By now many of you will have become aware of the brouhaha around the latest announcement from that fruity company in Cupertino, California.
ICYMI they announced a groundbreaking new device they call ‘Vision Pro’:
Apple Vision Pro: Credit Apple
Unlike the virtual world of legless avatars that young Zucky is flogging, Apple Vision Pro is something entirely different — and to be clear, it’s not a metaverse.
In my mind this new device accomplishes three things:
it unencumbers you from the limitations of physical screens and monitors
it enables truly immersive interaction with 3D content
it creates a ground breaking environment for sharing and collaboration
What then could provide a more perfect platform to interact with geospatial data?
I remember a demo, some 18 years ago, at the Esri annual user conference of a 3D touch table. People were wowed. Of course now Vision Pro would blow this out of the water.
Even Apple’s AR demo at the Apple Visitor Center at Apple Park seems quaint compared to what could be accomplished with Vision Pro:
With Apple Vision Pro I could imagine sitting in a room sharing some geospatial model of some landscape or perhaps a cityscape, pulling apart and visualizing different layers of data or watching as some geoprocess operates on the data — with all the interactions controlled with simple hand gestures.
And editing geospatial data would be similarly awesome. Perhaps you would dive into the scene to edit some little aspect of some pipe or fire hydrant, or perhaps you would fly back out to experiment with manipulating whole layers.
All at the same time your colleagues, remote or present in the room with you, would be able to experience the same thing and collaborate with you on the spatial interactions.
Geospatial story telling would be equally mind blowing.
Imagine the power of a presentation to make the case for some new policy or some new major investment. It would truly be a game changer.
Yes, dear Esri, it’s time to finally junk that mountain of COM based Microsoft Windows code you developed last century, abandon the complexity of UI ribbons and endless check boxes. It’s time get with the program and show the world what you can really do!
Done right your platform could become the prime enterprise example of the power Apple Vision Pro.
And who knows, if you’re busy junking that 20th century technology and revamping your platform, maybe, just maybe, ArcGIS for macOS may come easier than you thought. 😉
Just Imagine What ArcGIS Vision Pro Could be … Video Credit: Apple
In case you didn’t notice, we’re living in a world of revolutions.
Generative AIs are exploding and seemingly turning the world upside down. It feels very similar to the dot com boom of the 1990s, where your company wasn’t worth diddly squat unless its name ended in ‘.com’. Today your products better include some form of ML or AI — preferably generative — to grab any attention. It’s getting so ridiculous that I wouldn’t be at all surprised if Cadbury’s somehow integrated ChatGPT into their next chocolate bar.
In parallel with all this there’s another revolution happening in automotive manufacturing.
Here you are witnessing a wholesale switch in the methods for designing, engineering and manufacturing vehicles. It’s not just about replacing the internal combustion engine with an electric motor and lots of AA batteries, it’s about developing a completely new method of producing vehicles.
The Mercedes ‘AA’ — Credit: SNL
In the western world it’s Tesla that’s grabbing the limelight, spurring all the other auto OEMs to jump on the EV bandwagon.
The PC angle on the story for the OEMs is that it’s part of their mission to save the planet. However, it’s really about FOMA and increasing profits — there are orders of magnitude fewer parts in an EV vs. an ICE equivalent. The legacy OEMs’ strife and anxiety is being cranked up by Tesla’s use of its Giga Press1 which is resulting in yet another massive increase in manufacturing efficiency. No wonder Ford is sweating.
But what about the map making world?
Has anyone — or is anyone — doing something similar to revolutionize global map production?
And I’m not just talking about building the map, I’m talking about keeping it maintained and up-to-date.
Since organizations started the process of building global street maps in the mid 1980s the approach has more or less been the same:
Get rights to some existing reference maps or aerial photography
Develop some map editing tools that will scale (sorry @Esri)
Throw bodies at the problem. Thousands of them.
In the 2000s organizations started adding fleets of vehicles to the mix, first equipped with cameras and later also with LiDAR. This enabled richer data collection — for example, things like speed limits and lane info — and made it easier to verify ‘ground truth’.
But map production still took thousands of bodies.
Then around 2007 something magical happened. Smartphones came to be, first the iPhone and later other photocopies. Over time the proliferation of these devices has resulted in an abundance of new data. While this treasure trove of information has mainly been used by Location Harvesters, Personal Information Brokers & Assholes to turn you into a product, it has also proved useful for map making.
The anonymized and aggregated data from mobile devices can be used to both derive new products and help maintain existing ones.
The prime example is real-time traffic information: those red and orange lines you see overlayed on consumer maps denoting traffic jams are nearly always derived from movements of mobile devices.
But these movements can also be used to derive change signals. For example:
Where are devices traveling along a path where there is no road? Is that a new road?
Where are devices no longer traveling in a particular direction along an existing road? Is that a new one way?
Where are devices no longer turning left at an intersection? Is that a new turn restriction?
So that’s progress.
However, there’s a limit to its usefulness.
Even though the data is a large firehose, it’s fundamentally just movement data — and due to the current limitations of GPS it’s not always accurate enough to derive even simple information, particularly in urban canyons.
So, in other words, while movement data definitely can be used as a change signal, it’s extremely difficult to use it to derive map edits automatically.
While it’s progress it’s not exactly what you’d call a revolution.
So what do you need for a real revolution?
Well, I’d make the argument that it distills down to three things:
Eyes
Smart Processing
Streaming
Eyes
Let’s start with eyes.
By ‘eyes’ think of images like Google StreetView or Apple’s LookAround, but at a frequency that will make a difference. StreetView and LookAround images come from the dedicated fleets that the map makers employ, but the problem is they only drive the streets about once a year — if you’re lucky. Sorry guys, but that doesn’t cut it for revolutionizing map production.
To really stay on top of things you need eyes on every road everyday.
Where might that volume come from? Well from the cameras built into vehicles of course. There are two companies that I’ll highlight here that could bring the volume: Mobileye and Tesla.
Mobileye, recently spun off from Intel and subsequently IPO’d, sells systems to auto OEMs to enable them to provide driver assistance systems like adaptive cruise control, collision avoidance and ultimately, they hope, completely autonomous driving. They claim that their most basic system — which includes a front facing camera — is already installed in millions of vehicles.
What about Tesla? Well as of April 2023, Tesla has sold a total of 4,061,776 electric vehicles3. Each of them has eight cameras. That’s a lot of eyes.
Phew — sounds good right?
Alas there is one small problem in the way: lawyers.
Yes, dear readers, it turns out that the auto OEMs want to keep all that data to themselves, so it’s actually pretty hard to come by.
But it’s not just about eyes on the ground. You also need eyes in the sky. These eyes, used intelligently, can in theory be used to collect data automatically and can be used to detect change.
And the good news is there is an ever increasing plethora of eyes in the sky, not so much from drones (from which data is at best very limited and sporadic), but from birds. Small ones. They’re called earth observation satellites.
There’s a ton of activity going on in this space — volume, frequency of capture and resolution is increasing by leaps and bounds, sensors are evolving — and in the meantime costs are coming down by orders of magnitude. Pretty soon we’re going to be awash with data. For those of us in the map making business it’s going to be thrilling to watch because it’s going to change the way business is done.
