GIS Catchment Tool


  1. Map natural population catchment for health services in rural BC
  2. Evaluate the effectiveness of healthcare services
  3. Contribute to a more informed health service planning process


Our GIS Catchment Tool will consist of one-hour drive time catchment maps which are comprised of rural communities and a hospital that resides within the population catchment. Researchers will have the ability to use this tool to apply spatial analysis on patient flow patterns with respect to the level of health services that is available to them. The tool will link health service/interventions with health outcomes through isolated groupings of people found in rural communities, which will enable us to test out new models of health care. Furthermore, these catchments will help us determine the proportion of the population in BC that does not have access to healthcare services.

Samples of 1hr Drive Time Catchment


Process: Developing the Tool

We first identified the hospital within our community of study and then create a one-hour drive time catchment, which includes all the areas that are within a one-hour drive time to the hospital. We use the Network Analyst extension on Arc GIS to derive these catchments and use the road network data from Streetmap. Moreover, we account for catchments in close proximity and avoid overlap by placing ‘line barriers’ to separate them. We first searched up the drive time between one community’s hospitals to the overlapping community’s hospitals and then placed a line barrier halfway on the road that connects them. This way the solution avoids catchment overlap while allowing for unique catchments. Once the unique catchments are created, we use the intersect tool on Arc GIS to calculate the postal codes that reside in the catchment and the catchment population size. We derive the postal code and population data from ‘Abacus Dataverse Network’ (UBC Library).


Progress: What We Have Completed

We have completed building 1hr catchment maps for all Northern RSA communities. Additionally, a list of postal codes within the 1hr catchment of these communities has been acquired and validated. The process of collecting and validating the postal codes involves:

  1. Downloading a geospatial file that consists of postal codes areas from statistics Canada. Canada postal codes are six digits made up of the first three digits called the Forward Sortation Areas (FSA) and the last three digits, Local Delivery Unit Areas (LDUA). LDUA are significant in providing identification of a particular rural community. Thus, this file was used to obtain unique postal codes.
  2. Intersecting the postal code areas (LDUA) file with the 1hr catchment areas was used to acquire a unique list of postal codes.
  3. Removing expired postal codes and possible duplicates is essential. Postal codes are modified yearly; thus, the data file may contain expired postal codes. Postal codes are manually checked for validation. Census Canada, Google maps and Canada Post is being used to ensure that postal codes are being attributed to the catchment with the best fit.
  4. Organizing postal codes according to geographical location (e.g., Burns Lake, Woyenne and Burns lake Band are grouped due to proximity) and health service tier within the region (e.g., Woyenne and Burns Lake band will be secondary catchments to Burns Lake, as Burns Lake has the highest tier service)

We have also calculated the population size of the Northern catchments for the last three Census years (2016, 2011, 2006). The process is similar to obtaining postal codes. It involves

  1. Downloading 2006, 2011 and 2016 population size files organized by Census Subdivision (CSD) from Statistics Canada.
  2. The catchment area was intersected with the population file. This process returned a list of CSD’s and their population sizes. Before summing up the total population size from the CSD’s, percentages needed to be assigned to CSD’s covering a vast area. These CSD’s cover a large area but have a small population size. Most of these areas are inhabitable. Therefore, if a CSD intersects with more than one catchment, a portion of the population needs to be assigned to more than one catchment.
  3. Deciding the population percentage was based on the proximity of the CSD to the community hospital, habitability, other services and amenities in the CSD area that intersects with the catchment.
  4. After the CSD populations were revised, they were summed to get the catchments’ total population size (for the last three Census years). Line charts will be produced on PowerBI to display population size over time for the catchments. They will be included in the final dashboard for all RSA communities.


What we plan on doing

  1. Health centre/hospital data is currently being collected and organized to map the interior RSA communities. The process used to create the northern catchments, demographics and postal codes will follow for the Interior communities.
  2. Draft demos will be created for at least two communities (one from the Northern region and one from the Interior) to test the effectiveness of the dashboard and overcome any challenges that may arise.



  • It is difficult to create catchments for parts of the Interior that have several communities packed together. Although communities with primary services can be identified, attributing secondary catchments is challenging. These catchments are close to more than one community with a primary care service. It is even more challenging when the secondary catchment communities are equidistance to more than one community with primary care services with the same or similar level of service.
  • Specific rules will need to be created to ensure that the method remains systematic.



Although effort has been put in to minimize errors, the project has some limitations.

To calculate population size Census Dissemination Blocks (DB) have been used, which is the smallest geographic unit of the census. DB’s align to road networks, which were used to create the catchment and to provide a better estimate of the population size of the catchments. However, it is not always perfectly aligned with the roads, as it can include people that do not reside in the catchment,  vice versa. This is not a major error, as it occurs more towards the edges of the catchment and from our population pattern, it is evident that most of the population is clustered close to the hospital.

Streetmap was used to create the one-hour catchments for the communities. Streetmap only updates once a year or once every couple of years. Since our scope is limited to rural areas, we believe the road networks should not change much, however, our data would not be able to showcase these small changes until the street map is updated.

Human error is another limitation in this project, as the solution is only being run by one person. However, we have taken measures to lessen human error through our quality check system as mentioned above. There is yet a slight chance of some postal codes or population counts being missing from our dataset.


Next Steps:

Some of our next steps in developing this tool is expanding the scope of this project by increasing the number of catchments by including more rural communities in BC and including key services of interest such as emergency, maternity, anesthetic, palliative care services and more. We are beginning to account for the urban areas in BC by separating them from the rural catchments, so that rural communities don’t include urban areas that are close to in their catchment. When our dataset is complete, we strive to create an interactive map that will aid researchers to find appropriate data for their research.


If you would like to know more details about the project or to access this tool, please contact us!