Exploring Local
Mike Dobson of TeleMapics on Local Search and All Things Geospatial

What3Words – Not.Quite.Right

August 3rd, 2015 by admin

Recently, just for fun, I have been examining innovative grid offerings from What3Words, MapCode (TomTom-link) and Open Location Code (Google). What3Words seems to have caught the most attention, and in this blog I will present my thoughts about this specific effort at creating a more useful map grid for addressing. This is a really long blog. If you don’t have the time to read it, skip to the bottom section titled And Now a Word From Monty Python – it skips the details, but will give you the gist of my evaluation.

Three notes to start this off. First, the commentary that follows is not focused on detailed aspects of geodesic discrete global grid systems or their function as data structures. We are concerned here with simple location encoding systems, often called “finding grids” that can be used to provide an indication of the position of something, somewhere. Second, I do not intend to rehash the grids that have survived the test of time, other than to comment that there are a number of very useful grids that can be used for purposes of “finding.” Third, in an attempt at brevity, I am going to cut a lot of corners involving map projections, geoids, tessellations and other interesting areas and avoid discussions of theory that would leave you begging me to stop. Instead, let’s look at some basic notions involved in geographic grids, and then examine What3Words and what it (and other recent grid development efforts) may be trying to accomplish.

Map Grids – What’s involved?
At its basic level, the effort involves computing a grid comprised of cells relatively uniform in size that are used to tile, with no overlaps and no gaps, the area of geography in which you are interested. The coordinates defining these grid cells might identify the corners of a cell or they might identify the center of a cell. The method of annotation aligns with the goals of the producer of the grid.

Many grids that have been developed have been associated with efforts by militaries or other government agencies around the world interested in finding and naming locations in which they have or may field operations. Most of these efforts designate individual map grid cells by using short-codes that 1) avoid the need for users to be fluent with latitude and longitude, 2) eliminate the use of positive and negative grid values, and 3) do not require a detailed understanding of how the grid system was created.

“Finding” grids can be global or a local
In order to create a map grid one needs to decide the scope and parameters of the problem being solved. For instance, if you create a city street map designed to operate independently of other maps (i.e. other geographic areas); you might be satisfied by creating a local grid that bounds and applies only to the area covered by the map. Often these types of grids create cells are identified by coordinates called “bingo-keys,” as, reading a map index accompanied by local coordinate reference sounds like someone calling a bingo game, “A-29, I-32, etc.” Local grids should not be taken as meaning limited in extent to small areas. For example, the Township and Range system that exists only in some areas of the United States is defined on the basis of numerous, local baselines and principal meridians, but functions as an integrated land recording system across large swaths of the country.

Of course, another person might map the same area described above in the local street map example and decide that the geography involved should be represented as part of a global referencing system. In this case, the need for this map to integrate with the geography of the rest of the world is deemed of paramount importance to the developer of the grid.

Deciding whether a “finding” problem is local or global depends on your goals for the system, how you intend the grid to be used, and your plan for implementation and popularization of the grid. However, creating a global grid benefits from considerations related to how the new system could integrate with, or, possibly, replace existing grid systems. Unless the new grid provides a desirable functionality that existing grids do not, it is unlikely to be adopted by enough people to ensure its continued existence. Instead, it may be viewed as an unnecessary, duplicative addition in a field already crowded with worthy alternatives.

Grid Coordinates
As noted above, grid systems require a method for describing the location identified by the grid. In many cases these are reported in the form of linear or angular quantities that designate a position that a location occupies in a specific reference system. Coordinates from grid systems can be considered to serve as addresses. In its simplest form an address can be thought of as an abstract concept expressing a location on the Earth’s surface.

Two important questions follow. What does the creator of a grid mean when they use the term address to describe the locations in a new grid system? Second, how will commonly used existing addressing systems handle the form of address generated by the new grid? For example, from the perspective of a postal service an address might be defined as being: mailable, deliverable, locatable, and geocode-able. For some grid designers, locatable may be the only criterion of importance. For others, the address requirement might include the notions of it being hierarchical and topological. The notion of hierarchical can be seen in the address form used by go2 systems based on a long line of patents dating from 1996 (“Geographic location referencing system and method,” Patent number: 5839088) that in one embodiment, provides a hierarchical address in the form US.CA. LA.14.15. Other grids system coordinates may allow one to discern useful information about the relative distance and direction between coordinate pairs, thus providing a useful relational context to the “finding” problem.

So what is what3words?
On its website what3words (w3w) describes itself as, “… a universal addressing system based on the 3mx3m global grid. Each of the 57 trillion 3mx3m squares in the world has been pre-allocated a fixed & unique 3 word address.” On the same page of their website, the company indicates its opinion that the world is poorly addressed and that w3w provides a unique combination of just 3 words that identifies a 3m x 3m square anywhere on the planet. It claims that the grid cells are, “… far more accurate than a postal address, and much easier to remember, use and share than a set of coordinates.”

The ability to remember three words, as opposed to remembering a long pair of spherical coordinates is at the heart of the w3w system. W3w appears to be trying to introduce a system of geographic coordinates into widespread “public use,” as opposed to the more limited scientific and technical user populations associated with the use of many other geographic grids.

Example forms of the w3w coordinates are as follows: “remote.sun.palms,” ” feast.grab.bride,” or “madness.tags.curious.” As a further example, there are approximately 100 3m x 3m cells that fall within the boundaries of the property containing my home. If I enter my postal address using the w3w website, it appears to select a cell that is coincident with the center of the roof covering my abode. However, I could choose the coordinates representing any of the cells on my property as my w3w address. Presumably driveways or front doors might be a preferred choice for those presented with a large number of cells that could be used to identify the location of their home or business.

W3w is neither hierarchical nor topological. Any of the triplets used by w3w to identify a grid cell reveals nothing about the geographic relations between specific locations. In addition, w3w currently does not appear have a vertical component or any other method of ensuring precise addressing for multi-unit locations. I guess that people living in the same corner of a multi-level building might have the same w3w address and delivering anything to them might be a real puzzler. I suppose that’s part of why topology is so important in many addressing systems.

The approximately forty-thousand word English-language vocabulary used to identify the cells has been designed to avoid words that might be considered impolite or upsetting when combined with others. For example,” dogs.tinned.cats” is shown to identify a location in Japan, but the combination of words “dogs.eat.cats” or any related variant does not appear in the system. Singular and plural forms of words are included. The algorithm employed was designed to ensure that similar three-word combinations do not occur in the same geographical area. A variant form of a three-word combination used in one location (e.g. the use of plural form of one of the words in the coordinate triplet) might be used to describe a location on another continent.

Next, there are multiple language versions of w3w, although it appears that English is used in all versions for representing locations in the oceans and seas of the world. The triplet of words used to describe a specific land-cell using English bears no relationship to the three-word coordinate for the same cell in any other language, although these multiple representations point to the same world coordinate when analyzed by the w3w software. If you compared your w3w destination coordinates with someone who had used another language version of the grid, you both might be headed to the same destination, but, lacking a software application, would have no idea that the two seemingly unrelated grid cell designations were describing the same exact location.

As an aside, note that there appears to be some size parameter in work in naming locations in the ocean. While blank sections of water are named in English in all language versions, modestly-sized islands, such as Reunion Island, currently in the news, are covered with grids cells using words from the language version being used (e.g. French words if you use the French language version of the product). However, smaller islands (such as Flat Island and Round Island to the northeast of Reunion) are named in English, even when using another language version of the product. In further examination of this issue, I note that the Spratly Islands, involved in a territorial dispute between China, Brunei, Malaysia, Vietnam, and the Philippines are named using triplets of English words regardless of the language version of the product that is used. I Guess there might not be a strong appetite for the use of the w3w grid by China unless the naming algorithm is altered a bit.

The three words chosen as a coordinate for a location normally represent the center of the cell. These points, at least theoretically, “…will be within 2.12. metres from any adjacent square with a w3w address.” (Robert Barr –What3Words Technical Appraisal* is available here ). Barr further states that the w3w address is already a geocode (p. 16) and does not suffer from the problems associated with the geocoding and reverse geocoding process.

How about that? The w3w triplet is actually a pointer to the latitude/longitude grid that makes the system possible – but you must have already guessed that relationship.

In order to use w3w a user needs to have access to the w3w website or an app that uses the system. That means in order to identify their location and find the relevant grid address they need a computer, or a smart phone, or access to these types of devices and, at some point in the process, access to an Internet connection. The person hoping to find their w3w address needs to be able to point to their location on a map to select the grid cell that is going to be used to represent their location and whose coordinates will be used as their address.

If I had never seen an online map or an aerial image identifying my location on one, it might be a pretty hard task to accomplish. As a matter of fact even people who have had access to digital maps and satellite imagery often perform very poorly when attempting to use these types of spatial displays for purposes of locating features in the real world. What this means is that the adoption of w3w may be slowed by its users ability to access the required technology, as well as the abilities of users to locate their homes and businesses using the w3w platform. In addition, intervening opportunity make take its toll since the required technology can be used to solve the “finding problem” using alternative means.

In any event, after having identified the location of my home or business, I would need to remember the three-word combinations used to represent them. Of course, without access to the w3w software, no one else can determine if they are near me solely on the basis of the three-word coordinates. Nor can anyone help me out by referring to, say, a nearby address if I cannot quite remember my sequence, since w3w word-triplets are randomly connected to geographical space in the w3w system.

So, let’s recast the story. W3w grid cells are created based on lat/lon and then identified with unique three-word combinations. In order to use these “addresses” the three-word combinations point to a lat/long coordinate pair that can be used to tie into typical mapping and routing systems. Yikes! Just what benefit does w3w provide?

W3w seems to make a great fuss about the memorability of their three word triplets triumphing over the difficulties in using lat/lon coordinates. In other words, the w3w coordinates could be considered as a simple mnemonic for representing a location in a table that contains lat/lon.

Although I have never tried to memorize coordinate pairs, I agree that lat/lon coordinates might be hard to remember. Of course, so is memorizing and retaining the correct form of a random concatenation of three-words from a forty-thousand word dictionary that creates approximately 57 trillion unique variations of these coordinate triplets.

Perhaps more to the point, I cannot remember the last time I focused on remembering a specific lat/lon coordinate. However, I use lat/lon almost daily, but this action has been made opaque by mapping and finding technology. In my daily life, I no longer need an address for others to find me. I can call up a Google map and by tapping into my GPS chip it can calculate my location and tell others how to find me.

Indeed, if I point at a location on a map in Google Maps, right click and query, “What’s Here,” I receive the lat/lon of that location. If I put that lat/lon in a signature block, it would allow people to find me who did not know my postal address. In fact, the finding action in the above example seem to roughly approximate the same procedure people have to use to find the three-word coordinates in w3w that define the a lat/lon coordinate.

While the concepts of “finding” and “finding grids” might be considered a global problem, providing addresses for individuals and their businesses may, in fact, be an opportunity that is best considered a local problem. Further, assigning global addresses using a global grid when the grid system contains no recognition of the political and administrative geography involved may be an insurmountable problem. While this may sound short-sighted, I can assure you that addresses, addressing and the “authority” to establish them, to standardize their form, and to mandate their use are political hot buttons everywhere in the world.

Finally, note that technology may be bypassing the need for their beneficiaries to understand the complexities of grid systems. Consider, the mobile phone. You probably can’t remember the long sequence of digits that can be used to call your friends. Depending on the contents of your address book, it may also know your location and the locations of everyone you call. In addition, your phone records everywhere you go on the Internet and in real life. The phone doesn’t seem to need w3w to accomplish this feat and neither do you.

And Now a Word From Monty Python

Consider the fictional scenario presented below. I thought about scrapping the blog above and using this skit instead, but decided it might be better to discuss some of the issues with w3w in more depth. However, the scenario below is a pretty good summary.

It was a cold and dreary night. I had no idea where I was, so I called Rescue Services.
The operator asked, “What Three Words. Please?”

I replied, “I Am Lost.”

“No,” was the reply. “We couldn’t find any results for ‘I.AM.LOST’.”

I retorted, “But, I.AM.LOST.”

“No, sir. We require three words, not four words.”

I replied, “MY.CHOICES.ARE?”

