Lab 8: Raster Geoprocessing
Overview
Purpose
This week you will shift our focus to analyzing raster datasets. Raster analysis allows much more
flexibility for certain types of overlay analysis, for instance when you are looking for very small
geographic areas that meet large numbers of criteria. In this lab, you will be introduced to some
of the most critical raster analysis functions available in ArcGIS and will then apply them to
solve several environmental planning problems. Among the functions you will learn are the
creation of slope & aspect generation, raster queries, raster calculations, generation of
distance rasters, summarizing data via zonal statistics, viewshed analysis, and raster analysis
masks.
What you will need
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ArcGIS Pro
An internal clock reminding you to save your project “every so often” so you don’t lose
any unsaved work.
Lab 8 Manual – As a friendly reminder, it helps to read through the whole manual
before you start working.
The Lab 8 data (downloaded from Brightspace)
Submission
You are to submit all your work for this lab using Brightspace. Your lab must be submitted
before the deadline posted in Brightspace.
Notes
1. Use the recommended file names for any new data that you produce in this lab. Data
created this week may be used in subsequent lab assignments.
2. Remember, DO NOT USE spaces in filenames! If the pathname to your project folder
contains any spaces, there is a chance that the raster geoprocessing tools may fail in
strange ways (with less than useful error messages to help you diagnose the problem).
This is true of any folder in the pathname.
a. Good pathname: Q:\student\NR6430\Lab8
b. Bad pathname: Q:\student\NR 6430\Lab 8
3. Some of your answers in the lab might not match exactly with available choices. Pick the
closest answer but be careful to double-check your results if your answers are not close
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to the available choices. There should not be large differences between the values you
derive, and the available choices listed on Brightspace.
4. Take some time now to skim through the whole lab.
Part 1: Organize Your Data
Create a new project and unzip your data
1. Create a new ArcGIS Pro project called Lab8. Again, it is very important that the
2.
3.
4.
5.
pathname to your Lab8 directory does not contain any spaces.
Download the Lab 8 data package from Brightspace (always a good idea to put it in a
folder below your project called Downloaded), unzip the data and explore the result.
There are several datasets for Chittenden County and the Town of Richmond.
a. At the county level the data includes:
i. a Digital Elevation Model (from the National Elevation Dataset –
dem_chit.tif),
ii. land cover (from the National Land Cover Dataset – lc_chit.tif), and
iii. major roads (ChittendenRoads.shp).
b. For the town of Richmond there are:
i. parcel boundaries (RichmondParcels.shp),
ii. trails (RichmondTrails.shp),
iii. two Shapefiles showing proposed cell phone communication tower sites
(Tower1.shp and Tower2.shp), and
c. a Layer File (lc_chit.lyrx) that is used to symbolize the land cover data.
Import all the downloaded shapefiles into your geodatabase (Lab8.gdb). As usual, you
will want to work with geodatabase feature classes instead of the downloaded
shapefiles.
You will use a new geoprocessing tool, Raster to Geodatabase tool to import the two
raster datasets (lc_chit.tif and dem_chit.tif) to the Lab8 geodatabase.
Part 2: Create a Shaded Relief Map of Richmond
Suppose you are the Town Planner for Richmond, VT. You are busy helping to revise the Town
Plan, while also working to create maps and run analyses for various project needs. The first
order of business is to create an attractive map of the town’s land cover.
1. Add ChittendenRoads and the DEM (dem_chit) to your Lab8 Project. Use the
Geodatabase Raster stored in your Project Geodatabase, not the file you downloaded
which is in GeoTIFF (.tif) format.
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2. You also want to include land cover data, but instead of adding the raster dataset, add
the land cover Layer File (lc_chit.lyrx) (Hint: you will find it in your Downloaded data
folder).