One of the companies I’m watching is Satellogic, who claims to have got the costs of data acquisition down to $0.46 per km2 — two orders of magnitude cheaper than their competition:
Credit: Satellogic
One way or another all these eyes will produce the volume of data needed. It will just be a matter of time.
Smart Processing
The question is of course, what are you going to do with all this data? You’re talking many many petabytes at least.
To make good use of it you need to be intelligent about extracting information. Of course this is where machine learning models come in, not so much targeted at creating maps, but instead at detecting change.
Change can be detected from the ground, for example detecting construction zones by automatically detecting traffic cones. Or your machine learning models might also automatically pick up traffic lights, stop signs, speed limits, et cetera.
Detecting information from this street level imagery is already quite advanced. You’ll be familiar with it if you’ve ever ridden in a Tesla where the screen displays people and objects that the cameras see, and more importantly for map makers – traffic lights, speed limits and signs.
Ultimately all these eyes on the road might also be used to keep information about places and businesses up-to-date, including volatile information from signs on the windows indicating things like operating hours and ongoing sales. It’ll take some work, but it will get there.
Using street level imagery to help keep business information current — Credit: Apple Maps LookAround
Detecting change from the sky is more interesting. For example, there’s a homebuilding data company called Zonda which is using imagery to automatically detect phases of building construction, so you can tell when streets are in, framing has started or roofs are on.
Perhaps more interestingly, there’s a company called Blackshark.ai who has a service called Orca that is able to perform automatic detection on global imagery, e.g. for vegetation classification and building detection. I’ve not seen them produce a specific workflow for detecting road changes at scale yet, but I wouldn’t be surprised if they have something in the works.
Back in the hay days of printed road atlases everyone would be thrilled to get the annual update of their favorite road atlas from the likes of Rand McNally, Michelin or the Ordnance Survey.
Then ‘Sat Nav’ systems came along, and after spending $2,000 or more for a ugly stick map you only had to pay a ransom of a few hundred dollars to get your refreshed map CDs or DVD.
The cadence was still pretty much annual however.
Then, lo and behold, MapQuest came to be and suddenly you didn’t have to worry about DVDs or paying annual ransoms. The map updated itself!
But little did most of you know that organizations like MapQuest were beholden to the map makers of the day like Navteq, GDT and TeleAtlas. At best they sent MapQuest an update every quarter. On top of that it took MapQuest several months to process all the data, so by the time it got to customers the map was at least six months out of date.
It wasn’t until Google started making their own map that things really started to change. Because Google was developing the whole stack it gave them the ability to be in control of the release cycles.
Eventually the cadence of map releases became more frequent, first monthly and then with the ability to splice in critical updates, e.g. for highway and motorway intersections. But still the pipeline was geared towards releasing all the data in one big glued-together multi-layered lump.
The advent of displaying real-time traffic on top of the roads forced a change in architecture as traffic conditions change by the minute. This precipitated the need to stream at least some of the data.
The question is, is it possible to stream all layers of the data independently from one another — so an update in say roads can be streamed in separately from updates to parks or indoor maps?
To achieve such a goal might enable a true ‘living map’ with almost zero latency in updates.4
I’m not sure if any organization in the map data editing, processing and publishing business has truly achieved a layer independent, near zero latency streaming system yet. I’ve never seen the inner workings of Google Maps — perhaps they have, but I’d be surprised.
One organization that is certainly striving for such a system is HERE. That was the underlying story behind their recent announcement of Unimap at CES.
For example, they want to be able to take speed limit data that is recognized by vehicles driving around, quickly automatically verify it and then immediately stream the newly updated information back out to the their mapping services. HERE has an advantage in this space as their investors include BMW, Audi and Mercedes, so in theory at least those data could come in volume from the OEMs’ fleets.
This approach may be a little nascent as there aren’t enough BMWs, Audis and Mercedes vehicles on the road with the necessary ‘eyes’ yet, but hell, it won’t be too long before there is critical mass. So kudos to HERE for showing leadership.
Tesla has a ton of ‘eyes’ and could in theory stream the signs and objects their vehicles recognize back to their map. But for navigation at least Tesla doesn’t have their own map. They rely on Google.
Hmm — what does that particular data license agreement look like I wonder? Is there a quid pro quo that we don’t know about in place? Like Tesla’s object recognition in exchange for Google’s map data? Perhaps we should all ask Elon and find out.
Regardless of the relationship it clearly doesn’t result in near instant map updates, so the streaming architecture is not in place yet.
So Who’s Going to Drive the Revolution?
So, net/net — nobody has quite cracked it yet. As far as I can tell a ground breaking revolution in map making along the lines of what we’re seeing in generative AIs and auto manufacturing has yet to materialize.
But in time — and probably not too much time — somebody will crack it. There will be enough eyes, there will be enough smart processing and organizations will re-architecture their pipelines to enable near real time updates of all layers of the map independently of one another.
I can’t wait to see who will be first.
1 This 13 minute video is well worth a watch if you’re interested in the technical and financial details of how the Giga Press is benefiting Tesla:
4 Yeah, I know, I know — the Esri groupies among you will exclaim the virtues of Esri’s ‘Living Map’, but Esri is not a global map maker. Also, like it or not, there is still significant latency between the time when a change happened on the ground and when it appears in the Esri offering.
It’s a shame for all those Mac lovers who use ArcGIS. They either have to suffer through having to use Parallels, or worse yet, they have to endure the ignominy of using a PC.
Perhaps all of you avid Mac fans could start a campaign to convince Esri to invest?
For example, perhaps wear one of these rather delightful buttons at your next exciting GIS event?
If your day job is not in the mapping industry then you might find the title of this post a little yawn inducing. But bear with me, this is actually pretty momentous…
ArcGIS is a product that comes from the largest enterprise mapping technology company on the planet. That company is the Environmental Systems Research Institute, now commonly known as ‘Esri’. People call it ‘ezz-ree’ although for the longest time it was known to employees as ‘E-S-R-I’ or sometimes just ‘The Institute’.
Esri has both an impressive and illustrious pedigree. Started by Jack Dangermond and his wife, Laura, in 1969 they have built the company into an industry juggernaut. Through Esri’s work Jack has pioneered the geographic approach to technology, developing a foundation on something called ‘GIS’ or geographic information systems.
While the term ‘GIS’ is meant to be a generic term, it has actually become synonymous with Esri. In other words ‘GIS’ means ‘Esri’ and there are no other significant GIS players in the market.
Credit Esri
‘What about Google?’ I can hear you exclaim. Well to Esri, Google is but a pittance. While Google has become a master of consumer maps and navigation they have done relatively little in the enterprise mapping market. Sure, I guess you could say they ‘dabble’, but it’s not a core focus.
For Esri, mapping technology is central to everything they do. And as a result you will find it is used under the covers almost everywhere — national, regional and local governments, utilities, oil & gas, telecommunications, transportation, banking, insurance, retail, education — to name just a few.
It’s used by organizations not only to create super detailed maps of places and infrastructure, but more importantly it’s used for geospatial analytics — or what I’ve always liked to call ‘location analytics’.
You can use Esri’s software to map your cities — parcels, water and sewer lines, roads, bridges and parks. You can use it to figure out the optimal location for a store, a school, a cell tower or a wind farm. You can use it to assess risk or to plan for emergencies. You can use it to optimize emergency response. The list is essentially endless. At anytime when the question ‘where?’ comes up then Esri has a product for you.
