I know how I’m SUPPOSED to read it, but when I see this I can’t help but imagine a conversation like…
“What shall we call our car wash?”
/waves hand/ “I dunno.”
Tag: maps
Map of the Titan
Y’all seemed to enjoy the “overworld” map I shared the other day, so here’s another “feelie” from my kids’ ongoing D&D campaign.
The party has just arranged for passage aboard a pioneering (and experimental) Elvish airship. Here’s a deck plan (only needs a “you are here” dot!) to help them get their bearings.
Family D&D’s Overworld Map
In preparation for Family D&D Night (and with thanks to my earlier guide to splicing maps together!), I’ve finally completed an expanded “overworld” map for our game world. So far, the kids have mostly hung around on the North coast of the Central Sea, but they’re picked up a hook that may take them all the way across to the other side… and beyond?
Banana for scale.
(If your GMing for kids, you probably already know this, but “feelies” go a long way. All the maps. All the scrolls. Maybe even some props. Go all in. They love it.)
ChatGPT beats GeoGuessr
You’ve probably come across GeoGuessr already: it’s an online game where you (and friends, if you’ve got them) get dropped into Google Street View and have two minutes to try to work out where in the world you are and drop a pin on it.
A great strategy is to “walk around” a little, looking for landmarks, phone numbers, advertisements, linguistic clues, cultural indicators, and so on, narrowing down the region of the world you think you’re looking at before committing to a country or even a city. You’re eventually scored by how close you are to the actual location.
Cheating at GeoGuessr
I decided to see if ChatGPT can do better than me. Using only the free tier of both GeoGuessr and ChatGPT1, I pasted screenshots of what I was seeing right into ChatGPT:
That’s pretty spooky, right?
The response came back plenty fast enough for me to copy-and-paste the suggested address into Google Maps, get the approximate location, and then drop a pin in the right place in GeoGuessr. It’s probably one of my most-accurate guesses ever.
This isn’t a one-off fluke. I tried again, this time using only a single photo, rather than one pointing in each direction on the street:
This time, it wasn’t quite right: the actual location of the photo was Chittagong, not Dhaka, about 200km away.
But that’s still reasonably spectacular from only a single vantage from a single point.
Obviously my approach here was crude, but it’d be relatively easy to, for example, implement a browser wrapper that cheated on-your-behalf: while playing GeoGuessr, you’d just click a “send to AI” button whenever you fancied and it’d start working in the background, improving precision with each subsequent screenshot (so you could still “walk around” and pass extra details to it).
And similarly, a little prompt manipulation could have the output include estimated GPS coordinates, allowing the code to place the pin for you while outputting its reasoning on the side2.
The short of what I’m saying is: if you’re playing GeoGuessr against somebody you don’t trust not to cheat… they might well be cheating. And thanks to the explanatory output, they can even justify their explanations for the places they dropped pins.
What about other photos?
The other thought that comes to my mind is: could this be used to infer approximate geolocations from photos whose EXIF metadata has been stripped. Because if so, that’s the kind of thing that’s certain to get exploited by all kind of people who want to know where you are/were at a particular time. For example… law enforcement, stalkers, and other unsavoury types.
I wrote a prompt for gpt4o and put it to work:
1. Starting out easy
The AI guessed:
Location: Port of Spain, Trinidad and Tobago
Explanation: The photograph shows a large ship docked at a port with a building that displays the text “WELCOME TO PORT OF SPAIN”. This text is in English, indicating an English-speaking country. The mention of “Port of Spain” directly specifies the port’s location in the capital city of Trinidad and Tobago. The design and style of the port facilities, as well as the presence of a ship named “Logos Hope”, which is known to visit many ports globally, further suggests a Caribbean port, aligning with Port of Spain’s known characteristics.
Coordinates: 10.6549, -61.5011
✅ That was pretty easy, to be fair.
2. A little more challenging
The AI guessed:
Location: The Rollright Stones, England, United Kingdom
Explanation: The photograph depicts an area with several ancient standing stones arranged in a circle in a rural setting, characteristic of a stone circle. These are typically found in certain parts of the UK. The terrain is typical of the British countryside, with a grassy field and surrounding trees. The specific arrangement and size of the stones, along with the style of fencing and walking paths, are reminiscent of the Rollright Stones, one of Britain’s historic sites known for a circle of Neolithic or Bronze Age standing stones located in Oxfordshire, England.
