Exercise #4 — Measuring Distance & Connectivity
GIS Modeling, GEOG 3110, University of Denver
Team Members: Sharon, Graham
Date 3 Feb 2012
Part 1 – Calculating simple/weighted proximity
Question 1. Complete the analysis below that calculates “simple” and “effective”
proximity. Include screen grabs of the maps and your responses to the “notes”
and ‘questions” embedded in the instructions. Be sure your answers clearly explain the
similarities and differences between the proximity surfaces you generate …map values
(numeric distribution) and spatial patterns (geographic distribution).
Access MapCalc using the Tutor25rgs database—
Viewà select Roads
Map Analysisà Distanceà Spread
Enter, SPREAD Roads TO 20 Simply FOR Roads_simpleprox
Note the display differences in the 2-D lattice; 2-D grid; 3-D lattice and 3-D grid views
of the Roads_simpleprox map. Screen grab your opinion of the best display (Data Type,
Display Type and 2/3D buttons plus best Shading Manager mode, #interval and color
settings), insert the graphic below and add a figure title and brief caption describing the
map information you derived. (Hint: insert 1x2 cell table with the graphic pasted in the
top cell and the title/caption entered into the lower cell. Don’t use the “caption” option
in Word as has a history of fouling-up.)
Figure 1: Roads Simple Proximity and
Shading Manager.
What is the interpretation of the value “0” (zero) on the Roads_simpleprox map surface?
Zero on the Roads_simpleprox map represents the roads themselves. Because the map
represents distance from the road, a 0 value on the map represents the road surface.
How far away in “simple distance” terms is the farthest location from roads on the
Roads_simpleprox map surface?
The farthest location from the roads on the map is 10.66 cells but this location is on the
lake and is not useful for this discussion. The farthest location from the roads on land
is 8.41 cells. According to the map properties, each cell is 328 ft, so the farthest total
distance from the roads on the map is 2758.48 ft (8.41*328) which is approximately 0.52
miles.
What is the Column, Row coordinates of the farthest location from roads? (Hint: Mouseover the position in the display and note the Column, Row coordinates appearing in the
lower-left corner of the display)
The column, row coordinates (as found in the lower-left corner of the display) of the
farther cell from the roads are 1,25. In other words, this farthest cell is in the first column
and the 25th row (starting from the bottom-left corner of the map and counting across and
up). The coordinates of the farthest cell from the roads on land is 25,11. In other words,
this farthest cell is in the 25th column and the 11th row (starting from the bottom-left
corner of the map and counting across and up).
Enter, SPREAD Roads TO 20 OVER Elevation Uphill Only Simply FOR
Roads_uphillprox
Screen-grab the best display of the Roads_uphillprox map surface and insert below
including a descriptive caption …
Figure 2: Roads Uphill Proximity and
Shading Manager.
What is the interpretation of the value “0” (zero) on the Roads_uphillprox map surface?
The value 0 on the Roads_uphillprox map represents the roads themselves. Like the
Roads_simpleprox map, this map shows distance from the roads.
What is the interpretation of the value “20” (twenty) on the Roads_uphillprox map
surface?
The value 20 on the map represents cells that are not within the parameters of being
uphill from the roads and 20 cells. Therefore, these cells (represented in black on the
map) are either downhill from the roads or uphill and 20 cells away. In the case of these
specific cells on the map, none are uphill and 20 cells away; rather, they represent areas
that are downhill from the roads.
Disregarding the “20” values, how far away is the farthest away location from roads on
the Roads_uphillprox map surface?
The farthest location from the roads on the Roads_uphillprox map is 14.90 cells away
and occurs in the northeastern quadrant of the map. According to the map properties,
each cell is 328 ft, so the farthest total distance from the roads on this map is 4887.2 ft
(14.90*328) which is approximately 0.93 miles.
What is the Column, Row coordinates of the farthest location (disregarding the 20
values)? If it is at a different location than reported for the “simple” proximity
calculated previously, explain how this might happen.