And since 1969 that list of products has grown.
In the beginning Esri started with prefacing all their product names with the letters ‘Arc’ — as in ‘arc’, ‘line’ or ‘polygon’. This is much like Apple, who in the Steve Jobs days used to preface all their products with the letter ‘i’.
First there was ‘ArcView’, ‘ArcMap’ and ‘ArcInfo’ and more recently they’ve settled on prefacing all product names with the word ‘ArcGIS’ 1, for example ‘ArcGIS Pro’, ‘ArcGIS Enterprise’ and ‘ArcGIS Online’.
I’m actually quite astounded at how many ‘ArcGIS’ products there are now. When I last counted there were 110 of them — they even have a product for breakfast cereals (!!):
But if you look through the list carefully you might notice there’s one product that’s not listed.
Yes, there is no product for that operating system favored by the many millions of people who use computers from that large fruit company in Cupertino, California.
It’s true, dear readers, you will not find ‘ArcGIS for macOS’.
This is surprising, particularly given the popularity of the macOS ecosystem — not to mention its cool factor. It is also very surprising given Esri’s propensity and strategy to ‘get ‘em while they’re young‘.
I asked Perplexity about how much Macs are favored in universities. Even I was surprised at the number — some 71% of college students prefer Macs over PCs.
And real life backs this up. Here’s a screenshot of freshman students attending one of their first lectures at a well known US college:
Err — I think I can detect just one or two Apple devices in the audience!
But here’s the exciting news…
Thanks to some little birdies that have graciously kept me in the loop I can now tell you that your long wait is now almost over:
And I’m told this isn’t going to be some Windows lookalike hack either. No, it will be fully compliant with all the nitty gritty, pixel-perfect details of the Apple macOS Human Interface Guidelines. It’s also going to built from the ground up on Metal so it can make full use of Apple’s latest M-series chips. All-in-all it’s going to be gorgeous.
But wait, I hear you clamoring — just when is the exciting date?
Well I’m told that in deference to Apple it will be on the anniversary of Apple’s founding.
Go figure. 🙂
1 Although when I worked at Esri I sometimes heard people call it ‘ArghhGIS’ in frustration at the complexity of its UI.
So, in case you missed it, yesterday there was a momentous announcement from OpenAI. They released “ChatGPT Plugins”.
These are essentially brain implants that solve the woeful embarrassment that ChatGPT suffers from when trying to answer basic questions about, for example, mathematics or anything geospatial.
I can’t emphasize enough what a big deal this new plugin capability is: it’s just like the scene in the Matrix when the character Trinity is essentially given a plugin to learn how to fly a helicopter:
Only in ChatGPT’s case you can now upload one of these many brains:
So now ChatGPT can be immediately be given the power of any one of these sites, for example Expedia and Kayak for booking travel or OpenTable for finding and booking restaurants.
But the one I want to focus on is Wolfram.
For those (few?) of you that might not be familiar, Stephen Wolfram built an amazing site, Wolfram|Alpha, that was released 14 years ago in 2009. One of its key original intents was to be able to answer mathematical questions using a natural language interface. It did so admirably.
Alas, as Stephen Wolfram recently pointed out, this intelligence didn’t make its way into ChatGPT:
ChatGPT Failure to ComputeThe Correct Answer from Wolfram|Alpha
Stephen Wolfram pointed out ChatGPT’s ineptitude in spades in his article back in January. When I read the article I reached out to Stephen to discuss this in more detail. He put me in touch with Peter Overmann on his team.
Peter has worked at Wolfram|Alpha for many years, but he’s also worked at TomTom, so he knows geospatial. We had some great conversations. It was Peter who kindly gave me the scoop of what was really going on under the covers.
It turned out the ideas that I raised in my post were already being worked on by brains exponentially smarter than mine.
And, as of yesterday, ChatGPT is now also exponentially smarter than it was before:
But since 2009 Wolfram|Alpha has grown significantly. It can now answer questions on a whole host of subjects, not just mathematics:
And now you can access all these capabilities in ChatGPT.
If you want to experience it yourself I’m afraid you’re going to have to join a waitlist. Alas I don’t have access yet, so I’ve yet to enjoy these new superpowers myself.
Now while Wolfram|Alpha’s overall capabilities are outstanding, the geospatial capabilities are still, shall we say, somewhat rudimentary.
But as we all know things are moving fast. Very fast.
My question is: who will be the first to plugin a truly powerful geospatial engine into ChatGPT. Will it be:
Wolfram|Alpha extending its geospatial chops?
Mapbox?
Esri?
Wolfram|Alpha wrapping Esri?
Some new upstart?
It’ll be fun to see.
Stay tuned. I’m sure you won’t have to hold your breath too long.
So with all the recent froth about ChatGPT and Clippy 2.01, err, I mean the new Bing, I thought it might be fun to do a deeper dive and think about how all this might effect the geospatial industry.
In other words, what does the future hold for ‘Map Happenings’ powered by generative AI?
In order to write this article I started by doing a little research and investigation. I wanted to discover just how much these nascent assistants might be able to help in their current form. Now unfortunately I don’t yet have access to the new Clippy, so I had to resort to performing my tests on ChatGPT. However, while I suspect the new Bing might provide better answers, it might also might decide that it loves me or wants to kill me or something2, so for now I’m happy to stay talking to ChatGPT.
I picked a number of different geospatial scenarios — consumer based as well as enterprise based.
The first scenario is based on a travel premise.
I imagined I was planning a trip to an unfamiliar city, in this case to Madrid. I was pleasantly surprised with the results — they weren’t too bad:
But if you try using ChatGPT for something a little more taxing than searching all known written words in the universe, like, for example, calculating driving directions, you will quickly be underwhelmed.
Take this example of driving from Apple’s Infinite Loop campus to Apple Park. At first the directions look innocuous enough:
However, digging in, you’ll find the directions are completely and utterly wrong.
It turns out ChatGPT lives in an alternate maps universe.
Diagnosing each step:
“Head east on Infinite Loop toward Homestead Rd”: Infinite Loop does not connect to Homestead Rd. Get your catapult!
“Turn right onto Homestead Rd”: so after catapulting from Infinite Loop over the freeway to Homestead you turn right. OK.
“Use the left 2 lanes to turn left onto N Tantau Ave”: Err, you can’t turn left from Homestead to Tantau … unless the wind blows your balloon east of Tantau.
“Use the left 2 lanes to turn left onto Pruneridge Ave”: Really? Hmm. Wrong direction!
“Use the right lane to merge onto CA-280 S via the ramp to San Jose”: It’s actually I-280, but wait … Pruneridge doesn’t connect to the freeway… get out your catapult again!
“Take the Wolfe Rd exit”: but if you took “CA-280” towards San Jose then you were traveling east, so now you’re suddenly west of Wolfe Rd. The winds must have blown your balloon again!
“Keep right at the fork and merge onto Wolfe Rd”: Ok, I think.
“Turn left onto Tantau Ave”: You’ll be stumbling on this one. Wolfe and Tantau don’t connect.
“Turn right onto Apple Park Way”: wait, what?
Trying to make sense of ChatGPT’s incredibly bad driving directions.