Coordinates: 51.9743, -1.5731
✅ Again, spot on. If you’d never been to the Rollright Stones, you probably wouldn’t get that one, because while it’s a distinctive-looking stone circle… it’s not that distinctive-looking, right? Let’s be honest: it’s some rocks in a field.
3. Ramping up the difficulty
The AI guessed:
Location: Istanbul, Turkey
Explanation: The photograph prominently features the Galata Tower in the background, a well-known landmark in Istanbul, Turkey. Moreover, the presence of a ferry or similar boat and the architectural style of the surrounding buildings are indicative of Istanbul’s famous skyline. The signage in Turkish, as suggested by the word partially visible on the left, also supports this location as Turkey is a predominantly Turkish-speaking country. These elements combined make it highly likely that the photograph was taken in Istanbul.
Coordinates: 41.0082, 28.9784
✅ The coordinates aren’t exactly right – it’s placed us in the city rather than on a boat, which we clearly are – but it’s not done badly. Spotting the Galata Tower3 was a nice move.
4. Okay, now hard mode:
The AI guessed:
Location: Cardiff Bay, Cardiff, Wales, United Kingdom
Explanation: The photograph features a marina with modern residential buildings, which is characteristic of the Cardiff Bay area in Cardiff, Wales. The architecture, clear blue skies, and marina setting are reminiscent of Cardiff’s waterfront development. Additionally, the presence of English signage on the boat (“FINBAR”) supports a location in an English-speaking country. Cardiff Bay is known for its redeveloped waterfront area with modern apartments and leisure facilities surrounding the marina. The style and design of the building are consistent with developments in Cardiff’s revitalized docklands.
Coordinates: 51.4626, -3.1678
❌ Oops! Nope. I can see the thinking of what it’s claiming, there, but this was actually the Ipswich marina. I went for a walk to take a break from the code I’d been writing and took this somewhere in the vicinity of the blue plaque for Edward Ardizzone that I’d just spotted (I was recording a video for my kids, who’ve enjoyed several of his Tim… books).
So I don’t think this is necessarily a game-changer for Internet creeps yet. So long as you’re careful not to post photos in which you’re in front of any national monuments and strip your EXIF metadata as normal, you’re probably not going to give away where you are quite yet.
Footnotes
1 And in a single-player game only: I didn’t actually want to cheat anybody out of a legitimate victory!
2 I’m not going to implement GeoCheatr, as I’d probably name it. Unless somebody feels like paying me to do so: I’m open for freelance work right now, so if you want to try to guarantee the win at the GeoGuessr World Championships (which will involve the much-riskier act of cheating in person, so you’ll want a secret UI – I’m thinking a keyboard shortcut to send data to the AI, and an in-ear headphone so it can “talk” back to you?), look me up? (I’m mostly kidding, of course: just because something’s technically-possible doesn’t mean it’s something I want to do, even for your money!)
3 Having visited the Galata Tower I can confirm that it really is pretty distinctive.
4 3Camp is Three Rings‘ annual volunteer get-together, hackathon, and meetup. People come together for an intensive week of making-things-better for charities the world over.
Geocaching Convex Hull now includes Turkey
Thanks to finding a couple of geocaches here in Istanbul, my geocaching “2D convex hull” (the smallest possible convex polygon that covers an area), which I wrote some code to draw last year, just expanded a little further to the East. 🎉
I’ve got a lot of the world left still to encircle, but I’m slowly extending my reach…
(previous map, for comparison: https://danq.me/_q23u/2024/04/dans-geoing-hull-2024-04-03.webp)
OpenStreetMap rocks (especially on foot)
Especially outside of urban centres, and especially if you’re on foot, OpenStreetMap is way better than Google Maps, Bing Maps, Apple Maps, or what-have-you.
