Proximity to Neighbourhood Parks
Percent of Population within a driving distance of 1 km walk to a park
Method and Limitations:
Proximity to neighbourhood parks measures the closeness of a dissemination block to any dissemination block with a neighbourhood park within a 1 km walking distance. This measure is derived from the presence of all parks from a conglomeration of authoritative open data sources and OpenStreetMap
Over the last year, Statistics Canada (StatCan) and Canada Mortgage and Housing Corporation (CMHC) have collaborated on the implementation of a set of proximity measures to services and amenities. CMHC funded this collaboration to generate data and analytical work in support of the National Housing Strategy.
The result of this collaboration is the first nation-wide Proximity Measures Database (PMD). This database is now available as an early release to meet urgent information needs of departments and other stakeholders across Canada who are dealing with the COVID-19 crisis. The current situation involving COVID-19 emphasizes the importance of having timely and accessible information available to the public at all levels of government. Proximity measures developed for this project are relevant to the current situation by providing a wealth of information (at the granular level) in terms of proximity to health facilities, pharmacies and other essential services/amenities that can be used to make rapid informed decisions at different geographical levels.
This work is expected to be further developed and revised as new and updated information on the geolocation of services becomes available.
The database contains 10 measures of proximity and a composite indicator that combines some of the proximity measures.
All measures are at the dissemination block level (a block in urban areas or an area bounded by roads in rural areas), this provides the highest level of geographic resolution currently possible. Hence, the database has approximately half a million records. Some measures are more limited in their nationwide coverage as data is not always made readily available at the desired geographic level by its administrators. As the number of authoritative open data sources increases, a trend in recent years, future iterations of the proximity measures will have more comprehensive coverage spatially.
Proximity measures are based on a simple gravity model that accounts for the distance between a reference dissemination block (DB) and all the DBs in which the service is located (within a given distance) and the size of the services. The measure accounts also for the presence of services within the DB of reference.
All measures, except public transit, are based on network distances between the centroids of dissemination blocks (as opposed to straight line distances). For some measures a walking network is used while for other measures a driving network is used, as explained below. For public transit, the walking network distance is between the centre of a dissemination block and any public transit stop within a given range.
The size of the service is captured by total employment or total revenue of the service, or more simply, the presence of points of access to the service within a given distance. The measure of size of the service is specific to each measure and is explained below.
The measures are released as a normalized index value, meaning that the values resulting from computations were converted to a scale from 0 to 1, where 0 indicates the lowest proximity and 1 the highest proximity in Canada. The values are normalized at the national level in order to retain as much detail as possible. This allows for more intricate analyses to be conducted on more granular geographies. Dissemination blocks with no value are those with no service within a given distance.
The data sources and specifications used for each measure are described below. Note that businesses beyond the stated threshold of a reference DB are not included in the measure of that reference DB.
Due to the usage of different data sources for the various types of amenities, some inconsistencies between sources were found. For example, open data sources may suggest that a school exists within a dissemination block but the Business Register (BR) suggests that there is no source of employment within that same dissemination block. In cases like these, all proximity measures are suppressed.
Population percentages for proximity to parks use population counts from the 2016 Census of the Population. Census Subdivision (CSD) data is calculated by aggregating Dissemination Block (DB) data to the CSD level.
Statistics Canada. 2021. Proximity Measures Database
Proximity to Neighbourhood Parks in the Sustainable Development Goals
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11. Make cities inclusive, safe, resilient and sustainable
Cities are hubs for ideas, commerce, culture, science, productivity, social development and much more. At their best, cities have enabled people to advance socially and economically.
However, many challenges exist to maintaining cities in a way that continues to create jobs and prosperity while not straining land and resources. Common urban challenges include congestion, lack of funds to provide basic services, a shortage of adequate housing and declining infrastructure.
The challenges cities face can be overcome in ways that allow them to continue to thrive and grow, while improving resource use and reducing pollution and poverty. The future we want includes cities of opportunities for all, with access to basic services, energy, housing, transportation and more.