Field Activity 10: Arc Collector 2

Introduction

A majority of US adults own a Smartphone, which is capable of doing the same tasks a GPS is used for and can be online almost anywhere. The Arc Collector app utilizes these capabilities for accurate data collection in the field that can be shared online immediately. The previous lab dealt with data collection from a previously created database. In this lab the entire process will be done; first one must come up with a research question answerable using Arc Collector, create a database, and then use it to conduct field research.

Information about people can be gathered through observation of how they express themselves. People express themselves in many ways, from stickers on vehicles to the way they decorate their homes. One interesting method of self expression is through the use of lawn ornaments. In this lab Arc Collector and Arc Map will be used to create a database with domains that aid in answering the question of what proportion of homes in a given study area place lawn décor in their front yard as a method of self expression and of those, what percentage include animal lawn décor.

Study Area

The study area is a section in the southeast corner of the third-ward neighborhood beginning at the corner of Roosevelt and Garfield. This section was selected because it is out of the college rental section of the neighborhood and a majority of the homes are permanent residences. It is assumed that homes with annually changing tenants are less likely to have personalized yards. The study was conducted in the afternoon on a weekday so as to arouse fewer suspicions, as a stop in front of and observation of each home would be required. The weather was warm but cloudy and misting. This was not planned but assisted in the reduction of homeowners present during the survey.

Methods

After coming up with the research question, a polygon feature class of the study area can be digitized if necessary and a list of attributes capable of answering the question needs to be made. This lab required at least three fields for attributes; one text field for notes, a floating point or integer, and one with category items to choose from. The lawn décor survey includes four fields; two short integer, one category, and one text. The first short integer field indicates the presence or absence of lawn décor; in which 0 signifies absence of lawn décor and 1 signifies its presence. This field will allow for calculation of number of homes with lawn décor present. The other short integer field is for number of items present ranging from 0 to 20. It was assumed no one yard would exceed twenty items in their front yard alone. The category section is for type with options of animal, plant, other, combination (meant for those with animals as well as other items), or none. This field will be useful in calculating the percentage of home with lawn décor have at least one that is an animal. Lastly, there is a text field for notes on anything the surveyor feels note worthy. The ArcGIS for Collector web page has a very helpful tutorial with step by step instructions for creating the geodatabase with domains for each attribute, defining the feature class, setting up fields in the feature class to correspond with the previously created domains, and the steps to publish the map as a service. After the process has been completed, one should be able to log onto ArcGIS online and go to the My Content tab to find their map and open it in Map Viewer. When the map is open, choose Save As and then share it with the class group. The map service should now appear in the list of options when the Arc Collector app is open on a Smartphone device. 

It is a good idea to check to see if each attribute field is correct and functional (figure 1). For this survey the Notes attribute did not allow the actual insertion of notes. Upon further investigation it was found that when domains are being created, the text field type only allows coded values and no coded values were set for the Notes attribute. This was corrected by defining a new feature class and not associating the domain with the Notes attribute. Now the survey is ready for data collection using Arc Collector. Only the front yards of houses on each street within the study area were surveyed. Items also serving a functional purpose; such as bird houses, planters, and chairs were not included. Seasonal items were also not included. Wind chimes, while could be viewed as functional, were included. 

Figure 1.

Results

The published map created using Arc Collector was used to create a series of maps relating to the question of what percentage of people in a section of the third ward neighborhood have lawn décor pieces that are animals. There were a few survey points taken just outside of the study area, so the study area was corrected to include these points before the points were created.

Fifty-two homes were surveyed in the study area and of those 52, 21 home had some type of lawn ornament present in their front yard (figure 2). Of the 21 homes with lawn décor, 52% of them had at least one piece of décor in the form of an animal (figure 3). A graduated symbols map was made to reflect the quantity of lawn décor pieces at each house sampled (figure 4) and unique values were used to represent the type classification of each (figure 5).  

                

Figure 2.

             

Figure 3. 

