Lab Seven: Motorcycle Geospatial Question

Introduction:

Across the University of Wisconsin Eau Clarie campus there are several parking lots. Within these parking lots are areas designated to motorcycle parking. As the weather has been abnormally warm mild thus far this spring, there has been an increase in the presence of motorcycles in the area relative to the past months. This study will attempt to answer which parking area around campus has the highest average CC size (engine displacement in cubic centimeters) of motorcycle and what is the most common type of motorcycle in said area? In order to answer this question the ArcCollector mobile app was used in conjunction with a normalized feature class using numerous domains for data collection.

Study Area:

Figure 1. UWEC and surrounding areas study area.The study focused on the area around the University of Wisconsin Eau Claire (UWEC) Campus. 

This area has been further divided into several of the most used UWEC motorcycle lots (Phillips, Schneider, Towers), off campus street parking (between State Street and Park Ave from Roosevelt to Garfield), and the main Chippewa Valley Technical College (CVTC) motorcycle parking lot area. There are other areas around campus where motorcycles are parked but these are not as commonly used as the areas listed above. Figure one is a map symbolizing these areas.

Methods:

Figure two: Screen shot of choosing motorcycle classification with Arc Collector mobile app. 

The first step to this project was the creation of a feature class to aid in the collection of data to answer the geospatial question proposed above. There were three main fields and several secondary fields included for the point feature class:

• Main Fields: 
• MTRCL_Type: a text data type useing coded value domain for: Cruiser/Touring, Standard/Sport, Enduro/Motard, Scooter/Moped and Custom/vintage. This filed is to record the classification of each motorcycle observed (see Figure two). 
• CC: short integer data type with a range domain of 40-2300. This is used to record the engine capacity in cubic centimeters for each motorcycle observed. 
• Notes: a text data type with no domains other than a limit of characters. This is used to record observations or options not listed in the table. 
• Secondary Fields: 
• Make: a text data type used to record the manufacture of a particular machine. 
• Model: a text data type used to record the model of motorcycle 
• Year: a short integer data type with a range domain of 1930 to 2017. This field is use to record the approximate year of manufacture for each motorcycle. 
• Engine_config: a text data type with a coded value domain used to record the configuration of the motorcycle’s engine 
• Cyl: a short integer data type with a range domain used to record the number of cylinders for each motorcycle. 
• MAINT: a text data type with coded values used to estimate the mechanical and cosmetic condition of each motorcycle. 
• P_pass: a text data type with coded values used to record whether or not the observed motorcycle has a valid parking pass. 

After creating this feature class it was a streamlined process to export it to ArcGIS Online, create a map, download it onto a mobile device, and begin data collection. Data was collected over a period of five days with a start time for each survey. Each observation collection session took between 30 and 40 minutes to complete and was a complete survey off all areas.

Results:

Figure three. Embedded map of results for motorcycle survey.  

Figure four. Results of motorcycle survey via lot name. Note that “<Null>” represents no survey results.

Figure three is an embedded map symbolizing the locations of each individual motorcycle observation by its classification. It also shows the location of the parking areas and their average engine size. The accuracy of the GPS unit was under 15 meters for each point. Table one shows the compiled results of each individual motorcycle observation for the different parking areas. This was derived vie a spatial join between polygons and the points within them.

As a whole there were 16 total observations taken during 5 different survey sessions. 

Conclusions:

From figure three and figure four It can be concluded that Phillips lot has the highest average CC engine size (~756cc) of motorcycles on campus with touring/cruiser type bikes being the most common in the parking area. The CVTC lot ran a close second with an average engine size of 700cc. Towers lot is at the bottom of the list with a average CC size of only 450. Off street parking, located on lower campus had zero results.

Figure five. The distribution of CC size for observed motorcycles. 

The design of the feature class for this project was good. The domains and data type selection were thought out, which helped to prevent the omission of data while collecting. Despite this, there was one major issue with collecting data on motorcycles. Aside from a few die-hard riders, people do not seem to like riding in cold rainy weather. Aside from the first day of data collection the weather during this study was quite rainy and between 30 and 45 degrees Fahrenheit. Figure five is a distribution for the CC size of the dataset. While it may resemble a normal distribution, it is far to leptokurtic (peaked) and does not follow the central limit theorem. This means that this dataset is not a viable answer to the question of this survey. Put simply more observations need to be made, preferably during a time when the weather is more suitable for motorcyclists.