Notes and reflections on a curiosity driven personal learning journey into geo and rasters and animal movement trajectory categorisation and all sorts of things that weren’t the point when I started…
Somewhen over the last month or so, I must have noticed a 3D map produced using the
rayshader R package somewhere because I idly started wondering about whether I could use it to render a 3D rally stage map.
Just under three weeks ago, I started what was intended to be a half hour hack to give it a go, and it didn’t take too long to get something up and running…
I then started tinkering a bit more and thinking about what else we might be able to do with linear geographies, such as generating elevation along route maps, for example, and also started keeping notes on various useful bits and bobs along the way: some notes on how geographic projections work, for example (which has been something of a blocker to me in the past) or how rasters work and how to process them.
I also had to try to get my head around R again (it’s been several years since I last used it) and started pondering about a useful way to structure my notes and then publish them somewhere:
I use code just a matter of course for all sorts of things, pretty much every day, and also use it recreationally, so R has provided a handy escape route for my code related urges (maybe I should pick up the opportunity to learn something new? The issue is, I tend to be task focussed when it comes to my personal learning, so I’d need to use a language that somehow made sense for a practical thing I want to achieve…)
Anyway, the rally route thing quickly turned into a curiosity driven learning journey: how could I render a raster in a ggplot, could I overlay tiles on a 3D rendered map:
Could I generate a ridge plot?
Could I buffer a route and use it to crop a 3D model?
Could we convert an image to an elevation raster?
And so on..
When poking around looking for ideas about how to characterise how twisty or turny a route was, I stumbled across sinuosity as a metric, and from that idea quickly discovered a range of R packages that implements tools to characterise animal movement trajectories which we can easily apply to rally stage routes.
Enriching maps with data pulled in from OpenStreetMap also suggests how we might be able to use generate maps that might be useful in event planning (access roads, car parks, viewpoints, etc); and casting routes onto road networks (graph representations of road networks; think
osmnx in Python, for example) made me wonder if I’d be able to generate road books and tulip maps (answer: not yet, at least…).
I’ve written my learning journey from the last 20 days or so up at RallyDataJunkie: Visualising Rally Stages; the original repo is here. A summary of topics is in the previous blog post: Visualising Rally Route Stages (with help from rayshader and some ecologists…).
Reflecting on what I’ve ended up with, the structure is partly reflective of the journey I followed, but it’s also a bit revisionist. The original motivation was the chapter on the rendering 3D stage maps; to do this I needed to get a sense of what I could do with 2D rayshader maps first (the 3D plot is just a change in the plot command from
plot_3d()), and to do that properly I had to get better at working with route files and elevation matrices. Within the earlier chapters, I do try to follow the route I took learning about each topic, rather then presenting things in the order an academic treatment or traditional teaching route my follow: the point of the resource is not to “teach” linear geo hacking in a formal way, it’s a report of how I learned it, with some backdropped “really useful to know this” pointers added back to earlier stages as I realised I needed them for later things.
Something else you may note about the individual chapters is that there are chunks of repetition of code from earlier on: this is deliberate. The book is a personal reference manual for me, so when I refer back to it for how to do something in the future, there’s enough to get going (hopefully!) without having to keep referring explicitly to too many early chapters.
Another observation: I see this sort of personal learning as availing myself of (powerful) ideas or techniques that are out there that other people have already figured out, ideas or tools or techniques that can help me do a particular thing that I want to do, or make sense of a particular thing that I can’t get my head round (i.e. that help me (help myself) understand the how or the why of a particular thing). I don’t want to be taught. I want enough that I can use and learn from. In my personal learning journey, I’ll come to see why some things that were really handy or useful to help me get started may not be the best way of doing something as I get more skilled, but the more advanced idea would have hindered my learning journey if it had been forced on me. (When I see a new function with dozens of parameters, I stirp it down to what I think is all I need to get it to work, then start to perhaps add parameters back in…)
As teachers, we are often sort of experts, and follow a teaching line based on our expert knowledge, and what we know is good for folk to know foundationally, or that follows a canonical textbook line. But as a curiosity driven personal learner, I chase all manner of desire lines, sometimes having to go around an obstacle I can yet figure out, sometimes having to retrace my steps, sometimes having to go back to the beginning to collect something I didn’t realise I’d actually need.
I don’t care about the canon or the curriculum. I want to know how, then I want to know why, and at some point I may come to understand “oh yeah, it would have been really handy to to have known that at the start”. But whilst teaching is often about making sure everyone is prepared at each step for the step that comes next, learning for me is about heading out into the unknown and picking up stuff that’s useful as I find I need it. And that includes picking up on the theory.
For example, Finding the Racing Line collates a set of very mathematical references around finding optimal racing lines that I’ll perhaps pick into for nudges and examples and blind copying without understanding at times if it helps once I start to try to get my head round the lines rally drivers take round corners. Then I’ll go back to the pictures and equations and try to make sense of it once I’ve got to a position where things maybe work (eg visualised possible routes round a corner) but can I now figure out why and how, and can I make them work better. It may take years to understand the papers, if ever (I’ve been reading Racecar Engineering magazine for 15 years and most of it still doesn’t make much sense to me…), but I’ll pick the bits that look useful, and use the bits I can, and maybe go away to learn a bit more about something else that helps me then use a bit more of the papers, and so on. But doing a maths course, or a physics course wouldn’t help, becuase the teaching line would probably not be my curiosity driven learning line.
For me, playful curiosity is the driver that allows you stick at a problem till you figure it out — but why doesn’t it work? — or at least get into a frame of mind where you can just ignore it (for now) or park it until you figure something else out, or whatever… I’m not sure how the play relates to curiosity, or curiosity to play, but together they allow you to be creative and give you the persistence you need to figure stuff out enough to get stuff done…