ArcCollector 2: Project Activity

Introduction

          With experience in collecting data in ArcCollector for the microclimate, this activity had individuals plan and implement their own project through ArcCollector. The objective of this project was to develop a model for people to document litter in their surroundings. Trash has only become a bigger problem as time goes on. Besides not looking nice, improperly disposed-of trash can leech harmful chemicals into the environment, harm or kill wildlife, attract disease, and increase general hazards to people. By documenting litter, high litter patterns can emerge and professionals can utilize such information to implement solutions, preventative measures, and clean-ups (Figure 1).

Figure 1: A potential problem area on upper campus where the trees snag fly-away plastic bags.

           Before data collection is even possible, it's crucial to carefully plan and set up the project. Without a properly designed project, a collector will run into problems in the field and in the analysis, like false or inaccurate data points and unavailable documentation elements. In addition, a properly designed project will give accurate, relevant, and informative results.

Study Area

           At, first the study area was established to include UW - Eau Claire campus, Downtown, Owen Park, and a small neighborhood as shown by the red boundary lines in Figure 2. UW - Eau Claire includes upper and lower campus, Putnam Drive, and the property north of the Chippewa River. Downtown included South Farwell Street, Barstow Street, Graham Avenue, Wilson Park, and its perpendicular streets. The neighborhood connected Downtown to lower campus, bordered by the Chippewa River and State Street These study areas were meant to represent different types of spaces - campus, downtown, residential, and park spaces. However, the time to cover the total area was underestimated and made difficult given the weather, minimal daylight hours, and weak phone battery. Therefore, data was only collected in Downtown and the Campus property study areas. The objective of the activity still remains intact - collect litter data for documentation and analyzation. 

Figure 2: An image from ArcCollector of the different study areas and the where data collection took place.

Methods

          To begin the project, a file geodatabase was created in a personal folder. From ArcCatalog, the geodatabase's domains are established within the properties window (Figure 3). The domains chosen were - 

Amount - the number of articles of trash per approximate square meter

Bin - to designate whether a data point is a trash bin, recycling bin, compost, or no bin (thus a litter     data point)

Description - what type of trash it it

Area - downtown, residential, campus property, or park where the trash was found

Notes - a domain to mention relevant information of a point

          The domain must have a Field and Domain Type. This sets the boundaries for what type of information is recorded and can limit the possible errors made. Amount set as a Short Integer Field Type and a Range Domain Type. The rest were Text Field Types. The Text Fields were coded values in order to set up categories within the Bin, Description, and Area Domains. The Bin had values of Trash, Recycling, Compost, and No Bin. This way, trash, recycling, and compost bins could be documented too. If a litter point were to be uploaded, No Bin would be selected for its attribute. The Description Domain was set up as a coded value in an attempt to formally categorize the types of litter. The Area Domain also had coded values - residential, campus property, park, and downtown - in order to record the type of region the point was taken in (Figure 4).

Figure 3: The domain established in the geodatabase.

          

Figure 4: The coded values for the Bin, Area, and Description Domains that formed the categories in ArcCollector. Note, Graham Avenue was accidentally called Grand Avenue.

          Next, a feature class was created in the geodatabase called Litter and Bins. In the feature class properties window, the fields that will be apart of the feature class attributes are determined from the domains of the geodatabase (Figure 5).

Figure 5: The feature class properties window where the domains that will be used are established.

            Once the feature class was set up, it was published to ArcGIS online. In ArcGIS online bring in the  published layer onto a basemap. Nothing shows up because there are no data points in the layer. The map is shared and ready to be viewed in ArcCollector, and the fields that were defined for the feature class will be show up when plotting a point (Figure 6).

Figure 6: The fields that will be filled out according to the rules set up in the Domains when plotting a litter or bin point.

Results

       

          The first map shows the location of trash and recycling bins within the study areas (Figure 7). Downtown has a lot of trash bins but no recycling bins. On the other hand, the college campus displays plenty of trash and recycling bins. The recycling bins were usually attached to the trash bins, so the recycling bin points often overlaid the trash bin points on the map.

