I followed the classic Multi-criteria analysis methodology in my project, in which I identified the factors of Total 2016 Crimes, Crime Rate, Transit availability, and Transit Proximity, as well as the variables including Housing prices in Real Estate for Sale and Rental prices for Average Dwelling Rentals, which were relevant to my goal, and after standardizing them, I was able to cartographically portray the various correlations between these variables. I chose to use the variables of crime and housing price value in real estate together because I believe that there is a more realistic relationship between these variables than transit would have with home dwelling value or than crime would have with rental prices. I assumed that crime would have an impact on people looking to buy a home in a neighborhood and therefore crime would affect the value of the homes within various parts of the city. Likewise, I chose to examine the relationship between the variables of transit and rental prices because I assumed that the majority of renters would want to find a place that is near rapid transit with good connectivity (Revington and Townsend, 2016). I assumed that transit would have more of a correlation to rental prices than crime would have, and I also assumed that transit proximity and availability of transit would be more closely related to decisions involving home-renters rather than home-buyers.
The following is an outline of the methodology used in my project. First, I will depict the methods for Crime vs. Housing Prices. Second, I will illustrate my methods for Transit vs. Rental Prices. Here are the step by step methods:
CRIME versus HOUSING PRICES in Vancouver 2016: How Housing Prices in Vancouver Real Estate are Impacted by Crime.
Step 1.
Using 2016 Stats Can Data, I first created a map of Dissemination Areas in Vancouver and classified the data based on the Average Dwelling Value for 2016. The Average Dwelling Value can also be referred to as the Real Estate Value or Housing Price Value in the city of Vancouver. I used the Equal Intervals classification method in order to distinguish the different values of homes in the city. Furthermore, through Vancouver’s Open Data Catalog, I added a strongly viable red line to outline the Neighborhoods in Vancouver, and I kept this boundary on the final map, as it was relevant to depicting crime in the different neighborhoods.
Step 2.
Next, I went on the Vancouver Police department website and searched their database to acquire crime statistics for Vancouver in 2016. I also cross referenced this with the City of Vancouver’s Open Data Catalog to assure my data and statistics were accurate. As a result, I obtained data for all the Crimes that took place in 2016 in Vancouver, and I distinguished the different neighborhood crimes by using different colors. In other words, I labeled the amount of crimes that occurred within each neighborhood accordingly.
Step 3.
Then, I used the Vancouver Police Department’s data and statistics, along with Vancouver’s Open Data Catalog, to create my own metadata using Microsoft Excel.
Step 4.
Using Excel, I complied all the Crimes in 2016 and categorized the amount of total crimes into each respective neighborhood. As a result, I had a list of metadata that I could use to illustrate the information I wanted to convey about crime in the various neighborhoods.
Step 5.
My next step was to decide how I would portray this data. I chose to use proportional symbols on my map to convey this information, and I decided to use perceptual scaling for my symbols rather than absolute scaling because I wanted the data to appear more comprehensible and striking to the eye.
Step 6.
As a result, I was able to create the proportional symbols and the right sizes for the map. Now, I could move forward and complete my final map, depicting the Crime Statistics of 2016 for each of the neighborhoods.
Step 7.
Lastly, I put together all the components and created a final map that illustrates the various average Housing Prices or Dwelling Values in Vancouver’s Dissemination Areas in relation to the Total Crime occurrences in the various neighborhoods.
After completing the first phase of the project, I moved onto the next part, which was to identify and examine the correlation between transit and rental prices in Vancouver. The following are the steps of my methods:
TRANSIT versus RENTAL PRICES in Vancouver 2016: How Rental Prices in Vancouver’s Census Tracts are Correlated to Transit.
Step 1.
Using Stats Can Data, as well Vancouver’s Open Data Catalog, I first created a map of the Census Tracts in Vancouver for 2016 that displayed the Average Rental Prices of dwelling units in the various Vancouver CT’s. I organized the data so that it illustrated the various prices of rental units in the city. I used an equal intervals classification method to classify the price of rent, and here I noticed a bit of a divide between the prices in the Vancouver East-side in comparison to the West-side.
Step 2.
I gathered data from Translink and UBC Library’s Open Data in order to use the data that I needed to portray transit in Vancouver. I decided to use the bus stops and bus lines of only major transit lines, as well as the Sky Train and Canada Line. The reason why is because rapid transit which exists along these major transit lines is more favorable and optimal for renters, and this would allow a better and more comprehensible visual to meet the eye (Jones and Ley, 2016).
Step 3.
Using the features in GIS, I managed to create a map of all the Major Rapid Transit Bus Stops and Routes from the various layers of data that I gathered. In addition, I included the Canada Line and Sky Train Routes and Stations.
Step 4.
I set a buffer of 400 metres around each Major Rapid Transit Bus Stop and Train Station. The reason I chose my buffer to be 400 metres is because this distance can arguably be regarded to as a five to seven minute walk (Revington and Townsend, 2016), which would be ideal for the transit users that are renting and living in the different areas.
Step 6.
Lastly, I wanted to add the Land Use layers of Commercial Areas and Parks in my map, however, I found that this made the map look messy, and it made it harder to read and understand. For that reason, I decided to delete the Parks and Commercial Land Use Layers from my Final Map product. This is what it would have looked like:
Step 7.
FLOWCHART
Next, I took the data that I acquired and worked with it in GIS to create layers which resulted in a union to produce the final map. Here is a flow chart of analysis that I made to show the steps I took in the GIS process. I gathered the layers for Major Rapid Bus Stops, Major Rapid Transit Lines, Bus Routes, Sky Train Stations, Canada Line stations, as well as the Transit Line for the Sky Train and Canada Line, and ‘Clipped’ these to the boundary. Then, with the clips I made, I ‘Buffered’ the bus stops and train stations to a 400-metre buffer. The reason I chose a 400-metre buffer is because 400 metres is usually considered to be a five to seven minute walk, which would be ideal for the transit users renting and living in the area (Revington and Townsend, 2016). This buffer conveying a five minute to seven walk was set to be around each major rapid transit line bus stop or station. Then I created a new layer for the Major Rapid Transit Bus Routes, which was set in Red colour. In addition, this flow chart shows how I worked with the CT data from Stats Can and how I “overlayed”, “joined”, and “intersected” the layers and attribute tables to produce the final map. Lastly, I made a map consisting of the aforementioned variables as well as a layer for Land Use in the city, where I depicted Park and Green Space as well as Commercial Areas. In the end, I did not use the Land Use layers of parks and commercial areas in my final map because I thought it overcomplicated map, as there seemed to be too much going on and was a distraction to the map’s main goal.