Back in the old days1 we had to rely on our brains for navigation. Printed maps solved that purpose: they provided the necessary information so you could locate your desired destination and plot an efficient route to get there. The maps were all about representing road hierarchies — the motorways and highways, the major thoroughfares and the arterial networks. The detailed city maps provided the information necessary for short journeys. For long journeys small scale maps enabled you to plan your route to the nearest freeway or motorway and from there the best exit to reach your destination.
In the US in 1996 MapQuest appeared out of nowhere. Now you could type in your starting point and destination into Netscape Navigator on your desktop computer and get MapQuest to generate the directions. With those at hand you printed them out, put them in your briefcase and you were on your way. (But take a wrong turn at your peril — doing so would get you completely lost.)
A few years later the era of personal navigation devices or ‘PNDs’ arrived. One of these gadgets from Garmin, Magellan or TomTom would set you back many hundreds of dollars, but, boy, was it a stress reliever. No longer did you have to worry about missing that turn on your MapQuest print out and you never had the acute embarrassment of having to roll down your window and admit you were lost.
And for almost 16 years we’ve now had the delight of having a navigation device in our pocket. As a result our navigation anxiety has almost completely been eradicated.
For those of us that lived and worked in big cities or chose not to drive the situation was different but similar. Here you relied on public transportation. Your daily commutes were no different from those who drove: you knew where you were going and no map was needed. But if you had to get across town to an unfamiliar location you’d have to study a different kind of map — a map of bus routes, or more likely, the city’s subway map.
Made brilliant by the wisdom of Harry Beck, subway maps have become the ultimate navigation map by providing the minimum required information in its most simplistic and understandable form. Harry’s unique ability was to abstract just the essential essence of what you needed out of a horribly complex real world. He even took out the geography, realizing that it was the topology that mattered most.
But as those navigation apps in our pocket have continued to evolve even subway maps have become less relevant.
I remember visiting Madrid shortly after Apple Maps launched public transport directions in 2018. It was a godsend. No longer did I have to worry about trying to remember directions and train changes — I could just let Apple Maps guide me — even to the best exit from the subway station. My stress level and navigation anxiety was completely dissipated.
So given these apps do all the calculations for you, my question to you is this: how often do you actually peruse a map? And a follow up question: if you do peruse maps, what, exactly, do you use them for?
I put it to you that a ‘consumer’ map’s utility today is far, far less than it was 30 years ago.
But is it dead?
Well to answer that question let’s start by looking at the definition of what a ‘map’ actually is. Perhaps we can agree that the source for a good definition might be the National Geographic Society. This illustrious organization defines a map as being “a symbolic representation of selected characteristics of a place, usually drawn on a flat surface”.
I think the important key words here are “symbolic representation”.
Today’s subway maps are “symbolic representation” in its rawest form. Printed road maps are another good example. They show the important information necessary to help you make the decision on which roads to take. If it’s been a while, as a refresher, take a look at this map from the Rand McNally road atlas below. It’s completely true to National Geographic’s definition of a map: it’s almost entirely symbolic:
But if you look at consumer mapping apps today you’ll notice a new trend. It’s a trend away from symbolic representation and a trend towards recreating reality.
It started with Google’s Street View which launched in 2007. Clearly not a map. It continued in 2012 with the launch of Apple Maps and its Flyover feature. Again, clearly not a map. More recently we’ve seen this trend accelerate. In the last year Apple Maps has been launching detailed street maps that include (some rather delectable) three dimensional renderings of the key buildings in each city:
And earlier this year Google announced something called “Immersive view” which in their blog they characterize as a “A more immersive, intuitive map”.
But, going back to National Geographic’s definition, is Google’s Immersive view a ‘map’? With the greatest respect2, I would say no, absolutely not.
It’s all part of a trend, a downward trend in my opinion, that will result demise of consumer maps. Contrary to Beck’s approach to distill reality into its essential essence we’re moving in the opposite direction.
We are instead on a path to the dreaded metaverse, a virtual world where we should all be thankful and glad to wander around as legless avatars with the aspirational goal of reaching social media nirvana. I don’t know about you, but, ugh.
But surely there must still be a case for a consumer map, a map in the true sense of the word, as defined by National Geographic?
We’ve seen the need for navigation maps decline, which is fine. There’s nothing wrong with that trend. As a result consumer mapping companies are desperately trying to backfill that need by hoping they can help people “explore and discover”. Read Google’s blog and you’ll clearly see this is their ambition. And Apple Maps is no different with their push to re-create reality with detailed and colorful 3D models. But I don’t think either are quite succeeding in their lofty goal.
Google tells us:
“With our new immersive view, you’ll be able to experience what a neighborhood, landmark, restaurant or popular venue is like — and even feel like you’re right there before you ever set foot inside”
Well that may be their desire, but I think they will find that after they’ve spent gazillions of dollars launching Immersive view (and gazillions more maintaining it) the general reaction will be “it looks pretty” and, after a while “meh” — and the young ones will promptly move on to a TikTok video promoting a new donut store.
But what could these organizations do to build a better, more informative map, in a true sense of the word, instead of just focusing on recreating reality?
Well I think Hoodmaps is one site that is showing the way. The serial entrepreneur Pieter Levels created the site back in 2017. Pieter has had a history of creating different and entertaining products3. Hoodmaps was Pieter’s project to crowdsource information about neighborhoods and put it on a map. Pieter built Hoodmaps on his own in just four days.
Looking at Hoodmaps for any city and you quickly get a feel for the land (click on the image to visit the site for that city):
I don’t know about you but I think Hoodmaps has more or less nailed the characteristics of neighborhoods in these two cities. I encourage you to explore your own cities and draw your own conclusion.
Similar to the work of Harry Beck, Hoodmaps successfully distills down valuable and immediately comprehensible information about an area into an easy to understand map. Yes, you can argue about the design and the cartography — but the idea is spot on.
Think what could be done if a mapping organization dedicated a (small!) team to soup up Hoodmaps’ concept and then brought in their huge audiences to make it really resonate.
Well it turns out that Google is attempting this with a new feature they plan to launch in the coming months called ‘neighborhood vibe’. The general goal is to help you understand what’s popular with the locals. As the feature has yet to launch it’s difficult to say if they’ll achieve their objective, but judging from the screenshots it doesn’t look like they’ll be as raw, punchy or informative as Hoodmaps. But we can all hope.
If you’ve been in the mapping business as long as I have I’m guessing that you too will decry the lurch away from maps to this focus on recreating reality. Maps were invented for a reason — they reduce a complex world into something you can easily understand. A virtual reality can be very pretty, but it’s also just a photo on steroids — it doesn’t necessarily extract and present those golden nuggets of information you’re looking for. A map, however, can do that and it can do that extremely well.
So, are consumer maps dead?
For all of our sakes, I sincerely hope not.
1 Before the navigation systems were pioneered by Etak in 1985, before consumer mapping on the web was invented by MapQuest in 1996 and before Google Maps launched in 2004
2 For a British to American translation please see this handy guide
3 Pieter’s latest foray has been to create AvatarAI.me. It allows you to quickly create 100s of AI generated Avatars of yourself. Pieter made US$100,000 from the project in the first 10 days. For more information on Pieter check out Levelsio on Twitter
Before we delve into history1, let’s start with a multiple choice question:
When do you think the first road map was created? Was it:
The answer is, of course, none of the above.
The first road map of significance is arguably a map commissioned by the Emperor Augustus Caesar (63BC – 14AD). Augustus had his son-in-law, Marcus Agrippa, embark on a mapping project that took nearly 20 years to complete. The result was a map that stretched from Middle East all the way back to Britain. Like many of the maps the Romans created at that time it had multiple purposes. Maps were used both to conquer lands and to administer their vast Empire. But like most maps today, they were also used for commerce.
What was interesting about Agrippa’s map was its sheer scale. It measured almost seven meters long (~22 feet) and is 34 centimeters high (~1 foot). So it’s a linear scroll that somewhat conveniently rolled up for reference on long journeys. While it was a distorted format it still showed all the important details: the key settlements, the roads connecting them and distances between each settlement. I should emphasize that the geographic scope of the map was vast: it covered the entire Roman Empire as well as the near east, India and Sri Lanka. It even indicated the location of China.
Alas the map is long gone, but a copy was made in c1250AD and still exists today. It is known as the ‘Tabula Peutingeriana’ and is preserved at the Austrian National Library. It is considered one of their greatest treasures:
Rather than have me feebly attempt to describe this amazing work, I strongly encourage you to watch this 5 minute video from the BBC. I was was dumfounded — and I think you will be too:
But what of more recent maps?
Well let’s fast forward to the year 1500AD. It was then a map of Central Europe was developed by the compass maker and physician Erhard Etzlaub (1460–1532). It is the first known German road map. This was the era of the pilgrims and 1500AD was special — it was designated the ‘Holy Year’. In that year the pilgrims were expected to make their way to Rome and this map was specifically designed to help them find their way. It showed the routes to take and mountains to avoid. Perhaps, then, this is where we got the term “All Roads Lead to Rome”?
Moving on another 200 years, yet another advance in mapping was made in Britain. It was there in the year 1675 that a chap called John Ogilby published a seminal work called ‘Britannia’ — ‘an illustration of the Kingdom of England and Dominion of Wales; by a geographical and historical description of the principal Roads thereof’.