But wait, it gets worse:
ChatGPT runs out of energy at step 47 somewhere in New Jersey, presumably completely befuddled and lost.
Now this authoritative nonsense isn’t limited to directions.
Let’s look at some maths3.
First a simple multiplication:
So far, so good. But now lets make it a little more challenging:
ChatGPT certainly sounds confident. But is the answer correct?
Well’s here’s the answer you’ll get from your calculator, or in this example, WolframAlpha:
Credit: Wolfram|Alpha
Huh? It looks like ChatGPT not only lives in an alternate maps universe it also lives in an alternate maths universe.
Architectural differences between ChatGPT vs. Wolfram|Alpha Credit: Wolfram|Alpha
Stephen points out not only ChatGPT’s inability to do simple maths, but also its inability to calculate geographic distances, rank countries by size or determine which planets are above the horizon.
Credit: Stephen Wolfram
Stephen’s big takeaway:
In many ways, one might say that ChatGPT never “truly understands” things
ChatGPT doesn’t understand maths. ChatGPT doesn’t understand geospatial. In fact all it understands is how to pull seemingly convincing answers out of what is essentially a large text database. You can sort of see this in its response to the question about what to do in Madrid — this is likely summarized from the numerous travel guides that have been written about Madrid.
But even that is flawed.
In order to work efficiently the information store from which ChatGPT pulls its answers has to be compressed. And it’s not a lossless compression. It therefore is vulnerable to suffering from the same kind of side effects as audio, video or images that use a lossy compression.
Think of ChatGPT as a blurry jpeg of all the text on the Web. It retains much of the information on the Web, in the same way that a jpeg retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry jpeg, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.
In other words, don’t let ChatGPT’s skills at forming sentences fool you.
What’s missing?
Clearly ChatGPT’s loquacious front-end needs to be able to connect to computational engines. That is what Stephen Wolfram argues for, in his case for a connection to his Wolfram|Alpha computational engine.
I can easily imagine a world where a natural language interface like ChatGPT could be connected to a wide variety of computational engines.
There might even be an internationally adopted standard for such interfaces. Let’s call that interface CENLI (“sen-ly”), short for “Computational Engine Natural Language Interface”.
I challenge folks like Stephen @ Wolfram-Alpha and Nadine @ OGC to push such a CENLI standard. In that way we could build natural language interfaces to all sorts of computational engines. This might include:
All branches of Mathematics
Financial Modeling
Architectural Design
Aeronautical Design
Component Design
… and — of course — all manner of Geospatial
It turns out making a connection between a generative AI and a computational engine has been done already — by NASA. A chap called Ryan McClelland, a research engineer at NASA’s Goddard Space Flight Center in Maryland has been using generative AI for a few years now to design components for space hardware. The results look like something from an alien spaceship:
NASA’s AI designed space hardware — Credit: NASA / Fast Company
NASA is taking generative AI to space. The organization just unveiled a series of spacecraft and mission hardware designed with the same kind of artificial intelligence that creates images, text, and music out of human prompts. Called Evolved Structures, these specialized parts are being implemented in equipment including astrophysics balloon observatories, Earth-atmosphere scanners, planetary instruments, and space telescopes.
The components look as if they were extracted from an extraterrestrial ship secretly stored in an Area 51 hangar—appropriate given the engineer who started the project says he got the inspiration from watching sci-fi shows. “It happened during the pandemic. I had a lot of extra time and I was watching shows like The Expanse,” says Ryan McClelland, a research engineer at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “They have these huge structures in space, and it got me thinking . . . we are not gonna get there the way we are doing things now.
As with most generative AI software, NASA’s design process begins with a prompt. “To get a good result you need a detailed prompt,” McClelland explains. “It’s kind of like prompt engineering.” Except that, in this case, he’s not typing a two-paragraph request hoping the AI will come up with something that doesn’t have an extra five more limbs. Rather, he uses geometric information and physical specifications as his inputs.
NASA’s AI designed space hardware — Credit: Henry Dennis / NASA / Fast Company
“So, for instance, I didn’t design any of this,” [McClelland] says, moving his hands over the intricate arms and curves. “I gave it these interfaces, which are just simple blocks [pointing at the little cube-like shapes you can see in the part], and said there’s a mass of five kilograms hanging off here, and it’s going to experience an acceleration of 60G.” After that, the generative AI comes up with the design. McClelland says that “getting the right prompt is sort of the skill set.
What’s really interesting about McClelland’s work is that it is streamlining the long cycle of design -> engineering -> manufacturing. No longer does he need to pass off the designs to an engineering team who then iterates on it and subsequently passes it on to a manufacturing team who iterates even further. No. Now the generative AI tool compresses that process:
It does all of it internally, on its own, coming up with the design, analyzing it, assessing it for manufacturability, doing 30 or 40 iterations in just an hour. “A human team might get a couple iterations in a week.”
Jesus Diaz sums it up perfectly:
Indeed, to me, it feels like we are the hominids who found the monolith in 2001: A Space Odyssey. Generative AI is our new obsidian block, opening a hyper-speed path to a completely new industrial future.
So, given that a natural language interface to all sorts of computational engines is both possible and inevitable, what might a natural language interface to a geospatial computational engine look like and what might it be capable of doing?
First, let’s start with a consumer example.
I don’t know about you, but I love road trips. But I abhor insanely boring freeways and much prefer two lane back roads.
Many years ago when I lived in California I discovered the wonderful world of MadMaps4
MadMaps has developed a series of maps for people of my ilk. Originally they were designed for those strange people who for some reason like motorbikes, but for me, at the time when I had my trusty Subaru WRX, they were also perfect.
You see MadMaps’ one goal was to tell you about the interesting routes from A to B. So, when I was driving back to Redlands from my annual pilgrimage to the Esri user conference in San Diego, I would be guided by MadMaps to take the windy back roads over the mountains. It would take me about twice as long, but it was hellish fun.
Imagine if the knowledge of MadMaps was integrated into a geographic search engine or your favorite consumer mapping app. And imagine if it also happened to know something about your preferences and interests so that it could incorporate fun places to stop along the way.
It turns out I’m not the first person to think of this.
It was only recently that Porsche announced a revamped version of its ROADS driving app.
Porsche ROADS driving app — Credit: Porsche
ROADS is a valiant attempt to use AI to do what MadMaps does but in an interactive app. Unfortunately the generated routes are, well, pretty simplistic and not particularly enthralling. They lack the reasoning and context that you get from studying a MadMap.
However, I don’t think it would take a huge amount of work by the smart boys and girls at Google Maps and Apple Maps to do something similar, but much more powerful. Imagine this prompt:
“Hey Siri, I’m looking to drive from Tucson to Colorado Springs. I’m traveling with my dog and I’d love to take my time, but I want to do the trip in two days. Can you recommend a route that takes in some beautiful scenery and some great places to eat and stop for good coffee? And by “good coffee” I mean good coffee, not brown water or chain coffee schlock. I’d obviously like find good places to stop for walks to exercise the dog and I’d love to spend the night at some cute boutique hotel or motel close to some eclectic restaurants.”
If you try it today5 you will find what first appears to be a good answer, but on closer analysis it’s lacking in detail and is very vague in some places.
