Manual breaks rather than natural breaks should be used to compare the two cities because the data sets are different and the minimum and maximum data points are different for both, meaning that natural breaks for both of them are widely different so having different breaks means that the symbology created for both of them are using different amounts for each of them. Using manual breaks with identical intervals allows for both of the maps to have symbology that is based on the same breaks rather than having differing ones. It creates more consistency and becomes immediately easier to identify and analyze the map. Using standard deviation would mean that because the values for Montreal and Vancouver are different, it would result in having a different mean of the values which would cause it to be difficult to visually interpret the map and compare easily. While equal interval classification systems are only useful if the data would have the same intervals otherwise, it would lead to confusion and misinterpretation of the data. Attached map: lab4dataclass
This map (rentcostVM) represents the levels of rental affordability within Vancouver and Montreal. This data was sourced from CHASS which was derived from Statistics Canada and relates to the Census data. As all the source data used in this map was based on the Census where the average monthly rent price was looked at, that means that for certain areas there were zeroes, rather than numbers. This is because anything the Census does not count, it refers to as a zero. However, a zero just means that it is not available, such as reserves, which are not included in the data. This is a big source of error in this map because without having data for everywhere an accurate map is unable to be created and interpreted to gain a picture of the true level of unaffordability.
These next two maps (affordability & Lab4_S19_housing (2)) are measuring affordability in Vancouver and in Montreal. These maps are using the variable of percent of households that spend > 30% of income on rent, rather than rental unit cost, as in the previous maps. This is a better indicator of shelter affordability than rental unit cost because these maps show the amount of monthly income being spent on rent which does not take into account the affordability of the unit. The previously measured variable could be in a location where rent is high, but income is also high and it would not take into account that people could presumably afford it. Overall, affordability is a contributing factor in determining livability rather than a good indicator as there as other aspects that are also important. These could include the natural environment, pollution, air quality, cost of living, among other things. This is because somewhere could be very expensive/unaffordable and not be very livable.
By building on previously developed skills, this project allowed for more independence, more confidence in skillset to be attained and the ability to parse through data efficiently was learned which successfully resulted in maps and analysis to be done around housing affordability.