ArcCollector Part 1

Introduction
The purpose of this lab was to learn to utilize the ArcCollector app as a means for collecting data in the field and upload them in real time to a single destination where all the rest of the data collected by the class would be brought into a map.  Smartphones are becoming more common as a tool for data collection because they have more computing power than typical GPS units, and they have the ability to access online data.  The data points collected by the class had several attributes and each point was uploaded on the fly.

Study Area
The study area for this activity was mostly on the UW - Eau Claire campus, with a couple sections near Water Street.  The numbers in each section show the designated zones assigned for each student to collect data points, as shown in Figure 1.

Figure 1: The study area on UW - Eau Claire's campus and across the footbridge.  

Methods
First each student had to install the ArcCollector app, as seen in Figure 2.  The app allows you to choose a basemap, use the GPS on a smartphone to find location, and collect data points with multiple attributes that can be uploaded on the fly.

Figure 2: The ArcCollector app as seen on a smartphone.

Each student was then assigned a zone to collect data points within.  Each data point collected had the following fields to be input:
-Temperature (°F) 
-Dew Point
-Wind Chill
-Wind Speed
-Wind Direction
-Time

A Kestrel 3000 Pocket Weather Station (Figure 3) was used to find the weather information, a Suunto compass (Figure 4) was used to determine wind direction, and the phone was used for the time.

Figure 3: The pocket weather station used to collect weather condition data.  

Figure 4: The Suunto compass used to determine wind direction.  

The GPS on the phone and the map on ArcCollector were used to determine location and they helped to ensure data points were spread evenly across the assigned zone.  Over the course of an hour and a half, several data points were collected per zone and uploaded to a single destination where they could then be brought into ArcMap together to be mapped offline.  The resulting maps are displayed in the Results/Discussion section.


Results/Discussion

Figure 5: This map shows the temperatures from the recorded data points. 

By analyzing the temperature map in Figure 5 it can be observed that temperatures on both upper and the main lower campus vary considerably, in a range of about 15 degrees.  One possible reason this could occur could be due to some spots being in the shade and some spots being in the sun.  Another could be differences in the ways in which the students were collecting temperature.  If the pocket weather station was kept in the student's actual pocket before collecting data from that point, the data would be compromised because the temperature would be recorded higher than it actually was at that point and time.  Across the foot bridge the temperatures had a bit less variation, with temps ranging between 49 degrees and 58 degrees.  There does seem to be some observable clustering of temperatures in this area, with similar temps recorded being near other similar temps.

Figure 6: This map shows the dew points from the recorded data points. 

The dew point recordings on the map in Figure 6 seemingly show more patterns than the temperature map.  Dew point measures what temperature the air would have to be to become saturated, so it a measure of temperature and absolute humidity.  Upper campus shows the warmest dew points in general, while the recordings on lower campus and across the river show the colder dew points.  This could be because it is closer to the Chippewa River, the elevation is lower, or there were colder temperature recordings.

Figure 7: This map shows the recorded wind speeds and directions from the recorded data points. 

The map in Figure 7 displays the wind speed and wind direction.  It can be observed that wind directions on upper campus near the Southwestern end seem to mostly come from the Northeast.  Other than that they are varied.  On main lower campus they are widely varied in direction.  It could be speculated that the variance in wind direction recorded on both upper and lower campus are probably due to the fact that there are many large buildings scattered throughout campus, as well as forest.  These features tend to have a large impact on wind speed and direction.  It can also be observed that on the foot bridge and across the river the wind speed is generally much higher and follows a direction pattern.  The recordings along the river all seem to have wind coming from the East/Northeast.  This could likely be explained by the fact that wind can travel more easily over water than land with features like trees and buildings, because there is nothing obstructing it.

Figure 8: This map shows the recorded wind chills from the recorded data points.  

The map in Figure 8 shows wind chill, which is a recording of temperature with wind speed taken as a factor.  It can be observed that in areas on both upper and lower campus that are not near the river, the wild chill tends to be higher.  This is because both the temperatures recorded in these areas are higher and the wind speeds are lower.  Along the river almost all the wind chill recordings were between 36 and 52 degrees.  The variance in wind chill could be very dependent on wind speed variance.  Wind blows in gusts, so at any given time the recording could vary by several miles per hour, which would in turn have a big effect on the wind chill recordings.


Conclusions
This activity demonstrated that effective maps can be created using data collected with the ArcCollector app on a smartphone, a weather conditions collector device, and a compass.  The data was collected simply and quickly.  More precise collecting methods could be used to get a more reliable data outcome, but this activity demonstrated the effectiveness of the app and of a smartphone as a practical data collection device.