“No, we did not get any result for those three words”

I responded, “HELP.ME.OUT?”

“Sir, you need to use a three word combination that contains three words from the forty-thousand or so recognized by what3words.”

“OK” I replied. “WHICH.THREE.WORDS?

“No,” was the response followed by, “And, that’s WHAT3WORDS. Although if you choose to use French or Portuguese the dictionary is only twenty-five thousand words because they do not cover the oceans and seas. Are you asea?”

“No, but how do I get the correct what three words that locate my position?”

“Use a What3Words App to identify your location on a map and it will return the three words defining that position.”

“But if I gave you my What3Words, what would you do with them?”

“Convert them to lat/lon and run a route to you.”

I was incredulous – “WHY.DO.THAT? You can read my lat/lon directly from the GPS chip in my phone and you can JUST.FIND.ME!”

Conclusions**

Encountering new maps grids is always fun and the thought that one might contain something productively innovate is always a big lure for me. I admire the team at w3w for attempting to solve a difficult problem. Unfortunately, convincing the world to use a new grid is a very difficult task, even when you might have created something better than that which already exists. While w3w is being effectively marketed, it is my opinion that is it is unlikely to be widely adopted. It lacks what I consider to be a fundamental innovation. Further, its utility as a map grid is constrained by the simplicity that makes its use appealing to many.

Finally, I am no more enamored of the new grids Map Code and Open Location Code than w3w, but for entirely different reasons. But this blog is already entirely too long.

Letters, we’ll get letters…..

Best,

Dr. Mike

Notes:
*) Dr. Barr is an acquaintance and a professional of the highest caliber. His analysis of w3w is good reading and I recommend it to you. He appears to view w3w favorably.
**) It is my opinion that the w3w website software is not particularly well-disciplined. Its various language options appeared to me to be unstable when examined over several days using Firefox v39. I did not interrogate the website using any other browser.

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Posted in Authority and mapping, geocoding, geographical gazetteer, map coordinates, map grids, routing and navigation, Technology, what3words | No Comments »

Can Anyone Stay on Top of the Online Mapping Hill?

July 19th, 2015 by admin

Recently a colleague contacted me to ask my thoughts about a report indicating that Microsoft was selling some map-related assets to Uber. He noted his disappointment, as he had hoped that Microsoft would reinvigorate its mapping activities and, once again, become a notable player in the mapping market. The brief conversation led me to contemplate the world of online maps both past and future.

Microsoft has had a long and storied role in desktop mapping software. For a while they were the leading provider of consumer oriented mapping software, but that role relied on the company’s success in controlling the physical distribution channels for its products. In the age of packaged mapping software aimed at the desktop computer Microsoft was able to influence the popularity of its products by controlling the distribution channels that determined the availability of products for purchase.

Microsoft could afford to buy as much shelf space and as many end caps or stand-alone displays as it desired. Since physical space in stores was limited, Microsoft’s presence could restrict the competitive products that were available. In other cases when competing products seemed to offer more and better functionality, Microsoft often reduced the price of its software to “free,” or at a cost level that was not sustainable for most competitive products. Due to the ability to leverage its mapping brand across distribution channels and measure its mapping products profitability across all software product lines, Microsoft’s mapping software became a dominant force in the industry. This is not to say that Microsoft’s mapping software was uncompetitive, as it was often of better quality than the products of many other players in the mapping industry.

MapQuest’s launch of a free, online mapping product quickly changed the distribution paradigm. In what should have been a case study for the Innovator’s Dilemma, Barry Glick and Company offered Internet-based routing capability between addresses across the United States, even though no one, at the time, ever asked for one. While not quite as fully functional as some of the desktop mapping/routing software, it was often more up-to-date and offered none of the cock-ups that frequently accompanied the use of CD-ROM software, and the related idiosyncrasies of the operating systems of the time.

Microsoft’s response to the development of online mapping systems was quite timid, and, perhaps, more confused than anything else. Unfortunately MapQuest was the people’s choice, although Microsoft online map product was competitive. The more important point is that it was at this point that Microsoft’s inability to influence online distribution doomed its mapping efforts, as the company now would have to depend on functionality and innovation in its effort to lead the market without any of the revenue that accrued to the company from their desktop mapping. But like MapQuest and Yahoo, Microsoft had no idea how to make money from online mapping.

Google’s development of mapping as an infrastructure play designed to enhance its advertising business marked a turning point in the sophistication of online mapping functionality. Google had a financial reason to spend a great deal of money promoting innovative mapping and routing features. It was able to out-spend and out-innovate Microsoft and all other players in the mapping universe as a result. In turn, the threat of the position achieved by Google as a partial result of the global success of its mapping programs led Apple to develop its own capabilities in mapping. Apple realized that users of the iPhone expected quality mapping and the company was not interested in its customers being users of Google Maps. Apple’s spend on mapping has been to protect its brand.

In today’s online world of mapping Google and Apple, two companies with strategic incentives requiring mapping, rule the roost. Will this leadership continue?

I have previously noted in this blog my interest in how long Google might be able to sustain its “spend” on mapping software. I think we now have an answer. It is my impression that the heady days of map development at Google are over and that its map products will be maintained at or near their existing levels, but with little innovation, other than in regards to autonomous navigation systems, as we proceed into the future. Google, unfortunately, is approaching middle-age and is developing the concerns that accompany fiscal responsibility. Over the last year or so, Google Maps has experienced senior management departures and market abandonment (GIS). Now the company has new financial leadership and this will result in spending limitations leading to a lack of innovation that will certainly limit Google’s future in the world of mapping.

Although it is early in game for Apple, I doubt they will fare much better. In its favor, the company has been more circumspect about spending. It appears to have out-thought Google’s mapping innovations and found of way to reach near-parity without spending as much as Google. However, in the long run, Apple’s market is limited to its own customer base, and quality mapping will be too expensive to support without some fundamental change in Apple’s business model. I suspect that eventually Apple will find that it, too, cannot afford to support its mapping programs at the desired level of accuracy and functionality.

The problem for all companies involved in mapping is that supporting quality in spatial data is an effort that historically has nickle-and-dimed profitability. While some of the basic map “facts’ may remain unchanged for decades, other map features change with amazing rapidity. It is an unfortunate rule of mapping that you cannot just compile, then build your spatial database and stop. In order to be competitive companies need to update their map data in a cyclical and spatially comprehensive manner. In addition, technology is constantly changing and the spatial support systems that spin these databases must be upgraded, updated and rethought every two to three years. Most organizations simply cannot afford maintain these types of efforts on a worldwide basis.

Few CFO’s want to hear the answer to this question, “When will you be finished with the mapping database?” The answer, of course, is “Never!” The answer to “Can you spend less?” is “Of course, but the data won’t be as good and the functionality will suffer.” (While “active” crowdsourcing may be considered an alternative here, I think that it is, for several reasons, not a sustainable choice for major commercial map providers. However, crowdsourcing (either active or passive) is not the topic of today’s blog.)

That brings us to Uber. Obviously Uber is interested in mapping. It has hired key players from Google, made an asset-deal with Microsoft and submitted a bid for HERE. However, the HERE bid appears dead, which leads me to presume that new Uber employee Brian McClendon (ex-Google, once a mapping exec for the company) may be planning on recreating the Google Map Machine at Uber. I do not doubt that Uber could spend some of its money to build a great street-level spatial database for the world. Conversely, I hope someone besides me begins asking, “How many of these worldwide street-level databases are we going to build? Isn’t there a better way?” Maybe!

Although it may not make your day, next time I am going to write about map grids, an ever popular topic for dreamers. It might be fun – and, hopefully, informative.

Best,

Dr. Mike

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Posted in Apple, Google, google map updates, Google maps, HERE Maps, map compilation, map updating, Mapping, MapQuest, Microsoft, MindCommerce, OSM | 5 Comments »

Google Maps Stumbles Badly – Crowdsourcing is the Problem*

May 25th, 2015 by admin

Google Maps has had a rough go of it lately. Public relations problems generated by crowdsourced data are at the heart of the conundrum, but the problems are related to two different systems used to support Google’s mapping efforts.

Google Maps’ current public-oriented problems are these:

1) The editing system the company employs for using crowdsourced data that may eventually appear on Google Maps is not authoritative.

2) The local search “folksonomy-oriented” matching algorithm used to match names users enter to find locations on Google Maps was poorly designed.

Both of these “gotchas” are unfortunate and could have been avoided. I, and others, have offered plentiful, free-advice to Google about the company’s need to tune its spatial data capture to enhance its map data base, not to detract from it. Let’s look at the specifics of Google’s latest mapping problems.

Map Maker

In regards to Map Maker, the public relations fiasco focused on Google Maps apparently ingesting a curated edit of a road network whose content outlined an Android-like figure urinating on the logo of its competitor Apple. Indeed, it was reported that just to the east of the peeing Android was a sad face emoticon with the text, “Google Review Policy is Crap.” (See this source to view both images.) As an aside, it is good to see that Google has kept its sense of humor. While I was searching for sources on the “peeing Android,” the ad on Google’s search page was titled, “Manage Overactive Bladder.”

Of course, these have not been the only errors discovered in the company’s use of crowdsourced data – I am sure many of you remember the listing for Edward’s Snow Den located in White House. How could these types of map spam have been unexpected by Google? And yet, according to the Venture Beat news source cited above, Google’s response included this interesting comment, “We also learn from these issues, and we’re constantly improving how we detect, prevent and handle bad edits.” Hmmm. I never would have classified the Google Maps Team as slow learners. They are world-class brainiacs. Maybe they lack an appreciation of or familiarity with the nuances of cartographic practice?

In any event, in 2011 for example, I wrote a series of blogs analyzing Google’s Map Maker System and the company’s handling of crowdsourced data ( e.g. here and here, among other articles on the topic). To save you from having to read the original articles here is a concise summary – I examined Map Maker and its editing system and found that due to flaws in the system as it existed at that time the edited and “validated” information in Map Maker resulting from user generated data should not be considered “authoritative.”

Currently, Google has suspended Map Maker edits and is working on a solution to the “problem” of users contributing invalid, inappropriate, or otherwise erroneous spatial data for use in Google Maps. Let’s talk about what Google might consider doing to solve this problem near the end of the blog.

Google Map Search

Google Maps latest problem, highly documented in the press here, here, here, and here, is that it attempted to match unconstrained location identifiers (an uncontrolled vocabulary) entered by users during map search with actual locations on Google Maps (a controlled vocabulary). More specifically, the company chose to employ a purpose-built approach based on the use of an unconstrained folksonomy to match possible surrogate names entered by users during map search queries to find actual names and locations of the POIs (points-of-interest) symbolized on Google Maps.

I fully support the notion of a folksonomy-based approach to local search. As a matter of fact, in 2007, before Google or anyone else in mapping or location search was using the concept, I wrote a blog titled “Controlled Vocabularies, Why local search needs folksonomies.”

Google apparently understood the concept, but was not thorough enough in its implementation.

According to Google Maps’ own blog, the Google Team culled spatial terms from online discussion forums and related these names to known geographical locations. In some cases, the terms they gathered were found to be “offensive.” Really? How unexpected was this obvious, method-induced error? Did they think that they might not find associations between names and places that might be offensive? Have they never read about the riled-up public opinions on naming decisions made by the Board of Geographic Names of the United States? Nevertheless, Google authorities stated that they, “…were deeply upset by this issue, and we are fixing it now.” Hmmm. What other time bombs are yet to be found? Has Google not yet learned that maps and spatial information cannot be handled or considered “…just another information system?”

Maps and the information that they contain will bite you in the ass when you least expect it. My experience comes from years of teaching map making and over a decade spent as the person in charge of all mapping operations at a company that was, at the time, the world’s leading print publisher of maps and atlases. My mantra each morning was, “What’s it going to be today?” Google may be beginning to appreciate the problems of compiling accurate maps, evaluating map data for timeliness and appropriateness, calibrating authoritative editing systems, all while keeping your product up-to-date and editorially acceptable to your user base (it’s that old geographic names thing again).

Conclusions

Problems with their approach to crowdsourcing are at the heart of Google’s current, public, mapping blunders.

