Finding Home: A Suitability Analysis

My first quarter into my GIS certificate program at Portland Community College I had the opportunity to choose a final project subject of my interest as long as it incorporated the tools I had learned to use during the course of the class. The class, GEO 265, is an introductory course into the use of ArcGIS, the leading GIS program around the world. The idea of a suitability analysis quickly came to mind as my wife and I are moving to British Columbia and needed to find a place to live. We both deeply appreciate nature, a slow pace of life and a community that is connected. In terms of criteria that meant: being close to recreational sites such as trail systems, national parks and ski resorts; a small town that has a communal and environmental mindset.

Creating an Index

In order to “score” the potential places to live, I needed to create an index that would assign a value to each populace based on how well it fit the established criteria. As described in the map above, the criteria accounts for distances from national parks, trail systems, and ski resorts as well as the population of a community and its percent of environmentally friendly commuting. Phew.

Although those are not the only things that make us happy, they are some of the things we deeply cherish. Overall the criteria supports our health through physical activity, a positive community mindset, and most likely a place we can appreciate for its beauty.

I’m not sure the last time you looked at a formula for an index, but here is the one I developed.

Suitability Score = (DF ski area + DF trail network + DF national park + (%EFC * 100)) / 4.

Where DF = Distance From. %EFC = Percent Environmentally Friendly Commute

Woah, neat index Tyler!….But wait, won’t that index provide a score that is LOW for the most suitable place? Like golf? Hmmmm, you’re absolutely right, the most suitable place to live does have the lowest score, how will I display that on my map? Shouldn’t the best place to live have the highest score and largest symbol?

Luckily, a small number will go into 100 more than a larger number, so, if I were to divide the score by 100 I will be able to produce a number that scales with the suitability! The final version of the index is found below.

Suitability Score = 100/((DF ski area + DF trail network + DF national park + (%EFC * 100)) / 4)

Bingo!

The Suitability Index ranked the top three places to live, coincidentally enough, they are all on the eastern side of the province. Now, I’m not entirely sure why that is, but what it tells me is that we will most likely favor a town on the east side of the province versus a town on the west. The top three towns are also relatively close to one another, in fact, the time it takes to drive from Invermere to Revelstoke is 3 hours, passing through Golden on the way. There seems to be a sweet spot in this region of BC.

Now, you may be thinking “Jeez, Revelstoke scored nearly double both runner ups! Move there!”

Hold your horses! Let’s look into this a little more.

Revelstoke is ridiculously close to all the criteria, I mean 1.8km from a national park?! That’s nuts! Looking at both Golden and Invermere their furthest criteria is 14km away….how many miles is that? Ah yes, a conversion for my audience, sorry….8.7 miles! “Damn, that is close too!" you may be thinking. Well, it really is in the grand scheme of things.

So, let’s back up a bit and take a moment to zoom out.

Golden!

The place to be, a “Town of Opportunity”.

Taking a look at the third and final map, you can see that there is much more to a town than being the closest to the criteria I set. Golden is very central to several national parks and, not included above, it is also within a two hour drive of five, yes, FIVE, world-renowned ski resorts (check them out!).

Fortunately, we had a friend in Golden whom we lived with in New Zealand, and his place had an open room.

At the time I was creating these maps, we were already calling Golden home, we just happened to luck out on the coolest place to be in BC.

I hope you enjoyed my maps and stay tuned for more!