More importantly perhaps: it’salso just a text answer.
It’s not a detailed trip plan displayed on an interactive map that you can then tweak and edit. In other words, it’s only about 50% of the way there.
Switching gears, now let’s imagine a natural language interface to a complex geospatial analytics problem, this time applied to business.
As an example I’ll use the geospatial problem of something called “site selection”. This is a process of determining the best location for some object, some business or some facility. Traditionally this is performed with huge amounts of geospatial data about things like roads, neighborhoods, terrain, geology, climate, demographics, soils, zoning laws … the list goes on.
Organizations like Starbucks and Walmart have used these geospatial and geo-demographic analysis methods for decades to help determine the optimal location for their next store. Organizations like Verizon have used similar processes to help determine the best locations for cell phone towers based on where the population centers are and what the surrounding terrain looks like.
This methodology has not been limited to commercial use cases.
A long time ago I remember someone performing a complex geospatial analysis on the location of Iran’s Natanz uranium enrichment facility. They looked at things like the geology, the climate, the topography, access to transportation and energy. Using this information they spent a significant amount of time, energy and brainpower to determine other locations in Iran that might have similar characteristics — in other words: where else Iran might be hiding another such facility? I think there were only one or two places that the algorithm found.
What’s common about all these enterprise use cases is the complexity of getting to the answer. You have to set up all the right databases, you have to invent, develop and test your algorithms. And just like with the design -> engineering -> manufacturing process that NASA faces with component design, there is a feedback loop — for example, one of the challenges for locating a Starbucks is determining exactly what factors are driving the success of its most profitable stores.
All of this is compounded by the horrible complexity of the user interfaces to these systems. To get the best results you not only need to be well educated in something called ‘GIS’ 6, but it also doesn’t hurt to be an accomplished data analyst. My good friend, Shawn Hanna, who also happens to be a super sharp data analyst, used to work on these site selections scenarios for Petco. He can attest to the complexity of the problem.
But imagine if instead data analysts could issue a prompt to a geospatial computational engine to help them find the optimal answers more quickly:
“I’m looking to figure out the best location to open a new Petco store in the Atlanta metropolitan area. I’d like you to take into account the locations of current Petco stores, their sales and profitability and the location of competitive stores. I’d also like you to take into account the demographics of each potential location and match that against the demographics of my best performing stores. Also take into account likely population growth and predicted trends in the respective local economies. And, of course, information on which households own pets. When you’ve derived some answers, match that against suitable available commercial properties in the area. Rank the results and explain why you chose each location” 6
The trick, as McLelland at NASA says, will be in good prompt engineering.
And of course, you’ll have to have the confidence that your chatty interface is connected to a reliable, dependable and knowledgable computational engine.
It’s not going to eliminate your job, but it sure as hell is going to make you tons more productive.
We’re not there yet. But it’s coming.
Hell, we might even be able to do this:
Can you fly that helicopter? Credit: The Matrix / Warner Bros. Entertainment Inc.
Footnotes:
1 For those of you that don’t remember, here is Clippy 1.0 in action:
3 If you live in the United States, that translates to ‘Math’. Why I’m not sure. People generally don’t study ‘Mathematic’. Perhaps that’s why people from the US sometimes have a reputation for not being as good at mathematics as people in other countries? They don’t realize there’s a number bigger than one.
4 Here is one of my favorite MadMaps:
5 ChatGPT’s answer to a road trip challenge. It’s a reasonably good start, but the directions are pretty vague:
6 GIS stands for ‘Geographically Insidious System’
7 FWIW, here is ChatGPT’s answer to this prompt:
Acknowledgments:
The folks at OpenAI for letting me highlight ChatGPT
So the astute readers among you1 will have realized by now that this series of posts on the 12 Map Happenings that Rocked Our World are slowly advancing through history:
Part 1 was about The First Map which was probably invented about 45,000 years ago
Part 2 was about The Birth of Coordinates, specifically latitude and longitude, which happened in about 245BC
Part 3 was about the invention of Road Maps by the Romans somewhere around 20BC
Part 4 was about The Epic Quest for Longitude and how it came to be measurable at sea in 1759
Today we move forward yet again, this time to the year 1933 and the invention of the ’Tube Map’.
First of all through, what the hell is a ‘Tube’?
Well, if you’re not familiar, please let me enlighten you.
The Tube refers to the London Underground, which in 2023 is celebrating its 160th anniversary.
‘Love the Tube’ Roundel – Celebrating the 160th Year of the London Underground Credit: Transport for London
The first line opened on 10th January 1863 between Paddington and Farringdon Street. Initially the trains were powered by steam locomotives that hauled wooden carriages. It wasn’t until 1890 until the first deep level electric line was opened:
London Electric Underground Train in 1890 — Credit: Wikimedia
The London Underground first became known as the ’Tube’ in 1900 when the then Prince of Wales, Prince Albert Edward (later Edward VII), opened the Central London Railway from Shepherd’s Bush to Bank. This line was nicknamed the ‘Twopenny Tube’2,3.
Many maps of the Tube were created, the first being in 1908:
It wasn’t until 1949 that the Tube Map that that we all know and love truly came into being4.
The map was created by one Henry Charles Beck (4 June 1902 – 18 September 1974), a.k.a. Harry Beck.
Beck’s map was first published in 1933:
Beck’s First Work, Published in 1933
But It wasn’t until 1949 that Beck was completely satisfied with the the design:
Harry Beck’s Favorite Creation from 1949 — Credit: Darien Graham-SmithHarry Beck (1902-74) — Credit: Wikimedia
Beck had created something of beauty and it was truly a game changer: eliminating all extraneous information — even topography — to create the most simple and easy-to-understand map you could possibly achieve. Jony Ive would have been proud.
The history of how this map came to be and Beck’s trials and tribulations to get it approved is a story that has been told many times and, I hasten to add, with great comedic wit and wisdom. I could never come close to doing these prior works justice. Instead please let me point you to some delightful muniments worthy of your time:
One of my favorites is by Darien Graham-Smith who wrote about the History of the Tube Map in his article for the Londonist. In this article you will see the progression from the messiness of the pre-Beck maps to Beck’s 1949 masterpiece.
Another of my favorite history lessons is given by the amazing Jay Foreman who created two delightful 10 minute videos. They are full of acerbic British wit and most definitely a ‘must watch’:
The Tube Map nearly looked very different — Credit: Jay Foreman
What went wrong with the Tube Map? — Credit: Jay Foreman
So how much did Beck’s map influence the rest of the world? You only have to take a look at the official subway maps from around the globe to see:
Subway Maps of Beijing, Delhi, Mexico City and Tokyo
Even the city of Venice has adopted Beck’s style for its official maps of Venice’s water taxi network:
Now while I prattle on about Harry Beck, I’m sure the map purists among you are probably whinging5 that the London Tube Map is not a map, it’s a schematic. Well in the sense that the topography was trounced by topology that may strictly be the case. But the Tube Map accomplished what so many of today’s ‘maps’ fail to do today — distilling the horrible complexity of the real world into the atomic essence of the information you really need. And, let’s not forget, they still depict space, albeit without the equal scale of a traditional map.
But where is it all going?
