from Red to Yellow to Green.
Note that the default map display for the
Slope command is continuous data type.
Click on the Renumber icon
(under the Reclassify button) and specify…
Map Analysisà Reclassify à
Renumber
…by individually
entering the “assigning” phrases (ASSIGNING <New Value> TO <Old
value> THRU <Old UpperValue>) and pressing the “Add” button after each phrase is
complete to add it to the list.
Note that the default map display for the
Renumber command is discrete data type.
Click on the Drain icon
(under the Distance button) and specify…
Map Analysisà Distance à
Drain
Note that the default map display for the
Drain command is continuous data type.
Click on the Renumber icon
(under the Reclassify button) and specify…
Map Analysisà Reclassify à
Renumber
Click on the Compute icon
(under the Overlay button) and specify…
Map Analysisà Overlay à
Compute
Note that the default map display for the
Compute command is continuous data type.
Remember to switch to a “2D grid” display of the “discrete” data
type for a proper display of categorical data (2-digit code).
The following flowchart outlines the steps you just completed…
Question 5. Create a “narrative flowchart” with embedded maps displays
that describes the five processing steps of the erosion model. For example, the first step description would
include at a minimum—
Example answer for Step 1…
Step
1 Derive a map of Terrain Steepness
(Slopemap) using the command—
Slope Elevation for Slopemap
Note: Be sure you embed screen captures of
the input map(s) (Elevation in this case) and output map
(Slopemap) and include “Figure Title and Caption” with the all embedded screen
grabs, such as…
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Figure 5-1. 2D Grid Display of the Elevation Map (input). |
Figure 5-2. 2D Lattice Display of the Slopemap (output). |
Note: Be sure your answer includes a short
discussion of what is happening in the processing step. Be sure to reference the embedded figures and
identify the display and data types of the maps, such as…
Figure 5-1 is a 2D grid display of the Elevation data (Base Map;
figure 5-1) containing ratio data from 500 to 2500 feet. The terrain data is analyzed using the Slope
command to create a Slopemap
(Derived Map; figure 5-2) containing ratio data from 0% slope (flat) to 65%
(steep). Note that the steepest terrain
is located in the north-central portion of the project area (green tones).
Slope values for each cell are calculated using the eight neighbor
cells, that is, a 3x3 window is used for each calculation. The value is applied as a percent, to the
centroid of the center cell. The default
procedure aligns a best-fitted plane to the values in the window, and assigns
the slope of the plane to the center cell.
The window then shifts over one cell and the process repeats. Other options use the maximum, minimum or
average of the eight individual slopes in the slope window.
Now on your own, repeat the
descriptions for the other four Steps of the simple erosion model.
Step 2 Derive
a map of drainage (FlowMap) using the command – Drain
Over Elevation for FlowMap
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Figure 5-3. 3D Lattice Display of the SlopeMap draped
over the Elevation Map (input). |
Figure 5-4. 2D Grid Display of Slope_classes (output). |
Figure 5-3 shows the 3D Lattice Display of the
SlopeMap draped over the Elevation Map.
Slope is mathematically derived from elevation. Therefore, it makes visual sense to drape the
derived quantity over the original surface.
In this display mode, highest slope values are seen in steep areas of
the elevation map rather than as the sharp peaks and valleys that would result
in a map of slope alone.
The continuous ratio SlopeMap was renumbered
into discrete ordinal data
categories that generalize the amount of steepness (Figure 5-4). This
was done by a simple renumbering operation that assigns a new value to all
original values within a user specified range.
In this step, a value of one
was assigned to areas of slope value between 0% and 10% slope. A value of two corresponds to areas between
10% and 20% slope and a value of three refers to areas that had values greater
than 20% slope.
…blank line as new idea
The operation script was
RENUMBER SlopeMap ASSIGNING 1 TO 0 THRU 10 ASSIGNING 2 TO 10 THRU 30 ASSIGNING 3 TO 30 THRU 1000 FOR Slope_Classes.
A quick check verifies that
blue areas (value 3; steep terrain) correspond to green areas (steep slopes) in
SlopeMap and likewise with the other classes.
Step 3 Derive a
map of drainage (FlowMap) using the command – Drain Over Elevation for FlowMap.
…blank line to separate
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|
…good display …an alternative
display choice that dramatically shows the information in the DRAIN surface |
|
Figure 5-5. 3D Lattice Display of the Elevation Map
(input). |
Figure 5-6. 3D Lattice Display of the Flowmap draped
over the Elevation Map (output). |
The
base map for this operation is the Elevation Map shown above in 3D lattice display (Figure
5-5). The elevation map was processed
using the DRAIN
operation to produce the output Flowmap, shown in 3D lattice display over the
Elevation Map (Figure 5-6).
…blank line for
new paragraph as new thought is introduced
As
in Step 2, the Flowmap data is visually pleasing when displayed graphically draped over
elevation. High flow values make sense
when seen in low elevation areas.
Likewise, low flow values make most sense on peaks and ridges. Displaying the data in this way makes the
information in Flowmap more explicit and meaningful.