3. Chances are that when you add the Layer File to the Map Pane, you will see an
exclamation point next to it. This happens when ArcGIS cannot find the data source (the
actual lc_chit raster dataset) to use with the symbology rules that are stored in the
Layer File. (Even if the data appear as expected, you should follow the instructions here
to ensure you are using the correct version of the data.) To fix this, open the Layer
Properties interface for lc_chit and select the Source tab. Click Set Data Source, now
browse to your Lab8 geodatabase, select the lc_chit raster dataset and click “OK” and
“OK.” You should now see a properly rendered land cover dataset a customized color
scheme.
Question 1: The cell size of lc_chit and dem_chit are equal.
True
False
4. The study boundary for today’s lab is Richmond, VT. Add RichmondBoundary (the town
boundary of Richmond) and create a 2 km buffer around the polygon. Save the result as
RichmondBuffer_2km.
Stop! Before proceeding any further, you must specify your Environment Settings to control
the way that raster geoprocessing occurs within your ArcGIS project.
5. Select the Analysis Tab, and then click on the Environments button (in the
Geoprocessing Section) to open the Environments Interface. Under the Processing
Extent section set the Extent parameter to “RichmondBuffer_2km”. The Processing
Extent sets the spatial extent of future selections (for vector data) and geoprocessing
(for raster data). A group of coordinate boxes populated with the same bounding
coordinates as the Richmond buffer feature class appears below the Extent setting.
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(NOTE: If you want to confirm this, you can look at the Layer Properties -> Source ->
Extent for RichmondBuffer_2km.)
6. Next, in the Raster Analysis section set the Cell Size to “Same as layer Land Cover” and
the Mask to “RichmondBuffer_2km”. Again, make sure that the land cover layer is the
lc_chit raster from the geodatabase. The Cell Size parameter specifies the grid cell size
for all data derived in the ArcGIS Project, while the Mask specifies the region within
which data processing occurs. Data outside the mask will not be processed.
7. Also, set the Snap Raster to “Land Cover” to ensure that any raster datasets you derive
during the lab directly align with the land cover data. Note: Make sure that this is set to
the lc_chit raster in the Lab8 geodatabase and not the land cover .tif file in the
downloaded folder.
8. Click OK to finalize your Environment Settings.
Question 2: Take a screenshot of the Environments Interface and store it
as a JPEG (*.jpg). Insert the image into Brightspace for review. Be sure
that the settings you just customized are all clearly visible.
In lecture, you learned about several surface analysis tools contained in the Spatial Analyst
Tools toolbox. You can look for them in the Spatial Analyst toolbox or search for them. You will
use these tools for your Lab 8 analysis, starting with the Hillshade Tool. Recall, a hillshade
provides the shadows for a terrain relief map. The hillshade is for display purposes only. You
can drape any raster or vector dataset over the hillshade to create a “painted” 3D effect –
terrain map.
9. Search for the Hillshade Tool (or open the tool from Spatial Analyst Tools -> Surface ->
Hillshade).
10. Set the Input Raster to dem_chit and the Output Raster to RichmondHillshade. Accept
the default values for the remaining fields and click Run.
Question 3: What is the maximum value of the RichmondHillshade?
The last step should have produced a shaded relief map of the town of Richmond. Now let’s
explore how you can use the hillshade to alter the appearance of the land cover data.
11. Make sure the land cover data is situated on top of RichmondHillshade in the Contents
Pane and that both datasets are visible. Thus, you may need to rearrange the drawing
order of your map.
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12. Now, select the land cover dataset in the Contents Pane, and then click on the Raster
Layer Tab. Use the Layer Transparency slider bar or manually enter a number to set the
Layer Transparency for the land cover dataset to 40%.
The map should look like the image you see below, and you should be able to
distinguish between the area of the Map Pane that includes the RichmondHillshade and
the area that does not. If not, check to see that your Layer Transparency for the land
cover data is set appropriately.
13. Turn on RichmondBuffer_2km if it is not already displayed. Change the Feature Layer ->
Symbology so it only has a black outline. Now, Zoom to the spatial extent of
RichmondBuffer_2km. Be sure that the Raster Layer Tab is displayed at the top of the
interface, and that the only visible data are the RichmondBuffer_2km , Land Cover, and
RichmondHillshade datasets.
