COVID-19: Emergency shelter services hit hard
Organisation: Le Devoir
Organisation size: Small
Publication date: 17 Aug 2020
Credit: Ambre GIOVANNI, Philippe ROBITAILLE-GROU
“Stay at home!” was what we constantly heard at the beginning of the health crisis. While COVID-19 demonstrated the importance of home comfort, it also hit hard at organizations dedicated to homeless people, such as Montreal’s emergency shelter services. In order to find out how their situation was evolving, Le Devoir surveyed them using an online questionnaire and interviews. 19 of them participated in this fieldwork.
We were able to get a picture of the situation when little data were publicly available. The readers were therefore able to discover the reality experienced by emergency shelter services in Montreal from different perspectives.
This project required the use of several tools, both for data collection and visualization. To create the survey, we used the software Google Forms, which enabled us to collect information associated with each organization. The results were then converted as an Excel file, allowing further analysis. Then, we wanted to show on a map in one look the capacities of all shelters in Montreal before and during the pandemic. We therefore used QGIS, Json and D3.js to illustrate the geography of the city, its main streets and the location of each refuge, with disks of area, proportional to the number of available places. The D3.js library was also used to create the waffle charts showing the differences in available places, budgets and number of meals served before and during COVID-19.
What was the hardest part of this project?
The hardest part of the project was to find all the informations on the current situation, needed for a data journalism article. In fact, as the COVID-19 crisis was evolving very fast, all the data available on shelters in Montreal were quickly out of date. Furthermore, only few studies or academic literature representative of the reality existed because of the phenomenon of “hidden homelessness”. Thus, we decided to create our own data bank. For each shelter, we had to make several calls and send emails until we reached the person in charge, to talk about our project and ask to fill out our survey. Then, we had to verify all the collected data to ensure their consistency and uniformity, as well as to recontact several organizations to ask for clarification. Analyzing the results was also difficult, because we had to handle a large amount of data collected from all organizations and extract strong statistics representative of the overall situation in Montreal.
What can others learn from this project?
The article demonstrates how to overcome the lack of global data available on a subject. No source could tell us about the COVID-19 situation in the Montreal shelters, so we asked them all by ourselves. Our investigation enabled us to create a unique article, describing a striking problem presented nowhere else on such a large scale. The large data bank we gathered also allowed us to describe in words and figures both the overall picture of shelters in Montreal and the particularly worrying situation of certain types of organizations (for women, Aboriginal people and young persons). We were thus able not only to describe the difficulties experienced by the managers of the shelters that we interviewed, but also to illustrate their points with data visualisations, showing how the problem was generalized.