The
elevation map contains continuous interval data ranging from 500 to 2500
ft. The output Flowmap contains ordinal
data ranging from 1 to 200. The DRAIN operation calculates the
steepest downhill path from a starting location. When a path crosses a cell, the cell value is
increased by one. This is analogous to a
tally system the keeps track of which cells are visited most often by the
operation. The data values in the output
map will be whole number because fractional “visits” are not possible.
In
this calculation each cell
location in the
project area was used as a starting location because the parameter ENTIRE was selected. The minimum cell value is one because each
cell gets a tally when it is visited by the operation as the starting
location. The cells with the most
path drainage are cells shown in green in Figure 5-6.
…blank line as new idea
The
operation script was DRAIN Entire
Step 4 Derive
a map of flow classes (Flow_classes) using the command – Renumber FlowMap.
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Figure 5-7. 3D Lattice Display of the Flowmap draped
over the Elevation Map (input). |
Figure 5-8. 2D Grid display of the Flow_classes Map (output). |
Flowmap is an ordinal dataset with values ranging from 1 to 200
and is displayed in 3D lattice over Elevation (Figure 5-7). The logic for this display type is discussed in
Step 3. The output map for this step, Flow_classes, contains discrete ordinal
data and is displayed in 2D grid format (Figure 5-8).
This
map was renumbered by assigning 10 to low flow values, 20 to moderate
flow values, and 30 to the highest flow values.
The RENUMBER operation assigns new values to old values within a user
specified interval.
…blank line as new idea
The
operation script was RENUMBER Flowmap ASSIGNING 10 TO 1 THRU
10 ASSIGNING 20 TO 10 THRU 20 ASSIGNING 30 TO 20 THRU 1000 FOR Flow_classes.
Multiples of ten were chosen for these
values to facilitate a simple erosion code created in the next step.
Step 5 Combine
slope classes and flow classes for a map of erosion codes using the command - COMPUTE Flow_classes Plus Slope_Classes FOR Erosion_codes
…blank line needed separate
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Figure 5-9. 2D Grid Display of the Slope_classes Map
(input). |
Figure 5-10. 2D Grid Display of the Flow_classes Map
(input). |
Figure 5-11. 2D Grid
Display of the Erosion_classes Map (output). |
In
this step, two maps are combined
to yield a map of erosion potential. The
assumption is that both steepness of slope and amount of flow through an area
contribute equally to erosion potential.
Thus, areas of steep slope and high flow have the highest erosion
potential. If expert opinion advised
that flow and slope contribute disproportionately to erosion potential, the
input maps would need to be weighted appropriately. In this case, each input map has a weight
of 1.
To
determine the combined effect of flow and slope for the gridded area,
the Flow_class map (Figure 5-9) was added to the Slope_class map (Figure 5-10)
which produced the Erosion_codes map (Figure 5-11). Both input maps and the output map contain
discrete ordinal data types in their grid cells. Because of the carefully selected values in
the previous renumbering operations, the cell values in the erosion map are 2-digit codes that have
meaning …”tens”
digit identifies the flow class; “ones” digit indicates the steepness class. For example, a cell value of 31 is code for
steep slope and low flow.
The
erosion codes map is rather cluttered with all nine classes displayed
separately. A planning agency will
probably not deal with individual slope-flow combinations separately. Instead, it will be useful to separate the
areas with highest erosion
potential from the rest of the gridded area …good point. To do this, expert opinion is needed to
decide which slope-flow classes should be included in the high erosion
potential category. Question 6 below
illustrates one possible scenario of the classes to include in high erosion
potential.
48.0/50= 96%, A …super job. Your responses are clear and concise throughout.
Question 6. Considering the combined categories of “Heavy_flow and
Steep_terrain”, “Heavy_flow and Moderate_terrain” and “Moderate_flow and
Steep_terrain on the Erosion_Classes
you just created, use the Shading Manager
to graphically identify these areas of significant erosion potential as Red and all other locations of lesser
erosion potential as Light Gray. Embed a screen grab of your map below.
Figure 6-1. Graphic display of three highest erosion
potential classes. Re-coloration was done without changing the values
in the grid cells …you got it.
How
many acres and proportion of the total study area falls under the significant
erosion potential class (color= red)?
The number of acres with high erosion potential (shaded in red in Figure
6-1) is 192.642 acres which is 12.48% of the gridded area or 78 cells.
Use the sequence Map Analysisà Reclassifyà Renumber to generate a map called High_erosion that isolates the areas of
significant erosion potential with the new value of 1 assigned to areas with significant
erosion potential (as noted above) and
new value of 0 assigned to all other areas. Embed a screen grab of your map below.
Figure 6-2. Graphic display of three highest erosion
potential classes. The values in the
grid cells were renumbered into a binary format where high erosion potential is
represented by 1 (red) and all other classes are represented by 0 (gray).
How
many acres and proportion of the total study area falls under the significant
erosion potential class (value= 1)?
Where do they mostly occur?
