Below is a Tableau story about bicycle trends around the U of O campus. The original data was collected by Lane Council of Governments (LCOG) using 48 bike count stations around the Eugene/Springfield area. The stations use pneumatic tubes to count cyclists. The stations are also equipped to sense precipitation and temperature.
Introduction
City wide, bicycle counts are much lower during the Winter than other times. We see the same trend when looking at just Campus locations of bike counters but we also see a drop-off during the Summer. This is to be expected because campus enrollment and activity is lower during this time. Precipitation levels didn’t appear to explain Winter trends as well as temperature. Precipitation levels were lower during the Winter than other seasons, making it difficult to say that precipitation is the sole factor for a drop-off in dike counts. Temperature seems to play a larger role, at least during the Winter.
Temporal Resolution
I chose to look at only bicycle data for 2013 because that was the only year with ‘complete’ data. I also chose to bin the data by season since the data was only collected at specific times during the season, not for every day during the year, or even for each month.
Effect of Filtering Data
As I mentioned above, filtering the data by location (only showing data from bike counters around campus) shows a change in the overall seasonal trend. Citywide, the data shows only a drop-off in bike counts during the Winter. Filtering by location shows us that bike counts also drop-off during the Summer.
Expanding on Data Findings
To help expand on the data collected I think it would be necessary to expand the number of days that data is collected. Ideally, it would be great to see bike counts for every day, but that might be prohibitively expensive for city or county agencies. Supplementing data using other collection methods might be better suited for smaller agencies. For example, using a smartphone app to allow citizens to volunteer information about their bike trips might help verify trends present in the data or even identify where gaps in the data exist.
Data Limitations
In my opinion, the biggest limitation of the bike count data was that it was collected only on specific dates for, sometimes, brief portions of the day. This makes it difficult to say certainty that trends in the data hold true. Perhaps the data was collected on an unseasonably warm (or wet) day, thus affecting ridership for the day.