ArcCollector: Sunday Church Parking around Randall Park

Introduction:

This lab works as an extension to the previous lab on the class collective ArcCollector project. In this lab, students create their own ArcCollector mapping projects and experiment with the use of domains while building individual databases which will host the collected point feature class data of their choice. 

The project which was ultimately decided upon for field study was the influence that Sunday morning church mass has on parking in the Randall Park area, as seen in Figure 1. The park area is host of two separate church affiliations with First Baptist Church located on the corner of Fourth and Niagara Streets, and First Congressional Church located on the opposite corner of Third and Broadway Street. Both churches hold regular Sunday Mass sessions at 9:00 am and 10:30 am, making parking impossible to find in these hours. By noon, however, parking density usually subsides to its regular patterns. This lab attempts to capture the congestion of parking on the Park block as it occurs regularly on Sunday mornings, and attempts to capture the distribution of cars parking locations in relation to their corresponding church affiliation. 

Figure 1: Map of the Study Area

Methods:

Before any data could be collected from the field, students first had to set up their geodatabases to host the data points. After creating a new file geodatabase in ArcCatalog, students moved to ArcMap to set the geodatabase domain properties. Domains are set to restrict the entry options available for input in the field as an attempt to reduce data variations and user error. Domains also make it easier to classify the data during later use in querying and symbolizing specific attribute details. For the purpose of the Randall Park Project, attributes set included Church Affiliation, Notes, Streets, Time, and Vehicle Type. Most of the domains used coded text values. "Time" was the only exception, this option using short integer values. Options for Church Affiliation included First Baptist, United Methodist, First Congressional, Unknown, or None. Options for Vehicle Type were limited to Car, Truck, Van, or SUV.

Figure 2: Database Domain Properties

The next step required the creation of a point feature class, in this instance named 'parkedvehicles.' The Projected Coordinate System used was the WGS 1984 Web Mercator (auxiliary sphere) to account for the latitude and longitudinal point location readings in the field. A field was created within the point feature class relating to each of the domains listed in the figure above. Finally, the geodatabase was prepared to be uploaded to ESRI's ArcGIS online platform for connection to the ArcCollector App, and the mapped data could be accessed in the field.

Field data was collected on three separate hours to account for the parked car traffic around the Park's block. These time slots included 9:00 am, 11:00 am, (to account for the 9;00 am and 10:30 am masses) and 12:00 pm (to account for normal distribution of parking traffic). The results are provided in the next section.

Results:

The embedded map linked below in Figure 3 shows the overall distribution of points collected around the Randall Park area over the course of the three observed hours. 

                   Figure 3: Randall Park Distribution of Parked Cars



Figure 4 shows the distribution of cars and their corresponding church affiliations at the three different recorded hours. The maps provided show clustering around the Park block with parking on both sides of the street for the first two collections, while the last map shows an example of what regular street parking looks like around this block. It appears that both the 9:00 am and 10:30 am mass sessions are equally popular in terms of attendance. While it was difficult to track where every car's passengers diverted to after parking, it seems that most of First Baptist's attendees tended to park on Fourth and Niagara Streets while First Congressional attendee's occupied Broadway and Third Street.

Figure 4: Map of Collected Church Parking Data

Conclusion: 

The results from the collection of this data shows the congestion in parking availability that occurs around the Randall Park area every Sunday morning. For those who live in the area and have to rely on road side parking, this can prove to be an issue during these time frames. Diverting the traffic to other areas in the neighborhood could help to better relieve the parking congestion that occurs around the park block .

ArcCollector Demo

Introduction: 

Smart phone technology is increasingly becoming a staple piece of equipment to have while out in the field. In addition to its standard multi-use field functionality, operating as a watch, compass, GPS, timer, calculator, flashlight, and camera, various apps are now providing live data collection and mapping software accessible in the palm of your hands! 

ArcCollector is an ESRI product which allows for the live, multi-user collection of various point-data locations with the use of a cell phone. Additionally, the program allows various attributes to be updated based on preset domains from the convenience of the field. In this lab, students demonstrate the data collection efficiency of ArcCollector with an on-campus field survey which worked to collect weather data using multiple point data locations from various prescribed zones on campus. 

Study Area: 

For the purposes of this assignment, the University of Wisconsin- Eau Claire's campus was divided into a total of seven zones as shown in Figure 1 below. On Wednesday, March 29 between 3:30 and 4:30 pm, the class went set out to survey the campus and collect specific micro-climate data at sites within each of the zones. To ensure that no area was neglected, the class was divided into a total of seven groups, leaving two people to manage the collection of approximately 40 total data points within each zone. This write up speaks specifically to the data collected in zone four of the University. 