The significant erosion potential class (shaded in red in Figure 6-2)
contains 78 cells which is 12.48% of the gridded area and represents 192.642
acres on the ground. Most of the erosion
potential occurs in the center of the upper half of the map. This area corresponds to high values in the
SlopeMap and in the Flowmap. There are
also cells along the perimeter of the map that correspond to areas of high flow
accumulation values.
Describe the “significant difference” between the Graphic
(Shading Manager) and Numeric (Renumber) versions of the areas of high
erosion potential.
The
significant difference is in the values stored in the map grid. Graphically, the displays are is the same. But the binary map (Figure 6-2) can be used for mathematical and
statistical operations, while the ordinal map (Figure 6-1) is just a key
of erosion potential codes.
_________________________________
Note: Submit Optional question answers as separate Word document files with the Question number and your name (e.g., Optional_2-1_yourName.doc)…do not include them with the normal weekly lab reports.
Optional
Question 2-1 (extra credit= 3 points
possible; complete individually and submit your answer as a separate email).
Based on class discussion and your experience viewing the data
structures/formats for MapCalc, Surfer and ESRI Grid describe the
similarities/differences and advantages/disadvantages among the three
approaches to storing grid-based mapped data.
Use the Slope map in the Tutor25.rgs database
(MapCalc_Tutor25_rgs.txt, SLOPE_Tutor25_Surfer_grd.txt and
SLOPE_Tutor25_Esri_asc.txt in the …\MapCalc Data\Misc folder) for illustration
and discussion similar to the slide set presentation used in class to describe
the data structure/format of the Elevation map.
Insert your discussion with figures then email as separate file…
Optional
Question 2-2 (extra credit= 3 points possible;
complete individually and submit your answer as a separate email). Based on the class
discussion and assigned, optional and other readings you may find, write a
short essay (less than 750 words) discussing the differences in assumptions,
approaches, and applications in Computer Mapping, Spatial Database Management
and GIS Analysis/Modeling systems.
Assume you have been asked to write the essay for a magazine like
National Geographic with readers who have an interest and good but basic
understanding of science. Your charge is
to communicate the breadth of modern map analysis.
Insert your short essay…
Optional
Question 2-3 (extra credit= 3 points possible;
complete individually and submit your answer as a separate email). On your own, develop
and implement an additional new criterion to the Campground Suitability model
you completed in Exercise #1 that recognizes most campers want to be “within
and near forested areas.”
Hint: the solution requires manually entering a series of
commands similar to the criterion to be “close to roads,” however in this case
using the Forests map (base map), proximity to forests (Derived map) to create
a calibrated map (Interpreted map) such that 0 to 2 cells away is Most
Preferred (rating= 9) and a preference gradient of your choice to 6 to 100
cells away as Least Preferred (rating= 1).
Don’t forget to modify the Analyze command that computes the average
rating for all criteria (now six individual rating maps), as well as “masking”
for the constraints and user the same legend (Shading Manager settings) as
before for the final map.
Insert the additional commands you used (e.g., SPREAD Forests NULLVALUE PMAP_NULL TO 100 Simply FOR
Proximity_forests)…
Screen grab and then paste the
display of your final “masked” map (Potential_masked2) below…
Insert figure…
Discuss the procedure you
developed for favoring the location of the campground within or near forested
areas. Be sure your answer 1) identifies
the input map(s), processing operation and output map for each step, and 2) describes
the interplay of the input and output map values at each step.
Insert your answer…
Optional
Question 2-4 (extra credit= 3 points possible;
complete individually and submit your answer as a separate email). Note: this
optional exercise should only be undertaken by students with considerable
previous experience with ArcGIS. Use the
MapCalc cross-reference to Esri Grid commands available on the companion CD…
…and the ArcGIS Help (Spatial
Analyst) in the GIS lab to complete
the translation table below for the basic Campground Suitability Model
you completed in Exercise #1. Be sure
your answer addresses any disparities in the commands.
|
MapCalc Command |
Processing Achieved …what information is derived |
Esri Grid Command |
|
SLOPE Elevation… |
Creates a map of terrain steepness expressed in percent inclination;
input is a base map of Elevation; Esri SLOPE provides an option for output
values to be in degrees as well as percent |
SLOPE |
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SPREAD Roads… |
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SPREAD Water… |
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RADIATE Water… |
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ORIENT Elevation… |
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RENUMBER Slopemap… |
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RENUMBER Proximity_roads… |
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RENUMBER Proximity_water… |
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RENUMBER Exposure_water… |
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RENUMBER Aspectmap… |
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ANALYZE S_pref… |
Calculates a weighted average of a “stack” of map layers; input is
the set of derived campground preference layers calibrated from 1= least
preferred through 9= most preferred; Esri WEIGHTED OVERLAY requires building
a separate weighted overlay table |
WEIGHTED OVERLAY |
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RENUMBER Proximity_water… |
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RENUMBER Slopemap… |
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COMPUTE N0_slope… |
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COMPUTE Constraints… |
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