OpenStreetMap is especially good for walkers, with its more-comprehensive coverage of public footpaths as well as the ability to drill-down for accessibility information: whether a path ends in a gate or a stile matters a lot if you can’t climb the latter (or you’re walking with a small-but-muddy dog who’ll need lifting over).
Sure, you don’t get (as much) street view photography. But how often do you use that, anyway?1
I’ve heard it argued that OpenStreetMap, with its Wikipedia-like “anybody can edit it” model, cannot be relied upon. And sure, if you’re looking for an “official” level of accuracy and the alternative is an Ordinance Survey map, then that’s what you should go for.
But there’s nothing specific to, say, Google Maps that makes it fundamentally more “accurate” for most2 geographic features than OpenStreetMap. The vast of cartographic data on Google Maps is produced by humans, looking at satellite photos, and then tracing the features on them, probably with AI assistance. And the vast majority of cartographic data on OpenStreetMap is produced… exactly the same way, although without the AI “helping”.
Google Maps has mistakes, just like every map3. And it’s got trap streets, like most commercially-produced maps (including the Ordinance Survey). Google Maps’ mistakes tend to be made by somebody on the other side of the world from the feature, doing a bad job of tracing what they think might be a road… while OpenStreetMaps’ mistakes are for the most part omissions in areas that are under-explored by local contributors. And there are plenty of areas – like those near where I live, especially if you’re on foot – where the latter mistakes are much less-troublesome.
I fixed a couple of omissions on OpenStreetMap just earlier today. While I was out walking the dog, earlier, I added the names of two houses whose identities weren’t specifically marked on the map, and I added detail to the newly-constructed Deansfield estate. Google Maps shows there being only two houses on Deansfield Estate, among other inaccuracies, even though they’ve got up-to-date aerial and street photography.
Google Maps is fine if you want to drive to Sheffield, you need public transport connections to Plymouth4, or you’re looking for a restaurant nearby and you want the data about them to be accurate. But next time you’re walking somewhere, or when you’re looking for a specific address… I’d suggest you give OpenStreetMap a go. You might be pleasantly surprised.
Footnotes
1 I say that as somebody who uses street view and satellite photography a more than average amount, for geohashing purposes. But I can switch mapping software on-the-fly; nobody’s stopping me looking at “ostrich” photos when I need them.
2 The place that Google Maps really beats OpenStreetMap, in my mind, is in the integration of its business directory. If you search for a business in Google Maps, you’ll probably find it and get reasonably-accurate opening hours and contact details. But that’s a factor of two things: the Google My Business directory, and – more importantly – the popularity of the application and the fact that the mobile app “nudges” people to check on the places around them. By the way: if you want to contribute to making maps better in that way without becoming an unpaid researcher working to line Google’s pockets, StreetComplete is an app that helps fill-out business and related information on OpenStreetMap!
3 Google Maps used to show Vauxhall tube station on entirely the wrong side of the River Thames, for example.
4 Public transport’s another thing Google Maps does very well.
Trump’s Strategy
What do you reckon? Is he trying to go for a domination victory without ever saying “MY THREATS ARE BACKED BY NUCLEAR WEAPONS!”? His track record shows that he’s arrogant enough to think that the strategy of simply renaming things until they’re yours is actually viable!
After I saw Mexico’s response to Google following Trump’s lead in renaming the Gulf of Mexico, this stupid comic literally came to me in a dream.
Adapts screenshots from Sid Meier’s Civilization (1991 DOS version), public domain assets from
OpenGameArt.org, and AI-assisted images of world leaders on account of the fact that if I drew pixel-art world leaders without assistance then
you’d be even less-likely to be able to recognise them.
EXIF geodata, and what if you took a picture on the moon?
This is a repost promoting content originally published elsewhere. See more things Dan's reposted.
A conversation about staying private and stripping EXIF tags on blogs lead to shdwcat asking the question “what would happen if you took a picture on the moon?”
…
However, I figured we could do better than “a point high above the Earth.” If you could state the coordinate system, you should be able to list an actual point on the moon.
…
It’s a fun question. Sure, you need to shoot down the naysayers who, like colin, rightly point out that you couldn’t reasonably expect to get a GPS/GNSS signal on the moon, but still.