The column, row coordinates (as found in the lower-left corner of the display) of the
farther cell from the roads on the Roads_uphillprox map are 25,17. In other words, this
farthest cell is in the 25th column and the 17th row (starting from the bottom-left corner of
the map and counting across and up). This is a different value than the farthest cell from
the roads on the Roads_simpleprox map because the Roads_uphillprox map has the extra
parameter that the farthest cells must be uphill from the roads. On the Roads_simpleprox
map, the farthest location was on the lake, but in the Roads_uphillprox map, the lake
is downhill from the roads and therefore the values that represent the lake are not
considered.
Enter, RENUMBER Covertype ASSIGNING 0 TO 1 ASSIGNING 3 TO 2
ASSIGNING 7 TO 3
FOR Hiking_friction
What do you think the values “0, 3 and 7” mean in terms of the degree of hiking difficulty
in different cover types on the Hiking_friction map you just created?
Figure 3: Hiking Friction and Shading
Manager.
The Hiking_friction map reflect the degree of difficulty of the hiking in the different
covertypes. The red cells on the Hiking_friction map coincide with the Meadow
covertype on the Covertype map, and the value of 3 reflects the degree of difficulty
of hiking in that covertype. The green cells on the Hiking_friction map coincide with
the Forest covertype on the Covertype map, and the value of 7 reflects the degree of
difficulty of hiking in that covertype. The gray cells on the Hiking_friction map coincide
with the Open Water covertype on the Covertype map, and the value of 0 reflects that it is
impossible to hike in this covertype.
The values, 0, 3, and 7 are a quantitative and nominal numerically even though they seem
to be reflecting a quantitative ratio data type in which 0 is the absolute reference point
(reflecting no ability to hike there) and the degree of difficulty increases in (implied)
incremental steps. Since we have no evidence that there are hiking difficulties of 1, 2, 4,
5, or 6 on this scale, we cannot conclude that it is a quantitative data type, and since the
numbers 0, 3, and 7 are not separated by regular intervals, we cannot conclude that they
are ordinal. We must conclude, then, that they are a nominal data type.
Enter, SPREAD Roads TO 100 THRU Hiking_friction Simply FOR
Road_hikingprox
Screen-grab the best display of the Roads_hikingprox map and insert below including a
descriptive caption…
Figure 4: Roads Hiking Proximity and
Shading Manager.
What is the interpretation of the value “100” (hundred) on the Roads_hikingprox map
surface?
The value 100 on the map represents areas that cannot be hiked. For this reason, the
shading manager uses black to represent this value of 100 as an outlier. The reason these
cells are outliers and cannot be hiked is because they represent areas of open water. On
the Hiking_friction input map, water is represented in gray and is given the value of 0 to
indicate that it is not an area that can be hiked. When the Hiking_friction map was used
to create the new map, Roads_hikingprox map, the zero values on the Hiking_friction
map were carried over to the new map, forcing them into the outlier position (see Figure
5 for a visual representation of the data)
Figure 5: Histogram of Roads_hikingprox.
Disregarding the “100” values, how far away is the farthest away location from roads on
the Roads_hikingprox map surface?
The farthest location from the roads on the Roads_hikingprox map is the cell with
coordinates 25, 11 which has a hiking difficulty of 58.90 and is found in the easterncentral portion of the map. The Roads_simpleprox map shows that the cell with
coordinates 25, 11 is 8.41 cells away from the road. According to the map properties,
each cell is 328 ft, so the farthest total distance from the roads on the map is 2758.48 ft
(8.41*328) which is approximately 0.52 miles.
What is the Column, Row coordinates of the farthest location (disregarding the 100
values)? If it is at a different location than reported for the “simple” proximity calculated
previously, explain how this might happen.
The column, row coordinates (as found in the lower-left corner of the display) of the
farther cell from the roads on the Roads_hikingprox map are 25, 11. In other words, this
farthest cell is in the 25th column and the 11th row (starting from the bottom-left corner
of the map and counting across and up). This is the same value than the farthest cell
from the roads on land on the Roads_simpleprox map. The reason the farthest location is
the same on both the Roads_simpleprox map and the Roads_hikingprox map is that the
Roads_hikingprox map uses the same Simple Spread command as the Roads_simpleprox
map but multiplies the calculated values with the values of the hiking friction map. It is
possible that the farthest locations would be different between two maps created with
these commands, but on this Tutor25.rgs map they happen to be the same.