Ogilby only started map making in the latter part of his life. Prior to that he was a dancer, then he became director of Dublin’s theater. Returning to England in the 1640s he went on to translate and publish Aesop’s fables. He set up a printing shop in London which he used to produce a number of works that included travel guides and traveller’s tales. But in 1671 King Charles II commissioned him to make ‘a particular survey of every county’. What’s interesting about Ogilby’s Britannia is that it takes the form of a strip map, rather like a pre-cursor to the TripTik maps made popular by the American Automobile Association (AAA) in the 1930s. It was Ogilby’s atlas that set the standard for using 1760 yards for the mile, and a scale of one inch to the mile.
The first significant road atlas of the United States was the “A Survey of the Roads of the United States of America,” by Christopher Colles of New York in 1789:
Larry Printz describes the efforts of Colles in his excellent article for Hagerty: “Where the first automative maps roadmaps came from”. He explains:
“Despite such distinguished customers as George Washington and Thomas Jefferson, the effort faltered because there was little use for road maps in the United States. Most trips were short, made by locals who already knew where they were going. And besides, inner-cities roads were paved. Venturing any farther meant traversing unpaved, unmarked roads. Federal highways didn’t exist. Finding your way took time, patience and luck since most roads were originally trails carved out by wild animals or Native Americans.
It’s little wonder that until the early 20th century, traveling between cities was done mostly by rail, not by carriage.”
In 1901 a businessman and car enthusiast Charles Gillette from Connecticut created a series of maps called ‘The Automobile Blue Book’2 which covered the northeastern US from Boston to Washington DC. A few years later in 1906 the American Automobile Association (AAA) became the official sponsor of the Blue Book which dramatically increased its circulation. It wasn’t until 1911 that AAA produced its first interstate map, “Trail to Sunset,” a booklet of strip maps detailing a route from New York to Jacksonville, FL:
Now if you grew up in the US and were born before 1980 you might be wondering about Rand McNally. They actually got their start in 1868 producing railroad tickets and in 1872 railroad maps3. Their first road map wasn’t published until 1904. In 1907 they assumed publication of the Chapin Photo-Auto Guides — which were super cool — basically it was Google Street View or Apple Look Around about 100 years ahead of its time:
As automobiles and paved roads became more pervasive many other publishers got into the game. In Europe one of the most famous was Michelin. Their first publication, “Guides Michelin” for France, came out in 1900 which was several years before AAA and Rand McNally published their road maps.
What of today? Alas, most young ones can barely read a paper map, let alone know how to use one (street index anyone?). Paper road maps still do exist though! In case you don’t believe me… see the image of the latest Rand McNally road atlas below.
One really cool thing about Rand McNally’s road atlas is that it’s an atlas of the future — in this case for 2023! I’m not sure how the clever people at Rand do this, but perhaps the folks at Google Maps and Apple Maps could take note?
1 Warning to those of you born after 1990: smartphones have not always been ubiquitous. Before their invention one had the laborious task of having to refer to something called ‘printed maps’ to determine locations and routes to get there.
2 Not related to the Kelly Blue Book.
3 You can read more about Rand McNally’s history here
- World History Encyclopedia
- Jeremy Norman: History of Information
- Guinness World Records
- British Broadcasting Corporation (BBC) — especially for this video
- German History Intersections
- The British Library
- Larry Printz for his article “Where the first automotive roadmaps came from” on Hagerty.
- The David Rumsey Map Collection
- Rand McNally
First, for those of you who are not accustomed to the ‘American Way’, a short introduction to gerrymandering:
The term is used to describe adjusting voting district boundaries to create an unfair advantage for a particular party or group. Basically it’s a way to enable a minority of the population to win control of government.
The term gerrymandering is named after the American politician Elbridge Gerry who was the 5th vice president of the USA under president James Madison from March 1813 until his death in 1814. Prior to becoming vice president Gerry was the governor of Massachusetts.
In 1812, while Gerry was governor, the Jeffersonian Republicans forced a bill through the Massachusetts legislature to rearrange voting district lines to assure them an advantage in the upcoming senatorial elections. Apparently Governor Gerry only reluctantly signed the law. One of the districts was compared to the shape of a salamander, but when a particularly influential editor at the time saw it, he is said to have exclaimed: “Salamander! Call it a Gerrymander!”
As a result a cartoon-map depicting this district appeared in the Boston Gazette on March 26, 1812:
Ever since the term has had a negative connotation — indicating corruption of the political process.
So how does this nefarious process of gerrymandering work? Well let’s look at a simple example:
When creating voting districts there are certain basic rules put in place that prevent overtly egregious boundaries. For example, it is common to require that each district have equal population, thereby preventing the voters in one district having more influence than another. For US Congressional Voting Districts the variation in population is generally held to less than 1%. Obviously populations change over time and so countries use information gained from a national census as input to redraw the boundaries. In the US this happens every 10 years.
Continuing with a hypothetical example, let’s imagine that the requirement is to create 5 voting districts from a set of 50 precincts. Let’s assume each precinct has equal population. More importantly let’s assume we know the voting characteristics of each precinct — i.e. whether the voters in each precinct would vote for the ‘Purple’ party or whether they would vote for the ‘Orange’ party. Given these assumptions here are two different ways to draw the boundaries that result in entirely different outcomes:
If you think this is all rather academic and can’t possibly happen take a look at some of these contortions in 2022 US Congressional Districts. Now, I’m not saying that contortion implies gerrymandering, but it sure looks weird to me. And sorry @Texas — you win the prize for the most convoluted shapes:
[Huge credit to Alasdair Rae for providing the maps above. Alasdair is an internationally recognized mapmaker, data analyst, author and visual storyteller. Formerly a Professor of Urban Studies and Planning in the UK, he now runs Automatic Knowledge, a UK-based data, analysis and training company.]
What’s worse is attempts to create disproportionate outcomes based on race. The site All About Redistricting, is a great resource to learn more about the whole redistricting process. It has this to say about the various ploys used to achieve discrimination:
In redistricting, one ploy is called “cracking“: splintering minority populations into small pieces across several districts, so that a big group ends up with a very little chance to impact any single election. Another tactic is called “packing“: pushing as many minority voters as possible into a few super-concentrated districts, and draining the population’s voting power from anywhere else.
In the US discrimination like this is in theory prevented by Section 2 of the 1965 Voting Rights Act. However, this may all be upended in a new case being considered by the current session of the US Supreme Court, Merrill vs. Milligan. The London Guardian wrote about the case just a few days ago:
Merrill v Milligan concerns Alabama, where Republican lawmakers want to draw up congressional district maps that would give Black voters the power to send just one African American member to Congress out of a total of seven representatives, even though Black Alabamans make up a quarter of the state’s population. The map was blocked by three federal judges who ruled that it was racially discriminatory and that the state had engaged in racial gerrymandering.
In its brief to the supreme court, Alabama effectively invites the conservative justices to make it virtually impossible to challenge racial gerrymandering. Should the state’s view prevail, challengers would have to show that racial discrimination was the primary intent behind how district lines were drawn.
“That’s a very hard standard to prove,” said Paul Smith, senior vice-president of the Campaign Legal Center. Should the supreme court side with Alabama, Smith added, “it would allow legislatures to undo Black and Latino-majority districts and do away with the opportunity of people to elect their own representatives”.
The case was argued before the Supreme Court on October 4. It will be some months before the outcome is known.
So, given all this, how exactly does one go about creating voting districts in the first place? It must have been incredibly laborious to do it all by hand back in the days of Elbridge Gerry. Today of course we have technology at hand — and not just any technology — we have mapping technology!
According to Ballotpedia there are six packages designed for specifically governments1:
Software Package Developer Backend Technology Autobound Citygate GIS Esri ArcGIS Auto-Redistrict opensource opensource DISTRICTSolv ARCBridge Esri ArcGIS Esri Redistricting Esri Esri ArcGIS iRedsrict ZillionInfo ZillionInfo Maptitude for Redistricting Caliper Caliper Credit: Information from Ballotpedia
I was fully expecting the websites for these products to be emblazoned with colorful ads, perhaps something like this:
Alas — they are all quite boring and only talk about how they can be used to help in creating plans that “meet legislative requirements”. However, I have no doubt that any of these tools could be misused. One other note: none of them mention AI or ML. No doubt that’s coming though. I can only imagine what it will bring.
So is gerrymandering just a phenomenon limited to the US? And what can be done to prevent it?
“Canadian reapportionment was highly partisan from the beginning until the 1960s,” writes Charles Paul Hoffman in the Manitoba Law Journal. This “led to frequent denunciations by the media and opposition parties. Every 10 years, editorial writers would condemn the crass gerrymanders that had resulted.
Eventually, in 1955, one province — Manitoba — decided to experiment, and handed over the redistricting process to an independent commission. Its members were the province’s chief justice, its chief electoral officer, and the University of Manitoba president. The new policy became popular, and within a decade, it was backed by both major national parties, and signed into law.
Independent commissions now handle the redistricting in every province. “Today, most Canadian ridings [districts] are simple and uncontroversial, chunky and geometric, and usually conform to the vague borders of some existing geographic / civic region knowable to the average citizen who lives there,” writes JJ McCullough.
“Of the many matters Canadians have cause to grieve their government for, corrupt redistricting is not one of them.” Hoffman concurs, writing, “The commissions have been largely successful since their implementation.”
Implementing independent, nonpartisan commissions in the US is more complex. The decision is made at the state level, not the federal level. And I guess certain states (both Democratic and Republican) are perfectly happy to have the fox guard the hen house.
Again from Andrew’s article:
”There are no truly nonpartisan redistricting commissions in the United States,” political scientist Bruce Cain of Stanford University told me in 2014. Iowa uses a nonpartisan agency that’s not permitted to take party registration into account, but it still gives final say to the governor and legislature.
