Coming into 9th week, we’re a little behind where we’d like to be, but after getting some help from Austin we’re looking to finally finish data cleanup and move into the graphical aspects of our project. We’ll be working specifically with CityEngine and bringing together the raw data we’ve gathered into something more tangible.
So far, we’ve reached out to both the Carleton archives and ResLife concerning what data they might be able to offer us. We’re still waiting for an answer from ResLife, but the archives responded and unfortunately don’t have any information about room draw numbers for past years. They have confirmed that they have kept directory information in print for a certain number of years, which we would have to then transcribe into datasets.
As of now, this process is expected to be quite time consuming, which is forcing us to most likely reduce our sample size from an expected ~100 year period with yearly intervals to possibly four years or more. Analysis of room draw priorities might have to be dropped from the project because of our lack of data.
For now, everything else in the proposal (found here ) seems feasible and is on track for completion. We’ve begun our use of ArcGIS and will most likely start with SketchUp this coming week for the building models.
In the meantime we have mapped the distribution of students for this year as an example, limited to most of the residence halls (excluding town houses and northfield options). Map.
Hieu, Tristan and myself have decided on creating a project which analyzes the trends of students in the residence halls over the years, how this has changed and how this data might correlate with, say, field of study, interests and class year. We would also like to examine how the process of the Room Draw has changed and what weight each number truly has/what halls have been prioritized over the years. The idea is to create an interactive map which fluctuates according to the time period the client chooses. The map would display an accurate Carleton campus for the time, a graphical representation of the aforementioned groups and student distributions in each hall, as well as media of each hall (possibly as outbound links) in the form of either images or SketchUp files.
Most importantly we would need information regarding the housing information of all students at Carleton, a data set which is easily accessible for the current four years. For previous iterations we hope the Carleton Archive still holds some of this information.
This will most likely manifest in the form of a flat database, an excel spreadsheet or something of the likes, which we can populate easily. However, how we use the data is heavily dependent on what form it is provided to us in the first place.
The data would be separated first by year, and subsequently by residence hall, and presumably the percentage of each class (% freshmen, sophmores, etc.) that resides within. Most interesting would be the movement of freshmen since they are placed into housing by the school, whereas the rest of the population gets some semblance of a choice, so the disparities between those two sets should be interesting.
- A flat database of some sort (Excel, Google Spreadsheet).
- ArcGIS/GoogleMaps as the main display.
- SketchUp for complementary media.
- WordPress for main site.
By Week 7 – Finish gathering of data, create a realistic plan for the final version of the project with the data we have managed to find.
Week 7/8 – Organize the data into a selected database which we can easily manipulate using some of the other tools. Establish subsections of information.
Week 8 – Complete complementary media files; find pictures, create SketchUp files of each residence hall. Setup basis for the website the project will be hosted on.
Week 9 – Bring together databases and media into the map. Add finishing touches to the website. Create presentation.
Because our project is as unconventional as it is, there weren’t many projects acting as precise guidelines to what we want to do. However, many of the population tracking projects share a similar goal to what we have in mind.
Mapping Danish Population – Change in population over time in Denmark.
Animal City – Analysis of role of animals in San Francisco and where they lived.
Encompasses more of what we seek: “What urban spaces did they inhabit and how did those spaces change over time?” in regards to different classes (in our case).