Figure 4.

Figure 5.

Discussion

This turned out to be a very interesting study. The third-ward is a peaceful, beautiful neighborhood with many qualities to be observed. In this surveyors opinion, the best piece of lawn décor observed was a large stone turtle (figure 6). In addition to some interesting lawn décor pieces; there was also some eye catching architecture and landscaping choices. There was also a section of the study area in which the home`s backyards became Putnam Park, it could be observed from the road that this made for a most ideal backyard for someone choosing to live in the city but wanted to be able to feel like they were in the country.

Figure 6.

Field Activity 9: Arc Collector

Introduction

                A 2015 study by Pew Research Center found that 68% of U.S. adults owned a smartphone, 45% owned a tablet computer. and smartphone ownership increases to 86% for those 18-29 years old. The processing power of a smart phone or tablet is much greater than that of a GPS, therefore it makes sense that these convenient and almost omnipresent devices would make a good alternative to a standalone GPS device for the use of GPS data collection. Applications such as Arc Collector even offer the option to collect data while offline, similar to a GPS. You can download a map or use a basemap for reference and edits can be updated once a connection has been reestablished. In this lab, groups will utilize the same map while online to collect micro-climate data that will be updated on the fly.

Study Area

                The study area is an area about 1.2 square kilometers that covers almost all of UWEC`s main campus. It was divided into five zones; two zones were sampled by two groups, one zone was sampled by one group, one zone was sampled by three groups and one zone was not sampled at all (Figure 1). There was a small amount of zone overlap, but this did not cause excess data collection in any of these areas. Some geographic features noteworthy in micro-climate assessment are the large ridge running along the southern border of zone 3 and along the borders of zones four and five, the base map contains subtle contour lines that reveal this feature, and the two flowing bodies of water. Little Niagara Creek runs along the borders of zones two and three and then just within zone four before dumping into the Chippewa River. The river constitutes a large area within zone one. There are also zones with many buildings, as labeled in figure 1, and an area within zone three that is forested.

 

Figure 1. Map of study areas with zones and group data collection

Methods

                To begin this lab, everyone with a smartphone had to download the Arc Collector application on their phone, log into ArcGIS Online using a web browser to join the group containing the map necessary for data collection, and then log onto the application to open the map in Arc Collector. The map included polygons dividing the study area into zones and each group was assigned a zone in which to collect micro-climate attribute data at numerous locations within the zone. Each group received a Kestrel handheld ambient weather station (figure 2) and a compass to collect temperature, dew point, wind speed, and azimuth of wind direction. The groups consisted of two people; one for data collection and the other for input of data into Collector. Because everyone was online and data was being entered into a shared map; as data was entered into Arc Collector each group could see all points as they were added. The resulting point data as well as the zone polygons were then imported into ArcMap for analysis. Various interpolation methods were used to visualize variations between areas and predictions of data between points. The data was collected at random with one zone almost completely lacking data points and the river constituting another area lacking data points. The resulting dataset was not evenly spaced with points clustered together in some areas and others lacking points (figure 3).

Figure 3. Map displaying distribution of data points collected

Figure 2. Kestrel handheld Weather Station used to collect temperature, dew point, and wind speed

           

Results

Because the IDW interpolation method assigns values based on the values of known points nearby, it is an interpolation method that is not recommended for datasets with an uneven distribution of sample points such as this one. This is evident in the IDW interpolation of wind speed; the areas containing a higher density of samples are over represented with excessive value variation radiating from areas of high density (figure 4). The anomaly found in the southeast section of the study area can be explained by wind direction and the presence of the steep ridge directly south of the points. The wind was generally blowing at an azimuth between 180 and 270 degrees, leaving the area of data collection protected from wind by the ridge. Spline is another interpolation method not recommended for data with over and underrepresented areas. However, it does create a smooth transition between points and is ideal for gradually changing values. Temperature within a small study area like this will only contain gradual changes depending on location and the resulting interpolation contains change that is relatively subtle (figure 5). There are a few areas between points that have been given values that are most likely inaccurate. These could be explained by temperatures taken near heat vents and temperatures taken in areas with lots of shade and water, making it cooler. The kriging method of interpolation uses a similar algorithm as IDW to assign values to unknown points, but it also assumes correlation based on distance and direction from known points. This addition to the method makes it more ideal for the interpolation of dew point. The dew point of a space is partially determined by the moisture in the air, which will vary between points taken along water, taken near the swampy woods area, and the higher elevation of upper campus. Distance between these points will help prevent correlations between these points. The result provides a gradually changing interpolation of dew point (figure 6) compared to that of the natural neighbors result which resulted in distinct layers of change radiating from point clusters (figure 7).