Question 2. Evaluate the following GIS Model that determines the effective proximity
from the Ranch to all other land locations.
Prepare a narrative flowchart (brief text description of the processing flow and spatial
reasoning/logic) with embedded maps that identify…
Input map(s)
Analysis operation
Output map
…for each step. Be sure to generate the best display for each map and include a
discussion of map values and data types for each of the five steps. (Hint: insert 3x2
cell table with the graphics pasted in the top cells and the corresponding titles/captions
entered into the lower cell. Below the table summary, enter your overall discussion
relating the processing).
Step 2:
Figure 6: Roads.
Figure 7: Renumber
Command.
Figure 8: Allroads.
Step 2 uses the Renumber command to create a map of all the roads (Allroads) from a
map that categorizes the roads (and intersections) according to their quality. Whereas the
Roads map provides detail about the types of roads on the map, the Allroads map shows
where roads are and where roads are not. In this sense the Allroads map provides binary
numeric data that provides a qualitative level of analysis (roads or no-roads). The Roads
input map also provides qualitative data but it is ordinal because there is some order to
the distribution, but the steps between categories are not constant. Both maps present
choropleth geographic distributions, and as such, are best presented in 2D Grid displays
with the layer mesh on.
A possible real-life application for the Allroads map is a
simple road map that may be used by hunters to know where
roads are, and consequently where undisturbed areas of
wilderness are as well. Hunters may not need to know the
quality of the roads in the area, but they may want to know
where the roads are since the animals would probably be
away from those areas.
Step 3:
Input Maps
Figure 9: Hiking Friction and Allroads.
Command and Output Map
Figure 10: Cover Command.
Figure 11: Biking and Hiking Friction.
Step 3 uses the Cover command to combine the Allroads and Hiking_Friction maps. Both
input maps and the output map use qualitative numeric data, but the Hiking_Friction map
and Bike_Hiking_Friction maps use ordinal data while the Allroads map uses binary
data. All three maps are choropleth geographically, and 2D Grid displays with the layer
mesh turned on best represent these data types.
The output map essentially shows the areas for hiking and their levels of difficulty, as
well as the areas for biking (i.e. the roads). The map presents the roads as easy to bike on
with a value of 1, compared to the more difficult areas to hike with values of 3 and 7. The
lake and pond are represented with the value 0 which means that these areas are neither
suitable to bike or hike.
A possible real-life application for the Bike_Hiking_Friction map is a map for outdoor
activity in an area. By referring to this map, bikers and hikers can determine where they
can bike and hike and the relative difficulty of biking or hiking in specific areas.
Step 4:
Figure 12: Locations.
Figure 13: Renumber
Command.
Figure 14: Ranch.
Step 4 uses the Renumber command to re-categorize the Locations map in such a way so
as to isolate the ranch on the output map. The input and output maps both use qualitative
numeric data, but the Location map has a nominal numeric distribution while the Ranch
map uses binary data. Both maps are choropleth geographically, and 2D Grid displays
with the layer mesh turned on best represent these data types.
The values on the Location map are nominal because, though the locations seem to be
ordered, there is no pattern in their distribution.
A possible real-life application for the Ranch map is a map showing where an oil
company could drill on a specific area of land. Hypothetically, the oil company could
drill anywhere in within the area except on the ranch.
Part 5:
Input Maps
Figure 15: Ranch and Bike Hiking Friction input maps.
Command and Output Map
Figure 16: Spread Command.
Figure 17: Ranch Proximity.
Step 5 uses the Spread command to show the shortest effective distance from the ranch
on the map. It does this by starting with the Ranch map and Bike_hiking_friction map
and analyzing the distance from the ranch through the Bike_hiking_friction map values.