If all this leaves you rather depressed there is one ray of hope. A recent report by David Leonhardt in the New York Times “finds that the House of Representatives has its fairest map in 40 years, despite recent gerrymandering”.
I’ll leave you with a quote from Bernard Grofman and German Feierherd in a Washington Post article from 2017:
However, in most other countries, legal challenges [to voting districts] are limited, and there is not the same concern for strict population equality.
So perhaps the problem all boils down to lawyers? Ah, but then that’s a whole other topic, isn’t it?
1 There are other packages but they are designed more for the general public and educational purposes.
- Alasdair Rae for providing the maps of the US 2022 Congressional Districts used in this post.
- The US Library of Congress for their image of the Gerry-manner in the Boston Gazette.
- Professor Justin Levitt and Professor Doug Spencer for their detailed and informative site: “All About Redistricting”
- US Department of Justice: “Section 2 of the Voting Rights Act”
- SCOTUSblog: “Merrill v. Milligan”
- Ed Pilkington, The Guardian: “US supreme court to decide cases with ‘monumental’ impact on democracy”
- Ballotpedia: “Redistricting apps and software available for the 2020 cycle”
- Andrew Prokop, Vox: “How Canada ended gerrymandering”.
- David Leonhardt, New York Times: “Gerrymandering, the Full Story”
- Bernard Grofman and German Feierherd, Washington Post: “The U.S. could be free of gerrymandering. Here’s how other countries do redistricting.”
- Wikimedia and all its contributors
I grew up in a world of stick maps — street centerlines and roughly digitized curves. All topologically correct, but crude. This was the absolute minimum required to power the pioneering Etak Navigator back in 1985.
At Etak we took extremely simplistic digital map data from the US Census Bureau, called GBF/DIME files1, and using the information from the paper maps published by the US Geological Survey added shape, topology and any other missing data we could find. This was an extremely labor intensive process. At its peak we had about 36 workstations and ran 24×7 shifts. It took us years to get there, but eventually we digitized the whole of the US and much of Europe.
Fast forward to today’s world and organizations that want to make a map have it much easier, but it’s still really hard. If you want to own the map you can’t just copy Open Street Map. Just like Etak did in 1985 you have to start from scratch. But thanks to Gordon Moore and his law you now have a night-day-day technology advantage. And in the US at least you can start with the US Census Bureau’s TIGER files2 which are a tad more shapely than their GBF/DIME file predecessors.
You can lease a large fleet of vehicles, equip them with expensive cameras and LiDARS, drive all the roads and vacuum up all the data. But while this will get you a lot, it won’t get you everything. You’ll get lanes, street signs, traffic lights, speed limits and maybe if you’re lucky some addresses or businesses. But you won’t get post codes or administrative areas. Or rivers. Or golf courses. Or indoor maps. Or 3D building models.
And of course all this won’t come cheap. Plan on a budget that starts with a number greater than one and ends with a ‘B’.
At the end of it all you’ll have a beautiful map. But that construction your vehicle passed when it was collecting data? Well that was changing the intersection from a four way stop sign to one controlled by traffic lights. Your beautiful map is now out-of-date. Sucker!
Maintaining a general purpose map that is used for finding locations and turn-by-turn navigation is hard. Really hard. Believe me — I lived through it at Etak, at MapQuest and at Apple Maps. Even supposedly simple things like keeping speed limits up-to-date is horribly hard. There are four million miles of drivable road in the US alone. Your private fleet is not going to be able to drive the entire network every day, no matter what the market cap of your company is. It’s just not tenable.
So now let’s switch gears. Let’s up the ante. Let’s talk about creating a map not just for finding stuff and getting there. Instead let’s talk about a map to support autonomous vehicles. Now you really have to be on top of your game. Just about every company in the autonomous vehicle business will tell you that you need something called an ‘HD Map’ or high-definition map. It’s like the general purpose map from Google Maps or Apple Maps but with excruciatingly more detail and centimeter perfect accuracy.
There’s a ton of money pouring into the HD Map business. According to a report from MarketsAndMarkets, it’s projected to reach US$16.9B by 2030. The theory is that you absolutely need an HD Map to support Level 3+ autonomous driving system. The difficulty of producing an HD Map is illustrated by the fact that autonomous vehicles are commonly limited to specific geographic areas. This is partly due to climate — operators want to reduce risks of failure due to sensor obfuscation from road grime, but it is also due to the fact that the vehicles need an HD Map and those HD Maps are expensive to produce and so have very limited coverage.
I’m not an expert in autonomous systems, but I suspect many of them rely on a method of differentiation to operate. By that I mean the autonomous systems take what the vehicle sees and dynamically compares that to the HD Map as a reference. They use this comparison to deduce (1) where the vehicle is, (2) where it can go and (3) what’s around the vehicle that is not part of the map, for example other vehicles and objects.
Back in 1985 when I was at Etak we used to joke that the Etak Navigator would be like the introduction of the electronic calculator. Just like calculators eliminated humanity’s ability to perform arithmetic in their head, the Etak Navigator would eliminate humanity’s ability to remember how to get from A-to-B. Sure enough this prophesy has turned out to be completely true. My wife reminds me of it constantly — “Why do you need directions home? Don’t you know where to go?”
Etak used a system of cassette tapes to store the map data. We imagined cars roaming around aimlessly at the edge of our map coverage — their owners completely lost due to having no EtakMap. The brains inside many autonomous vehicle systems are like these poor owners of the Etak Navigator — they’d be completely lost without an HD Map.
So the big question is this: if it takes billions of dollars to maintain a plain Jane general purpose map, how can organizations possibly build and maintain an HD Map?
The theory is that eventually your personal vehicle will collect data as well as navigate. So if you’ve bought that snazzy new Waymo Bubble Car it will vacuum up data while it’s driving you around — and that data will help keep the HD Map current.3
Clearly the issue is scale. Today no member of the public owns a Waymo. Waymo operates 25,000 vehicles, but how often do you see one drive down your cul-de-sac? Not as often as an Amazon van I suspect. There are simply not enough vehicles in these dedicated fleets to collect the necessary data for an HD Map and, more importantly, keep it current.
But there is one way out of this conundrum.
What if your system doesn’t rely on an HD Map?
As I said in the title of this post — this is very much a religious question — and it’s the folks at Tesla who have a completely different religion. They don’t use an HD Map. This means that, in theory at least, their vehicles aren’t limited to drive in a particular area.
If you haven’t already watched the recent video from Tesla’s AI Day 2022, I strongly recommend you do so. It’s quite technical, but it will give you a very clear idea of how Tesla thinks — or to use Steve Jobs’ phrase: how Tesla “thinks different”.
If you distill everything Tesla talked about in their presentation — humanoid robots, full self driving methodologies and home-grown supercomputers — I think you will come away with the takeaway that Tesla is fundamentally about two things:
- Tesla is about scale
- Tesla is about efficiency
This is demonstrated by their effort to produce a humanoid robot called Optimus, which I predict is destined to become the Model ’T’ of robots. Yes, as predicted, the technical media immediately scoffed at Tesla’s efforts, saying it was nothing like what you see from Boston Dynamics. But Boston Dynamics started their dream 30+ years ago! 4 Tesla has been working on Optimus for just 13 months.
So the reaction is a little like the initial reaction to iPhone. I would be somewhat cautious about immediately dismissing their efforts.
Tesla is focused on building Optimus out of cheap, readily available materials — no carbon fibre for example — and they plan to manufacture it using techniques they’ve learned from making Tesla vehicles. Elon Musk predicts they could ultimately produce millions of units with each one costing less than a car. This is an example of how Tesla focuses on scale.
Tesla is also leveraging everything they’ve learned from their other work to speed development. This will help them leapfrog everyone else in the industry. For example, they’re using their full self driving software to give Optimus the brains it needs to navigate indoor spaces. This is an example of how Tesla focuses on efficiency.
If you look at Boston Dynamics by comparison, they’ve done some very impressive work, but now they have significant challenge ahead of them. They don’t have Tesla’s high volume manufacturing prowess, nor do they have Tesla’s autonomous navigation expertise, nor do they have a high volume factory floor that they can use to test and refine their robots at scale.
But let’s get back to the question of HD Maps:
For full self driving, Tesla uses a map, but to use their words, it’s a “coarse road-level map … this map is not an HD Map”. This ‘coarse’ map is used in combination with vision components built from vehicle camera data to dynamically derive lane connectivity in real time.
By choosing not to rely on HD Maps, Tesla has undoubtably chosen a much harder problem to solve as their vehicles have no theoretical ‘ground truth’ to compare to. But assuming their approach is successful it should result in a much more capable, intelligent and independent system that doesn’t have the extreme cost burden of building and, more importantly, maintaining an HD Map.
Tesla still spends an enormous amount of time, energy and money to process information used to train their neural networks, particularly the neural networks used for what they call “auto labeling” 5. Where does all this training data come from? Well of course a lot of it comes from all those Teslas driving around — there are now about 2 million Teslas on the road today. Given Tesla’s fleet is orders of magnitude greater than anybody else’s autonomous fleet they can further accelerate away from their competition. (And now they’re going to do the same in robotics.)
The cost of Tesla’s differing approach is still significant. For example, it includes the development of Tesla’s Dojo supercomputer to solve the massive parallel computing problems of processing petabytes of data. Their supercomputer architecture is significantly different from conventional approaches that traditionally amass banks of GPUs. As a result it provides significant efficiency gains. I don’t see GM, Toyota or VW developing a brand new supercomputer architecture like this anytime soon. Perhaps NVIDIA? We shall see.
