Hello all, my name is Alex Oestreicher, and I’ve spent the last 4 months working at Ottawa city hall as a GIS Assistant, as part of the Carleton University Department of Geography and Environmental Studies practicum program. The practicum program is a program for 4th year honours students that provides them with a professional (albeit unpaid) work experience for course credit. As part of the requirements of the practicum, I was tasked with producing a project (and associated deliverable) related to my position.

After discussing potential projects with my superiors in the GIS and Data Management unit, we decided upon the creation of a bicycle level of traffic stress (LTS) map for the transportation planning department. LTS is a methodology, devised by the Mineta Transportation Institute in San Jose, CA, of classifying segments of the roads of a city by their level of stress and safety they impose on cyclists (or the level of comfort that cyclists feel while cycling on roads). LTS classifies road segments into 4 discrete levels of traffic stress with LTS 1 representing segments safe enough for a child to ride on, LTS 2 representing segments safe for most casual adult riders, LTS 3 tolerable by cyclists who are “enthused and confident”, but still prefer having a dedicated space for cyclists, and LTS 4 tolerated only by those who are “strong and fearless”. Unlike other bicycle safety methodologies, LTS is relatively easy to calculate, as it depends mostly on physical attributes of the road itself, such as speed limit, lane count, and the presence or absence of bike lanes, and it does not require expensive or difficult-to-procure data such as traffic volumes.

Producing the dataset proved to be somewhat of a challenge. The necessary data was available, but was contained in multiple disparate datasets, which needed to be integrated together. For this, I made use of ArcMap (and in particular, the topology tools), and heavy use of the ArcPy Python library. While most of the geometry of the LTS dataset came from internally-held datasets, I also integrated service roads and parking lots from an extract of Open Street Map.

I also used a script I wrote with ArcPy to perform the LTS classification. I documented all steps of the process, and the dataset I produced should provide a strong foundation for future revisions. Overall, I had a great experience working at City Hall on this project. It not only challenged my technical abilities, but it will help inform transportation planners as they work to make the City of Ottawa a more bicycle friendly place.

I also began work this December as a GIS student consultant for the Carleton Library!