Can-ALE

Canadian Active Living Environments (Can-ALE 2.0) - 2025 Update

Meisam Ghasedi¹, Daniel Fuller¹, Gorham Achot², Nancy A. Ross²

¹University of Saskatchewan
²Queen’s University

Welcome to the official repository for the Canadian Active Living Environments (Can-ALE) 2.0 project. . This project extends the Can-ALE Original Project to include the 2011, 2016, and 2021 census years.

Introduction

This study replicates and extends the previously developed Can-ALE measures, which were initially available for the 2006 and 2016 census years. This update was accomplished in three primary ways. First, the entire data and code pipeline for the Can-ALE was rebuilt. This was necessary because the original point of interest data used for the 2006 measure was lost. Second, the Can-ALE was produced for the previously unavailable 2011 and 2021 census years, and with these additions, the Can-ALE is now available for 2006, 2011, 2016, and 2021. Although the Can-ALE is now available for four census years, it is not considered reliable for longitudinal analysis. This limitation stems from inconsistencies in the underlying data, such as the progressive completeness of OpenStreetMap (OSM) POIs over time and the availability of transit stop data for only 2016 and 2021. However, the measures can be used effectively to develop and analyze the ALE index for each census year independently. Third, we computed the “walk to work” and “active transportation to work” mode shares for 2011, 2016, and 2021, which allows researchers to examine associations between the Can-ALE measure and these commute rates. While active transportation is typically defined by walking and cycling, this study expands the definition to include public transit. This was done to create a more comprehensive “active and sustainable transport” variable that better aligns with the study’s objectives. These changes aim to enhance the measure’s overall accuracy and relevance by providing a more complete representation of ALEs across Canada.

Dataset	Data Download
2006 Can-ALE Dataset	CanALE_2006.csv
2011 Can-ALE Dataset	CanALE_2011.csv
2016 Can-ALE Dataset	CanALE_2016.csv
2021 Can-ALE Dataset	CanALE_2021.csv

Software

All code was written in RStudio (Version 2025.05.1, Build 513), and has been made available as open-source for use in future census years or for other analyses. Additionally, ArcGIS Pro 3.0.1 was used as validation software for all measures to confirm that the results from the R scripts were consistent with those produced by ArcGIS.

Data Collection and Definition

The updated Active Living Environment (ALE) index for 2011, 2016, and 2021 is based on five core measures: weighted population density, weighted dwelling density, transit stop counts, intersection density (≥3 legs), and a weighted points of interest (POI). For detailed definitions and data sources for each of these measures, please refer to Table 1.

Table 1. Definition of Measures and Their Sources

Measure	Definition	Data Source
Weighted population density	The number of people per square kilometer within a 1-km circle centered on a Dissemination Area’s (DA) population-weighted centroid.	Census (Statistics Canada)
Weighted dwelling density	The number of dwelling per square kilometer within a 1-km circle centered on a Dissemination Area’s (DA) population-weighted centroid.	Census (Statistics Canada)
Transit Stops	The number of available transit stops within 1 kilometer of population weighted centroid of the DA.	General Transit Feed Specification (GTFS)
Intersections with ≥3 Legs density	The number of ≥ three-way intersections on roads within 1 kilometer of population weighted centroid of the DA, excluding roads classified as motorways (highways, freeways) or slip roads (e.g., highway entrance and exit ramps)	Road Network File (Statistics Canada)
Weighted Points of interest	The number of points of interest (e.g., libraries, schools, hospitals) within 1 kilometer of population weighted centroid of the DA and weighted according to their importance and distance from weighted centroid of the DA.	OpenStreetMap

Geographic Unit of Analysis

To ensure the index accurately reflects the environments that people actually live in, all measures were computed for the area within a 1 kilometer circular (Euclidean) buffer centered on the population-weighted centroid of each Dissemination Area, rather than a simple geometric centroid. This weighted centroid represents the population’s “center of mass” and was computed by taking the weighted average of the centroids of its constituent Dissemination Blocks (DBs), using the population of each block as its weight. In cases where a Dissemination Area had zero population, the geometric centroid was used instead to ensure complete coverage and prevent any area from being omitted. Finally, the Can-ALE measures were estimated within each buffer using a precise areal interpolation method at the Dissemination Area (DA) level.

Methodology, Code Implementation and Results

This section details the methodology for calculating the measures, provides the corresponding code, and presents the results. Each code link includes a readme file with a step-by-step guide to facilitate future implementation and analysis.

Weighted Population and Dwelling Densities

Population and dwelling densities were calculated by analyzing a 1 kilometer buffer around the population-weighted centroid for each dissemination area (DA) within a given Canadian province, utilizing census data acquired with the cancensus R package. Population and dwelling counts from each intersecting Dissemination Block (DB) are then proportionally allocated according to the extent of their overlap with the buffer. These summed counts are subsequently divided by the area of the buffer to provide the final density estimates for each DA.

Transit Stops

To determine the transit stop count within each DA, transit data for both the 2016 and 2021 census years were systematically acquired using automated R scripts from the TransitFeeds website, which offers a General Transit Feed Specification (GTFS) database containing details on stop locations, transit schedules, routes, and trip directions. The code was written to select representative weekdays by excluding weekends, statutory holidays, and certain non-statutory holidays. The resulting transit stop locations were then arranged by province, transit agency, and categorized into Census Metropolitan Areas (CMAs) or non-CMA areas for subsequent analysis. In contrast to Can-ALE 1.0, this work included all stops found in both CMA and non-CMA areas. Finally, the transit stop counts for each DA were calculated by tallying the stops found within the 1 kilometer buffer around each DA’s population-weighted centroid.

