Nathaniel Jeffrey – August
Q: Tell us about yourself.
Before I fell into GIS, my studies in Environmental Science led me to a freshwater conservation project in Kenya, and down sewer pipes in my home city of Melbourne. Honestly, sewers are kind of fascinating if you have a background in biology. You think tapeworms can only survive in a digestive tract? Think again!
Professionally, for the last 10 years I’ve worked as a GIS analyst for Urbis, which is an international urban planning consultancy. It’s an ever-changing, data-driven job, which makes it a fun geo playground.
Apart from that: I cook, I eat, I game, I poke Raspberry Pis while frowning, and I travel (mostly to Japan, it seems).
Q: Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).
I’ve lived in Melbourne for 25 years after coming over from the USA with my parents as a kid. And as any Melburnian will tell you if you give them half a chance, it’s the World’s Most Liveable City.
So one of the big factors influencing “liveability” is the ability of a city’s infrastructure to adequately service its growing population. Melbourne has been growing at a rate over 2% per year for more than a decade, adding 80,000+ new people every year. Melbourne’s population has grown from just over 3 million in 1991 to 4.5 million today, and is projected to hit 6 million by 2031. I can’t do much to solve the many political headaches that spring up due to such rapid growth, but I sure can make a map.
Q: Tell us about the tools, data, etc., you used to make the map.
The population data I used is a mix of counts from past censuses (1991 to 2011), and future projections (2016 to 2036). I would have loved to go further back in time, but the small-area population data isn’t easy to come by.
I converted the population counts for each year into a raster surface representing population density, and then smoothed the heck out each one. This was a bit tricky, because I wanted to generalise the data enough to create an easily-readable map, but I didn’t want to misrepresent the truth in the underlying data.
Through trial and error, I then found a density value that more or less matched up with the edge of the suburban fringe for each year, based on aerial and planning maps. Applying that density cutoff to each year gave me the isopleths you can see on the map — lines of constant density.
Obviously this approach makes the assumption that the chosen density threshold has accurately represented to suburban boundary in the past, and will continue to do so in the future. This might not be the case, with a shift towards higher density developments at the urban fringe. But I think the approach is fine for a map that’s just trying to give a high-level view of the amoeba-like spread of Melbourne’s population. I would hope that no one tries to make any policy decisions based on this map!
Cartographically I went with a dead simple basemap — just roads and locality names for context. I made a deliberate effort to label locations where interesting things were happening in the data — lots of growth in a given year, for example. The colour scheme I chose for the isopleths is…striking. What can I say; it’s tricky to find ten colours that are distinct enough when placed next to one another, but still look reasonably harmonious as a whole. I had a bit of fun with the look of the title and legend — I’m no graphic designer, but I like to dabble in design, and steal things that look cool.