I suspect there will be other benefits to Tesla’s approach, some of which even Tesla has yet to anticipate or realize.
It’s ironic, but if anyone could build and maintain an HD Map that covers a large geographic area, like the USA or Europe — and keep it super current — it’s Tesla. They’re the only ones that have a large enough fleet to do it.
I pity other manufacturers. It’s going to be insanely hard to keep up.
In the meantime we’ll see who ultimately wins this religious battle.
1 GBF = Geographic Base File; DIME = Devilishly Insidious Map Encoding. Credit: Marv White
2 TIGER = Topological Illusion Generating Extensive Rework. Credit: Marv White
3 In the interim traditional OEMs hope that third parties like Intel’s Mobileye, Toyota’s Camera and Nvidia’s DeepMap will help them collect data from the cars they already sell. However, the issue is most of their vehicles just don’t have the cameras or sensors to collect the required data at the quality that is needed.
4 See https://www.bostondynamics.com/about: “We began the pursuit of this dream over 30 years ago, first in academia and then as part of Boston Dynamics”
5 This is the process of automatically identifying and categorizing features and objects that the cameras in Tesla vehicles see as they drive.
The Birth of Coordinates
As I considered the list of Map Happenings that Rocked Our World, it seemed obvious that the next most significant event after the invention of the first map was the invention of coordinates.
However, after I did some digging, I was a little surprised. The geographic coordinate system of latitude and longitude was invented way, way before the simple x, y, z coordinate system (also known as the Cartesian coordinate system).
Personally I would have thought some bright gal or bloke might have invented a local coordinate system using x and y for their village or town way before some other bright person invented a system for plotting your location on the entire world. But no, apparently that was not the case.
The concept of latitude and longitude was essentially invented by a Greek chap called Eratosthenes (c. 285-205BC). He wrote on a wide range of subjects — including mathematics, geography, astronomy, poetry and music theory. In about 245BC the King of Egypt, Ptolemy III, appointed him to be the chief librarian at the Library of Alexandria. Eratosthenes’ work included inventing the first global projection of the world and inventing the concept of parallels and meridians — thus lines of latitude and longitude.
One very interesting fact about Eratosthenes is that he is usually credited for inventing the term ‘geography’ and founding the science of geography. How about something like that on your résumé, eh?
But wait there’s more!
Eratosthenes’ other achievements include being the first to accurately calculate the circumference of the earth and the earth’s axial tilt, inventing the armillary sphere and for developing a simple algorithm for finding prime numbers. His masterpiece was a (now mostly lost) three volume work called “Geography” and an accompanying world map.
So what happened next?
Well let’s fast forward to the first century AD and the Roman province of Syria. It was there that the geographer and mathematician Marinus of Tyre was born. Like Eratosthenes, Marinus wrote a treatise on geography. Alas his work is lost and is only known due to being referenced by other scholars. But Marinus did some awesome work to advance coordinate systems:
His main legacy was assigning latitude and longitude coordinates to thousands of places — essentially he built the world’s first gazetteer. But this was well before the time when Greenwich, England was the prime meridian1. Marinus compiled a map of cylindrical projection, thereby also inventing the equirectangular projection. For longitude he designated the zero point to be the Fortunate Isles which passes through the Canary Islands (now ~ 14°1’W). At that time the world was thought to stretch from the Fortunate Isles in the west to China in the east. He designated the island of Rhodes in Greece as his zero reference point for Latitude. This is perhaps not surprising as Marinus lived in Rhodes.
Oh, one other thing — Marinus also coined the term Antarctic — that being opposite to the Arctic.
Yup, clearly another outstanding résumé.
Next up: another superstar geographer — Claudius Ptolemy2 (c. 100-170AD). Like Eratosthenes, Ptolemy lived in Alexandria and like Eratosthenes was also multi-talented: a mathematician, astronomer, astrologer, geographer and a music theorist. During his life he devoted most of his energy to astronomy, but geography was far from ignored. His seminal work in this area was “The Geography”, an eight volume masterpiece. Building on Marinus’ work he extended the gazetteer to over 8,000 places.
But it was Ptolemy who suggested that the lines of latitude be divided into degrees and minutes. The equator would be defined at 0 degrees and 90 degrees north at the North Pole. Lines of longitude were divided into 180 degrees east and west of a prime meridian, which, like Marinus, he kept at the Canary Islands. Ptolemy thus laid the foundation for all modern maps we use today. His work was so highly regarded it was copied and referenced for many hundreds of years and it was still influential in the Renaissance. Alas no manuscript of the Geography survives from earlier than the 13th century.
In the 18th century most countries in Europe adopted their own prime meridian, usually through their capital. Hence in France the Paris meridian was prime, in Germany it was the Berlin meridian, in Denmark the Copenhagen meridian, and in the United Kingdom the Greenwich meridian. It was the work of Nevil Maskelyne (1732-1811) that eventually tipped the balance towards Greenwich. Maskelyne was the fifth British Astronomer Royal. Between 1765 and 1811 Maskelyne published 49 issues of the Nautical Almanac based on the meridian of the Royal Observatory in Greenwich. This almanac was the first to contain data dedicated to the convenient determination of longitude at sea. Even the French translations of this almanac continued to reference Greenwich as the prime meridian.
In October 1884 the Greenwich Meridian was selected by the delegates to the International Meridian Conference held Washington, DC to be the common zero of longitude throughout the world. The French argued for a neutral line, but eventually even these cheese-eating surrender monkeys abstained.
Thus history was made.
But what of today?
Well a number of organizations have developed their own coordinate systems, with varying success. Countries did it to help them increase the accuracy of land surveys3. For example, the British have the Ordnance Survey National Grid and in the USA there is the State Plane Coordinate System.
On the commercial side various organizations are valiantly trying to wrench people away from latitude and longitude. Why? Because it’s really difficult to designate a precise, easily-to-remember designation for a location when that location has no address. And it’s certainly very difficult to remember long strings of numbers.
There are two companies active in this space that I’ll highlight: one is Google and the other is an organization called What3Words.
- Google’s approach to solving this problem is to assign six character alphanumeric codes to every place on the earth called Plus Codes. For example the Great Pyramid at Giza is located at X4GJ+98. Each individual code represents about a 14 meter by 14 meter square, which is about half a basketball court.
- What3Words’ approach is a little more ‘cute’, assigning, you’ve guessed it, three words to each location. So in their system the Great Pyramid of Giza is located at ‘rooms.collect.denim’. It’s also available in 50 languages, so the same location in Greek is “κανονικά.καλώδιο.φώτα”. What3Words is a little more precise than Plus Codes and has a resolution of about 3 meters.
While Google has open sourced Plus Codes they’re frankly still struggling to get traction. Personally I don’t know anyone in casual conversations that has given me, asked me for or even talked about Plus Codes. Unfortunately I think the general public has little or any awareness of their existence. But they exist in plain site. Go check Google Maps.
What3Words has been more successful — a number of well known organizations have adopted it. Even the whole country of Mongolia has adopted it. But What3Words has another problem. Google doesn’t need to make money from Plus Codes because it’s not central to their business. But coordinates are What3Words’ only business — so they have to make money. Unless some big rich company decides to buy What3Words out of the goodness of their heart (unlikely) — it’s going to remain a closed, proprietary system. As a result many organizations, particularly tax funded organizations, will continue to be trepidatious.
Both Google and What3Words tout their systems as being valuable for first responders and emergency services, and while it can help, it’s not always a rosy picture. I must credit Nick Heer at Pxlnv.com for pointing out the limitations. Nick stumbled across a catalogue of how What3Words is insufficient for emergency use: see What 3 Words is a Mess. Not a pretty picture. However, I’m also guessing there are probably as many good stories as there are bad and nobody’s put a similar catalogue together either.
But will these systems ever get adoption? I can’t see Google Maps using What3Words as it competes with Plus Codes. And I’d be very surprised to see Apple Maps adopting either system. I’m guessing the producers of other popular mapping apps in use around the world feel the same way. Time will tell.
Well that’s about it for this week — but there is one more thing…
Remember my earlier reference to the simple x, y, z coordinate system, also known as the ‘Cartesian’ coordinate system?
The coordinate system we commonly use is called the Cartesian system, after the French mathematician René Descartes (1596-1650), who developed it in the 17th century. Legend has it that Descartes, who liked to stay in bed until late, was watching a fly on the ceiling from his bed. He wondered how to best describe the fly’s location and decided that one of the corners of the ceiling could be used as a reference point.
Imagine the ceiling as a rectangle drawn on a piece of paper: taking the left bottom corner as the reference point, you can specify the location of the fly by measuring how far you need to go in the horizontal direction and how far you need to go in the vertical direction to get to it. These two number are the fly’s coordinates. Every pair of coordinates specifies a unique point on the ceiling and every point on the ceiling comes with a unique pair of coordinates. It’s possible to extend this idea, allowing the axes (the two sides of the room) to become infinitely long in both directions, and using negative numbers to label the bottom part of the vertical axis and the left part of the horizontal axis. That way you can specify all points on an infinite plane.
One last tidbit before we part ways: the word ‘Cartesian’ is an adjective meaning “relating to Descartes and his ideas”.
There you go.
1 The prime meridian, also known as the zero meridian is the origin or zero point of longitude.
2 Not to be confused with King Ptolemy III
3 The earth is not a perfect sphere. As a result compromises have to me made in global coordinate systems that reduce accuracy. A dedicated local coordinate system can compensate for this imperfection and increase the overall accuracy of a land survey.