GPS on Earth
GPS (and most other GNSS technologies) fundamentally work by the principle of trilateration. Here’s the skinny of what happens when your GPS receiver – whether that’s your phone, smartwatch, SatNav, or indeed digital camera – needs to work out where on Earth it is:
- It listens for the signal that’s transmitted from the satellites. This is already an amazing feat of engineering given that the signal is relatively quiet and it’s being transmitted from around 20,000km above the surface of the planet1.
- The signal fundamentally says, for example “Hi, I’m satellite #18, and the time is [some time].” Assuming your GPS receiver doesn’t contain an atomic clock2, it listens for the earliest time – i.e. the closest satellite (the signals “only” travel at the speed of light) – and assumes that this satellite’s time represents the actual time at your location.
- Your GPS receiver keeps listening until it’s found three more satellites, and compares the times that they claim it is. Using this, and knowing the speed of light, it’s able to measure the distance to each of those three satellites. The satellites themselves are on reasonably-stable orbits, so as long as you’ve been installing your firmware updates at least once every 5-10 years, your device knows where those satellites are expected to be.
- If you know your distance from one satellite, in 3D space, you know that your location is on the surface of an imaginary sphere with a radius of that distance, centered on the satellite. Once you’ve measured your distance from a second satellite, you know that you must be at a point where those two spheres intersect: i.e. somewhere on a circle. With a third satellite found and the distance measured, you’re able to cut that down to just two points (of which one is likely to be about 40,000km into space, so it’s probably not that one3).
- Your device will keep finding more passing satellites, measuring and re-measuring, and refining/averaging your calculated location for maximum accuracy.
So yeah: that tiny computer on top of your camera or within your wristwatch? It’s differentiating to miniscule precision measurements of the speed of light, from spacecraft as far away as half the circumference of the planet, while compensating for not being a timekeeping device accurate enough to do so and working-around the time dilation resulting from the effect of general relativity on the satellites4.
GPS on Luna
Supposing you could pick out GPS signals from Earth orbit, from the surface of the moon (which – again – you probably can’t – especially if you were on the dark side of the moon where you wouldn’t get a view of the Earth). Could it work?
I can’t see why not. You’d want to recalibrate your GPS receiver to assume that the “time” satellite – the one with the earliest-apparent clock – was much further away (and therefore that the real time was later than it appears to be) than an Earth-based GPS receiver would: the difference in the order of 1.3 seconds, which is a long time in terms of GNSS calculation.
Again, once you had distance measurements from three spatial satellites you’d be able to pinpoint your location, to within some sphere of uncertainty, to one of two points. One would be on the moon (where you know that you are5), and one would be on the far side of the Earth by almost the same distance. That’s a good start. And additional satellites could help narrow it down even more.
You might even be able to get a slightline to more satellites than is typically possible on Earth, not being limited by Earth curvature, nor being surrounded by relatively-large Earth features like mountains, buildings, trees, and unusually-tall humans. It’s feasible.
How about… LPS?
If we wanted to go further – and some day, if we aim to place permanent human settlements on the moon, we might – then we might consider a Lunar Positioning System: a network of a dozen or so orbiters whizzing around the moon to facilitate accurate positioning on its surface. They’d want to be in low orbits to avoid the impact of tidal forces from the much-larger nearby Eath, and with no atmosphere to scrape against there’s little harm in that.
By the time you’re doing that, though, you might as well ditch trilateration and use the doppler effect, Transit-style. It works great in low orbits but its accuracy on Eath was always limited by the fact that you can’t make the satellites fly low enough without getting atmospheric drag. There’s no such limitation on the moon. Maybe that’s the way forward.
Maybe far-future mobile phones and cameras will support satellite positioning and navigation networks on both Earth and Luna. And maybe then we’ll start seeing EXIF metadata spanning both the WGS-84 datum and the LRO-ME datum.
Footnotes
1 That’s still only about a twentieth of the way to the moon, by the way. But there are other challenging factors, like our atmosphere and all of the obstructions both geographic and human-made that litter our globe.
