Accomplishment Statement:
Obtained knowledge in downloading and importing spatial and tabular data which was used to assess and compare housing affordability in Vancouver and Montreal.
Quantitative Classification: dataclass
The following map shows the how the housing cost in Vancouver is displayed via the four different methods of data classification: Equal Interval, Manual Breaks, Natural Breaks, and Standard Deviation. Each method varies in its display of the data. Natural Break displays the most “error free” way of classifying data which would display the realities of extremely high housing costs in Vancouver. Within natural breaks, class breaks are identified that best group similar values and that maximize the difference between classes. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values (ESRI). On the other hand, Equal Interval classification method is a method that would make the data appear more affordable and therefore, more attracting to home buyers. The Equal Interval method divides the range of attribute values into equal-sized subranges. Although the equal interval method takes into account the outliers in data and the values at extremes, it does not for account for the distance between data points. Hence, some problems regarding ethical implications of the display of data may arise due to the generalization of ranges of values in each class. However, as a geographer, the most “error free” method is desired, which displays data reflecting the truth and realities of processes within a certain area, which in this case, the natural break method is preferred. Aside from these two methods, standard deviation displays the expense of a house with the median cost of housing, which may confuse its reader with its legend. Manual breaks, allows users to set their own breaks.
Housing Affordability: affordabilityVM
Within this lab, affordability is used to measure the ratio of median income of citizens to housing cost within the city. Using affordability, as opposed to just using housing cost alone, is a much better because it takes into account the financial ability of these buyers to the price of a home. This means that affordability would eliminate any discrepancy between a larger city where standards of living are higher to a smaller one with standards of living that are lower. The following housing affordability rating categories that are provided by “11th Annual Demographia International Housing Affordability Survey” include:
- Severely Unaffordable (Corresponding to a Median and Multiple value of 5.1 and higher)
- Seriously Unaffordable (Corresponding to a Median and Multiple value of 4.1 to 5.0)
- Moderately Unaffordable (Corresponding to a Median and Multiple value of 3.1 to 4.0)
- Affordable (Corresponding to a Medial and Multiple value of 3.0 and Under)
This rating is most likely trustworthy because it is highly suggested by the World Banks as well as the United Nations for assessing affordability, covering 378 metropolitan markets in nine countries, hence determined through trends in very large data sets. Through comparing Montreal and Vancouver’s affordability, we are able to compare the correlation between income and housing costs, however, if more factors were taken into account (such as poverty rates, employment opportunities), this map would be much more complete.