Credits and Acknowledgments:
- Cameron McPhail for his 2011 thesis “Reconstructing Eratosthenes’ Map of the World: A Study in Source Analysis”
- Chris Maeder at CivilGeo.com for his article Tales in Geography: Early Cartographers Shape Modern Mapping
- Nick Heer at Pxlnv.com
- Telemachus Odysseides for his wonderful site, Greatest Greeks, in particular his piece on Marinus of Tyre
- Wild.Maths.Org at the University of Cambridge, England
- Wikimedia and all those who contributed to Wikipedia.
First let’s talk about assholes.
It’s basically a primer designed to mentor people building companies and what you should look out for along your journey. It’s an easy read. I highly recommend it to anyone who is trying to build a business. One of the chapters in Tony’s book is about the various types of assholes you’ll meet along the way. In this chapter he does a splendid job of categorizing them into the following basic types:
- Political Assholes: the risk averse assholes who take credit for everything and who are focused only on reaching the top
- Controlling Assholes: the micromanaging assholes who strangle all creativity
- Asshole Assholes: the aggressive or passive aggressive assholes who suck at work and suck at everything else
- Mission-Driven Assholes: these are the ‘good’ assholes who are unrelenting and crazy passionate about the product. But they also listen. Yes… Mr. Jobs was a mission-driven asshole
So all this got me thinking.
Could the world of location harvesters and personal information brokers — or as I like to call them, “PIBs” — also be classified into various types of assholes?
Call me a chicken, but I suddenly hear the many lawyers I’ve come to know and love over the years whispering warning signals in my ear…
So on second thoughts maybe I’ll just leave the job of asshole classification to you lot.
Now of course much has already been written about location harvesting and location privacy. It’s been the topic of many articles and many blogs and I’m sure there’s more to come. Rather than regurgitate past articles I thought I would at least draw your attention to various ‘Happenings’ in this world. Hopefully I’ll provide a little bit of something you didn’t already know. Perhaps I can also provide an additional perspective.
So let’s look at the spectrum — from the good to the bad to the ugly.
And let’s start with the ugly.
I’m sure you’d agree that ugly part of location harvesting is surveillance. I’m assuming you’ve all read about misuse of Apple AirTags to track people and the emerging legislative efforts to prevent it so I’m not going to cover that in detail here.
Instead I did want to draw your attention to an organization called Fog Data Science that has been singled out by the Electronic Frontier Foundation (EFF). They provide “a proprietary platform [that] analyzes billions of commercially available location signals to provide insight into digital device locations and movement patterns”. Their typical customers are law enforcement agencies. To quote EFF:
Fog Data Science is a company that purchases raw geolocation data originally collected by applications people use every day on their smartphones and tablets. Those applications gather location data about where your phone is at any given moment and sell it to data brokers, who in turn sell it most often to advertisers or marketers who try to serve you ads based on your location. That’s where Fog swoops in. According to documents created by the company, Fog purchases “billions of data points” from some “250 million devices” around the United States, originally sourced from “tens of thousands” of mobile apps. Then, for a subscription fee that many law enforcement agencies are happy to pay, Fog provides access to a massive, searchable database of where people are located.
This means that police can open up their Fog map and do a number of things. They can draw a box and see identifiers representing every device within that geographical area at a given time frame. They can also use a device’s ID to trace that device’s precise location history over months or even years. Fog does not require police officers to obtain a warrant or other court order before acquiring this location data (unlike communication service companies that hold their customers’ location data and generally do require a court order). Likewise, many police departments that use Fog do not require their officers to get a warrant.
Just catching the bad guys? Or is this potentially very ugly?
Ok, so this is what happens, but let’s go into how the data get built…
Well, just about any mobile app developer on the planet wants to get analytics on how, when and where their app is being used. As an app developer you don’t have to develop your own analytics software, you can simply leverage a third party API — or strictly speaking — an SDK 1. Many app developers also want to generate advertising revenue, and of course there’s an SDK for that too. No surprise, but these SDK providers don’t provide you the SDK just for your benefit. They provide it primarily for their own benefit. They suck all the usage information out of the apps that use their SDK into one giant data “lake”. This can equate to trillions of locations, each with its own time stamp and device identifier.
That’s a ton of data.
But wait, there’s more.
Back in the old days, Personal Information Brokers, or PIBs, used to rely on data collected from national censuses to provide demographic data down to the city block level. Over the years these data have been refined and expanded with tons of other data, for example, information on financial transactions, product registrations, warranties, loan applications — the list goes on. Not only is the data now hugely enriched it’s now available at the household or even the individual level. These companies now know an enormous amount about your income & age, your immediate family, your lifestyle and your spending habits.
Here’s the thing though: the advent of mobile devices has brought a seismic revolution to this data marketplace.
No longer are you limited to just getting personal information on where people live. Now you can pick any location and get personal information on the people that are there now. Or even information on the people that are predicted to be at a location at some point in the future. And you can even tell where they came from and predict where they’ll go next.
How is this done?
One way to accomplish this is to take all the location data and timestamps mined from the app SDKs to see where devices spend the night. Bingo. Now you can marry the location data and device ID to the detailed income, age, lifestyle and spending habit data for a particular household or individual. And now all of that demographic data can travel with the ID of the device.
In other words: you now know the demographics of the people at any location at any time.
Because there is so much money in the advertising business there are a ton of companies piling into this location information business2. One such company is Placer.AI. Others include Foursquare, Near, PlaceIQ, SafeGraph, Unacast, and Veraset. In Placer.AI’s case you can take any location, for example, a shopping center, and get information on the visits by time period, the aggregated demographics of the visitors, where they came from and where they went to afterwards:
How is this data being used? Well one example is outdoor advertising, a.k.a. “out of home” or OOH advertising. More specifically it is being used for electronic billboards.
Using all the location data mined from apps — which is now married to rich demographics — billboard owners can not only tell how many people pass a billboard everyday but they can also tell the demographics of the people that pass it by time-of-day. So, they can run one ad on Monday morning to match the demographics of the people on their morning commute and a different ad on Saturday afternoon to match the demographics of the weekend traffic. And if you’re standing by a screen, say at a bus stop, the screen can use the SDK running inside an app on your phone to show you an ad that’s personalized to your device ID.
If you want to learn more, I suggest you read this great article in Consumer Reports from Thomas Germain: “Digital Billboards Are Tracking You. And They Really, Really Want You to See Their Ads.” Thomas explains:
When we go out into public, we are often surrounded by screens showing ads. They can be on the side of the road, at the gym, in store windows, in doctors’ offices, and in elevators. You might assume that the marketing messages are playing on a loop, but sometimes these ads are changing because people like you are nearby.
Data including your gender, age, race, income, interests, and purchasing habits can be used by a company such as Five Tier to trigger an advertisement right away. Or, more often, it will be used for planning where and when to show ads in the future—maybe parents of school-age children tend to pass a particular screen at 3 p.m. on weekdays, while 20-something singles usually congregate nearby on Saturday nights.
Then the tracking continues. Once your phone is detected near a screen showing a particular ad, an advertising company may follow up by showing you related ads in your social media feed, and in some cases these ads may be timed to coordinate with the commercials you see on your smart TV at night.
It doesn’t stop there. Advertisers are keenly interested in “attribution,” judging how well a marketing campaign influences consumer behavior. For instance, is it better to target people like you with online ads for fast food right after you see a restaurant’s new TV commercial, or to wait until after you drive by a new billboard the next day? The advertising industry looks for the answers by watching where you go in person, what you do online, and what you buy with your credit card.
It doesn’t stop there.
There are two additional ways you might be tracked:
- Ultrasonic Beacons in Ads: Ads on any TV, any radio or electronic displays can embed ultrasonic sound waves that humans can’t hear. But your device’s microphone can hear them just fine. Coded in the sound waves are data telling your device what ad is playing. The SDKs running in the background in that app your downloaded are happily listening out and transmitting the information back to a server. Now the advertiser knows how many people heard the ad, where it was heard and can also deduce the demographics of the people that were in the vicinity at the time. A lot of the tech for this seems to have been pioneered by a company called Silverpush about 10 years ago. They claim 150+ brands use them, including, eh-hem, Apple. So the technology is not new. Kaveh Waddell at the The Atlantic wrote a good article about it back in 2015: “Your Phone Is Listening—Literally Listening—to Your TV”.
- Facial Recognition in Stores: so here’s the concept: you go to a TV/appliance store. While you’re there you linger in front of the latest Samsung TVs. At the same time a camera is watching you and assigning an ID to your face. A little later you pick up a charger for your phone and head to the cashier to buy it. The cameras are still watching you. When you make the transaction the store now has your personal information from your credit card. Eureka! Now they can match that face ID to you. Now they know you lingered in front of those Samsung TVs. Nice. So now you can enjoy the ads for Samsung TVs with that special discount coupon when you get back home. For further reading take a look at Kim Hart’s article in Axios: “Facial recognition surges in retail stores”.
If you find all this rather depressing and you haven’t totally given up there are a few things you can do:
- On Apple devices you should definitely “Ask app not to track”.
- I’d recommend the location setting for most of your apps be “Only when using”.
- Don’t grant apps access to things they shouldn’t need. For example, does a weather app really need access to your microphone?
- Got a Smart TV? Make sure you check those information sharing settings very, very carefully. Better yet, don’t connect your TV to the internet. Instead use a third party device like Apple TV, Google Chromecast, Amazon Fire stick or Roku.
[Somebody from the land of Android — please chime in with some additional suggestions…]
Is there any good that can come from location harvesting?
