Methods | geob370

Methods

The basic methodology for this project was to first identify witch variables were important, or most accurately described the demographics of interest. These variables were Educational attainment, Age, Income and Density. Educational attainment was measured by percentage of people who received a college degree, age by the percentage of people over 35, income by average income and density by the number of dwelling units per acre. These were selected and normalized to a score between one and 10. Those scores were then given a weight based on the perceived importance of variables. Those were then loaded into Arch map and combined with the DA boundary layer. A Chloropleth map was created based on the range of combined scores for education, age, income, density. For language I created another Cholopleth map that represented percentage Chinese speaking. I also created a map of the ratio of detached housing units to apartments to separate out areas that are highly residential or apartment. I also looked at a land cadastral map that had lot sizes in Vancouver to get a feel for the different areas.

Weston Fritz: Geob 370

More Specifics:

Education: I selected out of the data set the population who had received a college diploma from ages 25-64 and 65 and above. These were added together and divided by the total population in each DA. These were then normalized to a score between 1-10 based on the formula below.

Age: I selected out the population statistics from the Age_sex data set. I added the total amount of people over the age of 35 and divided by the total population for each DA to get a percent. This was then normalized to a score ranging from 1-10

Income: I took the average household income for each DA and normalized it to a score between 1 and 10

Spoken language: To figure out which areas were predominately Chinese I took the total amount of people who marked Mandarin, Cantonese, or Chinese for the language most often spoken at home. These totals were made into percentages by dividing them by the total population

Normalization equation: ((x-minn)/(max-minn))* 10

Density: To calculate density I took the total amount of dwelling units per DA, joined it to the DA boundary file, and then calculated the dwelling units per acre. This was then normalized to a score from 1-10

Normalization equation: ((x-max)/(Max-minn))* -10

Combining Scores: For my main final map I created a weighted score for the age, education, income , and density variables.

Income*0.4 + Age*0.2+ Education*0.2+ Density*.2= Final score (1-10)

Income was weighted highest because no matter how positive the other variables may be it is a service that is only used when people have the money to pay for it.