2 Protip: it doesn’t.
3 Satnav voice: “After falling for forty thousand kilometres, you will reach your destination.”
4 The relativistic effects on GPS satellites cannot be understated. Without compensation, GPS accuracy would drift by up to 10km for every day that the satellites were in orbit, which I reckon would make them useless for anything more than telling you what hemisphere you were in within 5½ years!
5 If you’re on the moon and don’t know it, you have a whole different problem.
Multi-Phase Maps in FoundryVTT
FoundryVTT is a fantastic Web-based environment for tabletop roleplaying adventures1 and something I particularly enjoy is the freedom for virtually-unlimited scripting. Following a demonstration to a fellow DM at work last week I promised to throw together a quick tutorial into scripting simple multi-phase maps using Foundry.2
Why multi-phase maps?
You might use a multi-phase map to:
- Allow the development and expansion of a siege camp outside the fortress where the heroes are holed-up.3
- Rotate through day and night cycles or different times of day, perhaps with different things to interact with in each.4
- Gradually flood a sewer with rising water… increasing the range of the monster that dwells within.5
- Re-arrange parts of the dungeon when the characters flip certain switches, opening new paths… and closing others.
I’ll use the map above to create a simple linear flow, powered by a macro in the hotbar. Obviously, more-complex scenarios are available, and combining this approach with a plugin like Monk’s Active Tile Triggers can even be used to make the map appear to dynamically change in response to the movement or actions of player characters!
Setting the scene
Create a scene, using the final state of the map as the background. Then, in reverse-order, add the previous states as tiles above it.
Make a note of the X-position that your tiles are in when they’re where they supposed to be: we’ll “move” the tiles off to the side when they’re hidden, to prevent their ghostly half-hidden forms getting in your way as game master. We’ll also use this X-position to detect which tiles have already been moved/hidden.
Also make note of each tile’s ID, so your script can reference them. It’s easiest to do this as you go along. When you’re ready to write your macro, reverse the list, because we’ll be hiding each tile in the opposite order from the order you placed them.
Writing the script
Next, create a new script macro, e.g. by clicking an empty slot in the macro bar. When you activate this script, the map will move forward one phase (or, if it’s at the end, it’ll reset).
Here’s the code you’ll need – the 👈 emoji identifies the places you’ll need to modify the code, specifically:
-
const revealed_tiles_default_x = 250should refer to the X-position of your tiles when they’re in the correct position. -
const revealed_tiles_modified_x = 2825should refer to the X-position they’ll appear at “off to the right” of your scene. To determine this, just move one tile right until it’s sufficiently out of the way of the battlemap and then check what it’s X-position is! Or just take the default X-position, add the width of your map in pixels, and then add a tiny bit more. -
const revealed_tiles = [ ... ]is a list of the tile IDs of each tile what will be hidden, in turn. In my example there are five of them (the sixth and final image being the scene background).
const revealed_tiles_default_x = 250; // 👈 X-position of tiles when displayed const revealed_tiles_modified_x = 2825; // 👈 X-position of tiles when not displayed const revealed_tiles = [ '2xG7S8Yqk4x1eAdr', // 👈 list of tile IDs in order that they should be hidden 'SjNQDBImHvrjAHWX', // (top to bottom) 'tuYg4FvLgIla1l21', 'auX4sj64PWmkAteR', 'yAL4YP0I4Cv4Sevt', ].map(t=>canvas.tiles.get(t)); /*************************************************************************************************/ // Get the topmost tile that is still visible: const next_revealed_tile_to_move = revealed_tiles.find(t=> t.position.x == revealed_tiles_default_x ); // If there are NO still-visible tiles, we must need to reset the map: if( ! next_revealed_tile_to_move ) { // To reset the map, we go through each tile and put it back where it belongs - for(tile of revealed_tiles){ canvas.scene.updateEmbeddedDocuments("Tile", [ { _id: tile.id, x: revealed_tiles_default_x, hidden: false } ]); } } else { // Otherwise, hide the topmost visible tile (and move it off to the side to help the GM) - canvas.scene.updateEmbeddedDocuments("Tile", [ { _id: next_revealed_tile_to_move.id, x: revealed_tiles_modified_x, hidden: true } ]); }
I hope that the rest of the code is moderately self-explanatory for anybody with a little JavaScript experience, but if you’re just following this kind of simple, linear case then you don’t need to modify it anyway. But to summarise, what it does is:
- Finds the first listed tile that isn’t yet hidden (by comparing its X-position to the pre-set X-position).