Figure 7: Trash and recycling bin locations.

          The second map shows the bin points along with the points that designate litter (Figure 8). There appears to be more litter where there are less trash bins in Downtown, and over all, at a higher density than on the college campus.

Figure 8: Trash and recycling bins and litter data points.

          The third map displays the Amount domain, or, the number of articles of trash per square meter (Figure 9). Downtown has the highest density of trash, sporting points in the 16-50 range. This can be attributed to the patches of cigarette butts deposits that weren't really found on campus.

Figure 9: Litter displayed as a graduated symbols map showing the amount of trash articles per square meter.

          The fourth map in the Litter Report displays information on the different categories of litter for each point (Figure 10). No pattern really jumps out except that Paper and Food Wrapping seem to be the two top categories on the college campus.

Figure 10: Litter displayed as its category.

          The fifth map for the Litter Report shows the type of area that the litter was found (Figure 11). Clearly, Downtown and Campus-defined points are located in their previously defined zones. However, the some litter points in the Downtown study area are defined as being located in a park as that study did contain Wilson Park. Upper campus could potentially be labeled as a residential area because that is where the residence halls are located. They ended up constituting campus property since it is and it's not a traditional neighborhood.

Figure 11: Litter displayed as the location that it was found in.

Conclusion

           Even with the knowledge of how important a proper project design is, mistakes were still made in this project. For some reason, the Notes domain was set up for coded values that weren't there, instead of a plain text field to freely record notes. Thus, no notes were recorded. It was discovered that cigarette butts needed their own category base on their prevalence, and they were lumped into the Other category. The Assortment category was used more than desired because there were many instances where a data point couldn't be properly described from the variety of trash, making the Description domain useless sometimes. The Paper products category was used to gather data on potentially recyclable products, but it ended up including many non recyclable products, like tissues and napkins. There should have been a category for disposable drink containers (Figure 12). The Food Covering category was meant to cover everything from candy wrappers to take-out containers, but the extent of what fell under this category warrants more subdivisions. While the original study area was too large for this project, its design isn't necessarily meant to be contained to these study areas. It's main idea of documenting litter in relation to area and bins for further analysis and plans of action can be implemented anywhere. However, in an ideal situation, consumption and waste needs to significantly decrease to ultimately combat this environmental crisis.

Figure 12: A disposable cup that seemed to lack its own category.

Navigation at the Priory Field Activity

Introduction

          In October, field navigation maps were created in preparation for this field activity, which involved using a GPS and field compass for two separate excursions in a forest. The objectives were to gain experience in navigating in the field using the two different technologies, team work, problem-solving, and field work. The Bad Elf app was utilized once again to create tracklogs of the routes the students took in the field. The class was divided into groups of three, tasked in locating five specific marked trees using a navigation map and the two previously mentioned technologies. 

Study Area

          The navigation took place at Priory Hall of UW - Eau Claire and it's property. The navigation maps previously created display the boundaries of the study area (Figure 1).

          The navigation took place in the forests primarily north and east of the Priory building. The forest is thick with deciduous trees, samplings, and shrubs, with a patch of conifer in the eastern part of the study area. The ground is relatively level near the Priory building, but it gets increasingly variable slopes towards the edges of the study area. Deep gullies cuts into the land (Figure 2). The trees have shed their leaves, completely covering the ground, which makes traversing the steep slopes difficult.

Figure 1: Field navigation map showing the study area encompassed in the rectangle. 

Figure 2: An image of the terrain in the study area.

Methods

          Upon arrival at the Priory, each group was given five UTM coordinates and they were to mark the points on the navigation map chosen by the professor (Figure 3). The students were to use the points on the map and the coordinates to help them find the marked trees in the field. Then, a student per group paired their smart phone to a Bad Elf GPS, as demonstrated in a previous activity, and used the Bad Elf app to create a tracklog of the group's navigation to the points (Figure 4).