Well there is one land yet to be conquered — that fair city of the Big Apple, which so far has steadfastly refused to adopt Beck’s non-topographic mantra:
New York City Subway Map — Credit: MTA
And, I’m sorry to say that since Beck’s passing the London Tube Map itself has regressed. Somehow the attractive simplicity of Beck’s finest work in 1949 has now been lost to complexity and incoherence:
London Tube Map in 2023 — Credit: Transport for London
However, in my research I did come across one bright light. This is a map of the roads of the Roman Empire, in what is now a very familiar form:
So, perhaps it is the Romans we should thank after all? 😉
Footnotes:
1 By suffering through my blogs you have to be somewhat astute, or at the very least, patient and tenacious
2 Two things here:
‘Twopenny’ perhaps unsurprisingly means two pence. This was the initial cost of a ticket on this line
To those unfamiliar with proper British pronunciation, ‘twopence’ is actually pronounced ’tuppence’ not ‘two pence’
3 The term ‘Tube’ could also have come from the fact that, well, the tube looks very much like a ‘tube’. It could also have come from the concept of London’s Victorian Hyperloop, run by the London Pneumatic Despatch Company between 1863 and 1874.
A London Tube train emerging from the Tube — Credit: Wikimedia
4 You could argue that Beck’s first map actually dated from his 1931 sketch, drawn in pencil and colored ink on squared paper in his exercise book:
Sketch for a new diagrammatic map of the London Underground network by Henry C. Beck in 1931 Credit: Transport for London and the Victoria & Albert Museum Collection
5 ‘Whinging’ — pronounced ‘winge-ing’ (like hinge-ing) — is British for whining in a particularly irritating way. In other words, it’s much worse than simply whining.
Press releases about Apple Maps don’t come particularly frequently from Apple. If you include last week’s release there have been just four dedicated press releases about Apple Maps since 20161. The prior one was in September 2021, announcing their 3D city maps.
‘Apple Business Connect’ seems like a very specialized topic. Almost too much in the weeds for Apple to stoop so low and give it press release.
So what’s the big deal?
Well, now businesses and organizations are being given the opportunity to “Put your business on the map.”
Put Yourself on the Map — Credit: Apple
Huh, but weren’t all businesses on the map already?
Well, not always.
It turns out getting all those businesses on the map is hard — super hard. And it’s even harder to keep all the information about them current.
Having accurate, complete and up-to-date information about businesses is also absolutely crucial to the success of you map product: it doesn’t matter how pretty your map looks, it’s pretty much useless if you can’t find the organization you’re looking for.
The issue of how hard it is to keep the information up-to-date quickly became apparent with the onset of the pandemic. Restaurants and other businesses were suddenly closed or suddenly had very different operating hours. And it was extremely difficult to keep track of all the changes.
Keeping this information current is a constant struggle for all map makers, and Apple is far from immune.
So how does one even begin to address this challenge?
For you millennials in the audience, let me start with a little history:
Back in the old days we had something called the ‘Yellow Pages’. These were big printed books published by your national or regional telephone company. The yellow pages listed all the businesses in your city or region and complemented the ‘white pages’ which contained the residential listings2.
Yellow Pages were a big business: they generated a ton of advertising revenue for the phone companies. As a business you could buy a block of space — advertising your trade, your shop or perhaps your legal practice. If you really wanted to grab someone’s attention you bought a full page ad at great expense and renamed you business so it started with the letter ‘A’ — or indeed many As — so as to increase the likelihood that your listing was the first a prospective customer would see.
For you Millennials: This is what a Yellow Pages book looked like — Credit: WikimediaA Typical Yellow Pages Ad for a Lawyer Credit: Movie Posters USA
Being big, heavy and expensive to produce the phone books were published just once a year.
In the 1990s, with the advent of mobile phones and the quickly growing popularity of the internet, the business models of the phone companies began to change. The data started to move online. Suddenly the world became awash with something called “Internet Yellow Pages”. Back in their hay day Internet Yellow Pages were a key feature of both America Online (AOL) and Yahoo! The legacy of this era lives on today, for example with “Pages Jaunes” in France, but I’m pretty certain almost nobody uses it.
The issue in the 1990s was the currency of the data. These digital yellow pages were updated using the same low cadence methodology as had been used for decades with the printed yellow pages. The publishers would proudly tell you: “We call every business once per year!” 😱
Moreover, as these companies were making money from advertising, they were far more concerned with getting another year’s revenue from the lawyers, locksmiths & plumbing companies than they were about deleting listings for organizations that were no longer in business. So not only was there a currency issue, there was also a quality issue.
Back in the heady days of the dot com boom in the late 1990s I was one of the people at MapQuest that had to deal with these companies. Let’s just say that they didn’t move at the speed of the internet.
I remember dealing with all the various companies operating in the US at that time — InfoUSA, Dun & Bradstreet and Database America to name just a few — trying to understand their processes and their data quality.
A quote from a salesman at Database America sticks with me still to this day:
“It’s not a question of how good these databases are, it’s a question of how bad they are!”
So what about today? How do mapping organizations like TomTom, HERE, Google and Apple Maps keep their own ‘business listings’ current?
If you dig a little you can quickly find out that they don’t do all the work by themselves. And that’s true even for Google. It’s a massive aggregation and collation of data from dozens and dozens of sources. To get an idea of what sources are used you simply have to find the ‘acknowledgments’ page for each product. For example, here is the acknowledgements page for Google Maps’ business listings and here is the same page for Apple Maps3. These pages don’t list all the organizations that contribute data, but they list many of them.
At its inception Apple Maps relied solely on third parties, the most prominent being Yelp. Unlike Google and unlike Facebook, Apple has never seriously been collecting data about businesses.
That is until fairly recently.
It all started a couple of years ago in the latter part of 2020. Apple Maps suddenly gave users the ability to rate businesses in Australia as well as upload photos. It wasn’t long before this ability was extended to many more countries. This didn’t mean Yelp and other partners were suddenly swept aside, but it was a telltale sign that Apple was beginning to shift towards a homegrown solution.
Of course Google had taken the same approach many years before. It started with Google Local in 2004 and, via a long, winding and horrendously convoluted road, to the launch of Google Business Profile in November 2021:
The Evolution of Google Business Profile Credit: Bluetrain
Due to the enormous popularity of Google search and Google Maps businesses knew that they had to be found on Google and that they needed to be visible on Google Maps. Google didn’t have to do much to encourage businesses to seek out the page on Google where they could provide the information. Today Google Business Profile offers a myriad of options to enable businesses to not only add or correct basic information, but enrich it with details to entice people to visit:
Google Business Profile Marketing Page — Credit: Google
So what is Apple Business Connect?
Well, it’s taken them a while — err, 19 years4 — but it’s actually Apple’s response to Google Business Profile.
Like Google Business Profile you can add your business if it’s not listed, correct information if it’s wrong and enrich your listing with things like official photos, menus, special announcements and offers. The information you provide doesn’t just make its way to Apple Maps, but it also gets shared across the Apple ecosystem to services like Siri. Similar to Google Business Profile, Apple Business Connect also provides access to an analytics dashboard so you can see how users are interacting with your listing.
But here’s the $64 million dollar question: wiil businesses even realize that Apple Business Connect exists?
The problem — of course — is all about mindshare.
