An exploratory analysis on poverty and social inequality in Montreal neighbourhoods. Tidyverse, Leaflet, SF, Cansim and Cancensus are the main packages used in the analysis.
Low-income Cut-offs (LICOs) refer to an income threshold, below which economic families or persons would likely have spent a larger share of their income than average on the necessities of food, shelter and clothing[1]. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their income than average on food, shelter and clothing.
There are two LICO income concepts: The LICOs before-tax (LICO-BT) use total income while the LICOs after-tax (LICO-AT) use after-tax income.
The LICO-BT are based on the 1992 Family Expenditure Survey, which estimated that families spent 35% of total income on average on necessities of food, clothing and shelter. The LICO-BT thresholds were thus set to income levels where 55% of total income would be spent on these necessities. For the LICO-AT, it was estimated that families spent 43% of their after-tax income on average on necessities of food, clothing and shelter. The LICO-AT thresholds were thus set to income levels where 63% of after-tax income would be spent on these necessities.
LICO is the most established measure of poverty in Canada. It is the main poverty measure used by the federal government and is the most widely-used measure in general across Canada [2].
In the census LICO-AT values are represented as percentage values for census tracts.
To create analyses for different geographic units you can go through the Wikipeadia page on census geographic units of Canada
mtl_lico_at_65<-get_census("CA16",regions=list(CSD="2466023"),vectors=c(lico_at="v_CA16_2582"), geo_format="sf",level="CT")
head(mtl_lico_at_65)## Simple feature collection with 6 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.63785 ymin: 45.45439 xmax: -73.56055 ymax: 45.5608
## geographic CRS: WGS 84
## Population Households GeoUID Type PR_UID Shape Area Dwellings
## 1 3161 1756 4620149.00 CT 24 0.13819 1924
## 2 5217 2756 4620185.00 CT 24 0.39316 2982
## 3 3587 1492 4620222.00 CT 24 0.19894 1634
## 4 4359 1506 4620117.00 CT 24 0.27047 1614
## 5 5729 2592 4620124.00 CT 24 0.37836 2899
## 6 2847 1524 4620306.00 CT 24 0.22301 1635
## Adjusted Population (previous Census) CMA_UID CSD_UID CD_UID Region Name
## 1 3141 24462 2466023 2466 Montréal
## 2 5262 24462 2466023 2466 Montréal
## 3 3757 24462 2466023 2466 Montréal
## 4 4478 24462 2466023 2466 Montréal
## 5 5834 24462 2466023 2466 Montréal
## 6 2799 24462 2466023 2466 Montréal
## Area (sq km) lico_at geometry
## 1 0.13819 17.5 MULTIPOLYGON (((-73.56392 4...
## 2 0.39316 22.4 MULTIPOLYGON (((-73.57216 4...
## 3 0.19894 21.0 MULTIPOLYGON (((-73.63158 4...
## 4 0.27047 20.0 MULTIPOLYGON (((-73.63313 4...
## 5 0.37836 24.1 MULTIPOLYGON (((-73.61896 4...
## 6 0.22301 19.4 MULTIPOLYGON (((-73.56348 4...Mean percentage value for seniors living below lico-at:
## [1] 18.50472Maximum percentage of seniors living below lico-at in a region:
## [1] 65.3Minimum percentage of seniors living below lico-at in a region:
## [1] 2.4The concept underlying the LIM is that all persons in a household have low income if their adjusted household income falls below half of the median adjusted income. The household income is adjusted by an equivalence scale to take economies of scale into account.
The LIM-AT is more an indicator of social inequality rather than that of poverty since it is a comparison. While LICO is Canada specific, LIM is used across all OECD countries.
mtl_lim_at_65<-get_census("CA16",regions=list(CSD="2466023"),vectors=c(lim_at="v_CA16_2552"), geo_format="sf",level="DA") Mean percentage value for seniors lim-at:
## [1] 23.71518Max percentage value for seniors lim-at:
## [1] 100Min percentage value for seniors lim-at:
## [1] 0mtl<-get_census("CA16",regions=list(CSD="2466023"),geo_format="sf",level="CT")
mtl_lico_at_17<-get_census("CA16",regions=list(CSD="2466023"),vectors=c(lico_at="v_CA16_2573"), geo_format="sf",level="CT")
head(mtl_lico_at_17)## Simple feature collection with 6 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.63785 ymin: 45.45439 xmax: -73.56055 ymax: 45.5608
