CONTINUOUS DATA

1. 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.

In my analyse the environmental data did not seem to affect the bike counts. Regardless of the weather there are people biking more on weekdays and less on weekends. This is trend parallels very well with the demand data. More students have classes on weekdays, thus there are more bike counts weekdays as well. This leads me to conclude that the demand variable is a far greater driver of bike counts then the environmental conditions.

2. 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 counts were collected by an inductance loop. This is a sensor that is in place permanently and is good for counting bicycles. They work by creating an electrical loop and when ever a metal traverses near by the loop is disturbed. Each disturbance is counted as one bike.

3. 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?

There are 12 inductive loops barrier underneath the pavers. They are laid out in two rows of 6 in order to provide directionality. Each bike is counted and the data is stored in an onsite hard drive. Then Jacobs accesses it and provides it to us.

4. A brief description of the visualization you chose. Why did you choose it? How did it suit your data and your purpose/story?

I created a simple 3 page story. The map was created to add a spatial context to the story. For the environmental data I created a stack of graphs. I used line graphs for the temperature and humidity because they’re interval data and change gradually in the real world. I used bar graphs for the rain and counts data, because they are more of a ratio data type. I found the zero values to be distracting for the message when line graphs are used. For the last chart, I used a bar graph of the bike counts. I then symbolized the bars by the demand data. It’s a very simple graphic that provide a clear and concise message.