Using ArcCollector: Eau Claire Sculpture Tour

Introduction

Figure 1. Tourist could cast their vote on their favorite statues

Figure 2. Only the concrete slabs were left where the statues had stood.

The goal of this self-designed project was to gain experience using ArcCollector by creating a geodatabase and an online map. The objective of this map was to analyze and find a correlation between the Eau Claire, WI Sculpture Tour locations and the type of businesses they stand in front of. The point of displaying these sculptures is to use the arts to promote tourism, economic development, health and education (See website). By placing these tourism and economy enhancing pieces of art in front of businesses this organization could be promoting some businesses more than others. This project serves to uncover the methodology behind where this organization wants tourism and economic development. Along with location, this project  wanted to looked at the prices placed on the tags of these sculptures to find a correlation between the price and the intended demographic (rich or poor) of the business, however, at the time of this study the statues along with their tags were removed to prepare for a new set of statues (Figure 2). This portion of the study had to be abandoned.

Methodology
The first step of this project was to create a file geodatabase for this map in ArcMap. In ArcMap the geodatabase was created along with a feature class for the statues. In this feature class, statue name, type of business, Yelp review, intended demographic and notes field were created (Table 1).

Table 1. The Statue Name field actually refers to the name of the business the statue stood in front of.

Unfortunately the Statue Name could not be included in the study due to the previously explained circumstance. The Yelp Review of each business could not be included either due to user error. Therefor only the business name, type of business, intended demographic and location fields would be included in this study. Once the fields were created, geodatabase domains (Table 2) were created for the Business Type, Intended Demographic, Price of Statue (excluded) and Yelp Review (excluded) fields to strengthen data integrity.

Table 2. Although no prices of statues or yelp reviews were included, domains were created for those fields.

Once the geodatabase and feature class were set up the map was shared as a service to the UW-Eau Claire - Geography and Anthropology group on the ESRI Online website. By sharing it specifically to the Geog336spr17 group, only the members of the GEOG 336 group could see the map. After it was shared, data points were collected using the ArcCollector mobile application on an iPhone 6 (Figure 3)

Figure 3. The sculptures were located strictly along Water Street and S. Barstow Street, two of the most popular streets in Eau Claire, WI.

Results

The gathering of data using ArcCollector was successfully uploaded to the map saved online (Figure 4). Data was gathered on twenty-seven statues along Water Street and S. Barstow Street. In the online map pop-ups were configured so that the name, type of business and intended demographic could be viewed by clicking on a data point. From this location map it is obvious that there are more statues on S. Barstow Street than on Water Street.

Figure 4. The pop-ups offer directions to the data point as well as its attributes.

The next map (Figure 5) showed the type of business that each statue stood in front of. On Water Street 66% of the statues stood in front of restaurant, but over half of the statues on S. Barstow stood in front of businesses. This could be because the majority of businesses on Water Street are restaurants and the majority of businesses on S. Barstow Street are private businesses.

Figure 5. Private Businesses, Public Area, Restaurant and Retail were all domains created for the Type of Business field to maintain data integrity.

The intended demographic field was determined by the data collector so the data is subjective and less reliable (Figure 6). However, this could give insight as to why statues are placed in front of certain businesses.

Figure 6. The intended demographic of each business appeared to be random with regard to having a statue in front of it.

Seventeen of the twenty-seven businesses in front of the statues had no targeted demographic, which provides evidence that perhaps the statues are placed in a manner that does not favor businesses with a certain intended demographic.

Conclusion

The ArcCollector proved to be efficient in collecting geospatial data. This study could use many improvements, starting with having the sculptures present during the study. There does not seem to be relationship between the intended demographic or type of business and the location of the sculptures. With 77% of the statues located on S. Barstow Street it is clear that the Eau Claire wants to promote this area more. This could be due to the size of that area or the recent economic boom happening in that area. With that in mind, GIS could play a role in mapping the economic boom of the downtown area.

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).