DIALOGUE

Analysis

There are several reasons why these factors do not give us a complete overview of creativity in Vancouver's different neighborhoods. First, they are not the only ones that can give us a view into what makes creativity thrive in urban environments. Second, because we are dealing with a qualitative question with a barely defined idea of 'creativity' it makes it more difficult to analyse what can and cannot be used to calculate creativity. For example, the 'Greenways' map in the infographic can lead us to believe that certain neighborhoods don't have access to enough greenways. Grandview-Woodlands may not have enough greenways, but it makes up for this lack in park space. We must also consider that Grandview-Woodlands has a smaller area compared to another neighborhood such as Dunbar-Southlands.

Questions regarding lack of greenspace or park space in the southern neighborhoods can be answered by the 'Density' map on the first page. It seems that the lesser the density of a neighborhood, the lesser the park space and the transportation needed. This is also answered by the Apartments map. The majority of apartments are found in the northern neighborhoods of Vancouver because the southern neighborhoods are more family friendly with detached homes or duplexes. This is apparent if you take a stroll in these neighborhoods as well. This data shouldn't be new to a Vancouverite; these patterns are obvious if you take a stroll and observe the make-up of the neighborhoods.

However, what I will argue here is that since the northern neighborhoods show greater greenways, park space and walk scores, they will inherently be more healthy and people have greater access to nature and more ability to walk to achieve transportation than drive. However, this apparent healthiness is countered by a clear concentration of apartment buildings in the northern neighborhoods as well. These probably cater to single, small families and yuppies (young urban professionals) that are looking to work closer to the downtown core. However, in studies by Charles Montgomery in his 'Happy City' book, he claims that living in tall apartment buildings can increase people's loneliness.

At this point, we should question whether living in loneliness in apartment buildings is a greater risk than the benefits from access to nature. Given our data, we cannot answer this.

Results

There is a reason why multi-criteria evaluation wasn’t used in this analysis. The research didn’t indicate for me to find a particular neighborhood that was the best or the worst of them all. That would do the research a grave injustice.

Instead, the research required visualization of the comparative abilities of each neighborhood to realize their potential to be an inspiring one. In other words, choosing the best neighborhood would only serve the citizens who had enough resources to be able to choose the neighborhood they would like to live in. This is not meant to achieve that result. This infographic is meant to serve as a resource to planners, architects and urban enthusiasts who have the resources to serve those neighborhoods that require some attention. It is also meant as a resource to learn from those neighborhoods that are able to serve the needs of the demographics that live in them. However, as objective as this research is, it is equally as subjective.

For this reason, spatial analysis techniques of the sort acquired in this class, such as multicriteria evaluation and Boolean mapping would have done this research no justice.

Errors and limitations
Several problems were encountered in the course of this project. First of all, a serious problem with the mapping of the data occurred when the census table was joined with the City of Vancouver neighbourhood shapefile. This join caused some data to disappear. There were census tracts that entirely ceased to exist. With this happening, I couldn’t publish the information without presenting distorted or incomplete information. I had to use the neighbourhood shapefile from the City of Vancouver’s Data Catalogue instead.

The palette to display the maps in IDRISI was also unable to properly account for the values in the map. Instead of coloring in the specific values in the map, such as 4=red or 6=pink, the entire palette had to be assigned colors from 0-255. The values weren’t being read. This made analysis much more difficult.