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.