         Figure 4. IDW interpolation of wind speed,
with azimuth compass

Figure 5. Spline interpolation of temperature        

Figure 6. Kriging interpolation of dew point

Figure 7. Natural neighbor interpolation of dew point

Conclusion

                This lab offered a look at how the convenience of data collection using Arc Collector can then be easily used to analyze and manipulate data in ArcMap. The data analysis tools available in ArcMap make Arc Collector a very useful tool, but it is also useful for those who do not have access to ArcMap. Arc Collector itself can be used to create maps and collect data and very accurately track where you’ve been. 

Field Activity 8: Map and Compass Navigation

Introduction

                As most people have already experienced, technology can fail and should not be relied upon. The ability to navigate with a map and compass is an essential skill for anyone hiking or backpacking through the wilderness. In this activity groups will be using two navigation maps created in the previous week`s lab  and a compass to try to navigate to five points in Universal Transverse Mercator (UTM) coordinates located in the wooded area surrounding UWEC`s Priory. Both navigation maps contain two meter contour lines and a grid. One of the maps includes the geographic coordinate system with a decimal degrees grid and the other map is in a UTM projection with a 50-meter interval grid.

Study Area

 The Priory is a residence hall and children`s nature academy just over 3 miles south of UWEC`s main campus set on a 120 acre wooded lot containing a lot of relief. The navigation took place on a warm, fall day and the sky remained clear for the majority of the activity. Temperatures during the navigation remained at about 16^o Celsius.

Methods

                The class met at the Priory and upon arrival each group received; copies of their previously submitted maps, a course to navigate, a map compass, and a GPS unit. After a brief map compass (figures 1 and 2) tutorial, each group member marked the five UTM coordinates on their map and compared points for accuracy. To assist in the approximation of distance traveled, the two group members who would assume the pace counter role took a pace count for a 100-yard stretch in the parking lot (the third group member had an injury which prevented hiking through some of the terrain). Before embarking on this adventure three different roles were defined; the pace counter, azimuth control, and pace count recorder. The azimuth control would use the compass to determine the correct direction of travel and choose a landmark for the pace counter to travel to, then the pace counter travel to the specified landmark while counting the paces it took to reach the landmark and yell out the number of paces taken for the recorder to document and keep a running total. The 100 meter pace count could be used to produce a rough estimate of the number of steps needed to reach the points based on the map`s scale and measured distance from point to point.  It was now time to choose a starting point and determine azimuth from the starting point to the first point of the course. From a point easily locatable on the map; the map itself was laid on a flat surface, the compass was set on it and orientated true north by matching up the red arrow and the red outline below it, the map was then rotated properly so that is was also orientated true north. The edge of compass was then aligned with the anchor arrow at the starting point and the direction arrow aimed at the first destination, then the compass housing had to be rotated until the orienting lines were lined up with the lines of longitude on the map, thus facing north. The directional arrow now marked the bearing needed to travel to reach the first point. After the pace counter reached each landmark, the azimuth control would walk to that location and reassess the bearing by holding the compass so that it was facing due north. A new landmark could then be determined based on where the directional arrow was now pointing. The recorder would keep a running total of steps taken, adding a roughly estimated distance to account for relief as it was traversed, and alerting group members when the point, marked by a pink ribbon (figure 3), should be nearby. The group member not responsible for pace counting would then conduct a brief reconnaissance to see if it could be located before sending the pace counter further.  For each point the approximate bearing was recalculated and the process repeated. However, after successfully locating the first point, the second point was elusive and a pink ribbon was found on the ground some distance away from where it was thought the point should be. In an attempt to move on, the bearing for point three was determined based on the assumed approximate location of point two. After searching a broad area around the assumed location of point three without success, the GPS was consulted to approximate bearing and distance between actual location and marked location for point three. It was again assumed that another ribbon had been removed from the tree and point four was approximated from the current location according to the GPS. After yet again failing to find an actual marked tree, class was almost over and a hasty attempt to locate point five was made based on current location according to the GPS. These efforts were to no avail and it was time to use the recently practiced map and compass navigation skills to return to the Priory where the lone Dr. Hupy awaited our arrival.  