- If there aren’t any such tiles, we must have hidden them all already, so perform a reset: to do this – iterate through each tile and set its X-position to the pre-set X-position, and un-hide it.
- Otherwise, move the first not-hidden tile to the alternative X-position and hide it.
I hope you have fun with scripting your own multi-phase maps. Just don’t get so caught-up in your awesome scenes that you fail to give the players any agency!
Footnotes
1 Also, it’s on sale at 20% off this week to celebrate its fourth anniversary. Just sayin’.
2 I can neither confirm nor deny that a multi-phase map might be in the near future of The Levellers‘ adventure…
3 AtraxianBear has a great series of maps inspired by the 1683 siege of Vienna by the Ottomans that could be a great starting point for a “gradually advancing siege” map.
4 If you’re using Dungeon Alchemist as part of your mapmaking process you can just export orthographic or perspective outputs with different times of day and your party’s regular inn can be appropriately lit for any time of day, even if the party decides to just “wait at this table until nightfall”.
5 Balatro made a stunning map with rising water as a key feature: there’s a preview available.
Water Science #2
Back in 2019, the kids – so much younger back then! – and I helped undertake some crowdsourced citizen science for the Thames WaterBlitz. This year, we’re helping out again.
We’ve moved house since then, but we’re still within the Thames basin and can provide value by taking part in this weekend’s sampling activity. The data that gets collected on nitrate and phosphate levels in local water sources – among other observations – gets fed into an open dataset for the benefit of scientists and laypeople.
It’d have been tempting to be exceptionally lazy and measure the intermittent water course that runs through our garden! It’s an old, partially-culverted drainage ditch1, but it’s already reached the “dry” part of its year and taking a sample wouldn’t be possible right now.
But more-importantly: the focus of this season’s study is the River Evenlode, and we’re not in its drainage basin! So we packed up a picnic and took an outing to the North Leigh Roman Villa, which I first visited last year when I was supposed to be on the Isle of Man with Ruth.
Our lunch consumed, we set off for the riverbank, and discovered that the field between us and the river was more than a little waterlogged. One of the two children had been savvy enough to put her wellies on when we suggested, but the other (who claims his wellies have holes in, or don’t fit, or some other moderately-implausible excuse for not wearing them) was in trainers and Ruth and I needed to do a careful balancing act, holding his hands, to get him across some of the tougher and boggier bits.
Eventually we reached the river, near where the Cotswold Line crosses it for the fifth time on its way out of Oxford. There, almost-underneath the viaduct, we sent the wellie-wearing eldest child into the river to draw us out a sample of water for testing.
Looking into our bucket, we were pleased to discover that it was, relatively-speaking, teeming with life: small insects and a little fish-like thing wriggled around in our water sample2. This, along with the moorhen we disturbed3 as we tramped into the reeds, suggested that the river is at least in some level of good-health at this point in its course.
We were interested to observe that while the phosphate levels in the river were very high, the nitrate levels are much lower than they were recorded near this spot in a previous year. Previous years’ studies of the Evenlode have mostly taken place later in the year – around July – so we wondered if phosphate-containing agricultural runoff is a bigger problem later in the Spring. Hopefully our data will help researchers answer exactly that kind of question.
Regardless of the value of the data we collected, it was a delightful excuse for a walk, a picnic, and to learn a little about the health of a local river. On the way back to the car, I showed the kids how to identify wild garlic, which is fully in bloom in the woods nearby, and they spent the rest of the journey back chomping down on wild garlic leaves.
The car now smells of wild garlic. So I guess we get a smelly souvenir from this trip, too4!
Footnotes
1 Our garden ditch, long with a network of similar channels around our village, feeds into Limb Brook. After a meandering journey around the farms to the East this eventually merges with Chill Brook to become Wharf Stream. Wharf Stream passes through a delightful nature reserve before feeding into the Thames near Swinford Toll Bridge.
