Living In The Environment 4th Canadian Edition Pdf

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Samantha Figueredo

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Aug 3, 2024, 4:59:31 PM8/3/24

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A national metric of active living environments is desirable to facilitate the direct comparison of communities, national surveillance of population health and data linkage with existing Canadian national health surveys (e.g., Canadian Community Health Survey, Canadian Health Measures Survey) and investigator-led cohort studies. Currently, very few pan-Canadian measures of the active living environment exist and those that do are not readily accessible or free to use.Note 11

The purpose of this paper is to describe the development of the Canadian Active Living Environments (Can-ALE) dataset: a Canada-wide set of four individual and four summary measures that characterize the favourability of active living environments in Canadian communities at the dissemination-area (DA) level (Figure 1). This study reports on analyses which guided the selection of measures and derivation data sources to select for the dataset. The objective was to produce a national database entirely from open data and to evaluate the performance of open data compared with traditional or proprietary sources. A 2006 version and a 2016 version of Can-ALE are available to download online ( -ale/).

The principal unit of analysis was a circular, one-kilometre buffer around the centroid of the DA. Previous studies have shown differences in associations between measures and walking behaviour or health outcomes according to the type of geographic unit used to derive them.Note 13Note 14Note 15 As a result, buffer choices are often debated by researchers. To determine the most appropriate buffer shape and size for the national dataset, 12 active-living measures were derived using 4 types of buffers, which varied according to shape (circular versus street-network-based) and radius (500 metres versus 1 kilometre) for a subset of DAs on the island of Montral. Using walking-for-transportation rates derived from the 2013 Montral Origin-Destination Survey, 10 of the 12 measures had the highest correlations with walking rates when derived using the circular, one-kilometre buffer. In general, measures derived from the 500-metre network buffers were the least associated with walking rates (results available from the authors). Network buffers, by design, are a type of connectivity measureNote 16Note 17 and their use essentially causes connectivity to be counted twice in summary measures. The strength of the associations with walking rates and the confounding influence of network buffers led to the adoption of one-kilometre, circular buffers. Circular buffers are also favourable for computing resources relative to network buffers.

Two connectivity measures (three-way intersection density and four-way intersection density) were calculated three times, each time using a different methodological approach or derivation source. First, intersection density was derived using road intersections from the 2016 Statistics Canada Road Network File. Second, intersection density was calculated using OSM road features, which are identical, in principle, to the Statistics Canada file. Third, intersection density was calculated from OSM intersections of roads, footpaths and recreational trails. Limited-access roads (e.g., highways and freeways) were removed from each of the road files before calculating intersection density.

Four density measures were derived, and they varied in terms of underlying data (population count versus dwelling count) and calculation method (gross density versus weighted density). Gross population density and gross dwelling densitywere derived by dividing the population or dwelling count by the area of the DA. Weighted population density and weighted dwelling densitywere calculated by aggregating the population or dwelling count of each DA within the buffer and dividing by the area of the buffer. If a DA fell entirely within the buffer, its entire population and its dwellings were added to the count for the buffer. If a DA fell partially within the buffer, the population or dwelling counts of the DA were adjusted according to the proportion of the DA within the buffer. For example, if a DA with 1,000 inhabitants was only 25% within the buffer area, the buffer was assigned 250 of its inhabitants. Major coastal water bodies and DAs with no data were excluded from this calculation. The full and adjusted values were summed to determine an approximate population and dwelling count for the buffer. Population and dwelling counts were obtained from the 2016 Census conducted by Statistics Canada.