          

Figure 3: The UTM field navigation map used to plot the coordinate points for the Navigation Activity.

Figure 4: The location of the group given by the Bad Elf app.

          The groups set out on their given courses to their first point. One person was in charge of the GPS and tracklog. Another was in charge of relating the GPS location to the map. The the last individual kept track of the compass bearings and finding appropriate routes to the point of interest. In the field, however, the roles blended together and became less defined.

          It took some trial and error and frequent map and GPS checks to figure out how to relate the GPS to the map in order to confidently decide on the direction to travel. These pauses were necessary because navigating required both hands and full attention in order to search for the marked tree and to prevent injury from scratching branches, tripping, and falling (which happened regardless). 

          The group finally arrived at the first marked, but there was some confusion as to if this was the correct marked tree. The coordinate location of the tree didn't match the given coordinate on the map, but the two coordinates were within 50 meters - which was a qualification by the professor that meant it could be the correct marked tree. The fact that it was marked by a pink ribbon was also a concern. Some in the group were under the impression that the trees were marked with spray paint, while others thought ribbon-marked trees were a possibility from past classes. Since a spray-painted tree wasn't found in the vicinity of the ribbon-marked tree, it was decided that it must be the first point. A photo was taken to document the location. (Figure 5). Finding the bearing and relating the GPS to the map, the group set out to Point 2.

Figure 5: Point 1 marked by a pink ribbon.

          The group eventually found a tree marked by pink ribbon within the 50-meter radius of the given coordinate point (Figure 6).

Figure 6: Point 2 marked by a pink ribbon.

          The group discovered the third marked three, but were dismayed to find it clearly marked in spray paint (Figure 7). Labeled as C-4, P-3, there was no denying this was the correct tree. This made the likelihood that the previous trees weren't the correct trees.

Figure 7: Point 3 marked with spray paint, labeling the tree as Point 3 for Course 4.

          After studying the map, the group decided to navigate to Point 4 by traveling through the bottom of the gully that branched to the point. The ground was soft and muddy at the bottom of the gully, but because there were no thickets, navigating was much faster. Point 4, marked with spray paint, stood where it was expected, except at the top of the gully (Figure 8). Climbing the steep slopes was difficult due to the leaves making the ground slippery. The leaves had also almost hidden a deer ribcage, which startled the group as they unexpectedly almost stepped on (Figure 9).

Figure 8: Point 4 marked with spray paint, labeling the tree as Point 4 for Course 4.

Figure 9: A deer ribcage discovered by the group.

          The group decided to return to the bottom of the gully in order to navigate to Point 5, which stood on a ridge adjacent to the gully. However, under the of time, the group misinterpreted the grid system on the navigation map in relation to the GPS and climbed the wrong side of the ridge. The smart phone was on the verge of a dead battery, so the group returned to the Priory parking lot. There, the tracklog was downloaded to the smart phone where it could be sent to other group members as a KML or GPX file (Figure 10).

Figure 10: The tracklog of the GPS navigation excursion downloaded to the smart phone.

        After the tracklog was downloaded, the Bad Elf GPS was prepared for to record for another tracklog for the the second part of the field activity. This involved using the navigation map and a compass that has features like a Bearing Indicator, Orienting Arrows and Lines, Direction of Travel Arrow and Lines, and Bezel. The objective was to navigate by compass to three of the points that groups previously navigated too. To do this, take the edge of the compass to trace a straight line from the current location to the desired location (Point 1) on the navigation map from a flat surface. Next, align the Direction of Travel lines with the line that was drawn. Turn the Bezel so that the Orienting Lines are parallel to the lines of longitude on the map. Then the Bearing Indicator will point to the bearing on the compass. Since declination for Eau Claire is approximately 1 degree, subtract that from the bearing given by the compass and adjust the bezel to the bearing corrected for declination. Now hold the compass with the Direction of Travel in the opposite direction of the operator. The operator should now rotate until the red magnetic needle hovers within the red Orienting Arrow. Therefore, the operator proceeds to where the the Direction of Travel Arrow points to with the red Magnetic Needle remaining in the red Orienting Arrow. This will direct the operator to the point of interest.

