Field Activity #7 ArcCollector Data Gathering

Introduction

With the advancements of technology, mobile devices and tablets have become increasingly stronger and popular compared to GPS units. In this lab mobile phones were used to gather data points with the ArcCollector application. This allowed for data to be updated on the fly with online access. The University of Wisconsin-Eau Claire Campus was segmented into 7 zones for gathering the data and 121 data points were collected, as shown in Figure 1. Zones 5 and 4 were on upper campus; 7, 6, and 3 on lower campus, south of the Chippewa River; 1 and 2 were across the bridge, north of the Chippewa River and parallel to Water Street. The author of this blog was in zone 6 for collecting data. Data gathered included: Temperature, Dew Point, Wind Chill, Wind Speed, and Wind Direction. The data collection was made on October 24th, 2007 between the hours of 16:00-17:00.

Figure 1. Area of Study

Methods

The first step was downloading the ArcCollector app and joining a group (Geog336Micro_Section01) that our Professor created for us to upload data points. Once downloaded the interactive map showed the zones and boundaries to stay within. Figure 2. Shows the attributes given with each data point, Figure 3. Shows the ArcCollector App and the area of study segmented into 7 zones with data collection points. For zone 6, data was collected in various areas to try and show a variability in data. These areas included the shade, sun, in a wind tunnel (between two buildings), near buildings, and bodies of water. 

                    Figure 2. Attributes                        Figure 3. ArcCollector App displaying map  

A pocket weather meter was used to gather the Temperature, Dew Point, Wind Chill, and Speed shown in Figure 4.

Figure 4. Pocket Weather Meter

For gathering the Wind Direction a compass was used shown in Figure 5.

Figure 5. Compass

After the data was gathered it was then downloaded from ArcGIS online. Because ArcCollector and ArcGIS are both Esri programs they are compatible with each other and provide an efficient way to get data uploaded and do. The data was downloaded as a feature class in an already created geodatabase. Figure 6. Shows the attribute table of the data points collected in ArcCatalog.

Figure 6. Attribute Table

Results/Discussion

From the data gathered several maps were created to display all attributes. In these maps Temperature, Dew Point, and Wind Chill were all mapped using the tool IDW. This tool is found under Spatial Analysis--> Raster Interpolation--> IDW. Using this tool and creating a stretched symbology allowed for the depiction of the values listed above in the following maps. Figure 7. Shows the Temperature in UWEC. Based off of Figure 1. Showing the areas collected and the basemaps, a lot of collection points were taken near buildings. This can impact this temperature with whether the heat was turned on, being in the shade of the building, and the blockage of wind from the building. Figure 7. Also, shows a large pool of blue, this area represents the walk bridge on campus. This area probably came out blotched because of the amount of data collections created and it being above a body of water.

Figure 7. Temperature

Next, Figure 8. Shows a map of Wind Speed, Direction, and Chill in UWEC. The wind direction was created with graduated symbols of wind speed and then rotated by the direction of the speed. This allowed for multiple attributes to be mapped. This map shows the correlation of high wind speeds and low wind chill on the map, meaning colder temperature, around the Chippewa River. Also wind currents and direction change with the rise and fall of temperatures with the different heat capacities of land and water.

Figure 8. Wind Speed, Direction, and Chill

Figure 9. Depicts the Dew Point Temperature. Comparing Figure 9. With Figure 8. One can see some of a correlation with low temperatures and low dew point. This shows how dependent dew point is on temperature and the variations that attribute to it. Dew point is the temperature at which air could be saturated with water. The colder the air temperature the less moisture it is able to hold, meaning the lower the dew point.

Figure 9. Dew Point

Conclusion 

This lab displayed another way of collecting data with a mobile device rather than a GPS unit or more advance technology. This allows for easier use by the public in collecting data and uploading it for public or private companies. Other examples this method could be useful with is the sampling of tree species. Attributes for this study could be diameter, tree type, age, and height depending on how in depth the user wants to go. Having these alternate methods to data collection can help solve geospatial questions at a low cost.