Introduction
According to the Canada Mortgage and Housing Corporation, any household which spends 30% or more of gross income on housing has affordability issues. Given that the prices of rentals and homes have continued to surge within recent years, housing affordability is a pressing concern. Thus, in this lab we were required to create three maps to depict rental costs in Metro Vancouver using different classification methods and to compare rental costs and affordability in Montreal and in Metro Vancouver.
Quantitative Data Collection
The first map that was created, represented the median monthly cost of rent in Metro Vancouver using four different classification methods (natural breaks, equal interval, standard deviation, and manual breaks).
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- Natural breaks are the default classification methods used in GIS. Within this method, classes are created based on the natural groups present in the data and thus the distribution of the data is taken into consideration. For the purpose of looking only at Metro Vancouver’s median monthly rental costs, it’s not super ideal to use this method given that the data could be skewed. Additionally, in comparing to Montreal, this option would also not be ideal as it would consider the distribution of both datasets independently.
- The equal interval classification works by dividing the range into equal sizes. It works best for a uniform data set and it does not take into consideration the distribution of data. This method is also not very ideal for the purpose of looking at monthly rental costs as it is likely there is a skew in the data that won’t be captured by this method.
- The standard deviation classification method is utilized when there is a normal distribution of the data and when the purpose of the map is to show deviations from the mean of the data. Thus, once again this method is not ideal for looking at median monthly rent costs as we are not looking at how this value varies from the mean.
- The manual break classification method works by allowing the GIS operator to enter breaks. Thus, this method is definitely most ideal for working with median monthly rental costs as it lets us choose the breaks that align best with the type of analysis we are conducting. When comparing two datasets, manual breaks are also the best option given that they allow you to consider the distribution of the first dataset (ex. Metro Vancouver), determine the class breaks in accordance to the first dataset, then use those same breaks on the second dataset (ex. Montreal) to compare it to the first.
Rental Costs in Metro Vancouver and Montreal
The second map represents once again median monthly shelter costs for rental dwellings, except it compares both Montreal and Metro Vancouver. Using the median cost is ideal because it’s likely that the distribution of housing cost could be skewed by outliers where the cost of dwelling just happens to be extremely high or extremely low and not truly representative of that area which wouldn’t be captured by the mean cost. This monthly rental cost data was retrieved from the CHASS website (Canadian Census Analyzer) which retrieved it from Statistics Canada. The census tract data was also originally retrieved from Statistics Canada. The issue with using this variable of median monthly rental costs is that there often times there are a lot of no data areas. This is due to the fact that many CTs and many DAs are actually owner dwellings and not rental dwellings. In regard to using census data in general, the main types of errors include coverage errors where people may be missed or double-counted and sampling errors resulting from the long-form questionnaire of the 2016 census. Another issue of uncertainty that can arise from census data is the modifiable areal unit problem in which census data is aggregated over census administrative areas, thus, the results are based on these boundaries which may not be ideal to view the distribution of median monthly rent costs.
Affordability of Shelter
Affordability is a measure of the cost of shelter in relation to income, which essentially represents how much of total household income goes towards rent. This is a better indicator of shelter affordability because it takes income into account the fact that an area may have lower salaries but higher rental costs. If we were looking at rental costs alone, we wouldn’t have realized that the affordability is quite low in this area due to lower salaries. Thus, we created a third map to depict this indicator of affordability (based on the percentage of income > 30% spent on housing) and how it differs across Metro Vancouver and Montreal. In my opinion, affordability alone is not a strong indicator of a city’s livability. There are other factors such as access to healthcare and education, environmental quality, safety and stability, and infrastructure that must be taken into account alongside affordability to determine a city’s livability.
Accomplishments/Skills
Some of the tasks completed and subsequent skills and accomplishments gained from this lab include:
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- Created a geodatabase to organize and rename the datasets retrieved
- Utilized the select by attribute icon to get unique values required for this analysis (Metro Vancouver, Montreal, selecting for areas with > 0 values)
- Classified median monthly rental costs for Metro Vancouver using four different classification methods (natural breaks, equal interval, standard deviation, manual breaks) in order to determine how different methods of data classification influence interpretation of data on maps
- Joined tabular data (median monthly rental costs and income) to spatial data layers (CTs and DAs) using the CTUID to create a map of median monthly rental costs and affordability in Montreal and Metro Vancouver
- Standardized affordability data by using the variable of percentage of income > 30 % spent on housing to better visualize differences in affordability across Montreal and Metro Vancouver