In other words, it shows how long it takes to travel from the map to each point on the
map considering the difficulty of traveling through each area (represented by cells) on the
map. The Ranch Proximity map shows that the farthest (or hardest to reach) places on the
map by either biking or hiking and considers this out to 50 cells. The eastern-central cells
of the map (which represents the top of the mountain) are the hardest to reach by either
biking or hiking at 50 cells away, and the lake and the pond are also considered 50 cells
away because it is impossible to bike or hike to these locations.
The Ranch_prox output map displays quantitative ratio numeric data, even though the
input maps are qualitative. The Ranch_prox map calculates the distance (quantitative
data) starting at zero and counting up (i.e. ratio data) through the nominal data of
the Bike_hiking_friction map. Although the distances in the Ranch_prox map are
quantitative, since this quantitative distribution is confined to the nominal distribution of
the input maps, the Ranch_prox data has a choropleth geographic distribution. Therefore,
like the input maps, the Ranch_prox output map is best displayed in a 2D Grid display
with the layer mesh on.
Part 2 – Identifying optimal path(s) and path density
Question 3. Complete analysis operations below and describe the processing/logic that
occur at each step. Be sure your answer contains embedded maps and discussion that
identifies…
Input map(s)
Analysis operation
Output map
… including a discussion of map values and data types.
a) More on travel-time analysis—
RENUMBER Locations ASSIGNING 0 TO 1 ASSIGNING 0 TO 3 THRU 5 FOR
Cabin
Figure 18: Locations.
Figure 19: Renumber
Command.
Figure 20: Cabin.
The above table shows the input, algorithm, and output map for the renumber command
when processed for locations. In this case, the renumber command was used to define
the location of Cabin (2 in Figure 18) as one and the remaining locations as 0 (process is
shown if Figure 19). The output map from this function is shown in Figure 20, where the
location of Cabin (ID 1 in the legend) is isolated as the only distinguished feature on the
map.
The data display for Figures 18 and 20 is 2D grid with a mesh layer, discrete, and
qualitative, and the geographic distribution is choropleth. The data type for Figure 18 is
nominal, while the data for Figure 20 is binary.
STREAM Cabin OVER Ranch_prox Simply Steepest Downhill Only FOR
Cabin_route
Input Maps
Figure 21: Cabin And Ranch Proximity.
Command and Output Map
Figure 22: Stream Command.
Figure 23: Ranch to Cabin Route.
Figure 21 shows the input maps (Cabin and Ranch_proximity) used for the Stream
command. The Stream command (shown in Figure 22) was used to identify the
optimal path from the cabin to the ranch by identifying the steepest path possible
between the ranch and cabin locations. The output map, shown in Figure 23, shows the
calculated route. This map clearly shows that the optimal route from the cabin is not
a straight line. The route defined corresponds with one of the roadways located in the
Biking_hiking_friction map (Figure 15).
The cabin image data is the same as explained in step 1. The data type for the
Ranch_proximity is continuous, quantitative, ratio, and isopleth data, with a 2D grid
display accompanied by a mesh overlay. The data type for Figure 23 is discrete, binary,
choropleth, 2D Grid with a mesh display.
Extend Discussion?
COMPUTE Cabin_route Times Ranch_prox FOR Cabin_route_details
Input Maps and Command
Figure 24: Cabin Route (from Ranch)
Ranch Proximity.
Output Map and Shading Manager
Figure 25:Compute
Command.
Figure 26: Cabin Route Details and Shading Manager.
Figure 24 above shows in the input maps, cabin route and ranch _proximity, used to
compute (Figure 25) Cabin_Route_Details, shown in Figure 26. The compute function
acts as a calculator in that it performs basic mathematic functions on the input maps to
produce the output map. In this case, the input maps were multiplied together to yield
Figure 26.
Extend Discussion?
COMPUTE Cabin_route Times Roads FOR Cabin_route_deatils2
Input Maps and Command
Figure 27: Cabin Route (from Ranch) and Roads.
Output Map and Shading Manager
Figure 28: Compute
Command.
