In Lab 4, we ultimately were able to create maps on housing affordability for Vancouver and London. This allowed us to compare side by side the differences in affordability of each city.
Affordability was created by normalizing the housing cost data we already had and classified with the income of households. Using the 2017 Annual Demographic International Housing Affordability Survey’s affordability classification levels, we were able to create a new colour gradient depicting said affordability data.
Affordability is much better than housing cost alone because it takes into consideration of income. It is relative to income instead of an absolute price value. Having only an absolute price means nothing because in each city, housing price may be affordable or unaffordable relative to its area. Also, some cities average income of its population is much lower/higher than others so a price can be affordable/unaffordable for different cities. But once we look at affordability, it automatically takes income and area into account. This is a more reliable way of defining a cities living standard, economic growth, housing market etc.
The rating categories for affordability can most likely be trusted as it is created by Performance Urban Planning in their 2017 Annual Demographic International Housing Affordability Survey. Even though they are not government statistics, looking at their website, it is obvious they are accredited and strive to provide structured information to the public. Yet we should also keep in mind of their agenda and knowledge power when publishing the data they collect.
I think affordability is a good indicator of a city’s livability because as discussed before, it encompasses both income and house price. But it may not be the only good indicator because livability depends on many factors including environment, political ideologies, education etc.
Overall this lab gave us a better understanding of tabular data functions ArcMap offers like joining tables and classifying data. Through this, we were able to apply it to real world concepts of housing affordability based on income and shelter price.