          The members of each group were appointed jobs to make navigation more effective. The Leap Frogger is the individual that moves to a landmark within the bearing and watches to make sure the Pace Counter, the person in charge of counting the paces to establish the distance, stay on course. The Azimuth Control makes sure that the Leap Frogger and the Pace Counter stay within the bearing. Thus, each member of the group proceed to the point of interest.

          It was immediately apparent that the thicket made it almost impossible for the Leap Frogger and the Pace Counter to stay on course within the bearing, and the Azimuth Control needed to frequently direct the Leap Frogger back to a position in the bearing. The Leap Frogger ended up becoming the landmark for the other group members because landmarks weren't distinguishable in the brush. Paces stayed within 10-20, because the Leap Frogger quickly became invisible in the thicket. 

          The group discovered a marked three, but it was labeled as a point for a different course. Coincidentally, some 50 meters off, Point 1 for Course 4 was discovered by another group that was looking for the point found by Group 4. So the two group swapped their locations to document their point (Figure 11). It was then discovered that the Azimuth Control had the group follow magnetic north, which was nearly the same as the Direction of Travel. Incidentally, Point 1 was nearly north of the starting point. This is why the group was slightly east of the point of interest. It's also important to note that Point 1 was marked with spray paint, meaning the first Point 1 marked with the pink ribbon was not correct, and Point 2 most likely. Given the time, the group returned to the parking lot to return the equipment, end the tracklog, and download it to the smart phone.

Figure 11: The true Point 1 marked with spray paint, labeling the tree as Point 1 of Course 4 - discovered by compass navigation.

Results

          According to Figure 12, Group 4 was extremely close to Points 1 and 2, almost right on top of Point 1. This is curious because the group ended up documenting the wrong trees. Point 3 and 4 were correctly documented. A wrong path was taken before Point 3 and the group had to backtrack. The tracklog shows the group passing right through Point 5, but in reality, the group was on the other side of the deep gully. If one were to take the tracklog at face value, Group 4 appears to have done very well. It should also be noted that GPS failure created distorted sections in the tracklog that were corrected in ArcMap.

Figure 12: The tracklog of the path taken by Group 4 to the five points of Course 4.

          All the groups' tracklogs appear in Figure 13 (absences led there being no Group 2). All groups appear to have done well, except for Group 7 failing to navigate to two points. Perhaps that was due to time restraints.

Figure 13: The tracklogs of all the groups and their paths to their course points.

          The second tracklog (Figure 14) shows the groups relatively straight path north, when in reality, the group should have been traveling in a slightly northwestern direction. The tracklog also shows the group moving west to Point 1.

Figure 14: The path taken by Group 4 to Point 1 using a compass and map.

Conclusions

          For Group 4, the contour lines on the navigation maps proved invaluable in navigating to the points and determining where things were spatially. The scale bar and reference scale weren't really utilized in the activity. GPS proved to be superior in this terrain, as straight shots to the point of interest aren't always possible from the compass navigation. With more knowledge of the terrain and elevation, a compass navigator can plan out paths around such obstacles. There are instances when straight travel paths are more useful and save time. However, GPS navigation is sometimes inaccurate and can fail, which generated the faulty tracklog. Compass navigation is an invaluable skill, with or without greater technology. With GPS the group had a tendency to zigzag or backtrack when trying to figure out how to correctly relate the GPS to the map. The group also scaled plenty of steep slopes to reach the points. Since no points were at the bottom of the gully, the group might have avoided traversing up and down the ridges by traveling along the contours. However, this probably would have taken significantly longer. In the end, it depends on the terrain, time, and navigator. The group ended up making a few mistakes, but they only strengthened the learning experience.