2 Needless to say, we were careful not to include these little animals in our chemical experiments but let them wait in the bucket for a few minutes and then be returned to their homes.
3 We didn’t catch the moorhen in a bucket, though, just to be clear.
4 Not counting the smelly souvenir that was our muddy boots after splodging our way through a waterlogged field, twice
My Geo*ing Limits
I thought it might be fun to try to map the limits of my geocaching/geohashing. That is, to draw the smallest possible convex polygon that surrounds all of the geocaches I’ve found and geohashpoints I’ve successfully visited.
Mathematically, such a shape is a convex hull – the smallest polygon encircling a set of points without concavity. Here’s how I made it:
1. Extract all the longitude/latitude pairs for every successful geocaching find and geohashpoint expedition. I keep them in my blog database, so I was able to use some SQL to fetch them:
SELECT DISTINCT coord_lon.meta_value lon, coord_lat.meta_value lat FROM wp_posts LEFT JOIN wp_postmeta expedition_result ON wp_posts.ID = expedition_result.post_id AND expedition_result.meta_key = 'checkin_type' LEFT JOIN wp_postmeta coord_lat ON wp_posts.ID = coord_lat.post_id AND coord_lat.meta_key = 'checkin_latitude' LEFT JOIN wp_postmeta coord_lon ON wp_posts.ID = coord_lon.post_id AND coord_lon.meta_key = 'checkin_longitude' LEFT JOIN wp_term_relationships ON wp_posts.ID = wp_term_relationships.object_id LEFT JOIN wp_term_taxonomy ON wp_term_relationships.term_taxonomy_id = wp_term_taxonomy.term_taxonomy_id LEFT JOIN wp_terms ON wp_term_taxonomy.term_id = wp_terms.term_id WHERE wp_posts.post_type = 'post' AND wp_posts.post_status = 'publish' AND wp_term_taxonomy.taxonomy = 'kind' AND wp_terms.slug = 'checkin' AND expedition_result.meta_value IN ('Found it', 'found', 'coordinates reached', 'Attended');
2. Next, I determine the convex hull of these points. There are an interesting variety of algorithms for this so I adapted the Monotone Chain approach (there are convenient implementations in many languages). The algorithm seems pretty efficient, although that doesn’t matter much to me because I’m caching the results for a fortnight.
3. Finally, I push the hull coordinates into Geoapify, who provide mapping services to me. My full source code is available.
An up-to-date (well, no-more than two weeks outdated) version of the map appears on my geo* stats page. I don’t often get to go caching/hashing outside the bounds already-depicted, but I’m excited to try to find opportunities to push the boundaries outwards as I continue to explore the world!
(I could, I suppose, try to draw a second larger area of places I’ve visited: the difference between the smaller and larger areas would represent all of the opportunities I’d missed to find a hashpoint!)
The Road (Segment) Less Travelled
There are two junctions at which I can join the A40 trunk road from my house. When I drive East, I use the Easternmost of the two; when I drive West, I use the Westernmost; but I almost never drive the stretch of road between them!
A few years ago I generated heatmaps of my movements based on my long-running personal location log and, indeed, it shows a “cool spot” along this section of road too:
It’s inevitable I suppose that there should exist a “road (segment) less-travelled” right on my doorstep, but it still feels strange. Like when you live near a tourist attraction that you never get around to visiting. Except instead of a tourist attraction, I live near a major highway I rarely use.
Maybe I’m missing out on something great. Probably the commuters who use that road to get into and out of Oxford don’t think so.
Geohashing expedition 2023-03-10 51 -1
This checkin to geohash 2023-03-10 51 -1 reflects a geohashing expedition. See more of Dan's hash logs.
Location
North Leigh Common, West Oxfordshire.
Participants
Plans
My evening just freed up, so – weather-permitting – I might brave the sleet and cold and cycle out to this hashpoint this evening.
Expedition
Our dog had surgery at the start of the week and has now recovered enough to want a short walk, so I changed my plan to cycle for one to drive (with the dog) out to somewhere near the hashpoint and take her for a walk to and around it. Amazingly, I might have been faster to cycle: a crash on the A40 had lead to lots of traffic being re-routed along the exact same back roads that was to be my most-direct route, and on the local rat run through South Leigh I got trapped behind a line of folks who weren’t familiar with this particular unlit and twisty road and took the entire derestricted section at an average of 25mph. Ah well.