Surowiecki in his important work “The Wisdom of Crowds” provided a comprehensive look at user generated content and I urge you to read his book. Surowiecki postulated that taking advantage of the wisdom of crowds depends on the diversity of opinion, independence and decentralization in the crowdsourcing population, as well as the influence of the method used for soliciting contributions. Surowiecki felt that if the crowd contributing data cannot satisfy these conditions, then its judgements are unlikely to be accurate. If he is right, then Google may need to rethink its approach to crowdsourcing data for use in Google Maps, as it appears to me that its current procedures violate almost every aspect of these cautions.

In part, Google’s use of crowdsourced data seems to reflect a belief that the company would have been unable to create as comprehensive a map database on its own as it has been able to create using crowdsourcing. Google rightly reasoned that contributors to its spatial database might not have the same goals as Google in regards to map accuracy and authority. Presumably, it is for that reason that Google evolved a hybrid-edit practice, but then negated the efficacy of the system.

First, it employed internal editors who did not possess the specific local geographic knowledge to assess crowdsourced contributions supposedly describing local geography. Second, it further diluted its goals for the system by the manner in which it allowed its contributors to become one of the components of the authoritativeness of its edit system. In the long run, Google needs to find a way to exert control and authority over its edit system. Until it does, blunders like those described above, and ones that are even worse, will plague their map database.

Google’s goals for crowdsourced data often appear contradictory. While they want to be able to harness local knowledge from users, their system allows users to contribute to the system even when they do not have local knowledge, nor are located in the region for which changes are being contributed. Map Maker is a prime example of this mismatch. In turn, some review editors, also, appear not to have the local knowledge that one would think was required to analyze a contributed change made to some aspect of a “local” geography. Using imagery is an understandable, but poor substitute for local knowledge.

In other crowdsourced mapping systems edited data are pushed to a live site and, then, curated until it is “considered” correct (kind of like a ping pong match) by meeting the commonly held notion of what is correct by the community that evaluates it. Data in crowdsourced systems are supposed to be “self-healing” over time. Google, apparently, instituted its editorial review measures because they could not afford for live data to be batted back and forth until judged to be “healed.” For example, it is difficult to design a mapping system or a routing system whose features might be in a constant state of flux. Not only could this create incorrect maps, but non-navigable routes.

Google seems to have designed a system that that did not take the “extended” healing path, but one that was just good enough when its product was at a lower profile. Unfortunately, the system is no longer appropriate for the uses to which it is being put. Could these active sources of user generated content be used to navigate autonomous cars? We had better hope Google figures out a fix before that happens.

In regards to the map search problem, Google apparently was aggregating input from people who, presumably, were unaware of Google’s use of these data. While Google seems free to aggregate any information it wants, it boggles the mind that it would do so based on chat room conversations which were certainly not authoritative sources of information on local geography. Creating a folksonomy without consideration of the source authority, or the use of a filter for “appropriateness” were major, bush-league blunders. In addition, gathering crowdsourced data is influenced, see above, by the method used to solicit information from the targeted population. Google now knows that its method is in error, but will it be able to concoct a user-focused paradigm that elicits data accurate and useful enough for the purposes of Google Maps?

Whether or not Google can find a way to effectively engineer and police crowd-sourced systems is a topic of interest for them (and for me). My own opinion is that active and passive crowdsourced systems will be critical components in all future mapping systems. Google has the resources to monitor, evaluate, rank and adjust or regulate its crowdsourced geographical data to achieve its goals in mapping, but seems reluctant, or unable to mount the specific effort required to confront the problem.

As, I have noted here in past blogs, Google engineers don’t necessarily think they are smarter than everyone else, just that they have more and better data with which to examine a problem. Google should have the smarts, resources and the required data in their data lake/reservoir/swamp to analyze the likely validity and usability of crowdsourced map data by creating a consistent, authoritative vetting process. But, maybe not. Or maybe the effort would make it uneconomical.

Well, maybe Verizon will come up with something now that it owns the almost moribund MapQuest. Apple Maps? Well, they could certainly take better advantage of crowdsourced map data, but that does not seem to be of particular interest to them at this time. Although it is a technique that could really help them improve the quality of their maps and, especially, their business listing data.

And now for something completely different

While I spend most of my time on assignment for my consulting business or thinking about the problems of mapping and spatial data handling when not on assignment, I do find time for one hobby in particular. Don’t laugh – it’s bird photography. If you are interested in the world of shore birds you might want to take a look at some of my photos – DobsonPhotoArts.com . (While prints are for sale, I do not expect you, the audience of this blog, to buy any images; their purchase is part of a more complex strategy – and fun research in its own right. So don’t alter my sample.)

I hope you had a great Memorial Day Weekend.

Dr. Mike

The reference to the Surowiecki work is as follows:

Surowiecki, James, [2005). The Wisdom of Crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations, New York. NY: Anchor Books Edition pp 306

*Blog edited on 2015_05_26 to improve readability.

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Posted in Apple, Authority and mapping, business listings, crowdsourced map data, Data Sources, folksonomy, Google, Google Map Maker, google map updates, Google maps, map compilation, map updating, Mapping, MapQuest, routing and navigation, User Generated Content, Volunteered Geographic Information | 2 Comments »

Silicon Valley – the New Motor City?

May 11th, 2015 by admin

As you may have noticed, my last few blogs either directly or indirectly, have been nipping at the issues surrounding autonomous vehicles (AV) and the spatial data that might be needed to operate them. Today’s post offers a look at how Google, Apple, Uber and others might choose to compete in this market.

While the current automobile manufacturers and their suppliers obviously will attempt to compete in the AV market, it remains an open question as to whether or not these industry stalwarts will be able to effectively transition to a future AV market, in which vehicles being driverless,” and manufacturers being dealer-less may not be the most significant changes. The complex infrastructure that supports the motor vehicle industry (e.g. suppliers, dealers, repair shops, parking facilities, aftermarket services etc.,) will likely experience disruptive change as AVs emerge. In turn, this transition will generate enormous money-making opportunities related to the new and potentially unique infrastructure requirements required to support the AV market. (See this article from Forbes for some interesting insights. )

The implications of AV market development are incredibly complex and most of us have not thought through the changes that will accompany this market. Will cars that cannot crash need to be insured? How will local municipalities replace the income generated by motor vehicle violations when traffic fines disappear due to vehicles being programmed not to violate local transportation and parking ordinances? Will it be more economical to run fleets of AVs around the clock (as delivery vehicles in off hours), rather than park them overnight? Will houses need garages for vehicles? How will the transition be managed when SUVs and AVs share the highway before AVs replace old fashioned drive-it-yourself vehicles? Who is going to employ those taxi, truck, bus and other commercial vehicle drivers made redundant when AVs become commonplace? Obviously the questions are endless – as are the opportunities.

It is my position that what happens to the existing market for vehicles, its infrastructure in general, and current OEMs in particular, will depend, to a considerable degree, on how Google, Apple and Uber attempt to monetize and leverage the market for autonomous vehicles. I realize that some of you think that this scenario is implausible. Others might respond that this is the same mindset held by mobile phone manufacturers (e.g. Nokia and Motorola) when contemplating the news of iOS and Android-powered mobile phones. Note that disruptive innovation rarely comes from existing players in a market.

In this blog I do not intend to include a long background discussion on AV, as any search engine will result in tons of information on the technology. Instead, the paragraphs that follow outline how I see the future AV market unfolding mainly for the three key players that interest me today. Let’s start with market timing and then move on to Google, Apple and their approach to the AV marketplace. Comments on Uber’s strategy will be based on the potential strategies of Apple and Google.

While there is an amazing amount of ongoing work aimed at producing saleable AVs, it is likely that mass-produced autonomous vehicles are over a decade away. Before then, we will see semi-autonomous vehicles that require varying degrees of driver intervention. AV prototypes will become abundant during the next five years, but the market will remain miniscule over the next decade. During this market development period the existing manufacturers will attempt to show their ability to innovate, as well as their influence while lobbying for legislation that might tilt the table in their favor over companies threatening it from the software/technology worlds.

The possible strategies of Google and Apple

Google makes the Android OS, billed as the world’s most popular mobile OS, available to everyone, but produces and markets their own version of an Android phone to show mobile companies and software developers how the system should work. The original Android, aimed at creating an Open Source OS for mobile phones, was acquired and managed by Google to further its corporate goals, while sparking a communications revolution. In respect to those “corporate goals,” Android is advertised https://www.android.com/intl/en_us/ as having, “The best of Google built in,” which means that “Android works perfectly with your favorite apps like Google Maps, Calendar, Gmail and YouTube.” What that really means is that Google has you in the clutches of their massive and highly profitable advertising business.

Apple, on the other hand, developed its iPhone operating system (iOS) with the intent of delivering it exclusively for use in a handset developed by Apple and manufactured to its specifications. Apple’s amazing financial success is based on the fact that the company’s products are designed to provide a comprehensive user experience for its customers. In turn, Apple’s customers rely on the company’s integrated approach to product development, equipment manufacturing, support and device education, as it provides them an “upscale” product experience.

The approaches of Google and Apple to the mobile market potentially speak to two potentially different strategies for competing in the AV market. It seems to me that the goals of Apple and Google in respect to the AV market should be the same – they should not care about the car, they should care about controlling what goes on in it when cars become autonomous. It is at this point that, for the operator, the vehicle becomes a floating- office/living room/den/bar/restaurant etc. Relieved of the “duty to drive” people will want to use the car cabin for recreation/communication/lifestyle experiences or to conduct business. I am not sure that either Apple or Google needs to own the car to own the cabin, but Apple may well want to own the “car” and the entire vehicle experience.

Google may conceptualize the vehicle cabin as a “local search petri dish” – abounding with germs – each of which is a new thread opening a unique path to sell local advertising. Apple may see the car as another device whose sales will dramatically increase their income and include a cabin-based market for services that a captive audience will be willing to buy/lease/rent.

Google and its Johnny Cab

It is my opinion that Google is looking to kick-start their concept of how the AV World should work through the development of what will become a low cost, fleet-oriented solution to navigation that encompasses the concept of cars on demand and no need for individual ownership of the vehicle. While these are laudable goals, they may be so utopian in scope as to preclude success. The problems with implementing such fleets are enormous, but, then again, those who think of ways to do so will make fortunes.

Of course, Google’s goals are flexible and the company will likely produce a purpose-built automotive oriented OS running its Johnny Cab AVs to show other manufacturers how a Google-based system could benefit the development of AVs. Specifications for its Johnny Cab OS, including the details of the hardware, sensors and data (e.g. a map database, traffic database, vehicle restriction database) required to make the vehicle truly autonomous will also be available. However, it will allow its licensees adopt the OS to fit their own vision of AV production and style, just as it does today with Android in the mobile phone market. Google will, however, control and tightly license use of the databases key to operating the system. In this strategy, Google may become the best friend of existing automobile manufacturers and their suppliers, although providing the AV OS and support infrastructure will put Google, not the vehicle manufacturers, in the catbird seat. See Google’s pre-marketing for wireless cars here.

It is my opinion that Google already possesses the majority of tools, know-how, and databases required to create fully functioning AVs. Tuning their OS and optimizing the vehicle to meet their view of the future will take some time, but certainly they are the lead horse in the AV race. Their key concept of ride availability based on vehicle fleets rather than individual ownership may be further in the future, but the notion does solve a number of difficult mobility-related problems that society faces today.

Google’s strategic desire in their AV development effort is to ensure the production by others of a large population of AVs powered by Google as a method of extending the sphere of influence of its advertising business. I doubt that Google is interested in becoming a manufacturer of automobiles, just as it has not shown much interest in being a manufacturer of mobile phones, tablets, or computers. Apple, on the other hand, well, Apple will likely take an entirely different approach.

Apple and its iCar.

In some ways, Apple appears to be more realistic about exploiting the market, sensing that people want better designed products – an iCar OS for instance. Apple helped transformed the world of telephony into a world of social contact, software apps and declining voice communications. The company has similar dreams for transforming the AV world into a social experience that transcends the friction of distance by focusing each vehicle trip on you and your wants during the journey, not about the vehicle, or how to get it to your destination, or how to avoid other maniac drivers.

Apple’s presumed approach, however, requires more than just an OS. The iCar must be a complete user experience that Apple can control from design though production. Apple will likely want to develop, market and support its own branded vehicle and is currently reported to be executing this concept at a skunkworks in Silicon Valley .

