This lab builds on the skills developed in Lab 8, using the Arc Collector app to collect data using a previously created database. In this lab each student was to generate their own research question, create the database to be deployed to the Arc Collector app, and use the Arc Collector app to collect data to answer that research question.
For this project the research question was: which street of the main off campus student housing area at the University of Wisconsin-Eau Claire is the biggest party street?
To collect data on this research question the main 20 blocks of off campus student housing at the University of Wisconsin-Eau Claire were walked and data was entered in the Arc Collector app for every discarded alcohol bottle or can spotted.
To answer this question data was collected for each alcohol container on the following variables:
- Alcohol Percentage
- Container Type
- Name of Beverage
- General Type of Alcohol
- How full the container was
- Date
- Time
The study area, Figure 1, selected was the main 20 blocks of off campus student housing for University of Wisconsin- Eau Claire students. This section of housing is directly across the Chippewa River from campus and the houses located here are by majority filled with college students. The study area runs north to south from Water Street to Lake Street and east to west from 1st Avenue to 5th Avenue. On Water Street there are two campus buildings on the eastern side of the study area and on the western side of Water Street in the study area is a small down town area with restaurants, shops, and bars.
When data was surveyed, the sidewalk on both sides of every street were walked and any alcohol containers that were found in the street or lining the sidewalk in the front yards along the streets were used as a data point. Alley ways and deep into the yards of the houses were not used as locations to collect data from.
Figure 1. The study area used to collect data on discarded alcohol containers in off campus University of Wisconsin- Eau Claire student housing.
Methods
After developing the research question the first step to create a geodatabase that could be deployed to the Arc Collector app to collect the data for the project. To begin a file geodatabase specifically for the project was created. Within the geodatabase under properties in the domains tab, domains were set for the fields of data that will be collected. Domain names were created with a description, an appropriate field type was set, and coded values were created. Domains help enforce data integrity and make data entry easier by providing a list of valid values for the field.
After the domains were set within the geodatabase, a new point feature class was created within the geodatabase. The coordinate system was set to WGS_1984_Web_Mercator_ Auxiliary_Sphere. Field names were generated to match the domains created in the geodatabase. Most defaults were accepted and the feature class was generated. A summary of the field names generated matched with their geodatabase domains can be seen in Figure 2. In the table if no domain name or coded value is specified that means no domain was created and the value for that field was manually entered during data entry. These fields were selected as the data to collect because they will give insight into different factors that might help uncover which street is the biggest party street. It could be expected that the containers found with the greatest alcohol percentage could denote an area of greater partying. Areas with greater varieties of types of alcohol and greater variety of alcohol container types can also be assumed to be bigger party areas with more people bringing many different types of alcohol and containers. How empty or full a container is can help decipher why the container was there, was it discarded or left behind at this location? These fields will help answer the research question of which street is the biggest party street.
Figure 2. Table summarizing the domains that were created in the geodatabase.
Once the feature class was created, ArcMap was opened and the Map Document Properties were set to the newly created geodatabase. ArcGIS Online was logged into in ArcMap using the University of Wisconsin- Eau Claire enterprise account in the ArcMap viewer. A topographic basemap was added to the viewer and a new feature class was created in the project database by using the editor toolbar and creating a polygon feature encompassing the study area. The polygon was then symbolized as hollow to become a boarder of the study area. The new point feature class was added to the data frame and a test point was created to verify that all of the fields and domains were correctly operating, any problems were corrected in ArcCatalog. The basemap was removed from the viewer, and the ArcMap document was saved as a .mxd file. In ArcMap under File, Share, and then it was selected to share as a service. In the Service Editor Window all fields were filled out. Under Feature Access all boxes (create, delete, query, sync, and update) were checked, the item description was filled out, and it was shared as a service.
Next, ArcGIS Online was opened in a web browser and the University of Wisconsin- Eau Claire enterprise account using the student's credentials. Under My Content the map that was deployed can be seen. The map was saved and shared with a desired group if desired. The map then became available for data collection on the Arc Collector application. An iPhone was used for data collection. The data was collected on a Sunday morning as to maximize the chance of finding a greater number of alcohol containers after two generally big drinking days, Friday and Saturday. In Figure 3 all of the points of data collection (alcohol containers) can be seen the way they appeared in Arc Collector. In Figure 4 is the data collection window with all available fields. The data was collected by selecting the plus sign in the main window of the app over the alcohol container to record its coordinate location. Next the container was analyzed and the data collection fields were filled in with information corresponding to the alcohol container.
Figure 3. The points of data collection Figure 4. The data collection
as they appear in the Arc Collector app. window as it appears in the Arc Collector app.
As the data was recorded all of the alcohol container were collected and recycled. The collection of all 100 surveyed containers can be seen in Figure 5.
Figure 5. All 100 surveyed alcohol containers were
recorded as data points, collected, and recycled.
