Day 8 - WK 2

One of our GPS devices is giving off corrupted files so we worked to reset the GPS device, it seems to a bad formatted data card.  We have now been collecting data for 8 days.  Two of our GPS participants are now ridesharing, so we will be giving a device to another teacher.   Worked on the lesson plan today then we went to the library for research time after lunch.  We want to design a PBL, project based lesson for my students to work in teams, collect GPS data through various devices.  The first step for students will be to look at the benefits of ridesharing and the inhibitors to its success and discuss as a class.  I want students to become familiar with GPS and data collection and how it is processed by doing a geocaching activity with vectors and data collection. Research ridesharing techniques in various cities, the social issues of ridesharing, methods used to determine the best routes and how GPS calculates your routes.  Students will then develop a model and plan to reduce the carbon footprint from transportation and promote ridesharing in their school and community using their data results to support their model.
At this point we have no knowledge of how GIS could be incorporated to our project.  We do not have any data patterns other than looking at our data in lat/lon by date and time or in Google earth or on a map.
 Found articles over the social aspects of ridesharing and a website with research and connections for dynamic ridesharing  dynamicridesharing.org.  Found an interesting atricle that noticed a social pattern that women are more likely to ride share unless it was with a stranger. Men were more likely to rideshare with a stranger but in total it was less than 20 adults total willing to rideshare with a stranger. This was in the article Leveraging Social Networks to Embed Trust in Rideshare Programs. Another article actually had a formula they used to calculate CO2 emissions,the article Estimating the environmental benefits of ride-sharing: A case study of Dublin has several formulas that could be useful as we move forward to find a method to turn our distances traveled for each GPS data track into carbon footprint calculation and as well as a practical format that students and children at Techfest will understand.  One example might be how many trees it takes to absorb your CO2 emissions for a time period.


This is a picture of Jesse's track around the second floor of Discovery Park walking after our snap rover data collection failed today. Will bring in a faster RC car to attempt data collection again.