In essence, Apple may become the real threat to the current automotive manufacturers and their suppliers. Fortunately for them, Apple’s penchant for high-end, high-margin products will make it likely that Google or a Google-Like Company (GLC) will partner with the existing industry bringing the IOS vs. Android wars to the segment of the new marketplace that does not require “luxury” vehicles.

While hardware for AVs is not a crucial question (for an overview of the required hardware – see this from Wired) I have some doubts that Apple is up to the “whole car” challenge. Apple seems to be shy about working with anything that is mechanical in nature. Think of the evolution of all of Apple’s devices and you will notice that over time they have become smaller and less mechanical (dials replaced by touch screens, etc.). While I think Apple understands the world of “mechanical things”, I believe that the Company considers them the weak link in all use-scenarios. I wonder if Apple would be familiar with managing the life cycle of a mechanical automobile and dealing with the problems that such systems might present to their customers and to Apple as a product/service provider. Today, if you are having trouble with your still under warranty iPad, you go to an Apple store and once they confirm the fault, they simply swap out your unit. This type of service might be more difficult in the future AV market, but a modular car and component system could be maintained by remote diagnostics and serviced by “hot-swapping” equipment centers. Time will tell.

Note, that Apple’s map database might need some serious augmentation in order to support the company’s AVs. Today its database lacks the “map” details needed for autonomous navigation and also lack the roadway/roadside imagery and lidar data that could provide a significant benefit in the AV market, items that Google has been industriously collecting for some time. On the other hand, everyone involved in the potential AV market needs to ask themselves whether “map” database systems architected and designed for high-volume, Internet-based map serving will meet the needs of managing the tasks required to simultaneously, safely navigate million of AVs. I suspect there will be significant performance difficulties when using these map databases in AV applications – but that is not a topic to be covered here – at least not today.

And Now – Uber

Today Uber does not need to manufacturer or otherwise own a communications device or OS to support its business, since the phone and related apps are simply regarded as connecting the user to the Uber infrastructure. However, in the future, their infrastructure will be significantly transformed by AVs, their adoption rate and the nature of their ownership (personal or commercial). If my guess about the strategies of Apple and Google are correct, then Uber may have a difficult time competing, at least in some geographic markets, unless they reconfigure how their business operates.

Uber connects rider with vehicles and drivers. At its heart, Uber depends on driver/vehicle availability and matching these with the demand for trips generated by passengers. It may be that this scenario will become more difficult to manage if the scenarios for Apple and Google actually occur as outlined above. Owners of an iOS AV would, I think, be unlikely to make them available for use by Uber. Fleets of Google’s Johnny Cabs would already be programmed for maximum availability and, likely, not be available for use by Uber. While this issue could be considered a matter of logistics, Uber could take the uncertainty out of the equation by producing and owning a dedicated fleet of AVs. In this scenario Uber would continue to support its present business, but through its own fleet of AVs. The company could, also, expand its offering on a contract basis to provide individuals with transportation services that eliminate their need to own a car at all. Finally, Uber could add to its street visibility and market strength, by designing and fielding a fleet of custom AVs, in much the same way that the UPS brown trucks are unique and identifiable anywhere in the world.

It is likely that the notions described above are what has caused Uber to bid for HERE, as well as to partner with Carnegie Mellon on the Uber Advanced Technologies Center. From my perspective Uber could save a lot of money by being a licensee of Google’s forthcoming AV products, but, apparently, the company’s strategic interests in new markets makes them reluctant to partner with a likely competitor. At this point, however, Uber’s investment choices do not convince me that they are on track to play a winning hand in this game of chance.

Conclusions

I think the key concept behind Google and Apple’s participation in the development of autonomous vehicles involves each company capitalizing on the freedoms that will result when automobile users are finally relieved of the duty to drive. Driving is a demanding, tiring task that provides few rewards other than to transform space by spanning the distance between an origin and destination faster than by possible using other personal transportation devices. Those finding exceptional ways to fill the time that people will no longer spend driving vehicles will find a number of incredible new markets. Aftermarket modifications will become even larger in the world of AVs. Privacy? Don’t even ask.

For Uber the future of the cabin is largely a continuation the past. Its current users are those who have already chosen to be relieved of the duty of driving. What will change for Uber is the potential need to own a fleet of vehicles rather than contracting this availability from independent owner operators in order to sustain their business. The choice of whether to develop their own AV or not will profoundly influence the future development of this dynamic company. Total cost of ownership (TCO) will become a fundamental part of their business model, and one that they may wish they had avoided. They will find that paying drivers to use their own cars provided a much better return.

And one final note. How are all these quasi-sentient AVs going to find their destinations when they are not residential addresses? No company that I know of has either comprehensive or accurate business listings data, a theme that we have long hammered on in this blog. Add in the growing mold of link-rot and you have a toxic problem for the AV industry. Let’s face it, the AV will not know where the driver actually wants to go, it just knows which item on the list that was presented to the user was selected as the destination. In fact, the AV rider may not know this is the wrong business name/address pair until they arrive. What fun – a vehicle that finds destinations using a spatially indexed random numbers table. So what else is new?

Best,

Dr. Mike

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Posted in Apple, autonomous vehicles, Google, HERE Maps, Mapping, mapping business listings, Nokia, Personal Navigation, Uber | 2 Comments »

HERE Maps on Sale – The mapping derby begins

April 15th, 2015 by admin

As you may have read in the WSJ , Forbes, or other sources, Nokia’s mapping unit HERE is in play. While I do not find this item to be “news,” it has attracted a great deal of publicity and speculation on the estimated value of the company, the potential strategic benefits for the winner of the auction, and the companies that might be interested in acquiring the property. (Read my August 2014 blog on HERE, for more information on the company’s problems and its future – “here”, so to speak. The comments in my 2014 article seem especially pertinent given today’s news.)

Background

Let’s discuss some background items that I consider relevant to the discussion on the potential acquisition of HERE.

First, every company that has any association with navigation has known for a long time that HERE could be acquired for a “very reasonable price.” It is not as if owning HERE has provided any strategic or financial advantages for Nokia, especially after the sale of its handset unit. However, the diehards at Nokia and HERE will express indignation at this statement and respond that selling HERE was never considered before now. Yeah, right – if you believe that, well, wait until you get a job in senior management!

Second, HERE is not now nor has it ever been a consumer-facing business. Re-architecting it to function as a “visible” and valuable consumer brand, while maintaining the company’s role as automobile industry supplier, would likely not be an easy task for any potential acquirer.

Third, HERE revenue for 2014 came in at EUR 969M. Another evaluation had HERE’s EBITDA at $168M. Then, as now, the company’s operations ran a modest loss.

Insightful due diligence might reveal the reason behind the loss, but these problems might not be of significant interest to a strategic buyer determined to be a player in the navigation space. After all, Google Maps is the best game in town and you are not going to catch them by standing still or wringing your hands over something that needs improvement.

Although I do not have access to any “insider” information, I suspect the lack of significant growth in HERE’s financials is related to a) a lack of strategic leadership by Nokia, b) inefficiencies related to the loss of the “Navteq Corporate Memory” caused by the departure of numerous senior personnel from the company who, for one reason or another, did not continue with the company after its acquisition by Nokia (2008), and c) non-optimal revenue generation (i.e. below what was in the plan). My take is that sales have been difficult to conclude for, at least, two reasons. One weakness that I believe is providing inroads for competitors is the perception of declining data quality in HERE’s mapping and support databases. Second, HERE’s owner has “Nokia-ized” the sales process and its “telecom-based approach” to the automobile market has alienated current and potential customers.

Fourth, Nokia acquired Navteq for EUR 5.7 billion in 2008 and the value of the asset has declined since that time. In 2014 Nokia took a EUR1.2 billion impairment charge, as it revalued HERE at EUR2 billion. As noted in the articles linked to at the start of this one, preliminary current estimates of the “bid” value for HERE may range from EUR1 billion to EUR 4 billion.

The fifth background piece for the acquisition puzzle deals with the “potential” rights (if any) related to HERE that Microsoft may have received when it acquired Nokia’s handset division. Yes, there was a side deal for the use of HERE maps for an extended period of time, but there was also the issue of warrants and rights that were not definitively described in the press release on the deal.

Who might be interested in acquiring HERE?

A wide-range of companies could be interested in acquiring HERE. Let’s look at some of the most frequently mentioned (but not necessarily the likeliest).

Uber
Uber is at the top of everyone’s list, but I am not in that camp. However, when companies are not required to be economically rationale and have a high valuation, anything is possible. From my perspective, Uber is a user of map data, but does not need to own the map database (unless Google were going to acquire HERE and Apple were to acquire TomTom). Google? – Well, they didn’t want anyone else to own WAZE, but general antitrust considerations (along with their current problem in Europe) make it unlikely they will play this time around.

Back to Uber – Rather than buying Here, it should be hot at work investigating or developing methods to utilize its drivers maneuvers while on duty to map the paths used for transportation throughout the areas where the company operates. In other words, Uber could adapt the WAZE model to map its paths if it wants to transition away from commercial map data providers. Uber does not need to own a global database of streets, as there are limited benefits to maintaining data on streets and roads on which their drivers will never pilot an Uber-mobile.

Automobile Manufacturers/Suppliers Consortium
I can see HERE being attractive opportunity for a consortium of automobile manufacturers, as these companies fear the strategies of Google and Apple for invading the car. I suspect this would be the worst thing that could happen to HERE as automobile manufacturing companies are notoriously fickle and slow-footed. Indeed, it is hard to see a group of automobile manufacturers agreeing on anything over an extended ownership period. It would be extremely difficult for such a consortium to agree on a map compilation program that did not favor their most successful markets (what a battle that would be).
While owners can focus the resources of a company wherever they prefer, focusing map compilation on popular markets for in-car navigation could reduce the possibility of leveraging the data to other commercial markets and applications that need relatively uniform coverage everywhere. A limited map compilation strategy might result in decreasing profits and decreased map update frequency. Oh, here we go again. Isn’t this type of problem what happened to HERE over the last seven years?

One of the articles I read on this topic suggested there may be some discussions between Nokia and a consortium of German car makers. Hmmm. This could be the answer to the question I posed in last year’s blog when discussing why Halbherr would leave HERE before it was sold – but maybe not.

Microsoft
Microsoft could have done this deal numerous times before now if it were really interested. Nokia needed a lifeline when it sold its handset division to Microsoft, but the big Softy appears to have been satisfied with a long-term contract for map data. I do not see maps as a large part of Microsoft’s future, but the acquisition of HERE would depend on how much of an advantage owning HERE would be in helping Microsoft to become a significant player in automobile information and navigation systems. Microsoft’s map offerings are currently at relative disadvantage to Google regarding what it can offer the automobile manufacturers and may soon be behind Apple, as well. Whether Microsoft moves to acquire HERE may depend on how comfortable they might be in partnering with a potential new owner of HERE. I expect Microsoft may be able to exert some persuasion on the acquisition of HERE as a result of codicils in their previous deal with Nokia, but that is a supposition on my part and may be entirely wrong.

Apple
Apple certainly needs help if it ever wants to grow its mapping capabilities beyond the current IOS-based handset market. However, with the exception of Beats Audio, Apple seems more willing to develop technology on its own by acquiring small shops that exhibit abilities that Apple believes would help them tune their existing efforts. Their mapping acquisitions have been modest and their partnership with TomTom may preclude them from feeling any need to participate in the auction.

Utilities
While it may not happen, one of the most interesting options is for a technology infrastructure company to acquire HERE. Map data and navigation have become a utility and should be produced and distributed by a company that understands this type of business. Although it’s map data with all the inherent peculiarities and difficulties with data collection, processing and use, it is still data of various flavors that need to be delivered to a specific customer-type at the point of use. Networking companies, Intel, or other hardware/services/systems providers who understand the necessary model could be well poised to make a run at HERE. By the way, Intel is used only as an example. They have already been burned in the map market.