Figure 29: Cabin Route Details 2 and Shading Manager.
Figure 27 shows Cabin Route and Roads maps that were used to generate the Cabin
Route_Route_details2 map shown in Figure 29. The input maps both show discrete,
qualitative, and choropleth data displayed in 2D grid form with a mesh overlay.
However, the Cabin Route image shows binary data while the Roads map shows nominal
data.
The compute command in this case was used to multiply the two input maps together to
yield a second map (Figure 29) that displays details about the cabin route from the ranch
location.
The output map, Figure 29, shows the relative ease of traveling from the ranch to the
cabin. The Road map gives the values 1 to 4 to the roads on the map with 1 being
the worst quality road (Poor Road) and 4 being the best quality road (Heavy Duty
Road) (see Figure 27). In the case of the Cabin Route, the Light Duty Road (category
2) and the Poor Road (category 1) are used, and one cell of Medium Duty Road is on
the route at the point where the Poor Road and the Light Duty Road intersect. On the
Cabin_route_details2 map, the Light Duty Road is the light green range, the Medium
Duty Road is the dark green range, and the Poor Road is the yellow range.
b) Overland flow analysis—
DRAIN Entire OVER Elevation Simply Steepest FOR Flowmap
Figure 30: Drain Command.
Figure 31: Flowmap and Shading Manager.
Figure 30 shows the command used to perform the Drain algorithm in the Tutor25.rgs
project area. The Drain command is used to show the number of optimal paths present
from a location or set of locations within the study area. The more optimal pathways
present from a specific location, the higher the density.
Figure 31 shows the resulting image when the drain function is executed throughout the
project area with respect to elevation. Flowmap shows that the highest drainage occurs
along the perimeter of the project area on the south, west, and northern sides. The one
area with the highest drainage, shown by the green grid cell located in the southwest
corner of the image, has
DRAIN Forests OVER Elevation Simply Steepest FOR Forest_flow
Figure 32: Drain Command.
Figure 33: Flowmap.
Figure 34: Shading Manager.
Discussion?
Part 3 – Determining visual connectivity
Question 4. Complete the analysis below (not necessarily verbatim commands) that
calculates visual exposure to roads and housing. Screen grab the important map(s) and
prepare a narrative description of each processing operation.
Radiate Roads over Elevation to 100 completely for Ve_roads
Figure 35: Visual exposure from roads,
and accompanying shading manager for
tutor25.rgs project area.
The Radiate command is used to show visual exposure from a defined location (in this
case, roads through the project area).
Figure 35 shows visual exposure from the roads that run through the Tutor25.rgs project
area. The shading manager uses 13 equal ranges to represent the data in geographic
space. The number of ranges was raised from 7 to increase the resolution. With any more
than 13 ranges, the visual change in the display was minute and not particularly useful.
This continuous, quantitative, ratio data set is best displayed in a 3D Lattice display with
a mesh overlay.
This map could be useful in places with tourism that highlights scenic beauty that can be
seen from road routes. Alternatively, such a map could be used to show clients areas of
low visual exposure that might be useful places for hiding unsightly objects, such as a
trash dump.
Slice Ve_roads into 5 ZeroFill for Ve_roads_sliced
Figure 36: Visual exposure from roads,
grouped into 5 categories, and the
accompanying shading manager for
tutor25.rgs project area.
The Slice command separates continuous or discrete data into new categories based on
user-defined criteria. In this case, the input map (Ve_roads) is a continuous data set, and
the output map (Ve_roads_sliced) is discrete.
Figure 36 uses a 2D Grid display with a mesh overlay. The data is ordinal, qualitative,
and discrete, and the geographic distribution is choropleth.
The category that shows the highest visual exposure (19.2% of gridded area) is labeled
1 and represented in red on the legend and the map. Pink represents the lowest visual
exposure (7.36% of the gridded area). The category with the greatest coverage is the
Medium-High Visual Exposure category (represented in green) at 30.24% of the gridded
area. Also, note that the categories ranging from low to high indicate the amount of area
that can be seen from the locations as opposed to the lowest and highest points (in terms
of elevation) that are visible from the locations on the map.
