This function aggregates grocery store counts and computes additional variables based on raw census data. The function does not require any arguments but does require internet access.
Format
A sf dataframe with 49 columns, the additional columns compared to the raw grocery_DA:
- hsr_level
The quantile level transit stop number in that census DA (high, mid, or low)
- n_hsr_stops
The transit stop number in that census DA
- n_hsr_stops_density
The transit stop density per square kilometres in that census DA
- pop_density
Population density per square kilometres
- log_pop_density
Natural log of population density per square kilometres plus 1
- log_area
Natural log of land area in square kilometres
- dist_to_downtown
Euclidean distance from census DA centroid to Hamilton downton in metres
- log_dist_to_downtown
Natural log of Euclidean distance from census DA centroid to Hamilton downton in metres
- PCT_aged_under_24
Percentage of residents aged under 24 years old
- PCT_aged_above_65
Percentage of residents aged above 65 years old
- PCT_single_detached
Percentage of residents living in single detached houses
- PCT_married_common_law
Percentage of residents married or living in common law
- PCT_income_less_40k
Percentage of residents have household income less than 40k
- PCT_income_greater_100k
Percentage of residents have household income greater than 100k
- PCT_dont_know_official_language
Percentage of residents don't know/speak official languages
- PCT_not_speak_offcial_language_at_home
Percentage of residents don't speak official languages at home
Author
Zehui Yin, yinz39@mcmaster.ca
Examples
grocery_DA <- prepare_data()
summary(grocery_DA[,c("Freq", "PCT_single_detached")])
#> Freq PCT_single_detached geometry
#> Min. :0.00000 Min. : 0.00 MULTIPOLYGON :891
#> 1st Qu.:0.00000 1st Qu.: 37.86 epsg:26917 : 0
#> Median :0.00000 Median : 72.44 +proj=utm ...: 0
#> Mean :0.09877 Mean : 63.79
#> 3rd Qu.:0.00000 3rd Qu.: 93.94
#> Max. :4.00000 Max. :104.17
#> NA's :4