The purpose of this assignment was to give students the freedom to create their own project using ArcCollector. For this assignment in particular, the goal was to understand how the Eau Claire and Chippewa River have an effect on air temperature in relation to proximity. This student gathered 65 individual data points concentrated in the Phoenix Park area. Four domains were recorded for each point, these include temperature, windchill, wind speed, and land cover.
Study Area
The area these measurements were conducted on was Phoenix Park, everything between the shoreline out to Riverfront Terrace.
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| Figure 1: Satellite View of Phoenix Park |
Methods
Before recording the data in the field, a geodatabase was constructed in ArcCatolog. Four domains were chosen in this database. These included land cover, temperature, wind chill and wind speed. For land cover, coded values were assigned to classify different types of land surfaces. These included shoreline, grass, concrete (walking trails) and trees. Shoreline was a significant factor because temperatures at the shore of water bodies tend to be cooler compared with temperatures more inland (at least when the water is cooler than the air). Being in close proximity to trees can also have an impact on temperatures as well as increased wind friction. Concrete tends to store heat from solar radiation and could potentially have an effect on surface temperature. As for the other domains, wind chill and wind speed are correlated with one another when temperatures are below 50 degrees Fahrenheit and have speeds over 3 mph. The student looked to see if wind speeds had any influence on wind chill.
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| Figure 2: Text fields where domains were established |
Once domains were created, they were almost ready to be published to ArcGIS online in order to be used on the ArcCollector app. From Publish New Service the UWEC Geography page is connected and the service editor requires a brief summary of the project at hand before allowing one to publish.
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| Figure 3: Publishing in progress after filling out required item description |
Once the geodatabase is successfully uploaded to ArcCollector, the student is ready to go out to the field to start collecting. Accompanying him are a Bad Elf GPS and Kestrel 3000 pictured below. The Bad Elf was linked to an iPhone via Bluetooth to ensure the highest spatial accuracy for data collection. The Kestrel was used to measure wind speed, wind chill and temperature.
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| Figure 4: Kestrel 3000 |
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| Figure 5: Bad Elf GPS unit |
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| Figure 6 |
Results/Discussion
Figure 7 below shows a map of the temperature distribution. 65 points were recorded in total, and it can be observed by the temperature map below that the southern tip of Phoenix Park is a few degrees cooler than along the trail leading up to the bridge. The areas leading up to the bridge and beyond also seemed to be locations that were much higher above the river than the southern side, which slopes toward the Chippewa. Despite these temperature variances, the deviation was not as high as expected. To see a higher distribution of temperature ranges, the student probably could have plotted points more inland. Windchill also was equal to the air temperature. Perhaps this was due to the fact that it was over 50 degrees Fahrenheit and wind speeds never exceeded 3 mph, which are basic criterion for windchill measurements.
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| Figure 7. Temperature map |
The next map (figure 8) displays the distribution of windchill throughout Phoenix Park. The results are essentially identical to temperature, something that was not originally anticipated in the planning of this project. The fact that the temperatures were so high for this occasion likely was the reason there was a lack of deviation between windchill and temperature.
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| Figure 8. Wind Chill map. |
This interactive map below shows each and every individual domain value. By clicking on each point, one can observe wind speed, wind chill, temperature and land cover type.
Conclusion
It is important to have proper research questions when undertaking a project such as this one. This study was unique in that one of the factors (wind chill) simply was irrelevant because temperatures were too high (surprising, for November). The idea of this project was to show that the river has a significant cooling effect on air temperature and wind chill. On this occasion, the resulting data didn't quite meet the expectations the student had. It was a very warm November evening, but on a more typical November night the data would likely paint a more vivid picture of how the river has an impact on windchill and temperature. Overall, ArcCollector proves to always be a trusted app to use if one seeks to plot meaningful data.
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