## geographic CRS: WGS 84
## Population Households GeoUID Type PR_UID Shape Area Dwellings
## 1 3161 1756 4620149.00 CT 24 0.13819 1924
## 2 5217 2756 4620185.00 CT 24 0.39316 2982
## 3 3587 1492 4620222.00 CT 24 0.19894 1634
## 4 4359 1506 4620117.00 CT 24 0.27047 1614
## 5 5729 2592 4620124.00 CT 24 0.37836 2899
## 6 2847 1524 4620306.00 CT 24 0.22301 1635
## Adjusted Population (previous Census) CMA_UID CSD_UID CD_UID Region Name
## 1 3141 24462 2466023 2466 Montréal
## 2 5262 24462 2466023 2466 Montréal
## 3 3757 24462 2466023 2466 Montréal
## 4 4478 24462 2466023 2466 Montréal
## 5 5834 24462 2466023 2466 Montréal
## 6 2799 24462 2466023 2466 Montréal
## Area (sq km) lico_at geometry
## 1 0.13819 8.6 MULTIPOLYGON (((-73.56392 4...
## 2 0.39316 10.3 MULTIPOLYGON (((-73.57216 4...
## 3 0.19894 27.5 MULTIPOLYGON (((-73.63158 4...
## 4 0.27047 24.0 MULTIPOLYGON (((-73.63313 4...
## 5 0.37836 23.8 MULTIPOLYGON (((-73.61896 4...
## 6 0.22301 24.4 MULTIPOLYGON (((-73.56348 4...mean_mtl_lico_at_17 <-mean(mtl_lico_at_17$lico_at, na.rm = TRUE)
max_mtl_lico_at_17 <-max(mtl_lico_at_17$lico_at, na.rm = TRUE)
min_mtl_lico_at_17 <-min(mtl_lico_at_17$lico_at, na.rm = TRUE)
mean_mtl_lico_at_17## [1] 18.06996max_mtl_lico_at_17 ## [1] 55.7min_mtl_lico_at_17## [1] 0mtl<-get_census("CA16",regions=list(CSD="2466023"),geo_format="sf",level="CT")
mtl_lim_at_17<-get_census("CA16",regions=list(CSD="2466023"),vectors=c(lim_at="v_CA16_2543"), geo_format="sf",level="CT")
head(mtl_lim_at_17)## Simple feature collection with 6 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.63785 ymin: 45.45439 xmax: -73.56055 ymax: 45.5608
## geographic CRS: WGS 84
## Population Households GeoUID Type PR_UID Shape Area Dwellings
## 1 3161 1756 4620149.00 CT 24 0.13819 1924
## 2 5217 2756 4620185.00 CT 24 0.39316 2982
## 3 3587 1492 4620222.00 CT 24 0.19894 1634
## 4 4359 1506 4620117.00 CT 24 0.27047 1614
## 5 5729 2592 4620124.00 CT 24 0.37836 2899
## 6 2847 1524 4620306.00 CT 24 0.22301 1635
## Adjusted Population (previous Census) CMA_UID CSD_UID CD_UID Region Name
## 1 3141 24462 2466023 2466 Montréal
## 2 5262 24462 2466023 2466 Montréal
## 3 3757 24462 2466023 2466 Montréal
## 4 4478 24462 2466023 2466 Montréal
## 5 5834 24462 2466023 2466 Montréal
## 6 2799 24462 2466023 2466 Montréal
## Area (sq km) lim_at geometry
## 1 0.13819 14.3 MULTIPOLYGON (((-73.56392 4...
## 2 0.39316 14.6 MULTIPOLYGON (((-73.57216 4...
## 3 0.19894 34.5 MULTIPOLYGON (((-73.63158 4...
## 4 0.27047 31.6 MULTIPOLYGON (((-73.63313 4...
## 5 0.37836 33.8 MULTIPOLYGON (((-73.61896 4...
## 6 0.22301 30.8 MULTIPOLYGON (((-73.56348 4...mean_mtl_lim_at_17 <-mean(mtl_lim_at_17$lim_at, na.rm = TRUE)
max_mtl_lim_at_17 <-max(mtl_lim_at_17$lim_at, na.rm = TRUE)
min_mtl_lim_at_17 <-min(mtl_lim_at_17$lim_at, na.rm = TRUE)
mean_mtl_lim_at_17## [1] 23.74142max_mtl_lim_at_17 ## [1] 71.3min_mtl_lim_at_17## [1] 0mtl<-get_census("CA16",regions=list(CSD="2466023"),geo_format="sf",level="CT")
dummy<-get_census("CA16",regions=list(CSD="2466023"),vectors=c(vec_int="v_CA16_2168"), geo_format="sf",level="CT")
head(dummy)## Simple feature collection with 6 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.63785 ymin: 45.45439 xmax: -73.56055 ymax: 45.5608
## geographic CRS: WGS 84
## Population Households GeoUID Type PR_UID Shape Area Dwellings
## 1 3161 1756 4620149.00 CT 24 0.13819 1924
## 2 5217 2756 4620185.00 CT 24 0.39316 2982
## 3 3587 1492 4620222.00 CT 24 0.19894 1634
## 4 4359 1506 4620117.00 CT 24 0.27047 1614
## 5 5729 2592 4620124.00 CT 24 0.37836 2899
## 6 2847 1524 4620306.00 CT 24 0.22301 1635
## Adjusted Population (previous Census) CMA_UID CSD_UID CD_UID Region Name
## 1 3141 24462 2466023 2466 Montréal
## 2 5262 24462 2466023 2466 Montréal
## 3 3757 24462 2466023 2466 Montréal
## 4 4478 24462 2466023 2466 Montréal
## 5 5834 24462 2466023 2466 Montréal
## 6 2799 24462 2466023 2466 Montréal
## Area (sq km) vec_int geometry
## 1 0.13819 360 MULTIPOLYGON (((-73.56392 4...
## 2 0.39316 380 MULTIPOLYGON (((-73.57216 4...
## 3 0.19894 405 MULTIPOLYGON (((-73.63158 4...
## 4 0.27047 605 MULTIPOLYGON (((-73.63313 4...
## 5 0.37836 490 MULTIPOLYGON (((-73.61896 4...
## 6 0.22301 265 MULTIPOLYGON (((-73.56348 4...mean_mtl_int <-mean(dummy$vec_int, na.rm = TRUE)
max_mtl_int <-max(dummy$vec_int, na.rm = TRUE)
min_mtl_int <-min(dummy$vec_int, na.rm = TRUE)
mean_mtl_int## [1] 302.8112max_mtl_int## [1] 1315min_mtl_int## [1] 55