Handset Manufacturer Consortium
Samsung is one of the possibilities in this category, as it is unhappy with the strategic positions of Google and Apple in regard the smart phone market. Industry rumors abound that Samsung considered joining a coalition of companies organized to come up with an alternative to Google Maps and Apple Maps. Whether Samsung should seriously think that owning a map company can improve its strategic position in the handset market or as a technology provider, in general, remains an interesting issue for me. While Samsung could play the role of a “utility,” it is unlikely that it would have the expertise or management skill to develop HERE in an advantageous manner. It is for this reason that Samsung, or a similar company, might partner with other handset providers, as well interested automobile manufacturers or suppliers, to bid on HERE. Note: somewhere in the mix you will undoubtedly find a map company or possibly a traffic-reporting company.

There are tons of possible suitors, but this blog is already too long, so let’s cut to the chase and talk price.

Valuation
Nokia’s hope is that a bidding war erupts between companies that have no need to think of their valuation of HERE as “real” money. Traditional companies, such as those associated with the automobile industry, will be unlikely to drop a gazillion to purchase HERE. Internet services companies might be inclined to pay a higher price, if they consider the time value of money to be irrelevant in the face of acquiring what might become a sustainable competitive advantage in future markets important to their strategy.

If one were aiming to duplicate the range of attributes, data quality and data specifications that were engineered into the original process designed by Navteq, then building an asset base comparable to the current quality and functionality of the database owned by HERE would take a considerable amount of time and money (although less than was required by Navteq). The unknown is how well HERE has maintained, enhanced and or expanded its database since the acquisition of Navteq by Nokia and this question can only be answered by the requisite due diligence.

My belief is that significantly more than half the value of the company is based on the value of the data. Unfortunately, without testing the data it would be difficult to know: 1) the value of the database without examining the current data collection and processing infrastructure, and 2) the potential degradation of the database’s quality, if any, during Nokia’s ownership. Valuing the enterprise (infrastructure, brand, etc.) sans database would be an easier task, but no one would be interested in acquiring the company and not the data.

If pressed, on an intellectual basis I could agree with Nokia that a value of EUR2 billion for HERE could be justified. Unfortunately, I doubt that Nokia is willing to spend the money to maintain the data in a state equivalent to or exceeding its current quality. If this is the case, then the company is between a rock and hard place, since time will diminish the value of its assets. My conclusion is that the sale of HERE is tilted to the buyer’s advantage. In addition, if the quality of the HERE database has slipped, then the multiple will decrease and the the price should fall closer to EUR1 billion.

Time will tell.

By the way, we have retained Mr. Tudball and….ahhhh…. Misses Wiggins to represent TeleMapics in the bidding for HERE. Well, somebody has to interject comedy into this deal!

Best wishes to all.

Dr. Mike

Minor updates on 4/16/15 to correct one typo and the wording of one sentence.

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Posted in Apple, Google, Google maps, HERE Maps, map compilation, map updating, Microsoft, Mike Dobson, Navteq, Nokia, routing and navigation, TomTom, Waze | 2 Comments »

Silicon Valley Mapenings

March 11th, 2015 by admin

Recently I decided to put a new “topical” category in my Google News App in hopes of the service finding articles on mapping and navigation. Doing so has netted me relatively few articles. In fact, the same group of titles that were presented initially hasn’t changed since coverage started. However, two articles from Reuters caught my eye. The title of the first article was “Silicon Valley debate on self-driving cars: do you need a map?” The second article reported on Uber’s acquisition of deCarta. More on both articles below.

Silicon Valley and self-driving cars

Before I contemplate this interesting, but misdirected article, I think we need to pay homage to Lewis Carroll who wrote Sylvie and Bruno, as well as Alice’s Adventures in Wonderland, the Hunting of the Snark and many other humorous stories. In Sylvie and Bruno, we find his character Mein Herr commenting on a country map that had been created at the scale of a mile to a mile. When asked if the map was used often, the response was “It has never been spread out yet,” said Mein Herr. “The farmers object: they said it would cover the whole country, and shut out the sunlight! So, we now use the country itself, as its own map, and I assure you it does nearly as well.” It is interesting to note that Lewis Carroll in 1895 more clearly understood the Silicon Valley debate than many of those who are, apparently, now involved in it.

One never knows if reporters reliably report the gist of the conversations they have when developing a story, or if they actually understood what was being said to them. Also, reporters have been known to exaggerate issues when writing headlines in an attempt to a) sell their story, and b) attract readers. Whatever the case may be, I think that some clarification is required.
First, the premise in the headline of the article is misleading. Of course you need a “map” for autonomous vehicle development, but in today’s world a map is merely a representation of the data in a spatial database.

It is an overly simplistic, but important generalization to note that while maps once were the primary sources used to create spatial data (i.e. digitizing the map to create data for a spatial database), the trend is now reversing. With the avalanche of GPS devices capable of reporting the location and distribution of geographic phenomena, data are now being poured (both by end-users and database creators) into spatial databases that are, subsequently, used to create maps.

I think the map/spatial database confusion is exemplified by the Reuters article. All autonomous vehicles, regardless of the specifications and design goals, will require some degree of spatial data to perform their basic function.

For example, consider that systems capable of routing vehicles between destinations do not require a “map” to perform this function. Instead they require a spatial database that can be utilized to calculate the path and vehicle maneuvers required to efficiently navigate from one location to another. The human driver is the one who prefers to see a representation of these spatial data in the form of a virtual map, presumably for reassurance that the vehicle has selected the correct destination, as well as to provide information on other objects along the path they are traveling.

The question being debated in the industry involves the type and amount of spatial content that will be required to meet the specifications for the vehicle-type that the spatial database is intended to support. Google, for instance, is reported to be developing a spatially comprehensive inventory that can be used to provide navigation guidance, as well as additional spatial detail required to meet Google’s specific design for vehicles supporting autonomous navigation. Other developers may choose to create a sparse model of spatial information and rely on real-time sensing and associated signal or image processing to deal with conditions that they may feel can be better solved as they are encountered.

The efficient, but minimal, sparse database for navigation requires complete and comprehensive compilation of street and road geometry on those thoroughfares where automobiles can be legally driven. In addition, this database would be populated with street names, addresses, political jurisdiction, directionality, information on Points of Interest, and a host of variables that would ensure that the system could recommend a path between origin and destination that was safe, legal, efficient and “userful” (that is data that might be useful and informative to those being transported by such systems). How much more spatial data is required for an autonomous car depends on the design of the vehicle and how “autonomy” will be provided to the vehicle.

In my opinion, the gating factor for the complexity of the spatial databases supporting autonomous cars will turn on the efficiency and accuracy of the result of processing specific spatial data based on sensed, real-time inputs, versus the efficiency and accuracy of relying on previously reported spatial data purposefully compiled or tailored for a specific use.

And Now, onto Uber

As you must know by now, Uber acquired deCarta. In the article cited, the reporters humorously declared deCarta to be a startup. For those who do not know, the company formerly was known as Telcontar (note the Middle Earth link) and founded in 1996 by engineers from ETAK.

What you might need to know about the acquisition is covered in detail by Marc Prioleau , formerly an officer at deCarta. After reading Marc’s analysis, I was still left with a nagging question. However, let’s get rid of the “yes, but…” statements first.

Yes, routing algorithms are difficult to create and appropriately tune.
Yes, spinning large volumes of spatial data to answer routing queries is complicated.
Yes, Uber can benefit from the knowledge of deCarta’s present team of engineers, as well as the knowledge of numerous talented engineers no longer with the firm as encapsulated in the deCarta software.
Yes, Telcontar/deCarta powered Google’s earliest entry into mapping and routing, as well as that of Yahoo and other well-known internet based mapping applications.

But wait, if deCarta was involved with Google, and Yahoo and others in creating routing and mapping systems, why didn’t any of them acquire the company? Why did many of the Telcontar/deCarta customers prefer to develop their own mapping platforms, rather than take the easier road of adapting the system provided by deCarta?

Of course, we will never know the truth of the matter, but I offer some insights for debate.

I suspect that deCarta’s investors were tired of holding the bag/investment and realized that a really big payday was never going to happen for this company. I do not have any insider information, but another suspicion I have is that deCarta’s revenue and customer bases were contracting. Next, while I am sure that deCarta staff is outstanding, the company has lost some of its best talent and most of those are not recent departures.

More importantly, it is likely that Uber is not going to be a commercial map publisher responsible for creating maps in the volumes of the interactions that Google serves. Rather, it seems likely to me that Uber wants deCarta for help with the analytics related to mapping and routing that will enhance its ability to locate, pick-up and service its customer base. In essence, Uber wants to enhance their logistics management efforts by establishing predictive abilities that would allow them to manage the contention for the drivers and vehicles needed to serve their customers based on time-of-day and location.

I am not of the school that thinks that Uber acquired deCarta before someone else could have done the deed, although Samsung was a possibility. Heck, the deed could have been done by another company any time in the last nineteen years of the “startup’s” existence. For Uber, the cost of deCarta was likely a rounding error in their budget. Indeed, it may have been more efficient for them to execute the deal than to analyze it.

And so it goes. They don’t call me Mr. Warmth without reason.

Best,

Dr. Mike

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Posted in autonomous vehicles, Google, Google maps, map compilation, Mapping, Mike Dobson, Uber | 3 Comments »

The Incredible Lightness of Being…driven

January 20th, 2015 by admin

Have you ever noticed that passengers in cars often have little specific awareness of the details of the spatial locations through which vehicles navigate? Often, when these people are driving to a destination they visited previously as a passenger in a vehicle, will say, “Yes, I know we have been there, but I was being driven there and wasn’t paying attention to the route to take.” It appears that many of us conclude that when we are not driving a vehicle we have little reason to know the path between our origin and destination.

When relieved of the duty of navigating and driving between places, passengers are often multitasking while balancing communicating, reading, planning, thinking, napping or solving some goal that is momentarily important. It is very rare that any of these considerations involve navigation, or studying the path that is being followed by the vehicle. While this lack of attention to spatial detail has always been the norm for passengers using almost any mode of transportation, we are now entering an era where, “Technology is transcending geography” to everyone’s eventual detriment.

The growing problem of spatial ignorance is the philosophical issue discussed in today’s blog. It is my contention that society’s lack of attention to what I will call “spatial detail” has increased with the transcendence of mobile networking technology, including mobile phones, portable computers and various forms of navigation devices.

Consider someone who is in communication with other persons, none of whom shares the same geographical location. For everyone involved in this communication system real-world geography often has little or no immediate bearing on the “conversation,” other than its use in grossly categorical geographical descriptions, such as in, “I’m on my way the beach.” The person on the other end of the conversation has no need to travel to the beach to see, talk to, or communicate with the person who is en-route to or at the beach.

Indeed, a person’s location, when considered on a local, regional or national scale, makes little difference in the modern communications paradigm – unless the relationship between those communicating requires physical interaction. In large part the space warping capabilities of modern communications systems (voice, browsing video conferencing, video chat, texting etc.) collapse distance in a way that may render geographic knowledge irrelevant to the users of these systems.

It has occurred to me that the use of devices to assist navigation, such as smart phone maps and routing apps, have promoted a “spider web” geography that reduces the fabric of geographical landscape to a few major threads, diminishing both the importance of the landscape and our understanding of it in the process.

For example, many people who live in Southern California have a generalized mental map of: 1) the freeway system, 2) how some subset of local towns are connected by one or more of these thoroughfares, and 3) the general details of a few local streets that connect the freeways to a destination of interest. However, when requested to detour off the freeway (something WAZE suggests with regularity) the departure from known territory is often quite uncomfortable, as drivers may lack the geographical context that could inform them of where they are traveling and how that connects to the locations with which they are geographically familiar.

If WAZE or some other navigation app cannot accurately guide us, due to map errors for example, we may lack the familiarity with the geographical clues surrounding us that might help get us back on track. Unfortunately, the next generation of technology adopters may suffer even greater disconnects from real-world geography than many people experience today.

It is common to look into your car’s mirror while idling at a signal and see the people in the car behind yours on their mobile phones: texting, browsing, reading and, in a few cases, with the phone pasted to their ear. At the risk being guilty of profiling, it appears to me that, within limits, as the ages of the driver and their mobile cohorts decrease, their focus on networking communications increases while awareness of their geographical surrounding decreases.