Question 4: Take a screenshot of the entire ArcGIS interface, save it as a
JPEG (*.jpg) and insert the image into Brightspace for review.
To save on disk space and increase processing and analysis efficiency, you will clip the land
cover and DEM raster datasets to the study area extent (RichmondBuffer_2km).
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Clipping a raster dataset is different from clipping a vector dataset. Instead of using the Clip
Tool you learned about previously, you will use a similar tool intended for use with raster data.
14. Search for Clip Raster or Navigate to Data Management Tools -> Raster -> Raster
Processing -> Clip Raster. Set the Input Raster and Output Extent parameters to
dem_chit and RichmondBuffer_2km, respectively.
15. Stop! Be sure to click the check-box indicating that you want to use the Input Features
for Clipping Geometry as seen below highlighted in red.
16. Name the Output raster RichmondDEM.
17. Click Run.
18. Do the same for the land cover data and name the output RichmondLandCover. If you
turn off all layers except these two, new, clipped rasters, you should see that
RichmondDEM covers the same spatial extent as RichmondBuffer_2km.
Note: the newly clipped RichmondLandCover dataset was not rendered using the same legend
symbology as lc_chit (Land Cover). You could recreate that symbology manually; however, it’s
much easier to apply the layer file (lc_chit.lyrx) like you did earlier in the lab.
19. Use the Raster Layer Symbology interface to apply lc_chit.lyr to RichmondLandCover
(Hint: Look for Import from Layer File on a pulldown menu found in the upper right of
the Symbology panel). The colors and labels on the Symbology tab should now look like
those from the initial lc_chit (land cover) layer.
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20. Set the transparency for RichmondLandCover to 40%.
21. Zoom to the spatial extent of RichmondBuffer_2km, turn all data except
RichmondLandCover and RichmondHillshade off.
Question 5: Take a screenshot of the entire ArcGIS interface and store it
as a JPEG (*.jpg). Make sure to the legend for the land cover and
hillshade data are visible in your Contents Pane. Upload your screenshot
for review.
22. Before moving on to the next section, Clip the roads data to the extent of the Richmond
buffer. Name the output RichmondRoads.
23. Insert a new Map Pane and rename it Surface Analysis.
Part 3: Surface Analysis
In this section you will derive slope and aspect layers from the Digital Elevation Model (DEM).
1. Find the Slope Tool (HINT: It’s in the Spatial Analyst Toolbox.)
2. Set the Input Raster to RichmondDEM, the Output Raster to RichmondSlope and the
Output Measurement to DEGREE.
3. Leave the default value for the Z factor and click OK.
4. Use the Aspect Tool to create an aspect layer based on the RichmondDEM (use the
default Method). Name your Output Raster RichmondAspect.
5. Now, change the Raster Layer - Symbology for RichmondSlope. Use the Natural Breaks
Method with 3 classes. (TIP: If nothing displays, switch to “Stretched” display method
and then switch back to “Classify”.)
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Question 6: Take a screenshot of the RichmondSlope classification (in
the Contents Pane) showing just the legend (colors and break values).
Store the image as a JPEG (*.jpg) and upload the file for review.
Question 7: The Aspect Tool uses a ___ by ___ cell moving window to
process the data. (Hint: moving windows were discussed in lecture. What should
you do if you don’t know the answer to this question? Do a search for the ArcGIS
Pro aspect help file.)
Part 4: Development Suitability Analysis
Your office now needs to make a decision regarding a pending residential subdivision permit
application. There are several rules establishing siting guidelines for new development within
Richmond. To make a permit decision for the subdivision application, you need to identify sites
that are not developable for environmental reasons – the presence of wetlands, steep slopes,
high elevation zones and south-facing deciduous forests (what, really? That last one was added
for educational purposes).
You are going to use multiple raster queries to identify the places in town that meet these
criteria. Although this could be done in a single step, you’ll use multiple queries to make sure
you get the hang of things before trying more complex query syntax.
