Arc Collector Weather Data Collection

Introduction

          The purpose of this lab is to become familiar with gathering geospatial data on a mobile device using Arc Collector. The purpose of using Collector is the fact that smartphones have more capabilities than most GPS units, that being said there is no reason to not be familiar with collecting data using it. Additionally smartphones can access online data in real time and allow the user to gather on the fly and upload data instantly. Additionally photos of the study areas can be uploaded instantly, this helps give the data viewers an idea of where the data was collected.
           The study area for this exercise was on the University of Wisconsin Eau Claire. The class was split up into seven groups and each group was assigned as area, a map showing the seven different data collection areas is displayed below in figure 1. The study area that will be discussed in this report is area seven, which was the study area that was assigned by Professor Joseph Hupy. Each group member was to collect around twenty data points and the group members were instructed to split the zones up into smaller areas to ensure that data was collected evenly throughout the assigned area. Data collected for this lab was on Wednesday, March 29th between 3:30 and 5:00pm. The attributes that were collected in this lab were temperature, dew point, wind chill, wind direction, wind speed, time, and group number. There was also a notes section available, though there were a few issues experienced with that and they will be discussed later in the results section.

Figure 1

Methods

          Arc Collector is a very straight forward method of data collection. For this exercise the data collection table was set up previously by Professor Joseph Hupy and the data recorded was entered and then submitted online where all other groups could see the data points and data recorded at those points. The attributes were selected by professor Hupy and that was done by creating feature class within the geodatabase.

Figure 2

          Figure 2 to the left shows the pocket weather meter that was used to record the weather data for this lab. This instrument has many uses, as it collects temperature, wind chill, dew point, wind speed and it also had other features that were not recorded. When in the field the points for weather collection were random, the only stipulation was that the data collection points were spread out within reason. While collecting data the points were instantly displayed after submitting the points, also as the data collection progressed the more points from other groups were also visible. Figure 3 to the bottom left shows what the screen looked like after opening the app, from there the white plus was clicked on in the top center. From there Figure 4 below shows what the data collection screen looked like. The data being collected was abbreviated, GRP is group number, TP is temperature, DP is dew point, WC is wind chill, WS is wind speed, WD is wind direction, then notes and time are the two final attributes. The white symbol of a camera in the upper right was used to attach photos.


       

         As far as units go, the temperature was recorded in degrees Fahrenheit, as well as the dew point and wind chill. Wind speed was recorded in miles per hour (MPH) and the wind direction was recorded in degrees and entered as a number from 0-360. The time was entered in military time from 0-2400 and it was important that there was no semicolon between the numbers in the time.
        From here a number of maps were made that displayed the data that was collected. ArcMap 10.4.1 was used. The temperature, wind chill, and dew point maps were all interpolated using the IDW tool to give a better idea of how those variables change with elevation.

Figure 3

Figure 4

Results


           Below in figure 5 is a map that shows points at which the weather data was collected. With the fact in mind that there was no specific place to collect points, the data points are reasonably evenly distributed. As discussed in the methods section, each student was to collect data at twenty different locations.

Figure 5

         Figure 6 below is a map that shows the points collected by each group, very similarly to figure 5, though figure 6 shows how close each group got to the other groups areas. For example, the data collectors in group seven had a point collected that was very near group six, this could be explained from a possible error in GPS because of the hill, or the data collector was slightly off. Group one did a nice job of covering the northern side of there area, they made sure to record data at both the northeast and northwest corners. Group five should have covered a much larger area in the southern third of there study area. At first glance it appears the group only covered about 60% of there assigned area. Certain groups such as group four were very limited on where there data points could be recorded as there is a lot of buildings in there study area.

Figure 6

        Figure 7 below is map of the wind chill that was interpolated using the IDW tool in ArcMap. The study area on the west of the map has a high wind chill, this can be explained because that location is the top of the hill, therefore the wind hits the exposed area much stronger. The lowest wind chill recorded was 45.0592 degrees and the highest recorded was 62.892 degrees. The area in the southeast portion of the map had the warmest wind chills, likely because the wind was out of the northeast and it was blocked by Philips hall which is to the northeast of the data collection there. 

Figure 7

          Figure 8 is a map that shows the direction of the wind along with the speed in miles per hour. The wind direction is shown by the way the arrows are pointing. The bridge is a good place to look to get an accurate wind direction, all of the arrows are point to the southwest, which means the wind direction was from the northeast. Variations in the wind direction on campus can be accounted for because of the buildings. Wind tunnels can be created as the wind hits the side of buildings and funnels ways that it was not actually blowing.

Figure 8

          Figure 9 below shows the dew points collected throughout the lab. The lowest dew points recorded were 30.0257 degrees and the highest recorded were 57.9726. Almost all of the high dew points were recorded on lower campus in lower elevation areas. In comparison the east side of the study areas were almost exclusively done on upper campus which is a much higher elevation.

Figure 9

         Figure 10 below is a map displaying the temperatures that were recorded. There are some warm spots on the north side of the Chippewa river, in contrast there are a few cold spots in the south portion of the study area near the towers halls.

Figure 10

       

           It would be interesting and beneficial to complete this exercise again and to collect all the data with one or two data collectors in order to ensure that the data was all collected the same way. Much of the variation in wind speeds and temperatures could be because the collectors did not let the units get an accurate reading.




Conclusion

           In conclusion, Arc Collector was a very useful method of data collection. It is very efficient, easy to use and allows a number of users to collect data and report it in real time. This lab demonstrated that a smartphone or tablet can indeed do all of the operations that a GPS can when used correctly. It was very useful to see the data collected displayed in real time. One issue that would need to be resolved if this was a data collection for company is making sure that each data collector is clear on how to collect the data. To clarify, some students entered time the wrong way, also some were not familiar with recording wind directions and finally not everyone held the meter long enough for it to get an accurate reading on wind speed. As professor Hupy discussed before data collection, as our supervisor he could see where all the students were during class time, assuring that no one was skipping out. This is applicable in the real world because an employer could track down time and ensure that employees stay on track, which saves time and money.