In most countries Google Maps is nearly always top of mind5. So much so that many iPhone users will swear to you that they use nothing but Google Maps, but when you ask them to point to the icon of the app they use it turns out it’s not Google Maps, it’s Apple Maps.
So will the owner of Joe’s pizza parlor even even think about Apple Maps, let alone go on a hunt for Apple Business Connect?
I think we all know the answer.
‘No.’
Not unless Apple starts a major campaign to significantly increase the awareness of Apple Maps and Apple Business Connect.
But how?
It’s extremely unlikely Apple would start a massive billboard advertising campaign. Even if they could foist the costs of such a campaign on carriers, I don’t think this would ever happen.
A more logical approach might be to promote Apple Business Connect as part of the Apple Business Essentials, a program which helps organizations optimize use of the Apple devices they use at work.
A conjecture that seems to me to be far more likely, however, is that Apple Business Connect is just the start. The rumor mill has been rumbling about the likelihood of ads coming to Apple Maps. While I have no information to substantiate or refute such rumors, I wouldn’t be at all surprised if Tim and Luca would salivate at the prospect of recouping some of their massive geospatial investments.
Then promoting Apple Business Connect in order to effect more accurate, more complete and more up-to-date businesses in Apple Maps would be easy. They could just make use of unsold inventory.
One thing is for sure, however: Apple Business Connect is not a case of “if you build it, they will come”.
Let’s all stay tuned, ‘cos Apple is going to have to do something big to make your average Joe aware.
2 In some cases there was also something called the ‘blue pages’ for government listings
3 To get to this page on iOS, open Maps, tap the ‘choose map type’ button, then tap on the link at the bottom of the screen: ‘(c) OpenStreetMap and other data providers’
4 Google Local launched in 2004. Apple Business Connect launched 2023.
5 With perhaps the exception of China, Russia and South Korea
In case you didn’t realize, we live in a multiverse1 of global street maps.
It all started back in the early 1980s in the offices of two startups who were both based in Sunnyvale, California. One was Etak, the original pioneer of in-vehicle navigation systems. The other was a little company called Karlin & Collins.
In the case of Etak, founder Stan Honey and angel investor Nolan Bushnell had the vision of building a ground breaking navigation system. Stan knew they had a large number of hard problems to solve in order for the system to be successful, and the need for a digital street map was only one of them. In the very early days Etak somewhat naively thought, “Maps? That’s the easy part — we’ll just get those from the government!”
The Etak Navigator in 1985
Their assumption wasn’t totally lacking judgement.
It turns out that in 1965, almost twenty years before Etak was founded, the US Census Bureau had made the case for building a digital street map of the USA in support of the 1970 census. They called it GBF-DIME. The Bureau was a visionary of its time, realizing that such a map could not only be used in support of tabulating the national census, but it could also be used in many other areas, including education, transportation planning, emergency services and urban planning2.
Unfortunately Etak quickly came to realize that these US Census Bureau ‘stick’ maps didn’t quite meet the requirements of a navigation system. The data contained little information about curvature of the roads and highways were barely digitized. The quality of road connectivity — technically known as its topological correctness — left a lot to be desired. It was this hard reality that became the catalyst for Etak to get into the digital map business.
The other Sunnyvale start-up, Karlin & Collins, got its start in 1985 not because they’d invented a James Bond like navigation system like Etak, but because one of their founders, Galen Collins, had got lost driving in the Bay Area. Collins also saw the value in navigation, but focused on a much harder problem: developing a system that could provide turn-by-turn directions in addition to the map based guidance that the Etak Navigator provided. Collins’ desire for turn-by-turn directions added another whole level to the requirements — not only did you need to collect all the road geometry and street addresses, but now you also had to collect information about turn-restrictions and one ways. GBF-DIME definitely didn’t have that!
While Etak and Karlin & Collins discussed cooperating a number of times they rapidly became competitors.
Both companies realized ‘data was king’ and both companies started digitizing — the same cities, the same neighborhoods, the same streets, the same addresses. Not only in north America, but in Europe and Asia. All at huge expense.
Time moved on.
Etak was sold to Rupert Murdoch’s News Corporation who later sold it to Sony, who sold it to Tele Atlas. Tele Atlas was acquired by TomTom.
Karlin & Collins went through a series of rebrands, first to Navigation Technologies, then to NAVTEQ and later to HERE. HERE is now privately held by a number of corporate investors including BMW, Audi, Mercedes, Mitsubishi, Intel, Bosch and NTT.
The Genesis of TomTom and HERE
Both TomTom and HERE built a global map database. Both developed a successful business licensing map data to automotive OEMs. With the advent of the internet, they also licensed their data for use on the web, initially to a fledgling web mapping company called MapQuest3. The MapQuest site relied solely on map data licensed from third parties. This ultimately became an opportunity for Google. They swooped in, launching Google Maps in 2005. MapQuest was left to atrophy by its new parent, AOL, who remained completely distracted by its acquisition of TimeWarner.
But even Google had to rely on third parties for digital map data. Google initially chose NAVTEQ as their primary source and later switched to Tele Atlas. But on October 7, 2009 Google made what was to be a very significant change. Hidden in their announcement of their new “report a problem” feature for Google Maps was a move that would send shock waves across the mapping industry. Google had dropped the use of Tele Atlas data in the USA and had replaced it with their own map. This thus became the genesis for a third global street map. Today Google Maps continues to maintain a map of the entire planet, albeit relying on the foundation of many third party datasets.
Meanwhile in 2004, around the same time that Google Maps got it start, an English gentleman named Steve Coast became frustrated with the UK’s national mapping agency, the Ordnance Survey. Unlike the US government, who released all their geospatial data for free with no license restrictions, the Ordnance Survey insisted on (significant) license fees. In response Steve launched the OpenStreetMap (OSM) project and two years later established the OSM Foundation. It got off to a slow start, but a few key catalysts gave it the momentum it needed:
Contributions from organizations like AND (now Geojunxion), who provided some basic road networks; integration of US Census Bureau street maps
Access to aerial imagery, providing the necessary backdrop for map editing, initially provided by Yahoo! and later Microsoft Bing Maps
Money. In 2012 Google made the decision to start charging for access to its Google Maps API. The original catalyst for OSM was the lack of free access to good map data. Google’s move to add a paywall to its APIs added fuel to the fire. It precipitated moves by the likes of Foursquare, Wikipedia and AllTrails to switch from Google Maps to OSM. Many others followed.
‘Paid Editing’ — whereby corporations funded enormous numbers of edits to OSM. This started in 2017 and started to mushroom in 2019:
I’m not sure if Steve Coast’s original aspiration was to develop the ‘Wikipedia of Maps’, but it certainly turned out that way. Thanks to all the hard work of its millions of contributors, today OSM provides a beautifully rich global map:
Some nine years after Google launched its own map of the US in 2009, Apple Maps embarked on a similar journey, launching their home grown map of northern California in September 2018. Today Apple Maps has extended its coverage to many countries, including the US, Canada, most of western Europe, Australia, New Zealand, Israel and Saudi Arabia4. Filling in the gaps with OSM and other third party data Apple effectively has its own global street map5.
