20200311 16393000 1.0 TRUE <div><b>Title</b></div><div>Toronto Neighbourhood Wellbeing Map Metadata</div><div><br /></div><div><b>Tags</b></div><div>ryerson, toronto, wellbeing, index, weighted linear combination</div><div><b><br /></b></div><div><b>Summary (Purpose)</b></div><div>This map examines the spatial patterns of quality of life in the City of Toronto across all 140 neighbourhoods, based on five socioeconomic wellbeing indices.</div><div><b><br /></b></div><div><b>Description (Abstract)</b></div><div>Wellbeing Toronto is a tool that lets the user select various indices between the years 2008 and 2014, and map them to see the spatial distribution across the city. These include health, demographic, and economic indicators. The data can be downloaded as a graph or in tabular form. </div><div>Five indicators from Wellbeing Toronto were used. They were chosen from the 2011 reference period, as there were a greater variety of indicators that the 2014 reference period did not have (such as TTC Stops and Child Care Centres). All indicators were normalized by total neighbourhood population, except TTC Stops and Child Care Centres, which were normalized by total area. </div><div><br /></div><div>1.<span style=''> </span>Bachelor Degree or Higher </div><div>2.<span style=''> </span>Child Care Centres</div><div>3.<span style=''> </span>Local Employment (15+ years)</div><div>4.<span style=''> </span>Owned Dwellings</div><div>5.<span style=''> </span>TTC Stops (including all subway, bus, and streetcar stops)</div><div><br /></div><div>The rescaling procedure chosen was the maximum score procedure, s’ = s/smax (benefit criterion). This made the most sense because all indicators happened to be raw values and were benefit criterion, and the maximum score preserves proportionality. The weighting method used was ranking. The ranking of the indices (what is most desirable) and their weights were entered into Excel as follows:</div><div><br /></div><div>•<span style=''> </span>Bachelor Degree or Higher (Weight: 0.33)</div><div>•<span style=''> </span>TTC Stops (Weight: 0.27)</div><div>•<span style=''> </span>Child Care Centres (Weight: 0.20)</div><div>•<span style=''> </span>Local Employment (Weight: 0.13)</div><div>•<span style=''> </span>Owned Dwellings (Weight: 0.07)</div><div><br /></div><div>The max score value was multiplied by the ranking weight (e.g. 0.54*0.33). For the weighted linear combination (WLC), the ranked sums were added together, giving each neighbourhood a score that combined all indicators together. </div><div><br /></div><div>In the final map on ArcGIS Online, clicking on a neighbourhood will result in a pop-up showing the neighbourhood number (NEIGH_ID), neighbourhood name (NEIGHBOURHOOD), and weighted linear combination score (WLC). The maximum score WLC is 0.647 and the minimum score is 0.187. </div><div><br /></div><div>Map projection: NAD 83 Zone 17N</div><div><br /></div><div>Data from: https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/wellbeing-toronto/ and https://open.toronto.ca/</div><div><div><br /></div><div><b>Other notes</b></div><div>In the map, some of the neighbourhoods with the highest incomes in the city (such as Bridle Path-Sunnybrook-York Mills and Lawrence Park South) have WLC scores that are quite low (&lt;0.30). This is a reflection of the specific indicators that were chosen. There are not as many TTC stops or childcare centres in those neighbourhoods, which most likely brought the scores down. </div></div><div><br /></div> ryerson toronto A Toronto wellbeing map based on 5 indicators. 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