Microclimates and ArcCollector

Introduction
The purpose of this lab was to collect data while simultaneously uploading it to a desired geodatabase. Esri's ArcCollector is a mobile app that can do collect geospatial data more accurately than a GPS. Mobile phones serve as mini computers capable of connecting to the internet in an instant. ArcCollector was used to collect microclimate data throughout seven zones at the University of Wisconsin - Eau Claire (Figure 1). All of the students spent about an hour collecting microclimate data somewhat evenly throughout their designated zones. The students created sample points on the online map shared by all the students.

Figure 1. Students were assigned to collect microclimate data in seven different zones.

Methods

Figure 2. ArcCollector provides an interactive map.

The ArcCollector was downloaded on every students smartphone. In this app the user can request to join an organization that shares a map in which every member can add data points (Figure 2). The class used Kestrel 3000 Wind Meters, compasses and smartphones to collect the microclimate data.

The Wind Meter recorded:

• Windchill
• Dew Point
• Temperature
• Wind speed

Wind direction was captured using a compass and location was captured using the GPS in the smartphone. All of this was recorded on the ArcCollector app using the smartphone. In real-time sample points appeared on the shared online map (Figure 3).

Figure 3. Sample points were collected randomly throughout the campus. Half of zone three could not be sampled due to construction.

Results & Interpretation

Three maps were created to display temperature, wind chill, dew point, wind speed and direction of the collected sample points after they were exported into ArcGIS. The first map examines temperature. The temperature sample points were interpolated using the Inverse Distance Weighting (IDW)   interpolation method (Figure 4).

Figure 4. The IDW temperature raster was layed on top of a basemap at 15% transparency to help interpret the data.

The temperature data seems inconsistent in zone six. There seem to be cooler "dots" in temperature within this zone. This could be due to a higher density of sample points towards the middle, which could result in more accurate data. In the areas where there are less sample points the data could be skewed heavily due to sample points with higher temperatures being weighted more heavily that sample points in denser areas. On the eastern portion of zone 5 there seems to be higher temperature, but that could also be a product of scarcity in sample points. Also, the wind meter could have altered readings if the user holds on to it tightly or keeps it in a coat pocket, which warms the device and skews the temperature reading if it was not given enough time to calculate temperature of the air.

Next, a map of dew point was created (Figure 5). Dew point is the temperature at which the water vapor in the air is in equilibrium with liquid water (www.livescience.com).

Figure 5. The raster layer for dew point was created using the IDW interpolation method as well.

The dew point data shows an interesting pattern where the dew point is low in zones 1, 4 and 5, but high in zones 2, 3, 6 and 7. Perhaps the amount of vegetation in an area has an affect on dew point, because it seems that there is much more vegetation in areas where the dew point in high. Also, there seems to be higher dew point at the walking bridge, which tends to be a windy area. Perhaps wind has an affect on dew point as well.

The last map analyzes wind chill, wind speed and the direction of the wind (Figure 6). The wind chill raster layer was created, once again, by using the IDW interpolation method.

Figure 6. This final map combines three attributes of wind data.

The map seems to show a small relationship between wind speed and windchill. In zone 1 there were higher wind speeds accompanied by low wind chill. The rest of the map tends to show low wind speeds with high wind chill. Human error could have a huge impact on the direction of the wind. Students used compasses and the American flag flying high above Schofield Hall to determine wind direction. However, using a flag is not a sufficient indicator of wind direction, because it is stationary and only shows the direction of the wind above Schofield and not where the student is. Therefore, the wind direction data in this project does not seem to be reliable.

Conclusion
ArcCollector proved to be very efficient in collecting data quickly and uploading the data onto an online platform. This app could prove extremely useful in many instances that require swift collection of geospatial data. The app was very easy to use and the setting of domains allowed for better data quality. In the future, a domain should be set for time so that every student uses the same time structure (12 hour or 24 hour time).