Tag Archives: housing

Final Project: Assessing Vancouver’s Livability for Seniors

This project aimed to determine which neighbourhoods of Metro Vancouver are best suited for senior citizens. Our analysis was based on the assumptions that seniors would prefer areas of low housing costs, low crime rates, and that have senior-targeted amenities within an accessible walking distance.

Click here to see the Final Project Report!

Our team met at least once every week to construct the maps together. We typically had one member operating ArcMap, with another looking over their shoulder offering guidance and looking up instructions and online help. Another group member would be recording the steps taken in order to assemble a flow chart at the end of the project process. The final report was divided into sections amongst the group to be written up, but was edited together.

According to our analysis, the three best neighbourhoods for senior citizens in Metro Vancouver are:

  1.  Renfrew Collingwood

  2. Strathcona

  3. Marpole (eastern portion)

The project helped us be to become more familiar with the spatial join tool in particular. This skill allowed us to normalize data points, such as break and enters, over neighbourhoods. This suited our goal of comparing Vancouver neighbourhood crime rates and other factors.

We also became more comfortable with retrieving data from external sources. Census data was simple to find. We had originally planned to include grocery stores in our amenities analysis, but had significant difficulty in locating data. We acknowledge that this would be a good thing to include in future analysis of liveability in our report.

Group projects, particularly with GIS, face a multitude of challenges. There are several people working on the same set of maps which means that communication is key. We found that creating a Google doc to record notes, steps taken, and thoughts/ideas throughout the project process allowed all group members to stay on the same page. It was handy for both facilitating good group communication and later for creating the flow charts. It also made data management much more simple, because names of files were recorded with a description.

We also created a Facebook group for efficient communication. This was what we used to schedule group meetings and send documents/maps.

LAB 4 Part 2: Housing Affordability

In this part of the lab, we created maps of the affordability in both Vancouver and Ottawa. We used manual breaks as our classification method, setting the same break values in both maps. This allowed us to compare the housing affordability of both two cities to each other!

Click here to see my Housing Affordability maps!

Affordability is considers median housing cost normalized by income. So areas that have high housing cost and high incomes are considered more affordable than areas with high housing costs and low incomes. This is a better indicator of housing affordability than housing costs alone, because it is a more appropriate indicator of liveability. High housing costs become a more important issue when those living there do not have high enough incomes to afford it. For instance, housing costs in New York City are extremely high but its residents typically have high incomes. In contrast, Vancouver housing costs are high but residents have lower incomes. Vancouver is less affordable despite having cheaper housing costs than New York City.

The housing affordability rating categories used in my maps are:

  • no data
  • affordable (3.0 and Under)
  • moderately unaffordable (3.1 – 4.0)
  • seriously unaffordable (4.1 – 5.0)
  • severely unaffordable (5.1 and Over)

These categories were determined by the 12th Annual Demographia International Housing Affordability Survey: 2016 (www.demographia.com/dhi.pdf).  For the purposes of my map, I believe these categories are to be trusted.

Affordability is a good indicator of a city’s ‘liveability’, but should not be the sole indicator used. There are many other factors that influence ‘liveability’, including business conditions, environmental conditions, crime, transport networks etc.

LAB 4 Part 1: Quantitative Data Classification

In Lab 4, we accessed Canadian census data to map the affordability of Ottawa and Vancouver.  The objectives were to familiarize with the various methods of classifying data and to explore their potential ethical implications.

Click here to see my Data Classification Maps!

Part of the lab instructed students to pretend we were journalists hired to report on the affordability and liveability of Vancouver. Which data classification method would a journalist use to display affordability of Vancouver? As a journalist, I would likely choose manual breaks because it would allow me to compare a map of Vancouver’s housing costs to maps of other cities’ (that have the same breaks). This could illustrate that Vancouver’s housing is extremely unaffordable, even when compared to other areas (or visa versa) – the potential for a story.

However, if I were a real estate agent preparing a presentation  for prospective home buyers near UBC, I would use standard deviation as my data classification method.  UBC property appears to be relatively inexpensive with this classification. Because it is located next to areas that appear very pricey, potential buyers might view purchasing UBC property as an opportunity for investment (UBC might be predicted to follow the rising housing values of the surrounding area).

It is important to note that the different methods of classification may influence the interpretation of data on maps, and that there might be ethical implications. Is if fair to choose classifications that overemphasize ‘affordable’ and ‘unaffordable’ areas of Vancouver? That might mislead potential buyers. What if the division of your data classes is confusing to viewers?