Start by identifying all south facing slopes covered by deciduous forests. Since there are two
conditions to be met here (one for each dataset), deciduous forest and south facing, you’ll use
the AND logical operator for this query.
1. Before you continue, add a new Map Pane and rename it Suitability Analysis. Add the
following data layers to it: RichmondLandCover, RichmondSlope, RichmondAspect,
RichmondDEM, RichmondHillshade, RichmondRoads, and RichmondTrails. (Hint: You
can copy and paste these from the other map panes or add them from the Catalog.)
2. Double check that the Raster analysis environment is still set correctly. Refer back to
Part 2, Steps 5-8 on pages 3 and 4.
3. Open the Raster Calculator (Spatial Analyst Tools -> Map Algebra -> Raster Calculator)
and create a query whose syntax matches what you see in the figure below. Your goal is
to select those locations with deciduous forests that face south. The code for deciduous
forest in the land cover dataset is 41. The aspect data derived from the DEM has a
continuous numeric scale, and you want to select all cells within +/- 10° of true south
(i.e. 180°). Finally, use the AND logical operator demoted by &) to join the two query
components --- land cover class and aspect. Note: You can double click on the raster
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name to add it to the syntax area. Also, pay attention to how each of the criteria are
wrapped in parentheses.
4. Name your Output Raster RichmondStep1.
5. Click Run.
The output from this query is a raster dataset with two values, 0 and 1. As was discussed in
lecture, this is a “truth” raster. The 1’s represent locations that meet the criteria (true values)
and 0’s represent the areas that do not (false values).
6. Render the 1’s with a bright color and make the 0’s invisible by setting their Fill Color to
“No Color.”
7. Turn off all layers except RichmondDEM and RichmondStep1 to view the results of your
query.
Great, you have now identified some of the areas off-limits to development in Richmond, but
you still need to factor in the rest of the criteria.
Next, you’ll combine the results of your first query (RichmondStep1 = 1) with criteria for
wetlands (RichmondLandCover = 61 OR 62), steep slopes (RichmondSlope > 40) and high
elevations (RichmondDEM > 1500).
8. Open the Raster Calculator.
9. Use what you’ve learned earlier in the lab (Part 4 - Step2) to create a query in the Raster
Calculator that identifies all the pixels that meet any of the criteria specified in the
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preceding paragraph. Name your Output Raster RichmondStep2. Hint: There are no
“AND” operators in this query, only “OR” operators. Remember, the “OR” operator in
Raster Calculator is represented by the vertical line symbol (|) known as a “pipe”. Make
sure that each “piece” of the query is surrounded by parentheses (like you see in the
query syntax in section 2 above. Double Hint: The land cover criteria needs to have
double parentheses similar to this ((something here) | (something here))
10. Your output for RichmondStep2 should look similar to the figure below.
11. Open the Attribute Table of RichmondStep2 and examine the attributes. Recall that the
COUNT attribute indicates how many cells in the raster have that particular VALUE.
12. Multiply the number of cells (COUNT) by the cell size (Hint: look at the layer properties
if you don’t remember the cell size) to determine the total size in square meters of areas
identified as off limits (VALUE=1).
Question 8: What is the total area identified as off limits? (pick the
closest answer).
300,000 square meters
5,000,000 square meters
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52,000,000 square meters
130,000,000 square meters
Identifying land suitable for development
Now that you’ve determined locations suitable for subdivision development, it’s time to start
looking for areas in town that meet minimum suitability requirements for a new cabin. You are
interested in two specific criteria – the distance from roads and the distance from trails. You
want to site the cabin far from Interstate 89 and Route 2 (RichmondRoads) but near one of the
town trails (RichmondTrails). To do this, you need to compute a distance surface for each of
these features. A distance surface calculates the distance from a pixel (in a raster dataset) to
the nearest feature (in a vector dataset).
1. To create a distance raster based on RichmondRoads open the Euclidean Distance Tool.
(Ignore the warning about this tool being deprecated.)
