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The Real Census informs Neighbourhood Research in Canada

by Tracey P. Lauriault on Oct 28, 2010

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Ms. Tracey P. Lauriault discusses neighbourhood scale research using Census data. She introduces The Cybercartographic Pilot Atlas of the Risk of Homelessness created at the Geomatics and Cartographic...

Ms. Tracey P. Lauriault discusses neighbourhood scale research using Census data. She introduces The Cybercartographic Pilot Atlas of the Risk of Homelessness created at the Geomatics and Cartographic Research and will feature community based research (Halton, Hamilton and Saulth Ste. Marie) used to inform public policy as part of the Canadian Social Data Strategy (CSDS). She will feature maps and data about social issues in Canadian cities & metropolitan areas (e.g. Calgary, Toronto, Halton, Sault Ste. Marie, Ottawa, Montreal, & others) and will focus on the importance of local analysis and what the loss of the Long-Form Census could mean to evidence based decision making to communities in Canada’s.

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“ those facing the risk of losing their shelter either by eviction or the expiry of the lease, with no other possibility of shelter in view. Prisoners or people living in other institutions facing their release and having no place to go to are considered as part of this population.” (Springer, 2000:480). These are also individuals or families whose living spaces do not meet minimum health and safety standards, and do not offer security of tenure, personal safety and/or affordability. In the FCM QOLRS, the technical team has selected a variety of indicators associated with the risk of homelessness and these are: those that spend more than 50% of their income on rent, people living in substandard housing, those on social housing waiting lists, the poor and those who are living on low, insecure or feeble incomes, people on fixed incomes such as seniors or those receiving social assistance, and some demographic groups such as lone parent families. Also see the definition Affordable, Appropriate Housing (AAH).
Vacancy rates: Cicle total number of Rental Units The Radial Line is Vacancy Rates 50% + Circles #f Lone-parent family households with 50% or more of HH Income Spent on Rent Radial Line % of these private households over the total number of Private Households Renters for each year. Social Housing Waiting Lists Circles total number of households on the Social Housing Waiting List Radial Line percentage of households on the Social Housing Waiting List over to the total number of Rent Geared to Income (RGI) Units Housing Starts for Rental, Condos and Private Homes Circles total Number of Housing Starts for Rental Unit, or Condo or Private Homes Radial Line is percentage of a type of Housing Starts over the Total Housing Starts
EA for for 1991 DA for 2001 and 2006 This series of maps represents the spatial interpolation of the percentage of both the Low Income Cut Off (LICO) and the households spending more than 30% in rent (30% plus). This interpolation is based on data provided at the EA scale (1991) and DA scale (2001 and 2006). How to read this map: the darker areas represent the higher percentages, either in terms of LICO or 30% plus. For instance we can see an important increase of the percentage of households spending more than 30% in rent (30% plus) between 1996 and 2001. Low income cut-offs (LICOs) are income thresholds, family expenditure data, below which families will devote a larger share of income to the necessities of food, shelter and clothing than the average family would. Wanted to include the sSignpost study but could not as we could not get a boundary file of the health districts
Points display absolute values (numbers)‏ Colors display percentages for the same criteria. Logements sociaux et communautaires : NPO, Coop, total des logements sociaux et abordables existants peu importe leur année de création, le loyer est fixé en fonction du revenu des locataires et indépendamment du marché du logement. HLM : habitations à loyer modique • PSL : Programme de supplément au loyer • LAQ : Programme Logement Abordable Québec - Volet social et communautaire • ACL : Programme AccèsLogis * % = total number of rental housing by municipality, except for the 50% rate of effort which is calculated based on the total number of renter households. * The quantile method has been used to discretise the % (bottom map). This method allows comparison between series of maps (e.g. for each criteria once can see in which part of the distribution each specific municipality is located). For some criteria (e.g. % LAQ), the high number of zeros affect the classification.
Canada Post has these: http://www.canadapost.ca/cpc2/addrm/hh/maps/fsa/ON34.pdf and http://www.canadapost.ca/cpc2/addrm/hh/maps/FSA/ON36.pdf. By looking at these maps, you may notice that the FSA’s for Northern Ontario are huge in size. Most of the rural area of the Algoma District is covered in two FSA’s (P0S, P0R), with some area’s lying within the gigantic P0M area. There are 3 FSA’s for Sault Ste. Marie, and one for Elliot Lake. The P6A area stands out in SSM, as it covers the downtown core, the east side of the city and parts of two First Nation Reserves. In short, any data grouped by FSA (including the SAAD dataset) cannot be used at a meaningful sub-municipal level. For SSM, if we had to rely on the SAAD dataset to replace some information found in the census long form, we would need at least CT level geography. As far as I know, CT level SAAD data is prohibitively expensive. I’m pretty sure it doesn’t come at the DA level either. If it does, I can’t even imagine what it would cost. The problem with CTs is that the SSM Census Agglomeration area is tracted, while the rest of the Algoma District is not. Therefore, CT level data is not useful for rural Algoma. We have to rely on CSDs or DAs. I’m sure these problems exist in other municipalities, but I have attached a rudimentary map showing the CTs in and immediately surrounding the urban area of SSM. It seems, in areas with low population density, neighbourhoods can be grouped together rather haphazardly. Take a look at tracts 5900008.00 and 5900018.01. Notice how they both group neighbourhoods that are fairly far apart from each other?

The Real Census informs Neighbourhood Research in Canada — Presentation Transcript

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