Figure 1. Compass similar to the one used

Figure 2. Compass with parts labeled

Figure 3. A point marked by a pink ribbon

Results/Discussion

                The GPS track log mapped out with along with the plotted points reveal how accurately the course was navigated. Point one is the point furthest south, point two being the next one closest to that, and points three, four, and five are in a counter-clockwise order (figure 4). Oddly enough, it appears point one was missed and perhaps the point located was part of a different course. It also seems point two would have been successfully located and it was correctly assumed to have been removed. Navigation from point two to point three appears to be where things went wrong. The general direction of navigation is accurate, but the distances traveled were not at all sufficient to reach points three or four. There are two likely explanations for this error; first the approximation for point two`s location may have been off and the approximated addition of steps necessary based on relief was most likely off. Point five appears to have been in relatively close proximity; however hasty searching that had extended beyond the class’s meeting time may have caused the group to miss the last and final point.As for the navigation as a whole, another source of error could have been the overlooking of declination. A post lab calculation for the day the activity took place found it to be 1.14° W ± 0.39° changing by 0.06° W per year. This seems to be quite negligible and should not have altered the results too greatly, as omission of declination should still have resulted in close vicinity to the target.

Figure 4. The course points mapped with the navigation track log

               

Conclusion

Of the two maps created, the UTM map was most helpful. The feature of greatest assistance was the grid; the grid lines were left subtly visible on the map making it easier to assess location. The points were plotted on this map and the 50-meter intervals helped in assessing distance. After the GPS track log had been changed to this projection it also gave UTM coordinates, so the UTM map was also used in orienteering when a point seemed to elude the pace count method. After point one was located all other points were within areas of great relief and the contour lines were somewhat helpful when a point was thought to be nearby. They were used to decide if said point should be at the base, summit, or side of a ravine. If given an opportunity to repeat the activity, subtle three meter contour lines with a slightly more visible 25 meter grid might be of more use. Previous experience is also a very helpful asset in this sort of activity and the knowledge to take the concept of distance change in relation to relief into account will be taken away from this lab. This lab was a fun, educational challenge that taught a valuable life skill to someone who can frequently be found in hiking in the wilderness. 

