Introduction:
For this week’s activity, my class and I went about setting
up a data collection procedure by creating a geodatabase with feature classes
and deploying them to a Trimble Juno GPS unit to collect data at the Priory in
Eau Claire (Figure 1). The main purpose of this activity was to create a database for the
Priory with information on the area for recreational purposes and to help
restore it in the future. We were able to work in groups and choose one or two
features that we wished to map. Then we had to plan the fields and domains that
would accompany the features we would include for our portion of the map. There
were several options to select from including trails, view points, erosion
points, trees—dead or notable—or various man-made objects. My group and I decided
to focus on the man-made objects by mapping any signs or garbage that we found
in the Priory.
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| Figure 1: This is the Trimble Juno unit we used to collect data in the field. |
Study Area:
The area we have been working in is known as the Priory and
is owned by the University of Wisconsin-Eau Claire. It is located on the south
side of the city of Eau Claire in Wisconsin in a more rural setting. The Priory
is a hilly area with several steep ridges and gorges and is mostly wooded. There are some open areas near the Priory building, a house, and a
waste pond located on the site, however (Figure 2).
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Figure 2: This is an aerial image of the Priory in Eau
Claire, Wisconsin. The outline defines our navigation boundary. As can be
seen, most of the area is covered in trees.
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Methods:
Once we had chosen the two features that we wished to map,
my group and I had to come up with the fields and domains that each feature
would include. For the garbage, we decided to create three attributes—material,
size, and type of garbage. Then, we came up with several options for each attribute
and made domains from these. The domain type that was appropriate for this
situation was a coded value domain. The coded value domain allows the user to
specify specific options for the attribute that will later be selected from a
drop down menu. Once the values are set in the domain, the user cannot choose
any other value. For example, for the garbage material attribute, we created
four values that could be potential material types—plastic, paper,
aluminum/metal, and other. Then we set up the coded value domain to text and
entered in these options. Later, in the field, we could only select one of
those four material types. The advantage
of setting up the domain in this way is that there is less user error through
misspelling and it is faster/easier to enter in the values while in the field. The
rest of the attributes and domains for the garbage and signs can be seen in the
following table. Every domain was set to be a coded value text domain. All of
this work can be done within ArcCatalog and ArcMap and should be saved to a
geodatabase.
Figure 3: This is a table that includes the features and their attributes and domains. We used these to collect the features in the field and describe them by their attributes.
After the data had been set up in Arc, we had to deploy our
features to a Trimble Juno GPS unit. Before doing so, however, we had to change
the symbology of our features so that they could be easily recognized in the
field. We also had to ensure that the fields and domains were set correctly. We
used the ArcPad Data Manager Extension and toolbar to do this. Basically, we
converted the map we created in ArcMap (.mxd file) to an ArcPad map (.apm file).
ArcPad is simply software designed for mapping purposes on a mobile device. From
that point, we just copied and pasted the .apm file into the SD card of the
Juno GPS unit which we could pull up on the unit in ArcPad.
With the geodatabase and data deployed to our
units, we were ready to collect the data in the field. This was a relatively
intuitive process. We brought up our maps on our respective GPS units and then
selected the appropriate feature we were mapping as we stood near it. Once
selected, we had to fill in the fields we created earlier with the values generated
within our domains. My group and I also decided to pick up the garbage we were
mapping along the way to help clean up the Priory as well. After collecting the
data, we uploaded our points back into ArcMap and were able to view the
results.
Results/Discussion:
Although we were unable to collect data throughout the
entire Priory, we found some patterns with our data. Most of the garbage we
found was near the parking lot area and along the tree line going into the
forest. Once we were in the forest, however, we found little to no garbage. As
for the signs, we found two distinct areas/types for the most part. The first
was a group of parking signs surrounding the parking lot. The second was a
group of signs that delineated a trail through the woods. Other than these,
there were few signs to be found.
Figure 4: This is a map displaying the locations of garbage and the garbage material. Most of the garbage we found was plastic.
Figure 5: This map shows the garbage size. Most of the garbage we found were small items. We had one point that was actually a large group of garbage that we labled "other".
Figure 6: This map shows the garbage type by location. We found a wide variety of garbage around the area, so most of the garbage types are classified as "other".
Figure 7: This is a map displaying the locations of garbage for each group member. As can be seen, most of the garbage we found was near the side of the building in the center. This is likely due to the fact that there were two dumpsters near that spot, and the garbage flew out of the dumpster onto the ground.
Figure 8: This is a map of the signs we located represented by their color. Most of the signs were an orange color because they were trail markers along one of the trails in the Priory.
Figure 9: This is a map of sign material. A vast majority of the signs were made of metal, though we found one that was made of wood.
Figure 10: This is a map displaying the location of different types of signs. We had a pretty even split between navigational signs and informational signs. The navigation signs were trail markers and the informational were parking signs along the large parking lot near the building in the center.
Figure 11: This is a map of the signs collected by each group member. As can be seen, most of the signs followed a path along the northern part of the image and another large group surrounded the parking lot in the southwester part of the map.
This project, though seemingly simple, turned out to be a
bit difficult for the class. My group and I had little trouble using the GPS
units to collect our data, but other groups struggled with technical problems
with their GPS units and setting up their domains. The only issue my group and
I had was that we did not have the exact same fields for our data. One member decided
to omit the “Shape” field for our signs while another did include it. We also
forgot to include a notes field for the garbage feature, so we could not write
any additional information on those points. In the grand scheme of things, this
was a minor issue. We also were unable to walk throughout the entire Priory, so
our data is limited to the southwestern section of the area.
Conclusion:
Overall, this project helped the class learn how to set up
data for data collection from the ground up. We created our own geodatabases
and came up with features with unique attributes and domains. This is an
extremely valuable and powerful skill and will likely be used in the future. The
project also helped us to understand more about domains, how they work, and why
they are useful. Then, we used this data to collect the locations of signs and
garbage out in the Priory. My group and I were fortunate enough to run into
very few issues with our data, though we were unable to completely survey the area
we were working in.