Like the Ve_roads map above, this map could be useful in places with tourism that
highlights scenic beauty that can be seen from road routes. The sliced map simplifies the
display by categorizing the visual exposure into 5 easy-to-follow groups that would be
particularly useful when dealing with clients.
Radiate Housing over Elevation to 100 weighted for Ve_housing
Figure 37: Visual exposure from housing,
and the accompanying shading manager for
tutor25.rgs project area.
The Radiate command is used to show visual exposure from a defined location (in this
case, housing through the project area).
Figure 37 shows visual exposure from the housing located in the Tutor25.rgs project area.
The shading manager uses 13 equal ranges to represent the data in geographic space. The
number of ranges was raised from 7 to increase the resolution. Once again, using any
more than 13 ranges does not seem to be particularly useful because it does not increase
the resolution in a noticeable way.
This continuous (isopleth), quantitative, ratio data set is best displayed in a 3D Lattice
display with a mesh overlay.
This map could be useful for home owners or real estate agents. The map highlights
areas of high visual exposure on the map. By combining this map with a map that
highlights the scenic beauty (or lack of scenic beauty) of areas on the map, home buyers
could easily determine which areas on the map would be the most desirable places for
purchasing a home on the map.
Slice Ve_housing into 5 ZeroFill for Ve_housing_sliced
Figure 38: Visual exposure from houses,
grouped into 5 categories, and the
accompanying shading manager for
tutor25.rgs project area.
The Slice command separates continuous or discrete data into new categories based on
user-defined criteria. In this case, the input map (Ve_housing) is a continuous data set,
and the output map (Ve_housing_sliced) is discrete.
Figure 13 uses a 2D Grid display with a mesh overlay. The data is ordinal, qualitative,
and discrete, and the geographic distribution is choropleth.
The category that shows the highest visual exposure (54.08% of gridded area) is labeled
1 and represented in red on the legend and the map. Pink represents the lowest visual
exposure (14.08% of the gridded area). The category with the greatest coverage is the
High Visual Exposure category (represented in red) at 54.08% of the gridded area.
Like the Ve_housing map above, this map could be useful for home buyers looking for
locations that have high levels of scenic beauty and low levels of undesirable views.
The sliced map simplifies the display by categorizing the visual exposure into 5 easy-tofollow groups that would be particularly useful when dealing with clients.
Analyze ve_roads_sliced with ve_housing_sliced Ignoring PmapNull mean
for Vexposure
Figure 39: Visual exposure from houses
and roads, grouped into 9 categories, and
the accompanying shading manager for
tutor25.rgs project area.
Figure 39 shows Vexposure for the Tutor25.rgs project area. The color ramp was once
again changed to allow easier interpretation of the map values by using a consistent
color ramp. According to the values, the highest visual exposure is in dark green around
located in the North western area, as well as a small portion in the north eastern area, near
the top of the mountain. The lowest areas of visual exposure are located along the eastern
side of the project area. The numbers accompanying each color in the legend represent 9
sliced categories based on the values for the housing and roads maps.
The category with the greatest area is 1.5 at 382.814 acres (24.8 % of gridded area), while
the category with the smallest area is 4.0 at 66.684 acres (4.32 % of gridded area).
Slice Slope into 3 ZeroFill for Slope_sliced
Figure 40: Slope map grouped into 3
categories, and the accompanying shading
manager for tutor25.rgs project area.
The Slice command separates continuous or discrete data into new categories based on
user-defined criteria. In this case, the input map (Slope) is a continuous data set, and the
output map (Slope_sliced) is discrete.
Figure 40 uses a 2D Grid display with a mesh overlay. The data is ordinal, qualitative,
and discrete, and the geographic distribution is choropleth.
The category that shows the area with the gentlest slope (43.53% of gridded area) is
labeled 1 and represented in red on the legend and the map. Blue represents the areas
with the steepest slope (15.04% of the gridded area). The category with the greatest
coverage is the Gentle Slope category (represented in red) at 43.53% of the gridded area.
