These extensive road network and rail files were deposited into the University of Toronto Map and Data Library collection by Professor Matthew Turner, formerly of the Economics Department, University of Toronto. The data were created for and reported in the following publication http://homes.chass.utoronto.ca/~mturner/papers/unpublished/china_transport_all.pdf Requirement of use is proper citation.

DemoEnPoC2016.csv/DemoEnPoC2006.csv:

This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ).

DGRwithLable:

This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer.

Aggregation and SS Determination:

This script contains code for:

Aggregating postal code level data to the Dissemination Area (DA) level.

Eliminating rural DAs.

Converting environmental data into ordinal categories using quartile and even break methods.

Identifying sweet and sour spots for each DA based on these methods.

SSEJ Analysis:

This script includes code for:

Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).

Performing linear regression analyses between environmental categories and demographic variables.

SS Heatmap:

This script comprises code for:

Summarizing the results of the linear regression analyses.

Assessing changes in inequities among demographic groups between 2006 and 2016.

Visualizing regression coefficients through heatmaps.