But it doesn’t stop there. In the enterprise mapping world Esri has not been standing still. Esri created a global atlas which they call the “ArcGIS Living Atlas of the World”. Just like a paper atlas it contains many maps. And included in the list of maps is a highly curated global street map built from collating and aggregating numerous sources from around the world. The purpose behind this map is a little different from the other players. It’s designed to help Esri users get more value out of their investment in ArcGIS. If you want to be cynical — it’s to keep their users in the ArcGIS ecosystem.
So let’s review our multiverse of global maps. In no particular order:
TomTom
HERE
Google Maps
OSM
Apple Maps6
Esri ArcGIS Atlas of the World7
So now we have half a dozen organizations, all creating essentially the same thing. A global street map of the planet.
It’s as though there are six different organizations creating six separate sets of identical roads for you to drive on. Or perhaps it’s like having six different electrical companies, each creating an entirely separate electrical supply network to your house. Is that crazy or what?
But wait — there’s more!
Ladies and Gentlemen, now we also have the Overture Maps Foundation!
Credit: The Linux Foundation
The Overture Maps Foundation (OMF) was officially announced by the Linux Foundation on December 15, 2022 and has Amazon Web Services, Meta, Microsoft and TomTom as founding members. Clearly some heavy hitters. Together they will be “Powering current and next-generation map products by creating reliable, easy-to-use, and interoperable open map data”
Err, so how and why did this happen?
Well, having talked to a few people in-the-know, I think I can distill it down to three things:
Money
Control
Interoperability
Let’s Start with Money…
Building a high quality global street map from scratch is expensive. Super expensive. As I’ve said in prior posts: you had better start with a number greater than 1 that ends with the letter ‘B’. The hard work only starts after you’ve built the map. Now you’ve committed yourself to spending beaucoup bucks to maintain it. Only a very few companies on this planet have the financial means to do this and even they are under extreme pressure.
And as it stands in today’s economic climate pressures are now much, much greater.
Stock prices of all companies in this business have dropped — and for some — precipitously:
See Footnote 8 for Details
For a comparative benchmark consider the fact that the NASDAQ composite has dropped about 35% since its all-time high in November 2021. Alphabet, Apple and Microsoft are in this ballpark, but Amazon, Meta and TomTom have performed decidedly worse.
And there have been some hard realities for each of the OMF founding members:
Microsoft: Back in the Balmer days Microsoft was fairly bullish about mapping (remember ‘Bing Maps’?9). They even started going down the path of building their own map. It wasn’t until Satya took over that reality hit: the bulk of Bing Maps’ assets were sold to Uber. Uber got serious about maps for a while, but then their own reality hit. Let’s just call that one ‘Travis’.
TomTom: Since the heyday of a $2,000 navigation option for your shiny new vehicle, TomTom’s world has been shrinking inexorably. They got some solstice from licensing their data to Google Maps and later to Apple Maps, but now the Google revenue is gone. And Apple Maps is continuing to expand its coverage, so TomTom’s revenue from Apple has got to be shrinking fast.
Meta: For Meta, well, we all know it’s not been easy. 13% of their staff got laid off in 2022. I’m sure that just like Satya, Zuckerberg has no appetite for investing heavily in a global map.
So clearly each founding member must see OMF as a way to combine efforts and reduce costs.
Second Topic: Control…
Clearly Google Maps is a factor. It was a big factor for OSM getting its initial traction. But what’s wrong with OSM? Doesn’t that give these players what they need?
Well apparently not.
The founding members see specific challenges with OSM and they all relate to strategy and control.
While they can have some influence, no one company or group of companies that works with OSM has the ability to:
Direct OSM’s strategy:
what information is mapped, where it is mapped and in what order
Define how information is represented in OSM: each country or region can effectively define their own data models independently of other countries
Set QA processes: OSM leans toward manual and ground-truth verification processes; using input from massive sensor networks (e.g. vehicle sensors) to detect change or map errors is not widely endorsed
Prioritize internationalization: not surprisingly local map editors tend to favor their local language (thus all the German labels in the OSM map of the Berlin Zoo above)
Prevent vandalism: whereby malicious edits make their way into a widely published product
The last one is interesting. It caused a number of companies to get together to launch the Daylight Map Distribution organization which essentially puts OSM data through a data scrubber. Every day millions of contributions are made to OSM by thousands of people and it’s impossible to check everything in-real time. To quote Daylight:
“Some of these contributions may have intentional and unintentional edits that are incompatible with our use cases.”
In other words: they’d cause a major PR headache for any company that used the data.
My favorite piece of map vandalism actually took place not in OSM but in Google Maps, back in 2015. Some enterprising chap contributed the following edit:
Award for the Most Outstanding Piece of Map Vandalism — Credit: Google Maps
The thing is it’s still very difficult to use data built by one mapping organization in another mapping system.
So, for example, the way in which the city of Los Angeles defines, say, their data for building addresses is very different from the way that the city of Turin or Osaka might do it. So somebody building a global map has to deal with all these differences. My analogy is there is no equivalent to a standard shipping container in the geospatial world. As a result everyone is hurting — it’s incredibly inefficient and it’s a huge impediment to progress.
In my mind map-editing tool makers should enforce standard data models on their users — or at least very strongly encourage it — and make it insanely easy for their users to adopt.
But alas, that is not the case.
The resulting pain is thus felt by any organization that wants to use a high quality global map in their product.
So, in summary, the reasons for the birth of OMF seem to be valid and defensible.
But the Question is, Will it fly?
Let me start by pointing out that the ask of OMF is high.
If you want a proper seat at the table — by that I mean a seat on the steering committee — get ready to cough up $3M per year and to dedicate at least 20 full time engineers. If you assume the fully loaded cost of an engineer is, say, $250,000 per year, then you’re being essentially being asked to contribute $8M per year. No small chunk of change even for a wealthy organization.
At the same time, OMF is keeping its focus very narrow: streets, building outlines and basic information about places (or ‘POIs’). As far as I can tell they’re not even focused on street names or addresses at the outset. This narrow focus will increase the chances of success, but will this shallow foundation be enough to be useful? I guess you have to start somewhere.
There’s also the complex question of how the data will be licensed and what effect it will have on potential data contributors. This is too big a topic to cover in this post, but I suspect there will be some practical and very real challenges, particularly around “share alike” clauses.
To succeed OMF is going to need a lot more participation.
I think to increase the chances of success governments will need to contribute data at a frequent cadence and in volume. The good news is there is no membership fee for governments. But this isn’t a case of “if you build it they will come”. Additional incentives will be required as, like everyone else, governments are strapped for resources. Do they have the time or the money to contribute? What’s in it for them? So far OMF has not made any grand announcements about sharing back with the communities it serves.
What about the other global map makers? Will they join?
Esri? My guess is they’re probably the most likely to join. But will they take the lead and get all their government customers to participate? One can only hope.
So my final question is this:
OMF — is it ‘OMFG’ or just a big ‘MEH’?
Grab a drink. Get the popcorn. Let’s all watch and see.
Footnotes:
1 Note: I said ‘multiverse’, not ‘metaverse’
2 See ‘The GBF/DIME System’ published by US Bureau of the Census in 1978. Digitization courtesy Google.
3 At this time HERE was known as NAVTEQ and Tele Atlas was yet to be acquired by TomTom. There was also third company in the mix that licensed map data, Geographic Data Technology a.k.a. GDT. It was acquired by Tele Atlas in 2004.