The more important observation is that some members of Generation Y (Millennials) and Generation Z (the Boomlets) are or will likely in the future be living a lifestyle that, for practical purposes, is aspatial – i.e. generally lacking an active spatial context. Remember, these are the generations that are not interested in driving a vehicle, nor are they entranced by a car’s features and design. Instead, they want to be driven to their destination so they can optimize their time communicating with others. Cars, drivers’ licenses, automobile insurance, finding parking spaces and wasting time in traffic jams, or focused on driving are inconveniences that are quite reasonably not attractive to anyone, but are especially repulsive to younger generations. The need to optimize time has contributed to the success of Uber, Lyft, ride-sharing applications and other innovations that allow people to get into the back seat of a vehicle and productively, or at least pleasantly, use their time. Unfortunately, riding in the back seat produces tunnel vision in regard to understanding geography and how places are networked together.

This leads me to ponder this question, “If these generations don’t have personal familiarity with the spatial detail of local areas, who will and would that matter?” Some of you may consider this conundrum and, perhaps, think it interesting. Others may see around the corner and understand that “The incredible lightness of being…driven” may become a tragedy that will come to haunt us.

It is often the case that beneficial innovations have use-cases that generate unintended consequences. For example, it sounded like a great idea to put our power grid schematics and controls online, until we realized that the internet is not secure and, perhaps, never will be secure. Now we can see the spatio-temporal aspects of power flows and adjust power availability for them, but not so much for hackers who now can easily disrupt power systems. GPS, for instance, helps us track our location, but did you know that Global Navigation Satellite Systems (GNSS) drive PNT systems worldwide? P is for position, N is for navigation and T….well T is for timing. Financial transactions around the world are given a time stamp based on systems such as GPS and the time stamp directs the allocation of financial assets. Not too comforting is it, since GNSS systems have proven to hackable/spoofable? In a similar manner, the loss of local geographical awareness, an unintended consequence of the desire to focus on communication, may eventually be recognized as a significant loss.

Those cabbies in London who study the Knowledge understand the best ways to get around London at any time, regardless of traffic. Conversely, Uber drivers often have little idea of where they are driving in London or anywhere else and rely on TomTom, Garmin, Google and other devices for guidance. At some point this reliance on online spatial databases will render the notion of map accuracy even more important than it is today, because no one will actually know how to navigate where they are going or WHERE their destination is located. Others may be unable to evaluate whether the path they are being told to drive is something that should even be attempted.

So there it is – now you know why I blog about map accuracy and the sorry state of online spatial databases. Someday soon the default source of geographical knowledge – international, national and local – will be online spigots that pour geography from vast reservoirs of poorly curated spatial data. That idea raises many interesting questions – and having important, complex problems to solve is part of what makes life so interesting for those of us in geography, mapping and GIS. I guess some of us were very lucky when geography chose us to participate.

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Posted in Authority and mapping, Garmin, Google, Google maps, HERE Maps, Local Search, Mapping, Mike Dobson, Personal Navigation, TomTom, Uber, Waze | 1 Comment »

Business locations, map errors, address errors – Let’s stop the nonsense

January 15th, 2015 by admin

My colleague Dr. Stephen Guptill and I have been noodling about how to apply aspects of the formal theories of map accuracy and uncertainty in a manner that could be used to evaluate and report on the quality of business listing information commonly found on maps. The problems with business listings are not necessarily related to errors generated by the business listing systems side of the equation and may, instead, reflect various inadequacies of the map base or the mapping system used to fuse and present the data. In order to assess the mix of variables and interactions between them that we might encounter while attempting to formally study the issues involved, we have started examining the mapped presentations of businesses located in shopping areas known to us.

Dr. Steve is well-known for his work on issues of map accuracy and has published or presented numerous articles on the topic, some when he was one of the top scientists with the mapping division of the United States Geological Survey and other publications issued after he started his consultancy in GIS and Spatial Epidemiology.

Today’s blog is a co-authored effort by Steve and myself.

In order to get our heads around models, measures, dynamics, and usability related to the topic of map accuracy, we have been examining the mapping systems of online map providers in order to assess whether or not our ideas merit worth further examination. However, every time we look for examples and start examining local business listings, we fall into rabbit holes of extraordinary dimensions and complexity.

In a recent short discussion piece on Data Lakes, Michael Stonebreaker writes on why data lakes are actually data swamps. We recommend his article to you as it is a succinct description of the problems that result when companies misunderstand the complexity of data curation. While Stonebreaker is discussing a simple list, we believe that the curation of spatial data is an enormously complex situation. In the case of business listings, not only do we have quality issues related to the business listing data, but also quality issues related to the map to which the address is geocoded and displayed, as well as the data handling processes involved.

Almost every map provider collects some business listing information and then mixes it with licensed data from companies who profess to create accurate, well-researched business listings. Because some providers appear to be better than others in supplying specific categories of business listings, most map companies license business listing data from several companies, as well as adding in some crowdsourced listings, or perhaps listings that result from their own attempt at creating a business listing registry. The problem with these efforts is not in the provider’s ability to ingest the data collected or licensed, but in the ability to curate spatial data in a manner that results in reliable, accurate geographical information. Read the debacle below to better understand why Dr. Steve and I are nosing around a way to describe and measure the problems with the use and display of business listings.

The blog that follows outlines our search for a single bank and where it is actually located in Northern Virginia. The purpose of this particular blog entry is to provide some background on the complexities of the world of mapping business listings.

Detective Joe Friday of Dragnet fame was known for his monosyllabic summation of almost all investigations. “Just the facts, Ma’am.” He said. So here we go. It’s a long one, but there are lots of pictures.

There is a United Bank located in the area known as Fair Lakes Shopping Centers in Northern Virginia. There are three shopping areas at Fair Lakes, all managed by the same property management company. The United Bank is located in the shopping area known as Fair Lakes Center, shown by the blue map pin on the Google map that is provided on the website of the shopping center. (See Figure 1).

Location of the Fair Lakes Shopping Centers
Figure 1. Location of the Fair Lakes Shopping Centers

To start our search for the bank, we opened Google Maps and panned the map until we had the Fair Lakes Center centered on the map. No bank names or associated symbols were showing on the map so we entered “banks” in the map search bar. The relevant portion of the map that resulted is shown in Figure 2.

Search for
Figure 2. The bank of interest in this blog is the United Bank. It is incorrectly located on this Google Map.

Left clicking on the bank symbol labelled United Bank revealed the information shown in the following image (Figure 3)

Google Maps showing the address information it has for this bank
Figure 3. Google has the location of the United Bank as 13060 Fair Lakes Boulevard. Unfortunately, the location of the map pin is not that address.

Right clicking on the red dot at the bottom of the red map symbol labelled “United Bank” on the display above revealed the information shown on the map below (Figure 4). Note that the address information provided in the next map is not the same as the address information provided above as the location of the bank.

Google's alternative address information?
Figure 4. if you right click on the map pin the address is not 13060 Fair Lakes Blvd.

Right clicking on the location the blue dot displayed on the attached map revealed that Google Maps identified this street-stub as Fair Lakes Parkway and provided an address range for this entity (Figure 5). Note that the address information on the map below is not the same as the address information Google originally provided for the United Bank branch.

Another address for this location?
Figure 5. Right clicking the blue dot seems to indicate that Google thinks this street stub is an address range on a street named Fair Lakes Parkway.

The facts so far are these:
1. Google has located the United Bank at 13060 Fair Lakes Blvd.
2. The bank building is not visible on any of the Street View images on the maps that accompany any version of the address Google provides for the bank.
3. Google has stated that the address of the bank is 13060 Fair Lakes Blvd, but the geocoded location is adjacent to a street that is labeled Fair Lakes Pkwy.
4. The address 13060 is greater than the address range on the block named Fair Lakes Pkwy and the symbol for the bank is placed beyond the end of the street with the highest address, even though this is not the street referenced in the address Google provides for the bank.
5. Further, the location shown by Google for the geocoded location of the bank symbol places it on Federal Systems Park Drive in front of the parking lot for the Northrop Grumman Information Systems building.
6. The street stub the Google labels Fair Lakes Pkwy intersects a street, also, named Fair Lakes Parkway.
7. Route Planner by TomTom indicates that the street-stub labelled Fair Lakes Pkwy by Google is a named continuation of Fair Lakes Blvd and not named Fair Lakes Pkwy. (See Figure 6 below).
8. Neither company is correct, as the street-stub beginning at its intersection with Fair Lakes Pkwy is a private road named Federal Systems Park Drive. We drove by the location and photographed the sign identifying the street as Federal Systems Park Drive. (See Figure7 below.)
9. When queried to find the location at which TomTom geocoded 13060 Fair Lakes Blvd, TomTom located the address several blocks from the Google location. (See figure 8 below.)
10. The bank is not located where TomTom positions it.

TomTom's street address position
Figure 6. TomTom incorrectly labels the street-stub Fair Lakes Blvd.

Actual street sign photograph at the intersection of interest.
Figure 7. Oops. The street is actually named Federal Systems Park Drive, not Fair Lakes Pkwy, or Fair Lakes Blvd.

TonTom's geocode of the bank address
Figure 8. This location is where TomTom thinks the bank is located.

Well, maybe the bank knows its address and where it is located. Let’s look. We “Googled” the corporate website of United Bank to reveal the following information

Bank address according to United Bank website
Figure 9. This is the address of the bank provided by the United Bank corporate website.

Google and the bank agree on the address of the bank, but do they agree where the bank is on the map? The red map pin shown below is where the United Bank’s version of Google Maps shows its location (Figure 10).

Map used by bank to show its location to customers
Figure 10. The bank’s map agrees with TomTom, not Google, even though it uses Google Maps as a base.

However, as noted above, the bank is not located at this position. It appears that the bank’s version of its mapped location is based on lat/lon coordinate pair passed to Google Maps. If you right-click the location of the bank on this map and select “What’s Here?” the query will return a coordinate pair, a Street View image, but no address, nor does Google appear to return addresses for any location along Fair Lakes Blvd. As you might imagine, the Street View image does not include the United Bank (Figure 11).

No address range available using Google's What's Here button
Figure 11. Clicking “What’s Here” on the Google Map reveals a coordinate pair but no address range information. We could not find address range information for any segment of Fair Lakes Pkwy.

As a next step, we called the branch office of the United Bank in question to find out the location from the proverbial “horse’s mouth.” We asked for the address saying that we wanted to enter it into our GPS. The person we spoke to said very slowly that the address for the bank was 13060 Fair Lakes Blvd, and then carefully outlined how we would need to enter the Fair Lakes Center and navigate to the actual location of the bank, since it was not directly accessible from Fair Lakes Blvd.

As you might suspect, we knew the location of the bank from the beginning. In fact, the easiest way to find the United Bank in question is to enter the following address into Google maps: 13060 Fair Lakes Shopping Center (Figure 12). Of course, in order to do that you would have to know this address in advance. In this case, it would be pretty hard to find this address since neither Google nor the Bank provide this clue.

Oh gosh, look at this, a symbol exactly where the bank is actually located (Figure 12)

Houston, we have found the missing bank
Figure 12. Finally, the United Bank in all its glory!

And look, the Street View image shows the bank, and if you get fiddly with the controls you can position the image and see the address on the door facing the street named…Fair Lakes Shopping Center. Hmmm. (Figure 13)

Wow, you can even see the bank, and its address, in Street View
Figure 13. 13060 shows right on the door. But how about those photos?

Curiously, the Best Buy located next to the bank, provides its address as 13058 Fair Lakes Shopping Center, and not 13058 Fair Lakes Blvd. Perhaps we have discovered some confusion about mailing addresses and location addresses, but you should know that the Shop Fair Lakes website, which leases shops at this mall, lists the address of the Union Bank as “13060 Fair Lakes Center” (figure 14)

Where the Shopping Center Management appears to think the bank is located
Figure 14. It appears the mall management company thinks the bank’s address is on Fair Lakes Center.

In searching further, we found several websites that had the bank’s address at 13060 Fair Lakes Blvd, an equal number that had the address as 13060 Fair Lakes Shopping Center. Several of these sites had the United Bank erroneously located on a Google map in or near the position shown by the bank’s own map of its location, which was erroneous. Of the others that gave its address as 13060 Fair Lakes Shopping Center, two had it located in the shopping center, but not in the correct locations. We also found on Google+ that the address 13060 Fair Lakes Parkway, which does not lead you to the bank, was a verified address for the United Bank, as is shown by the circled shield in the following image (Figure 15). Verified maybe, but Google Maps does not know where to locate this address. Even if it did know the secret code for accurate mapping, it would still not have the bank located correctly!

