With the completion of the previous exercise, students were now familiar with the workings of ArcCollector. With this knowledge, each student was tasked with developing a personal project, building the necessary geodatabase, feature classes, domains, and collecting the data individually using ArcCollector. When setting up the project, designing and implementing the domains and fields of features was critical. Domains are what limit what kinds of data can be recorded in attribute fields of feature classes. Without these, the possibilities for for data entry error are numerous. In addition, if the domains and fields are not assigned the proper field type, proper spatial and statistical analysis cannot be performed.
For this project, the focus of the data collection was on the bike racks located on the University of Wisconsin, Eau Claire Campus. In the last three months, construction began on the Garfield Avenue project, resulting in this area being cleared for construction. This included removing the available bike racks located in this area. With this being the case, the survey focused on the question "at what bike racks are students parking their bikes on campus and how much available space is at each location?" This survey was designed to identify the areas with the bike rack locations with the least and greatest maximum storage and compare it to the amount of the space being used.
Study Area
Methods
First, a project geodatabase was created in ArcCatalogue. Within the Geodatabase properties, the domains were accessed. Several domains were generated to limit and monitor the future data collection:
- Bike Number: A long integer domain for the number of bikes currently present at a given bike rack. The range was set to 0-400, showing that a bike rack could be empty up to containing 400 bikes. This range was determined as it was roughly 60% larger than the maximum possible storage for a bike rack located on campus. This would allow for data collection beyond the current limit if the University ever decided to expand the storage at a given location
- Maximum Occupancy: A long inter domain for the maximum number of bikes a given rack was designed to hold. The range was similar set to 0-400 as the bike number domain, for the same reason. The bike number domain at a location could exceed the maximum occupancy if students stored their bikes in a way the rack was not originally designed to.
- Time: A long integer domain for the time of data point recording for each bike rack, with a range of 0-2359. This would allow for the recording of military time in a way that it could be easily separated later for analysis.
- Weather Conditions: A coded value text domain for the weather conditions at the given time of recording. This was set to codes for Clear (<50 cloud="" cloudy="" cover="">50% cloud cover), Rain(>90% cloud cover and raining), Snow (>90% cloud cover and snowing), and Other (Sandstorm, hail, other unlikely forms of weather).50>
- Two other domains for Date and Notes were created but ultimately not implemented, as they interfered with data entry for these fields. Students were instructed to avoid utilizing a Date domain due to the complexity and difficulty in implementing such a domain.
- A point shapefile for bike racks
- A polygon shapefile for the construction area on Garfield Avenue
- A polygon shapefile denoting the primary areas on campus, and the most likely locations for bike racks owned and maintained by the University to be located.
- Bikes: Long integer data field for the number of bikes at a bike rack. Connected to the Bike Number domain.
- Max_Occupancy: Long integer data field for the maximum occupancy of a bike rack. This was connected to the Maximum Occupancy domain.
- Time: Long integer domain for the time of data point collection, in military time. This was linked to the Time domain
- Weather. Coded text field for the weather conditions at the time of data point collection. This was linked to the Weather Conditions domain.
- Date: A text field for the date of data point collection, recorded as mm/dd/yyyy. This was not linked to a domain, but was given a maximum number of 10 characters in length.
- Notes: An additional text filed for any notes or critical observations on a given data point or bike rack. This field was generally left blank.
In ArcGIS online, the feature layer was saved as a remotely editable map. This allowed for the insert and removal of data to any of the feature classes online via computer or remotely via cellular device or tablet. Once this was completed, the data could be collected. As previously described, all but one of the data points for the bike racks in the evening (6:00 PM- 8:30 PM) were recorded on April 24, and the locations and corresponding fields were collected a second time on the morning of April 25. This was done remotely via ArcCollector on a mobile device. Once the data collection was complete, it was viewed in ArcGIS online. With this, an online, interactive, and public map was generated to display the data. In addition, the online map document was opened in ArcMap, and the feature classes saved offline. This data was utilized to created a series of maps which displayed the most critical results of the study.
Figure 2: An interactive map displaying the data collected during the bike rack usage ArcCollector Survey on April
24-25, 2017, on the University of Wisconsin, Eau Claire campus. The pairs of points are a result of collecting data for each bike rack twice, once during the morning and once in the evening. Data displayed also includes the study area and area of construction on Garfield Avenue.
Results
Looking at the results of the survey, several conclusion can be made(Figure 3). The number of bikes on racks in the western portions of campus don't really change, regardless of the time. However, racks in the northern and eastern portions of campus appear to hold more bikes during the morning hours than the evening. This was expected, as the eastern and northern portions of campus are primarily occupied by lecture halls which are more active during the morning to afternoon. What is surprising is the seemingly lack of change in the western bike racks, which primarily surround dorm halls. Only a small number of bike racks show a decrease easily noticeable decrease between 9:30 AM and 12:00 PM, a couple of racks located near center of the study area. This would imply that the increase in bike storage in the eastern and northern portions of campus, during the day, are largely a result students who live off campus biking to school in the morning. In regards to the number of bikes stored at a given located, it seems to be tied to the size and accessibility of the building next to the rack, in addition to the visibility of the rack. The more openingly visible racks next to large buildings hold the greatest number of bikes, which cannot be easily seen by looking at a map. However, these individual numbers matter less if the total possible storage is not looked into. Without knowing the maximum available storage, it cannot be determined which racks are filling up.
Discussion
Proper database creation and domain field implementation allowed for the proper data collection and analysis completed within this project. Without it, the bike number and maximum occupancy data could not have been properly displayed and analyzed. This study, up to this point, shows that bike rack occupancy increases in the eastern and northern portions of campus early in the pay and empties near the end. The southern, central, and western portions change little throughout the day. A small number of bike racks completely fill up, and these should be increased in size to fit the demand. However, these bike racks have the smallest maximum occupancy limit, and the largest bike racks which are shown to hold the greatest number of bikes do not fill completely. Unfortunately, weather data wasn't able to be properly explored, due to the limited time available for data collection. Ideally, this study could be repeated, with data collecting taking place over several weeks. That way, proper statistical analysis could be performed for weather data in conjunction with the bike rack usage data.
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