This lab explored ArcCollector, an ESRI app used to collect field data. ArcCollector can use crowd-sourced data and runs on any smartphone. Using a basemap, field measurements can be recorded with locational information taken through the phone's GPS. This can be used to create feature classes in the field. This data is automatically uploaded to ArcGIS Online, allowing other users to access it in real time.
In this lab, a series of attributes were created in advance, including temperature, dew point, wind speed, windchill, and wind direction. These measurements were recorded at many points throughout the study area. Using these data points, microclimates were mapped and assessed.
STUDY AREA
The study area of this lab was UWEC campus in Eau Claire. Prior to collecting the data, the study area was divided into 7 zones. The class was split into groups, each assigned to a different zone for data collection. These zones are shown below in figure 1. The zone I was assigned to was zone 3. This included the bridge connecting zones 1 and 2. Note that the Western section of zone 3, from the bridge over, was under construction and access was not possible. Measurements were taken on an overcast, ~50°F day.
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| Figure 1: The study area, divided into 7 zones used in data collection |
METHODS
The equipment used in this lab were an iPhone, a Kestrel 3000 handheld weather station, and a compass for determining wind speed. Two people were assigned to each zone, so I had one other person collecting data in zone 3.
To collect data, the ArcCollector app was used with the project opened. For each data point, measurements were read off the Kestrel and entered into a new data point on ArcCollector. Temperature, dew point, and windchill were entered directly. To find wind speed, the Kestrel was held vertically, perpendicular to the wind direction. Once the wind speed stopped climbing, the measurement was taken. The direction of the wind was measured using a compass. The Kestrel is shown below in figure 2.
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| Figure 2: The Kestrel handheld weather station measuring wind speed |
Measurements for each attribute were held to domains assigned to the feature class before collection began. Examples of these domains are limiting wind direction from 0 - 360 degrees, limiting temperatures to only reasonable values, etc. This was done to prevent some user error, for example if someone hit an extra number when entering temperature, entering it at 500°F instead of 50°F, an error window would come up to tell them there is incorrect data entered.
As data was collected, it was uploaded in real time to the database. This was easily seen in ArcCollector, with new data points popping up across the study area from each classmate collecting new points. Once data collection was complete, the data was downloaded onto ArcMap and surface maps were created.
RESULTS
ArcGIS online was used to download the data to the desktop ArcMap. The collected data contained 239 points. Interpolation was needed to create a continuous surface to better represent the data. Several methods were tried, and the kriging method turned out to be the best option. The results from kriging showed more detail than other methods tried. The first map created was a temperature map, shown below in figure 3. The maximum variance in temperature was less than 7°F. As mentioned, the day was overcast, so there were no drastic variances from sunlight and shadows. A sunny day would likely create more interesting data. That being said, there are some slightly warmer sections of the campus shown below, mainly in pockets along the southern edge of the study area. Note that the study area shape has been simplified. This was done to make the surface gradient seem less choppy and disconnected.
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| Figure 3: Temperature map of UWEC |
The next map created, shown below in figure 4, is the windchill map. Notice the similarity between this and the temperature map. They are almost identical besides the slightly lower minimum temperature on the windchill map. Warm and cold pockets are still found in the same areas with similar shapes and magnitudes.
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| Figure 4: Windchill map of UWEC |
The next map created was the dew point map, shown below in figure 5. This map has similar distribution to the previous two, but is not identical. Notice two warmer pockets are found on the eastern section, similar to the other maps. The pocket on the southwestern section does not appear in the dew point map, however.
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| Figure 5: Dew point map of UWEC |
The last map created was the wind map, shown below in figure 6. To create this map, raster surface maps were created for both wind speed and direction. This was done using the same kriging method as the previous maps. Once these were created, The raster was symbolized using symbols instead of a gradient. Once the direction was symbolized as arrows, the wind speed raster was used as a reference for the arrow magnitude. Notice the wind direction is mostly headed northwest, with the exception of some areas near the southern section. This could be explained by the hill that is found there. As for wind speed, the largest magnitudes are found in the western section. This is likely because this part of campus is more open, with less buildings and trees to block wind.
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| Figure 6: Wind speed and direction |
CONCLUSION
This lab served as an introduction to ArcCollector. This app will be explored in more detail in a subsequent lab. ArcCollector proved to be a powerful, yet simple tool for data collection. Using smartphones as a data collection device is a creative solution, as smartphones are readily available and have powerful GPS capabilities. It should be mentioned how useful it was for domains to be set up before data collection. This definitely cut down on user error and simplified the process. The app itself was easy to use and allowed the data collection from other groups to be seen in real time.
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