Wow the address of the bank is a verified location in Google+
Figure 15. Ahh, what does the mall management know about the location of properties it leases anyway?

Of course if you enter Google Search (not Map Search) using 13060 Fair Lakes Shopping Center Fairfax VA, Google will pop a map that shows the exact location of the bank and you can even see it on Street View (Figure 16). Hmmm.

Look what you can find in Google Search, but not map search
Figure 16. Look, if you use Google Search with the location address, you can find the bank.

However, if you combine the bank name with that address and search for “United Bank 13060 Fair Lakes Shopping Center Fairfax VA,” you will see a page of listings, but no map, as this is not the entity relationship in Google’s database (See Figure 17).

Using the location address in normal search does not directly reveal the location of the bank.
Figure 17. Hmm. If you use Google Search with the location address, you still cannot find the bank.

Similarly, if you search for “United Bank Fair Lakes Shopping Center Fairfax VA” you get some search engine results pages and an inset map from Google Maps of the incorrect location. (Figure 18).

More disappointing search for the bank
Figure 18. When you tie the location address to the entity known by Google, it must return an incorrect result.

It appears that even if you knew the correct “finding/location” address for the bank that you could not use Google to find it (Figure 19).

Using the correct finding address for the bank results in the incorrect location when using Google Maps
Figure 19. How frustrating!

Four final notes:

1. If you look at OpenStreetMap for this area in Northern Virginia the United Bank is clearly and correctly labelled.
2. Google (and likely all other mappers) appear to have no idea of the boundaries of shopping centers, although these can easily be found using Google Search. To some degree, the inability to map a business listing within a shopping area reflects the incompleteness of the spatial models used by the companies participating in the local search market. Hmmm!
3. I have seen articles by easily impressed reporters indicating that Google uses machine vision to read the signs in Street View. Maybe Google should spend some time using these capabilities to locate businesses.
4. Google – are you familiar with the concept of “fuzziness?”

Conclusions

The search for the United Bank detailed above is an example of the insanity in the world of mapping business listings. Companies that provide business listings frequently collect, scrape and/or merge incorrect locational information, improperly geocode it, and, in the process, make it impossible for consumers to find the businesses or services that they desire to locate. Why bother to put this stuff on maps if it is erroneous? If a human can’t use your system to find the business, do you think that Android Auto or CarPlay will do any better? Does this time consuming, erroneous process make consumers happy? Do advertising clients appreciate it when a customer cannot find its business due to a listings or map error?

Maybe it’s time to evaluate the scope of this problem once and for all so that companies wishing to publish the locations of business listing data can find better ways to evaluate the accuracy of the data they are offering. While it may be an attractive notion to think that you can provide this type of data without interactive processes and backtracking (see the Stonebreaker article mentioned earlier), those with a serious background in spatial data handling will understand that you do so at a considerable risk. Finally, it is unlikely that anyone can prepare authoritative and accurately mapped business listing data without some human involvement. The United Bank example may be one of those cases. Of course, the degree of the problem with business listings accuracy remains unknown and that is exactly why we are interested in examining it. More as we progress.

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Posted in Authority and mapping, Bing maps, business listings, Categorization, Data Sources, Geotargeting, Google, Google maps, Local Search, mapping business listings, TomTom | 4 Comments »

Google Maps and Business Listings – Better, but not quite there

December 16th, 2014 by admin

It’s time for that long winter’s nap – or perhaps a long read on a topic of interest to everyone – business listings and Google Maps. Thrill your boss, intrigue your friends and drown the conversation with data – all of this can be found below and it’s free, but only for the next millennium. Why, you might even gift your boss with a copy. I am sure they will thank you. Sorry about the size of the graphics and their extending out of the text frame, but I wanted you to be able to see them.

I have been following Google’s interest in fielding autonomous cars, their use of AI and maps to improve their navigation, and how Google thinks it will be pretty easy to expand their current map and related databases to support these efforts. (See this effort for an example of the coverage). In addition, I recently perused what was a very complimentary review in Wired of Google’s Ground Truth.

Hmmm. Perhaps I have slept through some major revolutions in mapping and map data quality. Well, actually I was in Italy for the month of October and maybe everything new happened then? Actually, I don’t think so because I had reason to use Google and Google Maps to find services I needed in the cities I was visiting. I was amazed. The business listings shown on Google Maps for several cities in Italy appeared to be even more incomplete than the Google Map to which I referred at home. Even more worrying, I was in established urban cores that should be well known to any serious providers of maps. Hmmm.

While in Italy, I had occasion to search for laundries, dry cleaners, stores, and business services, you know, the kinds of things related being away from home for an extended period. Since I was not familiar with the geography of businesses in the cities and regions I visited, I had intended to consult Google Maps to find locations so that I might be able to acquire those services and goods without undue exploration of the city. However, after using Google Maps in Italy this notion appeared to be a cruel hoax.

I had difficulty accepting that the types of services for which I was looking were not available, say in Rome or Florence. As I walked through the cities, I noticed a number of businesses that provided what I was looking for, but they were not shown on Google Maps. In addition, many of these stores had not appeared when I used Google Search to interrogate a category that I thought should contain a specific type of business. Often I could often perform a Google Search on the name of the business that I saw while passing by and found that it would now be displayed on the map as a search result. This process seemed sort of backwards to me, since I was using Google Maps to try and find potential targets in the real world, not using reality to find locations that could be symbolized on Google Maps if I searched for the name I saw on a storefront.

On arriving back in the US I decided to take a closer look at business listings on Google Maps. I was hesitant to once again look into business listings, as it is the scab of the mapping world that never seems to heal. However, as the venerable Yogi Berra is reputed to have said, “You can see a lot just by looking.” So, I headed for two shopping areas near my home after having equipped myself with a camera, GPS, note pad and a trusty sidekick who was willing to work in return for lunch.

About every two of years, for one client or another, I take a close look at the state of business listings on maps or navigation systems, shudder and tell them things were not any better than two years before. Once, I even designed a business listings system for a major player in the electronics space, but when they saw the complexity and expense of becoming a market leader they decided not to pursue their intended strategy. On the other hand, maybe it would be different this time and it sounded like a fun way to spend a few days.

Note: the study reported here was just for my information and does not have the detail of a client report. I think you might find it interesting – at the very least it will save you from having to go out and look for yourself – and if you are interested in business listings it will give you a lot to think about.

The study areas examined  for this research

Figure 1. The two shopping areas included in the present study are located in Laguna Niguel, California. Basemap courtesy of Google Maps. The areas were chosen based on accessibility to the author and because I had estimated that they would contain close to 100 businesses. Not a large sample, but ample to provide some food for thought on the topic.

Both shopping areas are outdoors and open, rather than an enclosed mall setting. Each consists of a large main building offset from the road, several pads or minor buildings scattered through the shopping area, and acres of parking. In both sites a few shops are located in buildings adjacent to nearby streets. In most cases the businesses in these centers are set-back far enough from the road to render reading business names impossible while driving by. Exceptions to this general rule are the huge signs for the anchor stores in each shopping area (e.g. Home Depot, Hobby Lobby or Wal-Mart).

I started the research by creating an inventory of the businesses operating in each of the shopping centers. I enumerated all businesses in the respective centers noting their individual position, name and general category of business. I entered the names of the businesses in list form with address information and also noted their location on aerial imagery. I, also, had maps of the businesses in each shopping area that I retrieved from the management web sites for the properties and used these to determine details of the geometry of the shops for further comparisons with building outlines and partitions that might be included on Google Maps. Although the maps provided by the management web sites were relatively up-to-date in terms of tenancy, my list of businesses was based on field observations.

In order to determine which businesses were shown on Google Maps I used a desktop computer (several actually) to interrogate Google Maps. The examination involved zooming, scrolling, examining images, examining Street View (when and where it existed), using various “developer’s consoles” associated with several browsers to determine details about the map tiles and Google servers involved, as well as using the Google Index to search for businesses that did not appear on Google Maps. When a Index search resulted in success, I then clicked the inset map that accompanied the search results and examined the location in Google Maps
.
Note that the use of Google Search and Google Maps are influenced by prior searches, prior Google Maps use and other information that Google knows about you as a user. The research results described here may be specific to my computers (several different machines were used during the process) and not reflect the experiences of others performing similar searches. However, without being more open about why, I have concluded that the results of my map viewing and searching should not be significantly dissimilar to those of most other users of Google Maps who view the Google products using an Internet browser and a desktop computer. Although I checked some of the business listings using mobile devices, this was to ensure that there was a basic compatibility between the business listings on Google Maps using various devices.

Note that after I had finished my original analyses at the end of last week, Google updated its map database and made changes to the businesses in both centers. I incorporated these changes in a second analysis that I will provide as part of this report. Finally, after having received a note from a friend on events at Apple Maps, I decided to include the business listings they had for the areas of interest, and will, also, comment briefly on their efforts in the area of business listings at the end of this blog.

Research Questions and Considerations.

It is my belief that Google or any company providing detailed street maps involving a wide range of scales for presentation should, at the appropriate scale or scales, attempt to name all business that occur with the boundaries of the areas they have mapped. The detail limitations on map content related to the lack of space available on printed maps are irrelevant in digital mapping systems designed to show extreme details in local areas. Users of modern mapping systems should be able to use large-scale maps as surrogates for reality. In the case of business listings this would equate to naming every business in a local shopping area, so that the user could find and navigate to any of the potential targets available and of interest to them.

It is clearly the case that not all businesses in an area are represented on Google Maps. One could postulate several possible reasons for the lack of a complete list of local businesses, although not all are equally likely:

1) Data quality issues – Google might not possess the required data to describe and/or locate businesses accurately within a specific area
2) Geocoding issues – Google might be unable to adequately determine the validity of the address data that must be geocoded to locate businesses.
3) Street View coverage limitations – Some entities, particularly, shopping areas with internal streets and roads may not allow Google vehicles access or, perhaps, Google has instructed drivers to not to capture street view in particular commercial locations (e.g. Google Street View imagery was unavailable for the majority of the internal roads in the shopping areas involved in this study). Alternatively, Google may have the necessary Street View data, but have not yet processed or published them
4) Google wants their users to search for data on unidentified businesses as they make considerable profits from such search activities versus the limited income directly generated by their mapping operations.
5) Google’s map design appear to me to be heavily influenced by their desire to capture more revenues from mobile devices, whose screen real estate could limit the usefulness of burdening the display with the names and positions of all business locations. Alternatively, there could be aesthetic design reason for limiting the number of displayed business tags on Google’s map

I thought that looking more closely at what Google was doing in my local area might be helpful in understanding Google’s display strategy in regards to business listings.I had six general questions that I wanted to answer during the course of my research:

1. How many businesses were in the shopping area and how many of these businesses were shown on Google Maps?
2. What was the accuracy of the locations of business shown on Google Maps?
3. When businesses were located in the correct building, were they correctly located within that building?
4. When businesses were not located on Google Maps, could Google Search map them and what was the accuracy of the locations on Google Maps revealed through Google Search?
5. When “searched” business locations were shown in the correct building, were they located correctly within that building?
6. What could Google’s performance in displaying business names on maps tell me about their strategies and challenges?

Let’s look at the details revealed by the research. There is a lot of data here, so I provide a series of graphics and brief captions, rather than spend a great deal of time in detailed explanation. The graphs generally follow the order of the questions provided above.

Plaza de la Paz

Businesses at Plaza de la Paz shown on Google Maps

Figure 2. Plaza de la Paz is comprised of 52 businesses. Google has mapped a portion of these and included 2 additional businesses that once operated here but are now out-of-business. Approximately half of the businesses at Plaza de la Paz were shown on the Google Map of this area.

Chart of the variablity of the locations used to represent the business listings

Figure 3. While most of the mapped locations were relatively correct (near where they should be located on the map or easily findable from that position if at the shopping area) those that were misplaced or significantly misplaced appeared to me to be errors resulting from some aspect of the geocoding process. Frequently, these locations were symbolized along external roads bounding the shopping areas, but generally not symbolized within the buildings that actually house these businesses and which Google shows on its map.

