The data displayed is the number of bicycles that passed the sensor. Changing variable to hourly for count and weather helped to look at the volume of bicyclist on a smaller temporal scale. Although I made this choice, weather conditions are an average, therefore, this story does not address extreme temperatures or precipitation incidences. Specifically average weather conditions do no dramatically effect volume of bicyclists. There is a correlation of the volume of bicyclist and the demand data of enrollment.
Bicycle counter at the entrance of the University of Oregon at Kincaid and 13th Street is an inductive loop. This counter was installed under brick work across the entrance. The inductive loop counter has a continuous electrical circuit, when a bike passes over the metal wheels and frames creates an eddy in the current. This counter is long term and collects data over extended periods of time. Inductive loops can be set to count only bicycles and does not detect pedestrians. This type of counter has been widely used in traffic monitoring and is trusted with a low error rate.
Weather station used Wireless Sensor Network (WSN) using Netatmo sensors measuring specifically temperature and precipitation. The sensors collected data and communicated to the central processing unit which aggregated and distributed the data to an Application Programming Interface (API). Data from the sensors then can be downloaded from the API link.
In the first visualization with the green boxes show the highest volume of student enrollment is Monday thru Thursday 8 am to 5pm. The next slide takes a closer look at volume Monday thru Friday. By trimming the data to days of the week the user can see which hours of the day has highest volumes of bicyclist. Comparing counts with the demand data of enrollment helps compare peaks in classes attended and bicyclists. The weather did not appear to have an impact when looking at averages. The day of the week appeared to be a bigger indicator on the volume of bicyclist. This visualization series the user can make decisions about the busiest times of the day.