Well it turns out yes — absolutely there is. Let me end with two good examples:
- Disaster Recovery: disasters happen all too often. When they do the First Responders need to know where people are, and more importantly who’s been left behind. A very visible example of this was Hurricane Katrina in New Orleans. But it’s not just in America. It’s a global problem. As I write this Hurricane Fiona is battering Puerto Rico and Typhoon Nanmadol is battering Japan. And then there are wildfires, earthquakes and wars. In all cases the people and organizations responsible for public safety are desperate to get a clear and current understanding of where people are or, as they call it in the industry, a ‘situational awareness’. Companies and organizations in the location harvesting business should look to do some good and not just focus on figuring out how to make the next buck. It’s very difficult for disaster response organizations to get a clear picture – the big companies that have this data don’t have any products designed to provide the needed information. I’ve experienced this having worked for a few large mapping organizations during my career. In the event of some disaster organizations would call, sometimes in desperation, to see how we might be able to help. There was never a clear answer. No product. No easy solution. Organizations have to go begging to anyone they can think of who might be able to help. To give them their due it is normally the cellular carriers that end up coming through. But still I’m not sure they have a readily available product for these situations. So, while there is tremendous potential there is clearly much more work to be done. I would respectfully suggest that the big boys — Apple, Google, Esri and the many hundreds of carriers of the world — step up to the plate and create a product that would benefit humanity. And yes — it can be done in such a way that doesn’t compromise privacy. @Google in particular: to a certain extent you do this already, it’s just not a product for first responders3
- Transportation Planning: this is a lesser example than Disaster Recovery, but nonetheless still very valid and important. I’m sure you’ve all been stuck in traffic jams. Additionally there have probably been times when you’ve wished there was a public transit stop or route where one didn’t exist. Or perhaps you pine for a dedicated bike lane on a particularly busy street. Addressing these concerns is the work of transportation planners globally. Getting the information they need to make data informed decisions is hard. Really hard. In a perfect world they’d be able to mine anonymous, aggregated data feeds from the organizations that have it so better decisions could be made. It’s the same situation as for disaster recovery — the data exist, it’s just not in a form that’s easily consumable by the organizations that need it. Kudos to Strava for getting into this business with their Strava Metro product. Ditto Uber for the Uber Movement product. But what about those big boys in Cupertino and Mountain View?
What’s stopping location data from being widely used for these two important cases? Mainly I think it’s cold feet from the big tech companies. They don’t want to be seen as sharing personal data, particularly with governments. But it’s happening anyway (see Fog Data Science). Big tech should figure out a way to enable location sharing for the general good in a non-creepy way. They’re smart — I know they can do it. Society as a whole would benefit.
Thanks for reading this far. I look forward to your commentary…
1 Good article from Shanika Wickramasinghe: “SDK vs API: What’s The Difference?”
2 I found this great chart in an article from Jon Keegan and Alfred Ng in the Markup: “There’s a Multibillion-Dollar Market for Your Phone’s Location Data” listing 47 companies in the business. It’s about a year old, so there’s probably more companies to add to the list:
3 Note Google’s “busyness score” in Green Park near Buckingham Palace in London during the Queen’s funeral on Monday. Note the unusual spike:
When you’re using an online map as a consumer, one of the things you will undoubtedly do is search for places — or as the mapping industry likes to call them ‘places of interest’, ‘points of interest’ or ‘POIs’ 1.
In performing these searches there are generally two factors: one factor is almost always proximity and the other is commonly quality.
Unless it’s something mundane like a fuel station this quality factor is incredibly important, especially for categories like restaurants, hotels and service providers.
But have you ever gone to a restaurant with tons of great reviews and then found yourself totally underwhelmed… questioning why you even went there in the first place?
Maybe it’s just my curmudgeonly self, but I’m guessing I’m not alone in this regard.
Apple Maps and Google Maps have been adorned with ratings and reviews for places pretty much since their inception. After an initial affair with Zagat’s, Google quickly went their own way and built their own home grown rating system — almost destroying Zagat’s in the process. For many years Apple Maps took a partnering approach, first with Yelp, and then later with organizations like TripAdvisor, OpenTable, Booking.com and LaFourchette2 . More recently you’re starting to see Apple’s own rating system creep gingerly into the picture, so it’s becoming a real melting pot.
The fundamental problem with all these rating systems is that none of them take your own preferences into account, so the ratings you see are this amorphous, unwieldy glob of data that provides little information that is tuned to the individual. Ipso facto you get good recommendations for crappy places and you may also get poor recommendations for places you actually think are quite good.
This, in my mind at least, makes all the rating systems pretty much useless.
Now of course there are work arounds:
- Work around number one: invest copious amounts of time delving into the reviews, and in doing so trying to pull out little nuggets of information that might indicate someone has said something that resonates with your own tastes or concerns. At the same try to guess which of these reviews were actually written by a human and whether or not someone was nefariously incentivized to submit the review in the first place. Ugh. If you’re like me you probably don’t have the energy to do this, especially as there’s no guaranteed success after expending all the effort.
- Work around number two: ferret through curated reviews from publishers. Now here you’re onto something perhaps a little more reliable. If you happen to know a place on one of these curated lists then you can use that nugget to deduce whether you trust their recommendations as a whole. So, for example, if a particular hotel is highly rated and you agree with their rating then the level of trust you can put in the rest of the ratings from that publisher might increase. Conversely, if the highly rated hotel was, in your opinion, ‘meh’ or an armpit then you can probably ignore this entire set of recommendations and treat them as places to avoid. The fact that Apple Maps incorporates dozens of curated guides from well known publishers provides useful fodder for this work around.
But it all kind of sucks.
So what have organizations done for rating systems in other industries?
If you look at the big, nefarious world of online retail — and Amazon in particular — then it used to be that you’d see “People who liked X also liked Y”. However I see that this approach is now being replaced by advertising (which also sucks):
If we look at music, Apple Music has something called “Similar Artists”, but it’s not clear what algorithm they use to determine the recommendation. I’m not a Spotify user, but it does appear they might do a better job at providing recommendations.
In the accommodations world, Airbnb doesn’t appear to offer any recommendation features other than generic ratings and reviews. In the restaurant world OpenTable now has a “news for you” feed, but like Amazon’s product recommendation feature it seems to be driven by promotional advertising.
Moving over to social media networks it gets a little more interesting. By having the ‘follow’ concept built right into their foundations these networks provide a framework to get recommendations from people who you know or at least trust in some way. For example, perhaps your friend Yevgeny might create his favorite list of Paris restaurants and share it as a guide on Instagram3. Like Julia Moon who uses TikTok, you might stumble across a particularly enticing video about some new doughnut shop, but frankly this a approach still a bit of a crap shoot and it all takes time and effort.
It seems that only Snapchat comes close to a genuinely useful concept — it combines the set of people I know, their activity (and therefore their likes) with a map, specifically a Snap Map. But again it’s not perfect as it all takes energy to sleuth out where my friends are going and what they’re doing. And the whole thing breaks down completely if I elect not to participate in social media networks.
What I’m really asking for is the perfect automatic recommendation machine that takes zero energy and is not sullied or adulterated by advertising.
So is there a solution or am I in fairy tale land?
Well perhaps I might posit one approach based on a classic Venn diagram:
There is the set of places that I like. If you combine that set of places with another person’s set of places that they like then they might overlap. So, for example, we might both like some of the same restaurants in London. But I might also like some restaurants in Paris that the other person does not know. Equally the other person might have liked some restaurants in Berlin that I don’t know. So the system recommends the Paris restaurants I like to the other person and recommends the Berlin restaurants that I don’t know to me.
I think the parlance for this kind of analysis is called ‘collaborative filtering‘.
The approach has several advantages:
- You don’t have to be a member of a social network
- You don’t have to know anything about the people from whom the recommendations are drawn
- You don’t have to spend endless time and energy rummaging through reviews trying to determine their pedigree
- It maintains privacy — other users don’t get to see my lists of favorites
- The recommendations are inherently personalized based on the set of people who have similar tastes to you.
Now obviously an actual system would need to be more complex and would need to process a ton of data. But in today’s world that’s not out of the question.
Why has nobody taken this approach for a rating or recommendation system for places?
Perhaps the reason is that there simply isn’t enough raw data from which to provide any useful recommendations.
To solve the data volume problem you need to make it super easy to rate places. Apple is doing this a little bit with their new rating system in Maps. Opentable encourages ratings after you’ve been to a restaurant booked via their platform. Airbnb does something similar. Google Maps has it even easier— they have so much mindshare that businesses themselves encourage you to rate them on Google. So surely there must be enough data?
Perhaps, like online retailers, companies are now much more interested in chasing advertising dollars than providing useful recommendations.
The lack of any decent recommendation system for places feels like I’m missing a wheel on my bicycle. I can’t get anywhere useful.
So in closing I have a number of questions to ask the Map Happenings audience:
- do you feel the same way?
- why is collaborative filtering not used more broadly?
- is there a better solution than collaborative filtering?
1 I recently rented a Nissan Murano and was surprised to see their navigation UI used the term ‘POI’. Are you kidding me, Nissan? Do you really expect consumers to know the meaning of that TLA?
2 Now part of TripAdvisor
3 Although the Yevgeny I know would never, ever do that because he wouldn’t want to let the world know about his secret culinary haunts.
The First Map
Now, before we get started, let me set the record straight. I am no historian, and certainly no map historian. My degree was in computer science. For the most part history classes did not get my full attention, although there were one or two exceptions.1
As a result I generally know diddly squat about history, so you’re just going to have to put up with that.
