Health Care Access in the City of Toronto

By: Shabnam Sepehri

Geo-Visualization Project: SA8905, Fall 2020

Project Link: Final Map

Final Product: An Interactive Map


There are many factors that contribute to an individual’s access to health care. Statistics Canada has defined the ‘Social determinants of health and health inequalities’ as the 12 major factors that affect access. First on the list is income and social status and near the bottom at number 11 is race and ethnicity. For this project, I was curios to see how these two variables are distributed across the census tracts in the City of Toronto; and if there are any overlaps with the locations of healthcare institutions. The software of choice will be CARTO, which is a Service cloud computing platform that enables the visualization and analysis of geographic data.

Data Acquisition

  • CHASS Data Center: used to collect census data by census tract (2016): Total population, total visible minority population, total aboriginal identity population, and median total income;
  • Statistics Canada: used to obtain census tract boundary files;
  • City of Toronto Open Data: Address Point files;
  • Geospatial Map & Data Centre: used to collect physicians data – Enhanced Points of Interest 3.1 (City of Toronto)
  • ArcMap (digitize): used to digitize hospital locations in the City of Toronto


After the data was acquired, the following steps were taken in ArcMap to organize the data before importing it into Carto. First, the census variables were joined to the census boundary shapefile. Then, a new column was created to calculate the sum of visible minority and aboriginal identity population density per 1,000 people.

Next, the hospital locations were digitized using the ‘Editor Toolbar’. following that, the physicians locations were geocoded using the using the address repository acquired from Toronto Open Source data. Lastly, , the non-spatial data (e.g. total median income) were joined to the spatial data (census tract boundaries) to enable the layer visualization. After all the necessary formatting was done, the data was uploaded onto Carto.

Once on Carto, I realized that the software allows the user to carry out different spatial functions such as geocoding. It also allows you to edit your dataset using SQL queries. This function is really useful in facilitating the data editing process and helps to reduce the back and forth between different mapping software’s.

CARTO: dataset dashboard

Carto allows you to import a total of four layers in your map. The hospital locations, physician offices, and the census tracts were added as the four layers, with census tract uploaded twice to show the two different census variables. The census variables were visualized as a choropleth maps, and the health institutions were visualized as points on top of the choropleth layers.


The interactive aspect of this map is mainly the users ability to switch between layers and toggle each map component separately. Moreover, the ‘pop-up’ option was utilized for the hospital points to show the name and address of each location. Similarly, pop-ups were created for the choropleth maps to show the median income and population density of each individual census tract. Lastly, the widget feature was used to create histograms to showcase the distribution of the two census variables among the census tracts. This feature allows the user to select for the tracts in different categories and zoom into those specific tracts on the map. For instance, someone may want to look at tracts with the highest median income and tracts with an average aboriginal and visible minority population density. Lastly, the choropleth layers are turned off and may be switched on as per the user’s interest.


The map shows that census tracts where the median income is relatively high, tend to have a low distribution of aboriginal and visible minority population density. The distribution of hospitals appear to be uniform throughout the city with a few more concentrated in the downtown core. Conversely, the physician offices appear to be more concentrated in tracts with higher income or close to the downtown core. That being said, this does not mean that higher income groups have better access than lower income groups. However, the map does identify areas where there is a low number of physician offices, and most often, these areas tend to be classified as having a low to medium income. There are of course other variables that must be considered when identifying access, however, due to the limiting number of layers this option was not feasible for this project. Overall, this map can be used to identify ideal locations for future health facilities and to identify groups that have limited access to these resources.

Limitations & Future Work

Initially, I wanted to include more variables in the map; the goal was to map median income, visible minority & Aboriginal population density, education attainment, and employment conditions. However, Carto only allows for the addition of four variables. This limited the diversity of the visualized variables. Ideally, exploring other geo-visualization software such as Tableau, ArcGIS Online, or the Esri Dashboard would aid in creating a more nuanced map.

Ideally, I would also want to map the change of these variables over time. For instance, to show whether the distribution of median income, and visible minority & Aboriginal population density per census tract has always been the same or if there are slight changes in pattern. It would be interesting to capture which census tracts had experienced better access due to changes in health determinants over time.