Error and Uncertainty

In my study, just as in any, there is always possibility for error and potential uncertainty in regards to the results and findings of Crime versus Housing Prices and Transit versus Rental Prices. Regardless, I am confident that my analysis and results are fairly accurate and also evidently realistic, but I cannot say for certain that my project proves that these variables are related or impacted from one another. My findings are based on the GIS analysis that I conducted and the maps that I have created, however it is not always 100% certain that the data I used is perfectly accurate and that the resulting projections are all precise. My project has also stirred up some uncertainty in regards to the CT’s and DA’s that were used to establish the different rental prices of dwelling units as well as the housing prices and real estate values. Some areas are very large in regards to their boundaries, whereas some areas are relatively very small. The uncertainty factor would be in the case or scenario where if one of these CT’s or DA’s was divided into two or more areas, would these areas all remain the same color in association with rental prices or housing prices, or would the colors and shades change accordingly as the prices in the different areas of the respective DA’s and CT’s might be significantly contrasted. The other uncertainty is that this data was collected from a year previous to the current year 2017 and upcoming year 2018. That means that the results and rental prices of the different areas might be completely changed next year. For that reason, I can only conclude that in 2016, Housing Prices or Values were indeed evidently impacted by Crime within the according Neighbourhoods, while Rental Prices of Dwelling Units are correlated to Transit Availability and Proximity within the different areas around the city.

Further Research

With this study, I have come to realize that more data and information is required to truly assess, to the highest degree, the correlations and factors behind housing prices and rental prices in the city. For further research, it would be interesting to include a map of bike lanes and bike routes around the city in order to analyze the ease of access from certain areas to the UBC campus. Biking is a growing trend in Vancouver and also cuts down time for people travelling or commuting (McKay, 2013). Including a bike routes map and analysis could be extremely useful. In addition, it would be insightful to examine and analyze these relationships of Crime versus Real Estate Value and Transit versus Rental Prices in the cities of Richmond, Burnaby, New Westminster, Surrey, Coquitlam, Port Moody, and Port Coquitlam, as they all have Crime occurrences and are all connected by either Sky Train, Canada Line, or Sea Bus, especially with the Expo line, Millennium Line, and the new Evergreen Line (Jones and Ley, 2016). Hence, these other cities can be compared to Vancouver and we can examine the similarities and differences between them. These newly analyzed relationships in the Lower Mainland would be exciting and would convey a lot of new correlations and findings between the variables that were used in my study.

Conclusion

In conclusion, my objective was to develop multiple models of maps conveying how crime impacts housing prices and how transit correlates to the rental prices in the Vancouver region. In all, my model and analysis was successful in scrutinizing these relationships, as I identified that crime has impacted housing prices in Vancouver around the various neighborhoods, and also illustrated that transit was correlated to the different rental prices in the city.