However, as I got thinking about the key Map Happenings that rocked our world, it became quickly obvious that the very first one had to be about the invention of the first map. So a dive into ancient history was unavoidable.
So, the question is, who started all this nonsense?
As you can imagine it turns out to be a somewhat difficult question to answer. There’s lots of information on the topic of early maps and, as as you’d expect, many differing opinions as well as quite a few disputes. We’ll get to all that in a moment.
The main problem is that these historical records don’t answer the question — there’s absolutely no way we can know who created the first map.
So let’s look at this in a different way.
If you think about any map let’s answer the fundamental question of what it is trying to achieve. I think the answer to this question is simple: it’s trying to communicate information. More importantly I think it’s trying to communicate information by providing an abstraction of the real world. So if we can all agree on this for a moment, let’s now think about it in the context of the human race. When did humans first become able to understand the concept of an abstraction?
If you can answer that question then it’s quite likely that around the same time some rather smart chap or chapess happened to take a stick, draw some lines in the dirt, and proceed to explain to their mates where to some find some juicy goodies. That, I believe, was the likely dawn of the first map.
To find out the answer as to when this might have occurred let me draw your attention to a rather good article from 2002 in the New York Times: “When Humans Became Human”, written by John Noble Wilford. In it he provides a brief overview of human history starting 2.5 million years ago and covers the various intellectual debates about the dawn of human creativity.
One point of view is that there was some sudden genetic advance and that in turn caused creativity to appear suddenly2. This is proposed in a book “The Dawn of Human Culture” by Dr. Richard G. Klein, a Stanford archaeologist:
In [Dr. Klein’s] view, 40,000 years ago was the turning point in human creativity, when modern Homo sapiens arrived in Europe and left the first unambiguous artifacts of abstract and symbolic thought. They were making more advanced tools, burying their dead with ceremony and expressing a new kind of self-awareness with beads and pendants for body ornamentation and in finely wrought figurines of the female form. As time passed, they projected on cave walls something of their lives and minds in splendid paintings of deer, horses and wild bulls.
It was around this time that the first known paintings were created, the oldest of which was a cave painting of a pig from some 45,000 years ago.
In my mind paintings are like maps and require abstract thought — although one could argue that maps require a little more abstract thought than a painting. I’m willing to bet that the first map was created somewhere around the same time as the first paintings.
But if we don’t know exactly when the first map was created or who created it, the question then becomes: “What do we know about early maps?”
The first known map might be the Çatalhöyük3 cave painting from 6,200BC near Konya in Turkey. It was discovered by James Mellaart in 1963. It is thought to depict a volcanic eruption around a Neolithic village:
Here’s what the painting is thought to represent:
As is common with ancient artifacts there is some controversy. Is it a map, or is it just a painting? Some think it might just be a drawing of a leopard skin. If you’d like to learn more, I suggest listening to this three minute article from National Public Radio.
A second contender for the first known map is a particular Babylonian clay tablet from around 2500BC. This was found in the ancient Mesopotamian city of Ga-Sur which is near Kirkuk in Iraq. According to the Mughal Library this clay tablet has been generally accepted as “the earliest known map”. It was unearthed in 1930:
Small enough to fit in the palm of your hand (7.6 x 6.8 cm / 3” x 2.5”), most authorities place the the date of this map-tablet from the dynasty of Sargon of Akkad (2,300-2,500 B.C.); although, again, there is the conflicting date offered by the distinguished Leo Bagrow of the Agade Period (3,800 B.C.). The surface of the tablet is inscribed with a map of a district bounded by two ranges of hills and bisected by a water-course. This particular tablet is drawn with cuneiform characters and stylized symbols impressed, or scratched, on the clay. Inscriptions identify some features and places. In the center the area of a plot of land is specified as 354 iku [about 12 hectares], and its owner is named Azala.
A third contender for the earliest known map is the Turin Papyrus Map which is an ancient Egyptian map, and according to Wikipedia is generally considered the oldest surviving map of topological interest from the ancient world. It depicts gold mines in Egypt’s eastern desert and was drawn about 1150BC, and so it is actually the earliest known geologic map. If you’d like to learn more I’d suggest reading this article from National Geographic. The map is on display at the Egyptian museum in Turin:
The oldest surviving map of the world is likely the Imago Mundi which is on display at the British Museum in London. It depicts the Mesopotamian world with Babylon in the center and dates from 700BC to 500BC. Contrary to American geography, Babylon was actually about 50 miles / 80 km south of Baghdad and is not located on Long Island.
So there you have it:
- First map: likely a scratch in the dirt, probably about 45,000 years ago
- Earliest known map, candidate 1: Çatalhöyük cave painting in Turkey from around 6,200BC
- Earliest known map, candidate 2: Babylonian clay tablet from Iraq, created around 3800BC – 2500BC
- Earliest known geologic map: Turin Papyrus Map from Egypt, created around 1150BC
- Earliest known map of the world: Imago Mundi from Iraq, created around 700BC to 500BC
There is just one more thing …
If you’re into old maps and happen to be in the San Francisco Bay Area I suggest you visit the David Rumsay Map Center at Stanford University which opened in 2016. Since the early 1980s David Rumsay has collected more than 150,000 rare maps from the 16th through 21st centuries. The Center contains maps and atlases in addition to interactive, high-resolution screens for viewing digital cartography. Sadly I’ve yet to visit myself, but I hear it’s tremendous. You can also see some of David’s collection by visiting his own web site.
So that’s this week’s Map Happening. I hope you enjoyed it.
Stay tuned for the next exciting episode.
1 There were two exceptions that I found quite gripping:
Exception 1: the British burning the White House in 1814.
Exception 2: the Second Battle of Canton, fought by the British and Chinese in 1841. The battle was triggered by a severe trade imbalance between the British and the Chinese. The British were importing tons of tea, silk and porcelain from China. To equal this trade the British exported opium to China. According to Wikipedia:
The number of people using the drug in China grew rapidly, to the point that the trade imbalance shifted in [Britain’s] favor. In 1839 matters came to a head when Chinese official, Lin Zexu, tried to end the opium trade altogether by destroying a large amount of opium in Canton. In response to Zexu’s actions, in January 1841 the Royal Navy bombarded Chinese positions near Canton and landed troops ashore in several locations. Local officials surrendered and signed peace treaties with the British.
2 An alternate point of view is expressed by Professor Ian Hodder, also from Stanford, but from the Department of Cultural and Social Anthropology. He wrote a book called “Consciousness, Creativity, and Self at the Dawn of Settled Life”. In his book Professor Hodder test the claims of cognitive revolution and argues that when the data are examined there is little evidence for it.
3 Pronounced “cha-tal hay OOK”
As you might already know I worked at Apple Maps for a little while. I had the privilege of joining the team back in October 2013 and I finally elected to part ways earlier this year. When I arrived in 2013 things were in a pretty sorry state and there was a huge mountain of work to be done. Actually — several mountains.
Back then Maps — as Apple calls the team internally — was pretty much the laughing stock. The product got grilled and rightly so. Tim Cook had to issue an apology and Scott Forstall, the SVP in charge of iOS software and responsible for Maps, got fired.
At the same time some of the criticism was actually pretty hilarious. One particularly acerbic piece of wit came from someone at Transport for London (TfL). For those of you not familiar, TfL often provides very specific information and advice in each London Underground station. In September 2012, shortly after the release of iOS 6, this sign was spotted in a tube station on the Victoria line:
My favorite criticism, however, came from a fantastically creative group called Puppet Shed Films who created a sumptuous parody of the Apple Maps Flyover feature.
Flyover allows you to, well, fly over city scapes using high resolution photography draped over 3D models. Today Flyover is actually very good, but unfortunately in its original form the 3D models were a bit, shall we say, melty. This resulted in some very peculiar and unintended effects like bridges that dissolved into rivers.
Puppet Shed picked up on this and developed this masterpiece video:
All joking aside things have changed quite a lot since 2012…
Ten years later Apple Maps is not too bad.
Now, before someone on the Maps team lynches me and inflicts grievous bodily harm, I should hasten to add that when I say “not too bad” I am writing in English, not American. If you speak English as a second language — and, yes, I include all of you Americans in that group — then you may not be aware that English people tend to take pride in the understatement. So “not too bad” in English roughly translates to “pretty fucking good” in American.
[Side note: If you ever struggle with understanding what British people mean then I advise that you refer to this rather useful translation guide. It’ll go a long way to keep you out of trouble. I’m not sure who wrote it, but whoever did deserves a medal.]
“What do you mean Apple Maps is pretty fucking good?” I hear the fans of the Mountain View mapping app and that popular cartoon mapping app say.
Well, let’s just agree that it’s come a long way. It now has some pretty useful features — some of which are now better than the competition. But let’s also agree that while the accuracy and reliability of Apple Maps has been vastly improved there’s still work to be done.
And like any product there are nooks and crannies that people don’t always know about.
So with that in mind, let us now switch to a few tips that I use all the time …
Tip Number 1: Quickly Flip Maps Between Dark Mode and Light Mode
Personally when I use iOS I much prefer dark mode1. I’m guessing many of you feel the same way.
However, when you select dark mode it also automatically flips Apple Maps to dark mode too.
Now I don’t know about you, but while the aesthetics of dark mode maps are lovely, for an aging curmudgeon like me they are a tad more difficult to read. Unfortunately there is no separate setting in Maps that allows you have light mode maps and keep dark mode for the rest of iOS.
So what is one to do?