2. Set the “Input raster or feature source data” parameter to RichmondRoads and the
“Output distance raster” parameter to DistanceToRoads. Notice that the Output cell
size parameter matches the cell size you specified when you established your
Environment Settings. The distance to roads raster layer you create should look similar
to the figure below but the colors may vary depending on the Symbology.
Question 9: What is the approximate maximum distance to roads within
the 2 kilometer buffer of Richmond (study area boundary)? (Pick the
closest answer)
9,300 meters
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15,500 meters
17,000 meters
29,000 meters
Next, create a cost-weighted distance surface to the trails within the study area. Pixels with
steep slopes contribute more “cost” to the distance value than pixels with gentler slopes.
3. Add the feature class called RichmondTrails (a trails dataset that has already been
clipped to the extent of the buffer).
4. Open the Cost Distance Tool. (Ignore the warning about this tool being deprecated.)
5. Set the “Input raster or feature source data” parameter to RichmondTrails, the “Input
cost raster” to RichmondSlope and the “Output distance raster” to DistanceToTrails.
Click Run.
You now have a raster dataset showing the distance of each pixel to the nearest trail, weighted
by the steepness of the slope steepness between a pixel and the nearest point on the trail.
Therefore, a pixel that is 100m from the nearest trail but is only accessible via steep slopes has
a higher cost-weighted value than a pixel that is 100 m from the nearest trail but is accessible
via flat surfaces.
Question 10: What is the maximum value in the DistanceToTrails
dataset? (Pick the closest answer)
83,000 meters
70.4 kilometers
102,000 meters
159,000 meters
Finally, to complete the analysis, you need to identify locations suitable for a cabin
(RichmondStep2 = 0) and identify those locations that are near trails when accounting for slope
(DistanceToTrails is less than 1,000 m), and far from major roads (DistanceToRoads is greater
than 2,000 m).
6. Create your query using Raster Calculator, and name the output RichmondCabinSites.
7. Display RichmondCabinSites so that the 0’s are invisible, the 1’s are a bright color and
the transparency is set to 40%.
Question 11: What is the total area of suitable cabin sites identified in
the analysis? (Pick the closest answer.)
2,200,000 square meters
3,963,000 square meters
4,880,000 square meters
6,540,000 square meters
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Part 5: Visibility Analysis
Your office has now been asked to review a permit application concerning the siting of a 100m
tall cell phone tower to minimize visual impacts to local residents. You will use viewshed
analysis to identify areas visible from (or able to see) the proposed cell phone tower sites.
Recall from lecture, a viewshed analysis maps those locations on the landscape that are visually
connected to a given point (or series of points) by determining site lines that consider
topography and other visual disruptions. The result of a viewshed analysis is a raster dataset
with values of 1s and 0s, indicating which pixels are visible (1) and not visible (0), respectively.
You will also use zonal statistics to determine whether a tower is visible from a landowner
parcel and how much of the parcel is visible by one or both towers. (Not sure what a parcel is –
hint, we’re not talking about something you receive in the mail. For more information, review
the information found at the Vermont Parcel Program page.)
As discussed in lecture, zonal statistics computes a statistical summary of all pixels (cells) within
some bounding area (also known as a zone). Review the help documents associated with the
Zonal Statistics as Table tool for a complete description of the computed output metrics.
Because a viewshed analysis produces an output raster with only values of 0 and 1, for the
purposes of this analysis, the MEAN value attribute represents the percent of the parcel that is
visible from a cell tower. Note: this is ONLY true when computing zonal statistics on a raster
dataset with values of 0 and 1! In subsequent labs, you will also use Zonal Statistics as Table to
summarize other information like percent tree canopy or forest cover. Remember this!!!
1. Because this is a new analysis, create a new Map Pane and rename it Visibility Analysis.
2. Double check that the Raster analysis environment is still set correctly. Refer back to
Part 2, Steps 5-8 on pages 3 and 4.
3. Add the Tower1 feature class to it. Choose a symbol that is large and easily visible to
display the tower. Also, add or copy the RichmondDEM from one of the other Map
Panes if you haven’t already added it.
