I’m just going to park this here for now. This is a quick map showing population change from 2016 to 2021 in southern Ontario municipalities (i.e, census subdivisions). The division between “southern Ontario” and all else was a bit arbitrary, and the color range doesn’t bode well with the baselayer (my usual choice, Stamen, was finicky with the plugin), but there are some interesting things here to discover all the same. Population change in the suburbs and exurbs is mad-high. (e.g, Collingwood, Waterloo, Kitchener, Barrie area, Peterborough). I suppose this confirms what the realtors were telling us about the covid.
Future changes I’m thinking about
– fixing the colour
– all of Canada. (comparatives won’t always be useful)
– the increase in dwellings and of population in K-W CT’s. (Watch the suburbs grow.)
Welcome back, blog. I suppose i should match the content to the URL.
One of the variables from the StatCan’s 2016 Population and Dwelling Counts that always gets a lot of attention is population density. This map shows the density of the Waterloo Region Census Metropolitan Area (CMA) by Census Tract (CT). Note that the Waterloo Region CMA doesn’t include all of Waterloo Region; a CMA consists only urban and suburban areas. Rural areas are not included.
Of no surprise, of course, is that the densest areas in Waterloo Region are the urban cores of Waterloo and Kitchener, as well as Cambridge. A line of blue runs east-west along Victoria St, with DTK just to its south; one can see how higher density communities are found in the old core neigbourhoods (e.g., East Ward) as well as in the resurgent downtown itself.
A few notes here:
Some Census Tracts do not have data. I’ve marked these in grey. If you look at the table from StatCan and then try to plot them yourself, you will see these three gaps in the Waterloo Region CMA. I haven’t found an official document yet, but I’m presuming that these three areas are suppressed for data quality and/or privacy concerns. (i.e., low populations). This is a common measure by StatCan. StatCan will not release data that might jeopardize a respondent’s privacy and anonymity, and nor will it release poor data.
The Census Tract encompassing the University of Waterloo campus isn’t as dense as some expect it to be. Recall that UW has a lot of land. And the census is enumerated in May. Even for a co-op-heavy school such as UW, this will have an effect on numbers.
Finally, note that I’m using census tracts here. The CT is the most common geography to use when projecting numbers onto “neighbourhoods”. CT’s have historic value since they’ve been employed for decades. I’m interested to project this map in the future, however, with StatCan’s new level of geography, the aggregated dissemination area. These are slightly larger than the CT, but I think they’ll give a better sense of community understanding than the CT can for medium-sized centres like Kitchener, which don’t have the density of the largest cities in Canada.
This map has been developed in QGIS and converted to leaflet with the QGIS2web plugin. I had been playing with Tableau recently but find that their maps take quite some time to render versus the leaflet.js code that is revving the engine of this one.
Here’s the first in a series of maps on Kitchener-Waterloo and the greater Waterloo Region from the 2016 Census. This map interprets population change per census tract between 2011 and 2016. Click on the full-screen icon in the lower-right corner to get the full view.
Some notes, here. Keeping in mind that we’re looking at population change:
I’ve rounded out the coloring on the high end and low end to take in account the outliers at both ends of the spectrum. Some of the rural census tracts show a significant population change between censal years. Double-digit percentage swings on low populations are more common than in geographies with higher populations.
There should be no surprise that the greatest positive change is occuring in the Waterloo and Kitchener cores.
Kitchener is benefiting from the redevelopment of its core, but it still has a ways to go in terms of residential
Waterloo near King and University is showing a significant positive change, given the construction of so much new housing in that area
Remember that a no-change or little-change population density census tract doesn’t mean progress or decline. it just means no change. A good example of this is in some of the old-stock neighbourhoods around uptown and downtown, where change is sometimes minuscule. They might be right on the edge of the downtown/uptown core, but residential change can be hard to come by since the neighbourhoods are firmly established with single-dwelling units and households.
This map was developed with Tableau. I used to hack out maps with leaflet.js but turned to Tableau to improve development times. I’m not too happy with the speed in which it renders to the user, though. A switch back to Leaflet may be in order.
This week, Kitchener City Council was presented with a 2016 Operating Budget that proposed large rate increases for water utilities (water, wastewater, storm sewer). You can see the budget here, and read the news here. The rate for water consumption, as presented, is due to increase by 7.6%. In a close 6-5 vote, Council asked staff to model numbers and actions based on 6.6% and 5.6% increases instead.
There’s been a flurry of news and social media items on these increases. Municipal staff argue that the increases are warranted and necessary, given the backlog of expensive maintenance required. Some people argue that no tax increase can ever be justified, while others argue that money well spent on services required and needed in the city is worthwhile and proper.
How does Kitchener’s water rates compare to its municipal neighbours? The chart below shows you. Bear in mind, though, that the number never tell the whole story. Some key points to remember are:
These figures do not explain why rates are what they are. A city might have a lot of maintenance to do. Or it may not. It may be taking on an appropriate number of projects, over-extending itself, or wilfully neglecting work given the will of the of the people. Either way, the figures are values and nothing more. Determining more relative figures such an opportunity cost or ROI is far more significant, and far more complicated.
These figures show only water consumption rates. This is very important.
There are many ways that a city will determine water charges. Stormwater charges and water service charges (e.g., the cost to administer the service outside of maintaining the flow of water in pipes to your residence).
A water utility rate in one city may not be comparable to the same rate in another town.
These figures compare only the cities present in table against one another. The bustling town of East Podunk, Ont., may have incredibly low or high rates, which would skew the scale considerably.
The following chart shows water consumption rates for Kitchener, Waterloo, Cambridge, the Townships, and also the City of Guelph. I have taken stormwater rates out of the equation since there are appear to be differences in how these charges apply from city to another. Administrative surcharges are not included, either.
All values above and sources below are current as of 24 November 2015. Sources include budget documents as well as municipal websites.