Your analysis of the data and any observations you make regarding the environmental data having (or not having) on the bike counts as well as the relationship of the demand variable.
The graphs from 2B.2 show correlations between weather, campus populations and LCOG bike data. The patterns I notice the most is that all activity is low in the summer months. This accounts for the fact that campus populations dramatically decrease in the summer due to classes being out. Although the weather in this instant is nice, the traffic flowing around campus is very little. Even though the bike counts around campus are low, the number of riders throughout Eugene on an average or higher everywhere else in Eugene. There are spikes in bike counts and populations between late August and October. School is back in session at this time and new years mean more students attending classes. In these instances the weather doesn’t seem to bother the bike counts or populations very much since people will need to be on campus either way.
A brief description of the sensor. Recalling our discussions and readings on sensor ontology and particularly bike counters –what kind of sensor is this? – What is actually being measured to produce a bike count?
The bike counter uses the same technology as the one on campus. the c3 and c5 sensors use magnetic pulls to count the bikes and an electromagnetic displacement wave to provide more accurate counts as it attempts to count the metal from the bike.
A brief description of the sensor network. Recalling our discussions and readings on sensor networks – how is the data getting from the sensors to you?
The sensors connect the an LCOG database. LCOG makes the data available on their website for free download. LCOG has made their own maps that show what the data is showing, and help users visualize what bike counter data may be or show.
A brief description of the visualization you chose. Why did you choose it? How did it suit your data and your purpose/story?
I chose to split most of the graphs due to the fact that since i had so many variables i didn’t want to overwhelm the graphs. The graphs that show up under the colored graphs are easier to view below each other instead of creating too busy of line graphs. I wanted the keep the graphs as simple as possible so that people reading them could kind of have an idea of what they are seeing without all the scientific terms for it. I feel like line graphs are the easiest to visualize, and read since they only require you to read possible points and observe a change over time. I felt like my story compares LCOG bike data with populations near campus and weather was straight forward about what it was trying to say. The data from LCOG is nice to have a visual for, since it also is part of my final. it gives you an idea of what possible data shared between the city bike share and LCOG could look like.