For the GSF we got the data from the utility data from 2010, then we compared that to the monthly kilowatt hours for each month and arrived with a number that reflected the kilowatt hours per square foot per month for each building

for people we did some cutting and pasting from the owu self service site and from there we were able to calculate how many students use each building in a given week on the academic side of campus, we then multiplied that by 4.29 the average number of weeks in a month (30/7=4.29)  and came up with the total # of people that use the science center in a month,  we also did this with the faculty, and on the residential side we had numbers of students that live in the building, and we multiplied that by 7 and then by 4.29.

For time we had data on usage in the dorms so that was used for time of use in the dorm in a month, for the academic buildings we figured out the building hours for each building, and then figured out how long that building was open in a month and compared that to the kilowatt hours.


Timeline (Tice, Pinkerton, Varner)

Project Timeline (Tice, Pinkerton, Varner)

Week 1 (March 19-23)

  1. Begin to organize and establish a spreadsheet with a all necessary data
  • Use data Sean sent us containing residential listings, energy costs, and a record of which buildings have air conditioning units.
  • Email reslife to obtain residential counts for all buildings listed for residential use.

Week 2 (March 26-30)

  1. 2.        Complete spreadsheet
  • Utilize data sent to us by reslife and add it to the existing spreadsheet
  • Establish how we want to normalize data in spreadsheet
  • Calculate averages depending on how we want to normalize data

Week 3 (April 2-6)

  1. 3.       Finalize datasheet and Begin creating map
  • Look over and check all information in spreadsheet to make sure there
  • Normalize data and compare with academic segment of project
  • If time begin creating image in Arcmap

Week 4 ( April 9-13)

  1. 4.       Work toward compiling and accurately reflecting all data on a table in Arcmap
  • Organize data sheet
  • Use address matching to pinpoint the locations of where our data is being represented.
  • Continue to compare and work with academic segment of project.

Week 5 ( April 16-20)

  1. 5.       Continue to work on map
  • Add keys/legends…ect
  • Work toward finishing map

Week 6 ( April 23-27)

  1. 6.       Finish up mapping and begin to prepare presentation
  • Finish touching up the map and adding detail to improve imaging
  • Begin to organize short presentation (be able to explain all steps of the project)


As of right now we have all of the electricity data for each building separated by month for one full year, in a spreadsheet.  along with that data we have the square footage of each building as well, and the buildings that are air conditioned are marked accordingly.

As far as finding the usage of buildings by students we were able to use the self service website, and extract the data that it has on people in classes, and put it into an excel spreadsheet and from there we reorganized it, and found the numbers of students enrolled in classes for each building.  From this we plan to find the average number of students who use the buildings by using spring course data, and fall course data.

One thing we still need to find is the building hours for all the buildings, which we believe public safety will be able to provide. We should be able to start mapping the numbers out, and start getting some useful information from the data fairly soon.  Some things we need to be aware of too, is the fact that the information that we have created ourselves was not readily available, making it hard for just anyone to access this data.  so one of our goals is to try and make this data more available.

Project Proposal: Academic Side

For our project on energy consumption we have decided to focus exclusively on electricity usage.  We chose to break up the work between the six of us: Silas and Jon will focus on academic buildings, while Adam, Sam, Keegan, and Mason will focus on the residential side of campus. We will determine the following:

  • Electricity used per Square foot– Simply the amount of electricity  used compared to the Sq footage of the building (this would be the usable floorspace in the building)    KWH per sq ft
  • Electricity used per person– The amount of heat and electricity used per student and faculty in the building (this will be found by obtaining a 7 day average of the amount of students and faculty that use the building  in that amount of time) KWH per Person per week (per month)
  • Electricity compared to the time the building is open and used– each building will have a time coefficient that will show the average amount of time on a 7 day average that the building is used and compare that to the electricity and heat use.  KWH per Usage in a week to KWH per usage in a Month
Eventually, we intend to give each building a rating, compiling as much pertinent information as possible and then assessing its energy efficiency.

The data we intend to use is shown in monthly intervals. The idea is to take weekly averages of students/faculty in each building, extrapolate it out to a month, and then compare energy usage per student/faculty member.


Week 8, Spring Break, Week 9

  • contact registrar for students enrolled in classes for academic side (we can potentially do this on our own)
  • Contact public safety for building hours
  • When student data is compiled, start crunching numbers
Week 10 – 11
  • Look at completed data, consult with Kinghorn and Krygier to ensure accuracy.
  • Address problems with non-academic or residential buildings, i.e., should we just consider faculty and staff of these buildings?
  • Start mapping

More to come…

This blog will document the work of six OWU students in GEOG355: Geographic Information Systems. Throughout the semester, we will update this blog with project progress, images, findings, and finally, our final report. Stay tuned!