Intersections with ≥3 Legs density

Intersection density metrics were calculated using Statistics Canada’s road network shapefiles for 2011, 2016, and 2021. Limited-access roads (such as highways and freeways) were first excluded from each road file, after which an R script was developed to identify intersections with 3 or more legs. To improve processing speed and optimize the calculation, provincial road networks were subdivided into smaller 10 km by 10 km tiles. Within each of these tiles, intersections with three or more road segments were identified and counted, then attributed to each DA buffer. Finally, the intersection densities were aggregated and exported separately for each province.

Points of Interest

For the Points of Interest (POI) calculation, OpenStreetMap (OSM) was chosen as the primary data source for the 2006, 2011, 2016, and 2021 census years. OSM contains a wide variety of mapped features (e.g., schools, shops, parks) as both points and polygons. It offers valuable data for unique, small-scale environmental features, like benches and fountains, which are typically challenging to map but are conceptually important for active living studies. However, due to insufficient data for the 2006 census year, this year was not included in the Can-ALE 2.0 project’s ALE calculation index.

For the 2011, 2016, and 2021 census years, the first step involved converting polygon-type POIs into centroids to create a standardized data format; this was then joined with the point shapefile to produce a single POI shapefile. Second, POI categories unrelated to active living environment variables were removed based on predefined OSM classification codes. Third, two types of weighting methods were applied to the POIs in the calculation process. The first weight was applied to ensure that POIs closer to a Dissemination Area’s population-weighted centroid were more likely to be used by people than those farther away. This was accomplished by applying the negative exponential decay function ( $1.0126e^{-0.0013x}$ ), where x is the distance in meters up to a 1000-meter threshold. The second weighting method, applied to increase the robustness of the counted POIs, consisted of weighting each POI type on a scale from 1 to 4 (1 = lower relationship with active living behavior; 4 = higher relationship). Table 2 provides a sample of the weighing coefficients used, and the POIs with assigned weight of 1 are available in Appendix. Finally, the count of POIs within 1 kilometer of the population-weighted centroid was determined using spatial intersection for both weighted and un-weighted POIs. The final POI counts served as an indicator of local destination availability that is supportive of active living.

Table 2. POI Weighting System

Weight Category 2	Weight Category 3	Weight Category 4
Post Box	Library	School
Post Office	Community Centre	Park
Town Hall	University	Playground
Arts Centre	Kindergarten	Sports Centre
Public Building	College	Supermarket
Pharmacy	Dog Park	Bakery
Hospital	Pitch	Convenience
Doctors	Swimming Pool	Greengrocer
Dentist	Stadium	General Stores
Theatre	Ice Rink	Market Place
Cinema	Restaurant
Hotel	Fast Food
Motel	Cafe
Bookshop	Pub
Butcher	Bar
Optician	Food Court
Sports Shop	Biergarten
Bicycle Shop	Mall
Vending Machine	Department Store
Vending Parking	Newsagent
Bank	Bicycle Rental
Atm	Picnic Site
Attraction	Toilet
Museum	Bench
Theme Park	Bed and Breakfast
Drinking Water
Waste Basket
Clinic

A complete list of Points of Interest (POIs) assigned a weight of 1 is provided in the Supplementary Appendix 1.

Results

The spatial distribution of ALE categories across four Canadian cities, including Montreal, Hamilton, Calgary, and Saskatoon, in 2021 is presented below:

You can find the final Can-ALE indexes and classes for each year separately through the links provided below.

Appendix

Table S1. Points of Interest (POIs) with a Weight of 1

Column 1	Column 2	Column 3	Column 4
Alpine Hut	Courthouse	Memorial	Tower
Archaeological	Do it yourself	Mobile Phone Shop	Toy Shop
Artwork	Embassy	Monument	Track
Battlefield	Florist	Nightclub	Travel Agent
Beauty Shop	Fountain	Nursing Home	Vending Any
Beverages	Fire Station	Observation Tower	Veterinary
Camera Surveillance	Fort	Outdoor Shop	Video Shop
Camp Site	Furniture Shop	Police	Viewpoint
Car Dealership	Garden Centre	Prison	Wastewater Plant
Car Rental	Golf Course	Recycling Glass	Water Works
Car Repair	Gift Shop	Recycling	Water Mill
Car Sharing	Graveyard	Recycling Metal	Wayside Cross
Car Wash	Guesthouse	Recycling Clothes	Water Tower
Caravan Site	Hairdresser	Recycling Paper	Wayside Shrine
Castle	Hostel	Ruins	Water Well
Chalet	Hunting Stand	Shelter	Windmill
Chemist	Jeweler	Shoe Shop	Zoo
Clothes	Kiosk	Stationery	-
Comms Tower	Laundry	Telephone	-
Computer Shop	Lighthouse	Tourist Info	-

Citation

We ask that you cite the following if you are using Can-ALE in published work.

Ghasedi, M., Fuller, D., Achot, G., Ross, N., 2025. Canadian Active Living Environments Database (Can-ALE 2.0) User Manual & Technical Document. Department of Community Health and Epidemiology, University of Saskatchewan and Department of Public Health Sciences, Queens University.