The areas with the steepest slope are generally located in the northeast quadrant of the
map, while areas of gentle slope are located generally around the perimeter of the map.
This map could be useful for displaying areas with high landslide or avalanche risk.
Also, this map could be useful to hikers, as the areas with steeper slopes will be more
challenging than the areas with gentler slopes.
Compute Slope_sliced times 10 plus Vexposure for Ve_slope
Figure 41: Computed Visual Exposure and
Slope maps, grouped into 28 categories,
and the accompanying shading manager for
tutor25.rgs project area.
The Compute command combines the input map, re-coding the categories to make new
categories for the output map. In this case, the slope map is multiplied by 10 and added to
the Visual Exposure map to create a two digit code (with one decimal place in this case)
to separate the data into distinct categories.
The two input maps use discrete data and the output map is consequently also discrete.
Figure 41 uses a 2D Grid display with a mesh overlay. The data is ordinal and qualitative,
and the geographic distribution is choropleth.
The category that shows the area with the gentlest slope and lowest visual exposure
(10.24% of gridded area) is labeled 11 and represented in red on the legend and the map.
Brown represents the areas with the steepest slope and highest visual exposure (0.64% of
the gridded area) and is labeled 35 on the map legend. Easter green represents the areas
with moderate slope and medium visual exposure (3.2% of the gridded area) is labeled
23. The category with the greatest coverage is the Moderate Slope Medium-low Ve
category (represented in forest green) at 14.24% of the gridded area.
The areas with the gentlest slope and lowest visual exposure are generally located in the
eastern side of the map. The area with the steepest slope and highest visual exposure is
in the northeast quadrant of the map. The areas with moderate slope and medium visual
exposure are generally in the mid-western portion of the map.
Due to the difficulty of interpreting Figure 46, the color scheme in the shading manager
was changed into 3 general color groups to identify the areas coinciding with Gentle
slope (red-purple), Moderate slope (green-blue), and Steep slope (yellow-brown). The
color ramp within each category was used to represent low visual exposure to high visual
exposure within each slope classification.
a) Explain the meaning of the values on the “ve_roads” and “ve_housing” maps and the
difference between “completely” and “weighted” options., and the interpretation of the
values on the “ve_” maps.
For both the ve_roads and ve_housing maps, the color ramp shows areas of highest visual
exposure in dark green, while areas of lowest visual exposure are shown in red.
The “weighted” option in the Radiate command takes into account viewer preference and
sums the preferences in conjunction with the visual connections, while the “completely”
only counts the visual connections for each location.
Based on the ve maps, the visual exposure for the roads is higher over a greater area than
for the housing locations. This is because the housing locations represent individual cells
and cannot move, while the road locations cover a greater number of cells allowing more
visual exposure.
b) Interpret the values on the “Vexposure” and the “Ve_slope” maps.
Figure 44 shows Vexposure for the Tutor25.rgs project area. The color ramp was once
again changed to allow easier interpretation of the map values by using a consistent
color ramp. According to the values, the highest visual exposure is in dark green around
located in the North western area, as well as a small portion in the north eastern area, near
the top of the mountain. The lowest areas of visual exposure are located along the eastern
side of the project area. The numbers accompanying each color in the legend represent 9
sliced categories based on the values for the housing and roads maps.
The category with the greatest area is 1.5 at 382.814 acres (24.8 % of gridded area), while
the category with the smallest area is 4.0 at 66.684 acres (4.32 % of gridded area).
Figure 46 shows the image for ve_slope. The category that shows the area with the
gentlest slope and lowest visual exposure (10.24% of gridded area) is labeled 11 and
represented in red on the legend and the map. Brown represents the areas with the
steepest slope and highest visual exposure (0.64% of the gridded area) and is labeled 35
on the map legend. Easter green represents the areas with moderate slope and medium
visual exposure (3.2% of the gridded area) is labeled 23. The category with the greatest
coverage is the Moderate Slope Medium-low Ve category (represented in forest green) at
14.24% of the gridded area.
