How I reported it

It turned out that there were a lot of initiatives to teach kids about climate change. But in my research, I found a study that showed climate change was actually making it hard for kids to learn about the very thing that was a factor in their vulnerability.

I then found data a study from the National Bureau of Economic Research, that showed kids do worse on tests in hotter temperatures. I found the same data that they used online from the Stanford Center for Education Policy Analysis, and found the same result.

I also wanted to know how many schools had issues with air conditioning and building maintenance, since an LA Times article said that there were 1,709 complaints about heating, ventilation and air conditioning systems in the Los Angeles Unified School District at the beginning of the 2018 school year. Also, one of my interviewees, Heather Randall said that structural problems were one of the large problems associated with how vulnerable students are not just to heat but climate change in general. I wanted to know where these schools are? Are they in the same "disadvantaged communities" that CalEnviroScreen identified?

After obtaining all the maintenance requests from LAUSD over the 2017-2018 and 2018-2019 school years (up until November 26, 2018), I used the school code from each request to join my maintenance data with another spreadsheet with addresses of each school. Then, after a clean-up (there were some repeats) I grouped the requests by school in SQL to get a count of each request per school.

Then in QGIS I mapped the school maintenance data on top of the poverty level by census tract data from CalEnviroScreen. At first glance it seemed that there were more maintenance problems in higher poverty areas but I didn’t trust my eyes enough to make that claim.

So, I did a spatial join in QGIS of the number of requests for all the schools in each census tract. I downloaded the spreadsheet and did a linear regression in both R Studio and Excel on the poverty level of the census tract and the number of requests per census tract. This resulted in a p-value of 4.2e-06, which being less than .05, led me to concluded that my eyes were correct, and the correlation between the two were statistically significant.

In between all the data analysis, I also set out to get some real voices about real experience with this issue. It was difficult to get in contact with teachers, but through my connections at USC I was able to find a teacher and a school psychologist who had real experience with this issue, especially since Jackie Melendez doesn’t even have air conditioning in her room!

In my talks with researchers (Like Heather Randall) and the educators, I found out that epilepsy and asthma is a huge problem in the heat, and asthma specifically since it tends to affect low income students more.

It's an important topic to me, since there are so many initiatives to teach about climate change in California, but if the kids can't learn, how will they combat it in the future?