Field Activity 7: Development of a Navigation Map

Introduction

Stored within the hippocampus of the human brain there is an inherent sense of direction that enables the process of navigation to happen. A study done at University College in London indicated that those who perform navigational tasks regularly have more developed hippocampi compared to those who do not(Maxwell, 2013). In order for one to utilize this inherent sense of direction to successfully navigate, a few additional tools are necessary.  First, a location system is needed to identify location in reference to surrounding area. Often location systems employ a projected coordinate system to accomplish this. These coordinate systems entail a number of systems more precise than latitude and longitude in order to facilitate navigation on a large geographic scale. The second component for successful navigation is an actual navigational tool such as GPS technology or a map. In this lab, two large scale maps of UWEC`s Priory on Eau Claire`s southwest side (figure 1) will be created for future use in navigation of the area. One of the maps will use the Universal Transverse Mercator (UTM) coordinate system to give spatial information in meters and the other will use the Geographic Coordinate System to display the same spatial information as decimal degrees. The UTM coordinate system is divided into 60 zones, each being six longitudinal degrees wide(esri). Each zone is then split at the equator to form north and south sections of each zone (figure 2), the navigation area falls into zone 15N. The UTM coordinate system is ideal for land navigation using large-scale maps because it is measured in meters and can be tied to a distance measuring system. The Transverse Mercator projection used by this coordinate system is a cylindrical projection that does not maintain direction on small scale maps, however it is appropriate for the navigation area because it is a large scale map and falls within one single UTM zone. The geographic coordinate system references latitude and longitude to identify location in terms of decimal degrees. This is helpful information to have when using a GPS, as the technology uses this system to identify location.

Figure 1. Location of navigation activity from Google maps

Figure 2. UTM zones of contiguous United States nps.gov

Methods

                The area to be mapped was UWEC`s Priory and a geodatabase with the navigational boundary, remotely sensed imagery, and a contours map was provided. Begin with a blank ArcMap document, it is then best to copy and paste the geodatabase into a private folder connection where it can be altered. The first item added to the map was the navigation boundary and the source information inspected. The layer had a UTM projected coordinate system, making the UTM the first map created. While creating these maps, it is important to bear in mind that they will be printed and used for navigation.

Before doing anything else, change the layout to an 11X17-landscape format. The next step is to create contour lines of the area; the land surrounding the Priory contains a lot of relief and contour lines will assist in spatial navigation of the area. To do this, the existing 2-foot contour lines layer was added to the map for examination. Contour lines every two feet seemed a little excessive and a new two-meter contours layer created from one of the elevation model layers using the contours tool. After this was accomplished, each provided raster image of the navigation area was placed on the map to determine the best backdrop. One of the true color raster images seemed to be the best fit and set to a 40% transparency allowing the contour lines to be more visible. The selected image then needed to be projected into the Transverse Mercator projection with the appropriate UTM coordinate system. With the navigation boundary, contour lines, and raster image background in place, it was time to switch to layout view and add all the necessary elements. One of the more tricky requirements for this map was the grid with appropriate labeling. It is necessary to be in layout view to accomplish this task and can be found in data frame properties, under the grid tab. For the UTM map, make sure all layers included in the map are in the appropriate Transverse Mercator projection and the grid should be at 50-meter intervals on the X and Y-axes. The remaining elements include a north arrow, scale, information about projection and coordinate system, source information, and your name so no one else can take credit for all your hard work. The map with geographic coordinates in decimal degrees requires all the same elements however; the appropriate project tools must be used to change all layers to a geographic coordinate system. After this has been accomplished and all the UTM layers removed, a grid with decimal degree divisions can be placed over the map.  

Results/Discussion

                 Both maps were created with the goal of being user friendly and usable for those without field navigation experience. The two resulting maps both include two-meter contours in hopes that relief can be used as a guide for spatial orientation in the field. They also include aerial imagery backgrounds to assist in spatial orientation once on location. The UTM grid has finer spacing to facilitate the tracking of distance navigated (figure 3). The GCS grid does not include spacing quite as fine as the UTM, but if given a current location in decimal degrees, one should be able to use the map to determine a relatively accurate location within the navigation area (figure 4).

Figure 3. Map with UTM grid

Figure 4. Map with GCS grid

Conclusion

                The ability to perform a simple spatial navigation activity using a map is an important skill to have. Not only have studies shown that it promotes activity in the hippocampus, but they have also shown that this method of navigation may be better for the brain that reliance on a GPS for navigation (Maxwell, 2013). Furthermore, technology may fail and one may need to utilize their inherent sense of direction to navigate out of a remote area or to make it to a job interview using landmarks and directions. This lab challenges one to consider what map characteristics would be useful for navigation and is an excellent precursor to an actual navigation activity.