Well, here is a workaround:
Go to Settings > Control Center and add Dark Mode to the list of Included Controls:
Then, whenever you want to quickly switch from dark mode to light mode in maps, just swipe down from top right and tap this button:
Voilà! You’ve switched iOS from dark mode to light mode — and therefore also switched maps from dark mode to light mode too. When you’re done looking at Maps you can quickly switch it back.
Tip Number 2: Create & Share Lists of Your Go-To Places
For some time now Apple Maps has included curated guides from well known publishers around the world. They’re pretty cool and they can be quite informative and useful. Maps has also given you the opportunity to create and save favorite places like home or work (not that ‘work’ may ever be a true favorite of yours I realize).
But what you can also do is create your own lists of places, or to use Apple Maps vernacular — your own Guides. I have a number that I’ve set up in Maps myself:
To create one simply tap the “add to Guides” button on the place card for a business or landmark. You can then very easily add the place to an existing Guide or create a new Guide:
The cool thing is it’s also very easy to share these personalized guides once you’ve created them.
I did this only the other day. One of my good friends was on her way to London, so I shared the list of my favorite London restaurants with her:
Guides can easily be created, edited, renamed, deleted and shared in Maps on iOS, iPadOS or macOS. On macOS you can also easily drag and drop places from one Guide to another.
Number 3: Use Siri to Effortlessly Share Your ETA
Now some of you may already be familiar with the “Share ETA” feature in Maps. In iOS 15 you get to it by tapping “Share ETA” when in navigation mode.
Now when you’re in the midst of driving this can be a bit of a pain, particularly if the person with whom you want to share your ETA is not in the pick list that Share ETA provides.
So, instead you can use Siri.
Now, personally I hate Siri. Well, I don’t hate Siri per se, I just hate having to say “Hey Siri!” I wish Apple would allow you to rename the wake words as Alexa does… then I could issue a command like Captain Kirk: “Computer!”, or perhaps if I’m feeling particularly grumpy: “Oy, Bozo!” 2
The workaround for this dilemma is of course simple: press and hold the crown of your Watch for a second or so, or on iPhone, the right side button. Siri will then thankfully activate without you having to utter those nauseating wake words.
With Siri thus engaged you simply give the command to share your ETA, for example: “Share my ETA with Tina!”
There are two nice things about this feature:
- First it will automatically give the recipients updates if you get delayed, for example, due to traffic
- Second, the feature plays nicely with Android users: they’ll be sent SMS texts instead of Maps notifications
So that’s this week’s Map Happenings. I hope you enjoyed it.
Thanks for reading and stay tuned for future episodes!
1 Settings > Display & Brightness > Appearance
2 This of course raises the whole ethical topic of AI assistant abuse. I’m sure we’ll see much discourse on this topic going forward. John Gruber has already mused on this: “I like saying thanks to my AI assistants. My wife thinks I’m nuts. But I worry we, collectively, are going to be dreadfully rude to them by the time they’re essential elements of our daily lives.”
So Ron Knox recently wrote a great piece for the Atlantic: “Amazon’s Dangerous New Acquisition”.
In it he covers Amazon’s planned purchase of iRobot, the maker of Roomba, and talks about how we should be concerned about Amazon’s latest foray:
Owning Roomba would give the world’s most dominant spy-tech maker yet another portal into our homes and lives. It could map where we live, what we own, and what it should be selling to its hundreds of millions of captured customers
The latest Roomba models capture information that Amazon, at the moment, doesn’t have access to. iRobot’s new operating system maps the floor plan and contents of the spaces in which it operates. The vacuums are now equipped with a camera so it can respond to commands like “Clean in front of the couch.” But that means it knows what kind of couch you have—and crib, and dog bed, and so on.
His piece dovetails nicely with my recent post on indoor maps.
Yes, in case you didn’t know iRobot’s latest vacuums automatically create an indoor map of your home. Here’s an image courtesy of iRobot’s website:
So, let’s now put on our super-paranoid hat and riff on this idea a little, but in jest of course…
Let’s say our mission is simple: make as much money as possible so our largest shareholder can hand out even more sub-orbital joy rides to his friends and family.
And let’s take a quick inventory of some of our successes so far:
- Global domination of online retail: Check. Achieved with Amazon.com
- Global domination of product search: Check. Also achieved with Amazon.com
- Global domination of warehousing & logistics: Check. Achieved with Amazon.com infrastructure
- Global domination of delivery: Check. Well on the way.
- Global domination of compute utilities: Check. AWS
So continuing with this Dr. Evil theme, what else could we dominate?
Some of you have opined that Amazon is clearly going after global domination of the home. After all, consider their products and acquisitions so far:
- Amazon Alexa
- Amazon Ring people monitors, err, I mean doorbells
- Amazon Blink security cameras
- Amazon Eero routers
- Amazon Fire TV Sticks
- Amazon Prime Video
- Amazon Music
Now the acquisition of iRobot helps complete the picture, right? Not only do we get Roomba robot vacuums and Brava robot mops but we also get Aeris air purifiers.
But I put it to you that Amazon’s strategy is not about dominating the home. Yes, sure, selling you some more gizmos for your house can help fund more sub-orbital joy rides and perhaps another very expensive yacht that won’t fit under a bridge, but that’s not where the true value is.
The value is in the data — and this value could be substantial.
I believe it was Clive Humby who originally coined the term “Data is the new oil” back in 2006. In 2011 Peter Sondergaard, formally of Gartner, took the concept further: “Information is the oil of the 21st century, and analytics is the combustion engine.” 1
So, now let’s say you’re anointed to be the new product manager in charge of global domination of data. More specifically you’ve been put in charge of a new product called “Complete View” which of course is designed to get a complete view of every consumer on the planet.
So with your company’s existing assets you’ve got some great ingredients. You already know what people are buying, when they are buying it and at what frequency. You know what music they listen to and what TV they watch. And from the home routers you know what people look at online. And you also know when people are at home, when they are away and when someone comes to their front door.2 Pretty cool.
The missing component?
You don’t have a map to stitch it all together and add another dimension to your data.
But if you vacuum up indoor maps via Roombas you can create a much more holistic picture for your Complete View product. From system setup and room designations you can learn which room is which. You can even record who sleeps in each bedroom from the users who want to enable commands like “Alexa, go clean Timmy’s room!” And of course from the camera you can associate objects with rooms as well as their current condition.
So now with the data tied to rooms and potentially also to individuals you can start to analyze this treasure trove to your advantage.
Imagine fast forwarding a few years and experiencing the following scenario: you’re sitting comfortably on your couch and suddenly Alexa perks up and says:
“I just wanted to let you know, your Aeris air purifier noticed a high concentration of ScaryPox-25 in Timmy’s bedroom. Do you want me to get Timmy a doctor’s appointment at Amazon One Medical? I can order an Amazon Zoox to pick Timmy up at 11am and take him to the doctor when you’re back from your errands!”
“I noticed the couch in your living room is looking a bit ratty. I found a great couch sale that’s going on right now. Do you want me to make some recommendations?”
So if Amazon were to create such a data product, what would they do with it? Would they compete with traditional data vendors?
The market for this kind of data is actually very mature and perhaps ripe for disruption. Organizations like Claritas have been in this kind of data business for almost 50 years. They can tell you where people live, their demographics, their psychographics, their lifestyles and their lifestage. And they can tell you about spending patterns and habits. And it’s all tied to location. One of the ways in which they do this is through something they call “segmentation”. Claritas’ segmentation product is called PRIZM and it’s actually rather fun:
If you live in the US (or know somebody that does), enter your ZIP code into Claritas’ web site here. Try it, you’ll enjoy it I promise!
The result you get is a breakdown of the different categories (or ‘segments’) of people that live in that ZIP code. The segment names do a wonderful job of describing what kind of people live where. For example in the infamous Beverly Hills ZIP code, 90210, the dominant segments are ‘Upper Crust’, ‘Movers & Shakers’, ‘Money & Brains’, ‘Gray Power’ and ‘Urban Elders’, whereas in ZIP code 36617, which is in Mobile, Alabama, the dominant segments are ‘Toolbelt Traditionalists’, ‘Bright Lights’, ‘Li’l City’, ‘Lo-Tech Singles’, ‘Struggling Singles’ and “Park Bench Seniors”. Each segment is backed up by details, so for example ‘Toolbelt Traditionalists‘ have the following lifestyle and media traits:
- Owns a Lincoln
- Eats at Long John Silvers
- Shops at Stein Mart
- Attends NASCAR events
- Cruises on Carnival
- Visits AARP
- Listens to Gospel
So what am I saying here? Is Amazon going to go full tilt and swoop in on Claritas’ legacy business? They certainly have the means and the raw data to do it.
But will they?
It seems more likely to me that Amazon would want to keep the data proprietary and for only for themselves.
I think the answer is simple. They don’t have to sell or license the data because they already have a derivative product.
It’s called advertising.
Amazon can use all the data they collect to support their mushrooming ad business3. So if your company wants to reach families who live in 3 bedroom homes, with three kids and a dog, who buy lots of Keurig coffee pods and watch Fleabag then, boy, do we know exactly who those families are! We’ll put up an ad front and center when they search for your product on Amazon. The conversion rate will be amazing — just you wait.
Am I being too paranoid?
Does Amazon’s planned acquisition of iRobot/Roomba mean that Amazon is going to use it to help create a world dominating “Complete View” data product? Or does it simply mean that Amazon is trying to trounce Dyson?
1 The metaphors have been discussed at length by many. I throught Amol Mavuduru’s article on this topic was rather good: “Is Data Really the New Oil in the 21st Century?”
2 Yeah, I know Amazon may not actually do all this snooping, but it’s fun to speculate, right?