Figure 1: Group Zones and the Study Area

Methods:

For this exercise, students began by first downloading the ArcCollector app on each of their mobile devices. Students could then sign in to their enterprise accounts and ask permissions to join the class group on the department home page. With Professor Hupy's granted permission, students could then access the geodatabase made for the purposes of this assignment. One of it's most convenient features, the ArcGIS online functionality allowed for students to each update data points and attribute fields in real time, simultaneously. The class was given roughly an hour to collect their data point site locations and update each of their corresponding attributes using only their cell phone and a Kestrel 3000 pocket weather meter shown in Figure 2. While in the field, a point could be established on the online map by selecting the "add point" tap within the mobile application. The selection proceeds to an editing page which allows for modification of attribute fields (guided by preset domains), before finally establishing the point and updating the live-feed map. Some of the attributes collected at each of the sites included:

Figure 2: Kestrel 3000

• Group (1-7)
• Temperature (F)
• Dew Point
• Wind Chill
• Wind Speed (mph)
• Wind Direction (azimuth)
• Time
• Notes

The final locations of the data points collected by the class is showcased in Figure 3. 

Figure 3: Micro-Climate Data Point Collection Sites

Once all data points were obtained, the data and its corresponding attributes could be viewed and manipulated using the user friendly functionalities found in ArcGIS online. Some examples of ArcGIS online functionalities include proportional symbol mapping and IDW interpolations as demonstrated in the micro-climatic maps displayed in the results section below. Additional modifications could be made again after downloading the layers into ArcMap desktop software.

Results: 

The first map represented here as Figure 4 reveals the interpolation of temperature using the temperature attribute data collected in the field. Light blue colors symbolize the lowest ranging temperatures (with the lightest color ranging between approximately 49 and 51 degrees Fahrenheit) while the darker blues indicate the highest temperature range (the highest between 57 and 60 degrees). Looking at the dispersion of temperature differences, the map indicates that temperatures were at their lowest in areas around the river on lower campus. At home near Milwaukee, they call refer to these type of significant temperature differences near the Lake Michigan coastline as "The Lakefront Effect," where the lake is responsible for producing cooler temperatures in its coastline areas during the summer, and warmer temperatures in the winter. In Eau Claire, perhaps this would be referred to as a "Riverfront Effect." Contrastingly, temperatures appear to be significantly warmer within specific areas of upper campus (in some areas, up to 10 degrees warmer!). This could be due to the tendency of warm air to rise, in this case, to a slightly higher elevation. It could also be a result of its increasing distance from the Chippewa River. 

Figure 4: Temperature Interpolation Map

The next map (Figure 5) focuses on representing the wind speed and wind direction data that was collected at each of the plotted data point locations. Speed is symbolized by the size of each arrow symbol while the arrows direction indicates the direction from which the wind was blowing. The map shows wind speed to occur at its highest speeds around areas on upper campus, the bridge, and within parking lots. This can be expected as they each are effected by a higher elevation and/or a lack of wind disruptive features such as buildings or trees.

Figure 5: Wind Speed and Direction Map

The last generated map (represented as Figure 6) once again uses the ArcGIS online interpolation function to illustrate the distribution of data collected by measuring the dew point of each site location. The map illustrates high dew point levels occurring in the area occupying lower campus, especially near to the Chippewa River and Little Niagara Creek. This makes sense as these areas may be expected to have a higher moister content in the air. Areas on upper campus, contrastingly, had much lower dew point observed numbers as a result of it's significantly higher elevation.

Figure 6: Dew Point Interpolation Map

As with all good field experiences, the completion of this lab was met with a few minor complications and faced some known areas of error. For starters, while the ArcCollector app can prove a useful infield tool, the tool can only remain useful for as long as the cell phone battery is able to last. This created a problem in the field when one of the cell phones used by a data collector in the area four zone died mid-way through the collection of these data points. Luckily, there was enough time left over for the zone's second point collector to cover the remaining of uncovered ground in the area. A second example of error occurred in the set domains of each of the provided attributes. Domains were set so that even though the Kestrel 3000 read its values to the nearest tenth, the data could only be recorded as a whole number in the online geodatabase. This resulted in a rounding of values which may have skewed the numbers in the overall class collective dataset.

Conclusion: 

ArcCollector is an efficient and easy method of in-field data collection. Its multi-user, simultaneous accessibility allows data surveying to be completed on mass scales at a rapid pace, and the overall user-friendly functionality linking the data and attributes to ArcGIS online makes the manipulation of various datasets quick and resourceful. In all, the program proved to be a useful tool to keep close at hand for any surveying endeavors that might arise in the future.