Results/Discussion
ArcGIS Online also allows for the creation of web maps and visualization of collected data. Below is a web map created in ArcGIS Online has been embedded. This feature of ArcGIS allows for easy data interpretation if ArcGIS Desktop is not available. This one is for the simple use of locating the data points collected in this exercise. Each point should be able to be clicked to read about the attributes of the alcohol container found at that point. It was just by chance that exactly 100 alcohol containers were located and recorded as data. The number 100 was not a number desired to attain or maximum/minimum number that was set. From looking at this web map it can be seen that most of the alcohol containers were found in the southwest corner of the study area. This area is both close and far away from the University of Wisconsin Eau-Claire campus which is located closest to the study area in the southeast corner. The most alcohol containers were located close to Water Street which is where all the bars and a small downtown are located. The increased number of alcohol containers near the bars could be due to people discarding their alcohol bottles on the street before entering the bars. But in contrary with this conclusion is the minuscule number of alcohol containers located on Water Street. An explanation for this could be that the city of Eau Claire does a good job keeping the downtown clean by cleaning up the alcohol containers. Overall the most alcohol containers were located closer to campus, specifically at Chippewa Street and 4th Avenue.
Figure 6 shows the spatial patterns of where discarded alcohol containers were found based on how full the container was when it was found. The more full containers were found closer to campus, closer to Water Street. This could be due to people discarding their containers before moving to Water Street for the bars. The most half full containers were located in the middle street of Broadway. The empty containers were scattered overall all over the 20 block study area, with no observable pattern.
Figure 6. The locations of surveyed alcohol containers by the fullness of the container when it was found to be surveyed.
Figure 7 shows the spatial patterns of what the collected alcohol container was made out of. It appears that the most popular container is an aluminum can, the second most common is the glass bottle and the least common is the plastic bottle. This is interesting and leads someone to believe that this is a mostly beer drinking campus, with beer being a common alcohol drank out of an aluminum can. Glass bottles were located closer to campus with none being located in the farthest two blocks from campus. The greatest variety of container types falls along Chippewa Street suggesting it is the biggest party street, bringing in a larger number of people who would bring in a variety of containers.
Figure 7. The locations of surveyed alcohol containers by the type of container found when surveyed.
Figure 8 shows the spatial patterns of what type of alcohol the collected container was for. It can be seen that beer is the most popular beverage of choice making up 78% of the collected containers. Other forms of alcohol with no specific category make up 16%, vodka makes up 4%, and rum and wine both make up 1% of the collected alcohol containers. This area of off campus student housing prefers beer over other types of alcohol. Looking at the locations of the non-beer containers shows a pattern of more varieties of alcohols being closer to campus falling mostly along Chippewa Street.
Figure 8. The locations of surveyed alcohol containers by the type of alcoholic beverage the container is for.
Figure 9 shows the spatial patterns of the alcohol percentage of the alcohol containers collected. The greatest number of containers fell in the range of 4.0% to 4.6%. This is due to the large number of beer cans collected, beer alcohol percent usually fall within this range. The greatest number of high alcohol percents fell along Chippewa Street, Niagara Street, and 4th Avenue. All of these areas lie closer to campus within the first three streets from campus. Conclusions can be drawn that these streets are the biggest party streets because typically hard alcohol would be consumed in a social setting like a party.
Figure 9. The locations of surveyed alcohol containers by the percentage of alcohol in the beverage found on the container.
A concern that could result from using discarded alcohol containers as the method of determining which street of off campus housing is the biggest party street is that the streets that have the most parties are clean and remove all alcohol containers are removed from the yards. Even given that concern there were some interesting spatial patterns that yield some significant results.
Overall the results displayed that the streets closest to campus, except Water Street, are the bigger party streets of off campus housing. These streets have the greatest variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed. All of these factors suggest that these streets are bringing in a variety of people with different beverage choices suggesting partying. These observations can also be supported that drunk college students coming from on campus housing might not want to travel very far into off campus housing an go to parties closer to campus. Looking further into the results of the two streets Chippewa and Niagara which are the two streets closest to campus, not including Water Street which has minimal alcohol containers suspected to be because of downtown city cleaning, Chippewa Street is most likely to be the biggest party street due to its increased variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed. Chippewa Street from this surveyed data is the biggest party street of off campus student housing at the University of Wisconsin- Eau Claire.
Conclusions
Creating the correct design for a project is very important for successful completion of the project. It is extremely important to include a notes field so that any concerns or extra data that should be recorded can be noted while in the field. For this project the notes section was crucial for recording data about a field that was missing after being deployed to the Arc Collector app. It was concluded that Chippewa Street is the biggest party street due to its increased variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed. If this project were to be conducted again a larger study area could be done, there are other areas of off campus student housing at this university. Also multiple weekends could be surveyed and the data could be compared, maybe other streets have more parties on other weekends and this specific weekend the survey was conducted was just a big party weekend for specifically Chippewa Street.
