This story explores the inequality of heat in Asian cities and how urban planning has left more marginalised or lower income communities with less access to life-saving greenery in the context of global warming.
In the last few years, more investigative reporting has been done on how redlining has resulted in the unequal distribution of greenery and tree cover in urban cities and how this leaves the most vulnerable and disadvantaged communities at risk from air pollution, extreme heat and other effects of climate change.
However, coverage on this issue has largely been US-centric, so we wanted to bring this conversation to Asia and look at inequities in the urban Asian landscape. We have received feedback that the story was a much-needed one to be told, particularly in Singapore where urban greenery is taken for granted. We hope this story drives urban planners in Asia to make tree distribution more equitable.
In order to map open space and tree distribution in relation to income in two Asian cities, Singapore and Hong Kong, rigorous data gathering and data processing had to be done to produce a comparable result.
We calculated the cumulative median income population for each income bracket, by planning area, using Excel. We then found the cumulative median population to find the corresponding income bracket. The midpoint of this income bracket is used to estimate the median income of the planning area. Next, we split both sets of data into tertiles each, labelling them A-C and 1-3 by tertile of the respective variables. Finally, we merged both variables into one label. For example, a planning area with low income and few trees would be labelled A1. To map these, we assigned each unique label (e.g. A1) a colour along a bivariate spectrum.
For Hong Kong, we first split the data into two groups: below and above average. For the ‘above average’ group, we split that equally into two. This was done as Hong Kong’s average countable open spaces per capita is already much lower than international numbers, making it meaningful to compare districts that fall even further below this average. We used the same methodology of labelling and mapping the labels along a bivariate spectrum.
What was the hardest part of this project?
The biggest challenge was in defining “greenery” or “green space” and finding the right datasets that were comparable between Singapore and Hong Kong. In mapping Singapore, we were able to obtain data on tree distribution, while Hong Kong only had data open space, which includes green space.
What can others learn from this project?
Although the issue of redlining and inequities in urban planning is underreported in Asia and available data might not be as abundant as in other regions, this story showed that with enough digging and analysing existing data, one is able to produce a compelling story that addresses urban development issues that are both unique and shared.