4. Open the Viewshed tool (Spatial Analyst toolbox), set the “Input raster” to
RichmondDEM, the “Input point or polyline observer features” to Tower1 and the
“Output raster” to Tower1Viewshed.
5. Accept the other defaults and click Run.
Question 12: How many cells were identified as visible in the Tower1
viewshed analysis?
5,100
32,100
49,400
55,000
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6. Now add RichmondParcels to the Visibility Analysis Map Pane, set the Fill Color to “No
Color” and the Outline Color to a line weight of 0.5 with a color that contrasts with the
other data. It should look similar to the figure below.
7. Add a new field called TowerVisibilityCategory to the RichmondParcels feature class
with Data Type set to Text. Don’t forget to save your changes.
Next, use the Zonal Statistics As Table tool to determine the number of impacted parcels and
the area of impact within each parcel. The Zonal Statistics As Table tool summarizes the value
of all pixels within the bounding geometry of the features of the zone dataset (in this case, each
parcel is a zone).
8. Open the Zonal Statistics As Table tool. Set the “Input raster or feature zone data” to
RichmondParcels, the “Zone field” to PAR_ID and the “Input value raster” to
Tower1Viewshed. Name the “Output table” Tower1ZStats. Accept the other default
values and click Run. (NOTE: You may receive a Warning indicating that not all zones
were rasterized. This means that some of the parcels were smaller than the area of the
cell size. As a result, they are ignored in the analysis. What do you suppose that means
when you join your output table to the parcel data? Still not sure? See OBJECTID = 8 in
the image below.)
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9. Dust of your table joining skills. Join Tower1ZStats to RichmondParcels using PAR_ID
and PAR_ID, respectively. Remember that you are joining the Tower1ZStats table to the
RichmondParcels data so make sure RichmondParcels is the Input table and
Tower1ZStats is the Join table.
10. Open the parcel data attribute table. The second OBJECTID column (see figure below) is
where the results of the zonal statistics (for each parcel (zone)) begin.
Question 13: What does it mean if a parcel has a MAX value of 0?
(Hint: Don’t over think this. Scroll through the table and look at the MIN and MAX values
compared to the MEAN. Remember we are concerned about visibility here, and as noted
above, the MEAN value attribute represents the percent of the parcel that is visible from a cell
tower.)
All of the parcel is visible from the tower
Some of the parcel is visible from the tower
Most of the parcel is visible from the tower
None of the parcel is visible from the tower
Question 14: What does it mean if a parcel has a MAX of 1 and a MIN of
1?
All of the parcel is visible from the tower
Some of the parcel is visible from the tower
Most of the parcel is visible from the tower
None of the parcel is visible from the tower
Question 15: Based on the results of the zonal statistics operation
performed on the Tower 1 viewshed data, how many parcels in
Richmond have at least one cell that has line of sight with the proposed
Tower? (Hint: Use your select by attribute skills here)
352
390
422
497
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Suppose the developer proposes a second tower site and wants you to compare the visual
impacts of each tower to see which site has a less significant impact on the residents of
Richmond.
11. Add the Tower2 feature class, which like Tower1 includes a single point.
12. Run a viewshed analysis for Tower 2. Name the output Tower2Viewshed.
13. Use the Zonal Statistics As Table tool to summarize visibility by parcel for Tower2 (using
the same approach as Tower1). Name the “Output table” Tower2ZStats.
14. Join Tower2ZStats to the parcel layer. (Note: Because you already have the zonal
statistics table from the Tower1 analysis joined to the RichmondParcels feature class,
the Zone Field will be listed as RichmondParcels.PAR_ID instead of simply PAR_ID.
Again, as a friendly reminder, RichmondParcels is the input table and Tower2ZStats is
the join table.
There are now two tables (Tower1ZStats and Tower2ZStats) joined to the RichmondParcels
layer. It is not obvious from the Table View which attributes are from which zonal statistics
output. This confusion carries over into the Select By Attributes interface. However, you can
always switch/toggle to the SQL Mode (in the Select By Attributes interface) to check your
syntax. The attributes for the parcel data, Tower1ZStats and Tower2ZStats begin with
“RichmondParcels.”, “Tower1Zstats.” and “Tower2Zstats.”, respectively.