The areas with the gentlest slope and lowest visual exposure are generally located in the
eastern side of the map. The area with the steepest slope and highest visual exposure is
in the northeast quadrant of the map. The areas with moderate slope and medium visual
exposure are generally in the mid-western portion of the map.
c) Suggest a real-world application that might use this visual exposure analysis.
As mentioned above, the visual exposure analysis would be useful for analyzing locations
of high exposure to “scenic beauty,” that can be used for real estate agents to market
property to home buyers. It can also be used to show project managers locations of
low visual exposure so they can put ugly features in these locations (i.e. dumpster),
and locations of high visual exposure that can be used for beautiful features (i.e. art,
architecture)
Question 5. The following questions require you to solve the problems with no
guidance—your group is on its own (email if clarification is needed …no penalty). In
solving the problems you will use combinations of reclassify, overlay and distance
operations that you have studied in this and the previous exercise set. Complete any
three of the following five questions (that means don’t do two unless you want to
complete them for extra credit …see Optional Question 4-1).
Q5-1) Using the Tutor25.rgs database, determine the average visual exposure
(Vexposure map from question #4) for each of the administrative districts (Districts
map). Screen grab the important map(s) and briefly describe your solution as a
narrative flowchart.
Figure 42: Visual Exposure.
Figure 43: Composite
Command.
Figure 44: Average Visual
Exposure of Districts.
The Vexposure map is the input map, and the Composite command uses the Districts
map to “cookie-cutter” the average Vexposure for each region category on the District
map.
The district with the highest Vexposure is
Q5-4) Using the Island.rgs database, create a map that identifies locations that are
fairly steep (15% or more on the Slope map) and are westerly oriented (SW, W and NW
on the Aspect map) and are within 1500 feet inland from the ocean as depicted on the
Land_mask map (hint: the analysis frame cell size can be identified by right-clicking and
then selecting Properties Source tab on any map display). Screen grab the important
map(s) and briefly describe your solution as a narrative flowchart.
Figure 44: Flowchart showing the process used tocompute the
Slope_Aspect_Coastprox_pref over Elevation map.
Figure 44 shows the process used to determine the preferred areas in the island.rgs
project area. The input criteria were slopes greater than 15%, westerly facing aspects,
and areas within 1500 ft. (18.18 cells) of the coastline. Using the renumber function,
the Land_mask was used as an input map to produce the Land_mask_pref ma that
reclassified the ocean as 0 and land as -1. This was done so the spread function could be
used to calculate land proximity to the coast as opposed to ocean proximity to the coast
(computation that would occur if values weren’t changed). The second step used the
Land_mask_pref map as the input to the Compute algorithm the spread from the coast
line up to 1500 ft. (18.18 cells) away. Finally, the coast_prox map demonstrating areas
within the designated distance from the coastline was produced using the Renumber
function on the Land_mask_pref map.
Step 2 involved renumbering the slope map so that areas with a slope greater than 15%
were classified as a 1, while areas with a slope less than 15% were classified as a 0.
The resulting data for this map is binary with a 2-D grid mesh display and a choropleth
geographic distribution.
The third step used the Aspect map as an input for the renumber function to produce
Aspects_westerly, which displays areas that only have western aspects. The display type
is 2-D grid with a mesh display. The data type is binary and qualitative with a choropleth
geographic distribution.
Step 4 uses the output maps from steps 1, 2 and 3 to compute the output map
Slope_aslpect_coastprox_pref. This map is shown in a 2D-grid display with a mesh
overlay, and the data type is binary, qualitative with a choropleth distribution.
The final step takes the Slope_aspect_coastprox map and lays it over a 3D elevation layer
so the locations with the defined criteria can easily be seen. The display is a 3-D grid
with a mesh overlay, the data being displayed is discrete, binary, quantitative data with
a choropleth display shown over a 3D- map of elevation (quantitative ratio data with an
isopleth geographic distribution).
Figure 45: Shows the preferred slope and
aspect proximity to coast line using compute
function and accompanying shade manager.