Out of laziness, I didn’t bring my GPSr or make a tracklog; I just used the Geohashdroid app and took a screenshot when I got there. South Leigh Common is pleasant, but it was dark, and my photos are all a little bit hard to make out! But the stars were beautiful tonight, and the dog loved one of her first outings since her surgery and enjoying running around in the long wet grass and sticking her head into rabbit holes. At 19:00 precisely I got within about a metre and a half of the hashpoint – well within the circle of uncertainty – and turned to head home.
I also took the time while there to update OpenStreetMap by drawing in the boundaries of the common, replacing the nondescript “point” that had marked it before.
Photos
Geohashing expedition 2022-12-05 51 -1
This checkin to geohash 2022-12-05 51 -1 reflects a geohashing expedition. See more of Dan's hash logs.
Location
Bridleway behind Cokethorpe School, West Oxfordshire, UK.
Participants
Expedition
When I saw this hashpoint appear I thought to myself: that’s eminently achievable! I hoped I might be able to slip away from work for a lunchtime cycle to claim it.
But the gods of technology didn’t approve of my plan and turned my workday into a catastrophe of the kind that only a computer can, and the chance of taking a long lunch evaporated quickly. But fortune dealt me a second hand when the weather held off into the evening, and I instead opted for a post-dinner huckle in the dark out to this hashpoint.
I set out around 18:30, South through Stanton Harcourt then North up the adorably-named Ducklington Road. It took some time to sight the somewhat-concealed bridleway around the hill of Cokethorpe School. And then, another challenge – navigating by OpenStreetMap I missed my turning and went straight through a farmyard, and had to carry my bike over a fence at the other end. Turns out the map is wrong and I later found a sign indicating the true course of the bridleway; I’ll get that corrected.
I abandoned my bike for the final 50 metres, trekking through the thick grass of an unmown meadow to the hashpoint and arriving around 19:00. No panoramic photo today it’s too dark – but you get a silly grin.
Pleased with this fast expedition, I diverted on my route home to the Harcourt Arms pub for a pint of their surprisingly-delicious seasonal guest ale, Fairytale of Brew York, which genuinely tastes like stollen. There, I wrote up this expedition report, but I’ll have to get home before I can extract my GPSr‘s tracklog.
Tracklog
Photos
Automattic International
(This is yet another post about Automattic. Seee more posts about my experience of working at Automattic.)
Off the back of my recent post about privileges I enjoy as a result of my location and first language, even at my highly-multinational employer, and inspired by my colleague Atanas‘ data-mining into where Automatticians are located, I decided to do another treemap, this time about which countries Automatticians call home:
Where are the Automatticians?
To get a better picture of that, let’s plot a couple of cartograms. This animation cycles between showing countries at (a) their actual (landmass) size and (b) approximately proportional to the number of Automatticians based in each country:
Another way to consider the data would be be comparing (a) the population of each country to (b) the number of Automatticians there. Let’s try that:
There’s definitely something to learn from these maps about the cultural impact of our employee diversity, but I can’t say more about that right now… primarily because I’m not smart enough, but also at least in part because I’ve watched the map animations for too long and made myself seasick.
A note on methodology
A few quick notes on methodology, for the nerds out there who’ll want to argue with me:
- Country data was extracted directly from Automattic’s internal staff directory today and is based on self-declaration by employees (this is relevant because we employ a relatively high number of “digital nomads”, some of whom might not consider any one country their home).
- Countries were mapped to continents using this dataset.
- Maps are scaled using Robinson projection. Take your arguments about this over here.
- The treemaps were made using Excel. The cartographs were produced based on work by Gastner MT, Seguy V, More P. [Fast flow-based algorithm for creating density-equalizing map projections. Proc Natl Acad Sci USA 115(10):E2156–E2164 (2018)].
- Some countries have multiple names or varied name spellings and I tried to detect these and line-up the data right but apologies if I made a mess of it and missed yours.