Citizens Sensors | Round 1 | Update 1

We’re in Week 3 of our Citizen Sensors project with two local schools in Durban. In my last blog, I mentioned that we were spending time putting together a curriculum and programme with different themes that align with the objective of the project. As I mentioned, we looked to the Umkhumbane Schools Project for some insight into how to plan and deliver sessions with young learners, especially considering we are covering a topic that could get pretty complicated. They had a lot of really great advice that we incorporated into our curriculum design, and here is what we’ve come up with.

We planned for five sessions — one introductory session in the beginning, and four sessions to cover the content. Each session’s theme is geared around the learners being able to broadly answer a question. The session questions are as follows:

1. What is data journalism? What is air quality monitoring? What is this project about?
2. What are some of the main issues affecting you and the community around that you feel aren’t right? (a facilitated discussion on hyperlocal issues)
3. What is air quality? Why do some communities get exposed to more pollution than others?
4. Why is it important to know how the air and environment around us affects our health? (looking at New York City data)
5. What is happening in the air around my community? (The good and the bad)

Each session is broken up into roughly three parts (discussion, writing and some sort of data skill) and we try to keep each session format different, dynamic and exciting. For example, in week 2, we conducted a makeshift unconference with the learners highlighting issues that are affecting them directly in their community. We combined this activity with a bar graph making activity that turned out to be a total hit. Here is how it went:

We asked students to write two issues, one per piece of paper, then take that piece of paper and put it up on the wall. Once all the issues were up on the wall, the class spent some time putting similar issues together and making some general themes. Then, students were asked to vote on their top 3 issues. The mentors then tallied the votes, and wrote the number of votes for each category onto the cards so students were able to see what issues got the most votes and which ones got the least. Then, based on these votes, students were led through the process of drawing a bar graph indicating these results. The point of this activity was two-fold: on the one hand, we wanted to get students thinking about challenges specific to their community and spark the concerned citizen in all of them, and on the other hand, we wanted to show how easy it is to generate something that can be turned into data, analyse it, and visualise it. It is important to demystify the concept of “data” for the students to make it more approachable, showing that you don’t need fancy technology or to have tertiary education to be able to analyse data.

In week 3, we started to get more into some of the air quality concepts. I was a bit worried that this one would be too technical, but the learners seemed to grasp it completely. The question for this session was: What is air quality? Why do some communities get exposed to more pollution than others? Firstly, we showed them a few short films and presented a few graphics explaining some air quality concepts such as what affects air quality, what chemicals are in the air as a result of humans, and what the most dangerous pollutants are in the air. From what the mentors reported back, the short videos were really useful and students learned and remembered a lot from them. Next, we spoke about the South African context — who are the big pollution contributors, spatial inequality of apartheid, and how during apartheid, townships were often placed downstream or downwind from big industrial plants, exposing these residents to harmful chemicals more so than wealthier, white South Africans living in white areas. To show that this spatial inequality legacy still exists today, we then showed them a graph of Particulate Matter in a region in South Africa (close to Soweto) from 2007–2016. Students were asked to collectively interpret this graph and explain why this area had such bad pollution and what the potential residents were like. The last part of this session was showing a photo of an informal settlement clearly being affected by pollution and asking learners to describe what is happening in the photo and what this photo tells us about air quality and health for poor communities.

So far, the students have been outstanding and very engaged in the programme. They seem to be really grasping the concepts and eager to participate. With only a few weeks left in the programme, we will begin introducing the sensors and the data generated from the sensors soon. The mentors have also really stepped up to the plate for this programme, and I’ve been really impressed with their ability to deliver the sessions according to what I’ve set out for them to do, while still allowing for some flexibility and creativity. They’ve also been super reliable which has made coordinating them very pleasant. It’s been a learning process for me as well, and I’m looking forward to getting their feedback on the experience and letting us know what we could have done better.

- Sophie McManus

Originally published at Open Data Durban.