Two active transportation rates were calculated from Question 43 a) of the 2016 Census long-form questionnaire.Note 25 This question asks members of the labour force aged 15 or older with a fixed workplace address how they get to work. These rates were calculated by aggregating the number of pedestrians, cyclists and public transportation users for all DAs intersecting the circular, one-kilometre buffer of the DA centroid. If a DA fell only partially within the buffer, a smaller number of commuters proportional to the area of the DA within the buffer was aggregated. For example, if a DA was 25% within the buffer area and reported 40 pedestrian commuters, it was estimated that only 10 pedestrian commuters lived within the DA buffer. The walking-to-work rate reflects the proportion of this population that reports walking as their primary mode of transportation to work. The active-transportation-to-work rate reflects the proportion of the same population that walks, cycles or uses public transportation to get to work. Public transportation use was included as active transportation, as public transportation has been shown to generate physical activity via walking to and from transit stops.Note 26Note 27Note 28

Pearson correlation coefficients were calculated to assess the association between walking-to-work rates, active-transportation-to-work rates and the 13 active living environment candidate measures (Table 1). To assess whether there was a regional bias in the coverage of user-contributed OSM data, the proportion of OSM POIs to DMTI POIs in five geographical regions (Atlantic, Quebec, Ontario, Prairies and British Columbia) was calculated. It is important to note that OSM and DMTI POI datasets contain different types of records. The DMTI EPOI file mainly consists of records of businesses, while OSM POIs contain certain commercial businesses (e.g., grocery stores, restaurants, clothing stores) as well as features of public spaces and streets (e.g., benches, picnic tables, tennis courts, food stalls, ATMs, postal boxes). Accordingly, the number of points in each dataset cannot be directly compared, and instead, the proportion of OSM to DMTI points was examined for different regions of the country. The strength of the correlations in this analysis and the spatial and regional distribution of the input datasets were the primary findings that informed the selection of Can-ALE measures.

K-medians clustering was performed to classify Canadian DAs into five categories that characterize the favourability of the active living environment. K-medians clustering is a partition clustering method where the user specifies the number of clusters (k), then an iterative process is used to assign observations (in this case DAs) to a group with the closest median values. The cluster analysis was based on the three pan-Canadian DA-level measures selected for Can-ALE:Note 1 three-way intersection density of roads and footpaths derived from OSM,Note 2 weighted dwelling density derived from Statistics Canada dwelling counts, andNote 3 POIs derived from OSM. The k-medians clustering method was used, as the right skewness of the active living environment measures made the k-means approach unsuitable. A descriptive analysis compared walking-to-work rates; active-transportation-to-work rates; and average connectivity, density and POI values by cluster group.

Dwelling density measures were more strongly associated with walking-to-work rates than population density measures, and the weighted density derivation method was more strongly associated with both walking-to-work and active-transportation-to-work rates. Gross population density (R = 0.23) and gross dwelling density (R = 0.29) were both modestly associated with walking-to-work rates, while both weighted population density (R = 0.82) and weighted dwelling density (R = 0.82) were strongly associated with active-transportation-to-work rates.

Five cluster groups had the lowest variation of median values for each component measure within the cluster groups and the most variation across groups. The cluster groups were ordered according to the favourability of the active living environment: DAs in Group 1 represent the least favourable active living environments and those in Group 5 represent the most favourable active living environments in Canada. A cluster group was not assigned to the 500 DAs where dwelling density could not be derived (i.e., areas where Statistics Canada does not disseminate data on dwelling counts).

This study informed the development of the first pan-Canadian database of active living environment measures derived entirely from non-proprietary sources. Database content was guided by principles according to which the measures selected needed to be associated with walking rates, suitable to a variety of built environments (e.g., urban, suburban, rural), and openly available to researchers and the public health community. Four individual measures (three fully pan-Canadian; transit stop proximity was available only for a subset of DAs in urban areas) were ultimately selected for the database, which also contains composite measures. Three-way intersection density using the OSM road and footpath features was selected based on its higher correlation with active-transportation-to-work rates, relative to the measures with roads alone. Three-way intersection density was deemed more appropriate for the national database, as four-way intersections are concentrated in cities with a grid-like street pattern and are less common in rural and certain suburban areas of Canada, which may lead to the connectivity in these communities being underestimated. Weighted dwelling density was selected because of its higher association with walking-to-work rates relative to population density measures and the much higher association between the weighted density methods and active-transportation-to-work rates. OSM POIs were selected because of their stronger association with active-transportation-to-work rates relative to DMTI POIs and good evidence of regional similarities in feature coverage. Additionally, the transit stop measure was selected and derived for DAs within CMAs and was strongly associated with both walking-to-work and active-transportation-to-work rates.