Suppose the Richmond Selectboard wants to know which tower has a smaller impact on the
residents in their town. They ask you to answer the following questions. Remember, the
viewshed analyses extend beyond the boundaries of Richmond, but the Selectboard is only
interested in the land area inside the town. There are several ways to compute this, and you
have learned at least two approaches in this lab already.
Question 16: How much area within Richmond is visible from Tower1 in
millions of square meters?
7.9
16.4
19.2
21.6
Question 17: How much area within Richmond is visible from Tower2 in
millions of square meters?
7.9
16.4
19.2
21.6
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Map the Visibility Analysis Results
Note: The following steps must be performed in the specified order to achieve the desired
result. You will need to experiment with the different formatting tools and interfaces to alter
the appearance of your Map Layout to match the image below.
1. Query the RichmondParcels data to find all the parcels with at least 25% (i.e. >= 25%) of
their pixels in the line of sight of Tower 1.
2. Set the value of TowerVisibilityCategory to “Tower 1” for the selected records. (Hint:
Calculate the field.)
3. Do the same thing for Tower 2 (i.e. Query the Richmond parcel data to find all the
parcels with at least 25% (i.e. >= 25%) of their pixels in the line of sight of Tower 2) and
set the value of TowerVisibilityCategory to “Tower 2” for the selected records.
4. Next, query the data to select the parcels with at least 25% of visibility of both proposed
tower locations and set the value of TowerVisibilityCategory to “Both Towers”.
5. Select all the records where the TowerVisibilityCategory Is Null, and set the value for
these records to “Neither Tower.”
6. To enhance the cartographic quality of your final output map, select those “parcels”
that are water and roads. Select where PARCELNUM is equal to WATER, set the value of
TowerVisibilityCategory to “Water”. Now, select where PARCELMUM is equal to ROAD
set the value of TowerVisibilityCategory to “Road”.
7. Zoom to the spatial extent of the RichmondBuffer_2km.
8. Create a Map Layout of the RichmondParcels layer draped over the RichmondHillshade.
Adhere to the following guidelines:
a. Use “TowerVisibilityCategory” as the Value Field and select Unique Values as
the display method for the RichmondParcels layer.
b. Use whatever color scheme you want to display the data. Set the Layer
Transparency to 40% for RichmondParcels.
c. Include the two tower locations in the map (using the ArcGIS2D - Radio Tower
symbol).
d. The only visible data in this map layout are: Tower1, Tower2,
RichmondTownBoundary, RichmondHillshade and RichmondParcels.
e. Use the same north arrow that you see in the image below.
f. Create a scale bar using kilometers as the linear unit. The scale bar should have 5
divisions and 0 subdivisions, should span 0 - 5 km, and should be labeled as km
(instead of kilometers).
g. Adjust the data layer names to match those in the legend in the image below.
Center the word Legend and make it Tahoma, 16pt bold font, The Heading (for
the parcels dataset) should be Tahoma, 12pt bold font, and labels should be
Tahoma, 12pt regular. (Hint: To adjust the font formats, double-click on the
Legend Element (in the Map Layout TOC) to open the Format Legend Item
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Interface. Then, use the dropdown menu in the top left of the Format Legend
Interface to select the specific legend item to format.)
h. Completing this will require you to look for functionality we have not covered in
detail previously. Look at the ArcGIS help and online resources to attempt to
figure this out on your own. If you remain stuck after trying to resolve any issues,
ask for help, but be prepared to demonstrate / describe what you've tried.
i. Save the Map Layout as a Web JPEG and insert the file into Brightspace for
review.
Question 18: Export the map layout from ArcGIS as a Web JPEG and
insert the file into Brightspace for review.
Question 19: How did this lab go for you? What did you like? What if
anything did you find challenging?
NR 6430
18