Microclimate Data Collection

Introduction

The purpose of this activity was to utilize Arc Collector to collect micro-climate data from many different locations on the UWEC campus. Everyone was divided up into two person teams and sent to specific zones within the study area. The class as a whole was supposed to walk around and collect climate data for specific points. The data that was collected included the temperature, dew point, wind speed, and wind direction. This data was all able to be collected by the use of a tool that could digitally measure all the required data. 

Study Area

The study area for this project was the main Eau Claire campus. This excluded Mcphee Center and any buildings South of that. The Study area was divided up into zones so that each team was able to collect data in a smaller region. My group was assigned zone 2. Zone 2 included areas around Schofield Hall, Schneider Hall, Centennial Hall, Hibbard Hall, and the Zorn Arena. Figure 1 is a map of the entire study area and the different zones within the study area. Figure 2 shows zone 2 within the study area.

Figure 1 is the entire study area and the zones
it is divided into.

Figure 2 highlights zone 2.

Methods

The methods in this assignment were relatively straight forward. Each group was deployed to their zone and were told to collect somewhere around 20 points. In zone two, the best strategy that was developed for the collection method was to start on the left side of the zone, and move north towards Hibbard Hall. Once at the top of the zone, we zig-zagged back South to try to cover the middle and Eastern portions of the zone. At each point, the temperature was recorded along with the dew point, wind speed and wind direction. 

Some of the points that were taken were in areas that may have been blocked by the wind. There were some points taken in the shade to see if there was a temperature change. All of the data collection was done on each persons smart phone through the Arc Collector app. Because the map itself was shared between the class via ArcGIS online, the map would constantly be updating with other groups collected data points. 

Results

The results for the micro-climate data collection were all compiled in the ArcGIS online map that the class shared. Because the points and data were so easily combined, the only thing that needed to be done was bring the data into ArcMap and create maps showing the results. Maps showing the results of the temperature, wind speed and wind direction were all created. 

Figure 3 shows the temperature data for each point collected. This was an interesting assortment of data. The values range from a max temperature of 66 degrees to a minimum temperature of 48.6 degrees. Some of the factors that could have changed the temperature so much would be direct sunlight versus shade. Another factor that was found was heating vents in the sides of buildings.

Figure 3 shows different temperature data collected.

Figure 4 shows the difference in wind speed. This category was a little more difficult to measure because gusts of wind could effect the maximum reading. Our group tried to find the average reading over roughly 20 seconds. Throughout the groups, the maximum wind speed collected was 10 mph. The minimum wind speed was zero. places where no wind would be found would be directly behind buildings. The two windiest spots collected were both located under the Hilltop bridge. This bridge would create a sort of wind tunnel effect. My group's highest wind speed was collected right off the edge of the Chippewa River on top of the bridge.

Figure 4 shows the wind speed data collected. 

The last map that was created is shown in Figure 5. This map shows the direction of the wind collected at each point. I believe that this map is not as accurate as it should be. This is because all of the groups didn't go over the same method of collecting the wind direction. My group collected the angle in which the wind was coming from.

Figure 5 shows the direction of wind at each point.

Each point has all of these pieces of data stored inside of it. To show this, figure 6 shows what happens when you use the identify tool and click on a point. Figure 7 then shows a sample of what the attribute table looks like for the point feature class.

Figure 6 is the data that is stored in each point. 

Figure 7 shows a sample of the attribute table for the point class.

Discussion

It was interesting to use Arc Collector for this assignment. It seems like it is an effective way to collect simple data and easily record it into ArcGIS online. The major issues that my group ran into were issues involving cell service and phone battery. Today many phone GPS are very accurate, but a lot of the time the accuracy can be effected heavily if the cell service is lacking. Both my partner and I didn't have an accurate GPS position until we were outside. Regarding phone battery, my phone died just before we finished. This could have been a lot worse if we weren't in groups. Overall, for a relatively simple survey, it seems like Arc Collector works very well.  

Conclusion

Overall, this assignment helped to familiarize the class with Arc Collector and a new way to go out and collect data. Using ArcGIS online was also a relatively new experience for me. It was nice to experience such an easy transfer of collected data into a GIS. This assignment proved that Arc Collector is an easy way to create a geodatabase of surveyed data.