By Sara Kmair
I have been watching people outside the metro stations. My eyes widen with horror at the numbers of homeless that pour out of the trains each day. Just because I have seen worse does not mean I am able to diminish each homeless person I cross.
The Toronto Street Needs Assessment 2018 results further prompted me to seek more data I could analyze. Were there enough shelters in the city? What were their occupancy rates like? The federal definition of chronic homelessness is being homeless for six months or more in the past year. 48% of all respondents reported being homeless for six months or more. Further, over one-third (36%) of respondents reported being homeless for more than one year according to the study. Among top sources of income for respondents across the shelter types (outdoors, single adults, family, youth, 24-hour respite sites, VAW) are Ontario Works and ODSP.
I set out to learn more about the distribution of shelters in the GTA and what sectors they served. The data set I picked provides a listing of all the active shelters serving the City of Toronto area. Included in the dataset is the name of the shelter, program name, sector served (i.e. men, women, youth, families) addresses, the space capacity (i.e. beds or cots available) and the number of people that occupied those spaces at 4 AM the next morning. For example, the occupancy count of January 1st would be taken on January 2nd at 4 AM. For reasons of confidentiality, information regarding Violence Against Women shelters are removed from the dataset, and any personal information also removed from this dataset.
The dataset has 13 variables with two numeric attributes Occupancy (number of people occupied these places) and Capacity (the capacity for each shelter).
- X_id : unique digit to identify the visit
- Occupancy date
- Organization name
- Shelter name
- Shelter address
- Shelter city
- Shelter province
- Shelter postal code
- Facility name
- Program name
- Sector: what sector the shelter is serving
First step is data cleaning: Removing NA values if any. Removing attributes that will not affect our analysis.
The bar chart below shows the number of shelters in each area and what sector they serve:
Etobicoke has one shelter for youth. North York has two shelters for youth. Scarborough has one co-ed shelter. Toronto has the highest number of shelters in all sectors.
Now let’s compare the average shelter capacity with the average occupancy for each month:
The bar chart below shows the average capacity vs the average occupancy per month. The average capacity increases slightly during the year along with the occupancy:
While I was disappointed with the lack of direction in the data, given the disparate situation that is evident on a daily commute to anywhere in the city, it seemed like a worthy experiment, and I will keep seeking out that ideal dataset to also further explore what shelter conditions might be keeping occupancy rates lower than full. There are some studies out there that delve further.
Sara Kmair is a passionate problem solver, challenge seeker and a highly motivated data analyst with a Bachelor’s degree in Mechatronics, Robotics and Automation Engineering from Tishreen University, Latakia. You can visit more of her work here: https://github.com/SaraKmair