Percent of businesses mapped in the correct building

Figure 4. As noted previously most of the businesses that were mapped were located in the correct building, although some of these were generously misplaced within the building that they occupied. In some instances this was not a major problem, but in other cases, it put the business on a side of the building that was not visible if you were looking for the business near the location where Google had located it on its map. I moved a business into the misplaced category when it was unlikely that most people could figure out where the business was based on the symbol location. Note – the main building in this shopping area is several blocks long, so the measure of “occurring within the correct building” is a very liberal measurement.

Locations that required the use of Google Search to discover

Figure 5. One-hundred percent of the businesses not shown on Google Maps had locations within the shopping area that were known to Google. In order to find these businesses I entered each business name (known to me only because I had observed the name in the field) in Google Search (not Map Search) and then clicked on the map that appears to the right of the listing on the search engine response page. In most cases, these businesses were located in the correct building, although several were shown along streets or in parking lots, rather than within the building outlines that Google had extracted from satellite imagery.

Search locations mapped in the correct building
Figure 6. Searching the Google Index revealed eighteen businesses that were mapped located in the correct building. A modest number were misplaced within the building and shoppers would likely experience difficulty finding these businesses using the locations provided on Google Maps.

Next, let’s see the results to the same questions for the second study area.

Marketplace

Businesses at the Marketplace in Laguna Niguel

Figure 7. Google had a much better grasp of the businesses that were located at the Marketplace as compared to Plaza de la Paz. In part, this may be that the layout of the shopping center is “open” and the buildings surround central parking rather than being embedded within buildings surrounded by parking areas, as at the Plaza de la Paz. Google did not map any out-of-business locations at Marketplace.

Location of the businesses at the Marketplace Laguna Niguel

Figure 8. Not only did Google list more of the businesses in the Marketplace, but it was able to locate them more accurately than in Plaza de la Paz.

Mapped in the correct building

Figure 9. Unfortunately, while able to identify the correct building containing the business, Google was less able to accurately locate the businesses within the building. My conclusion was that these were geocoding errors resulting from attempting to geocode the address data of the businesses without detailed reference information for the units involved. For example, these businesses have addresses such as 27150 A, 27150C, 27150F. There are no units labelled B, D, or E. Without knowledge of the omitted unit identifiers, it would be difficult to properly geocode the actual addresses for units that do exist or to even know where to start geocoding within a building.

Search locations at the Marketplace

Figure 10. Only one of the existing Marketplace businesses omitted from Google Maps could not be found and mapped using Google Search. Most of these results were correctly located.

Search locations in correct building

Figure 11. In general, location accuracy of the results from Google Search was commendable. Although several locations were misplaced within the correct building, it should be noted that the sample size was small.

Thoughts so far

Google Maps clearly did not represent all businesses in the study areas. Approximately one-third of the businesses in the areas studied were not included on Google Maps. Moreover, when Google Maps was able to locate the business in the correct building structure, it frequently mislocated some of these businesses within those confines. In addition, approximately one-quarter of the businesses shown on Google Maps were either shown in the wrong building, symbolized in the parking lot or along an adjacent road, or were, in a small number of cases, no longer in business at the shopping areas examined. The businesses not on the map could be searched and mapped if you knew the name of the business from some other source. In general, the accuracy of the locations of businesses found through search was slightly inferior to that of the businesses appearing on Google Maps but not requiring search to symbolize them. However, when these “searched” businesses were located in the correct building, they were as likely to be accurately located within that building, as those businesses initially shown on Google Maps in the correct building. The last finding was unexpected. If the locations that required search can be shown on the map with approximately the same accuracy as those that are normally shown, why not show all of these data on the map to begin with?

More detail

In order to get a better understanding of Google’s strategies in naming businesses, I needed to see a little more data.

I decided to investigate if there were categories of businesses that appeared to be favored by Google and other categories that seem disadvantaged by Google’s approach. As is well known, attempting to categorize almost anything results in instant argument. And so it goes. I decided on the categories to use and feel that they adequately represent the variety of businesses that were discovered in the field.

First, I categorized the businesses in each of the two study areas and, then, lumped them into one pool of businesses representing both locations. The results were as follows.

Business segment percentages

Figure 12. It appears that there is a good mix of business types in the two areas studied. Dining dominates, followed by Specialty Retailing (mainly shopping for general stuff), Services (banking, FedEx, etc.), Salon Services (beauty shops, nails, etc.,) and Home Furnishing, Decorating and Supplies.

Business segments on Google Maps

Figure 13 Wow; guess Google feels that people in “South County” like to dine out since Google Maps really emphasize the Food and Restaurants category. It appears that the businesses names published by Google on its maps reflect the general distribution/occurrence of these categories within the two shopping areas. The segments emphasized by Google are those commonly thought to be of most interest to mobile users.

Mapped businesses by market segment

Figure 14. This is probably the most interesting of the graphs as it shows the performance of Google Maps in terms of businesses mapped in each of the business categories. Google provides good coverage of the most popular gategory Food and Restaurants. In the two shopping areas studied it does a relatively poor job of representing Pet Services and Supplies and Pharmacy & Medical Services.

Indeed, in several categories it seems to be almost a fifty-fifty chance that an individual business will appear. Oh wait, it’s generally worse than that since, in most categories, national or regional chains/brands are well represented on Google Maps, at the expense of …small businesses. However, my experience is that this problem really exists at the level of business listing aggregators who supply their lists to Google and other providers. It is quite easy for these companies (or Google) to contact major retailers and solicit the location of their stores and outlets (or scrape them from the web). Finding the small business owners is much harder and that appears to be reflected by the lack of representation of small businesses on Google Maps.

Well, now that I had a better grasp of how Google Maps was performing, I had one more issue to deal with before concluding –

What about those Google Maps updates?

Before I could post this blog last weekend, Google decided to push out new map updates early Saturday morning. So, realizing that this might be an interesting opportunity, I processed the new data and made some new graphs. It appears that Google Maps really is trying to improve its business listings, but still under represents business listings. The update made only one minor adjustment to the Marketplace shopping area and this was to indicated a Redbox existed in the parking lot (not where it is located). Since the businesses mapped at the Marketplace were unchanged I did not update the graphs for the Marketplace. Plaza de la Paz, however, received a significant upgrade to the number of businesses listed.

Plaza de la Paz (after Google Update)

Businesses on Google Maps after update

Figure 15. Mapped locations in Plaza de le Paz increased by seventeen percent in last weekend’s Google Maps update.

location and updated business listings

Figure 16. The number of mapped locations at Plaza de la Paz increased from 28 to 39 in the most recent Google Maps update, but some of the additions were misplaced and the number of erroneous businesses entries increased from two to three.

Updates and correct building location

Figure 17. While the number of businesses mapped in the correct building increased by ten, the number of businesses locationally misplaced in the correct buildings increased as well.

Next, I wondered about how these updates were applied across the business segments?”

detail by market segment on Google Maps update

Figure 18. The recent Google Map updates was focused on the Food and Restaurants segment. With this update Google has identified all of the businesses in this category that exist in the two shopping centers! Other changes were the addition businesses to the Service category, as well as single business additions to Salons, Home Furnishing and Pharmacy.

As can be seen on the graph, a number of segments are still under-served and in most of these the businesses that are not represented are small businesses. One interesting exception is that Google continues to snub a sizable AT&T shop in Plaza de la Paz, although you can see the unnamed individual aisles in the Home Depot store instead, if that would be of interest to you. All in all, Google is making good progress in putting businesses on the map, but one has to wonder how soon they will accomplish the goal of complete representation and accurate location of local businesses.

Some thoughts about the location accuracy of listings.

The accuracy of business location on Google Maps is generally good for major chains (which often are in a stand-alone unit that makes it easier to identify the business), but less accurate for small businesses that are, often, clustered along the length, or sometimes the perimeter of a buildings.

In most cases in this study Google appears to have relied on geocoding to locate the addresses of their business listings (since they appear not to have Street View Data for these areas) and in many cases the quality of the address data, the map match, or the geocoding algorithm produced, or combined to produce, insufficient accuracy in the name placement results. In the previous sentence, the “map match” refers to the detail available to place addresses within a building in which all of the units may have similar addresses. For example, in both shopping areas there was one large, elongated main building that ran almost the length of the entire shopping area. Unless you have access to the building plans that detail the size and location of the units that house individual businesses, you will have difficulty matching the address to a reasonable location within the larger unit.

In both shopping areas, the quality of location data, as shown by business placement on Google Maps was quite unimpressive and less advanced than I had hoped. While Google was able to identify the building outlines within both shopping areas, it was often unable to accurately determine where the business was within the unit. If Google actually desires to field autonomous cars that can deliver customers to a commercial destination, it is my belief that the accuracy of the business location placement on Google Maps will need to be vastly improved.

Street View is probably a portion of the required elixir, but being up-to-date would require the vehicles to explore all shopping centers on a recurring update cycle. To be of benefit, the update cycle should be based on a “change” index calibrated to economic indicators and other local and regional trends that effect the provision of services and retailing.

While Street View would provide some of the required geometry, I find myself wondering why Google does not appear to gather the details concerning store placement that are available from most property management companies. People who make their money leasing property know the units, their details, and even their clients. Of course, they may be pursuing these data and may not yet have compiled it for the areas studied here.

An aside

Before I end this already overly long blog, I feel compelled to comment on the utterly unhelpful “what’s here” tab that shows up when you right click on or near a location on Google Maps. In my experience the result is an address and a Street View image, if one is available. Oh, that’s really helpful. Once in a while you will see an address and a business name, but that happens infrequently. Given the details about locations that Google has amassed, why is this button’s functionality so lame?

Over a decade ago I gave a presentation about the utility of the GIS-powered “What’s Here” functionality. When you click a spot on the map the system responds with map details showing what is around the point you have selected and links allowing further exploration. This is a trivial spatial search that would add a world of utility to maps such as Google’s. Why hasn’t Google or anyone else invested the time and effort to generate a space filling functionality that would make those empty maps incredibly useful to anyone searching for anything close by the point of interest?

Now back to the topic

One final note, a colleague mentioned some news about Apple Maps and the charge it is making in displaying business listings on its maps. Well, take a look at the next figure.

Apple Maps shows some business listings

Figure 19. Apple critically lags Google in providing business names on its maps. Worse yet, Apple does not appear to provide building outlines, nor have access to any specific referential data that might improve its geocoding. As a result, it relies on street-face geocoding to place most of the businesses it names. The result is that the majority of the businesses are named along the surrounding city streets, even though this is not where the businesses are located. In the shopping areas examined in this study, street face geocoding and mislocating the business is better than not locating the business at all, but not very valuable to the consumer. Unless Apple Maps changes its current strategy for naming and locating businesses, it will remain severely behind Google in the Names Race. One ray of hope, Apple showed some of the small businesses that Google is missing. How about that?

General conclusions

Near the start of this blog I asked what Google’s performance in displaying business names on maps tell me about their strategies and challenges? My conclusions are quite simple:

1. Google is clearly working on improving the quality and abundance of the business listings shown on the basic Google Maps.
2. Google Maps continues to suffer from incomplete coverage of business listings and complicates this with less than accurate locations used to show the businesses that do appear on their maps.
3. Google Maps clearly under-serves the small business community. Although it has worked on strategies for improving this coverage it does not appear to have a significant advantage in this segment.
4. While the businesses in an area that are not shown on their maps appear to be known to Google, for some reason the company appears not to have enough confidence in these listings to present them as included content on their basic maps.
5. It remains unclear to me whether the incomplete listings reflect a quality control issue or is an indication that Google does not feel a need to provide complete coverage of businesses on its maps.
6. The completeness of business listings on Google Maps is a critical issue with implications for the utility of Google maps, as well as their ability to support navigation and autonomous vehicles in the future.
7. Google is obviously spending cycles trying to improve the quality and comprehensiveness of their business listing data and likely spending a ton of money to do so. What are its competitors going to do to compete if Google creates a highly accurate business listings database to go with its highly accurate maps? Either pay the toll, be shut out of the market, or continue to wonder why inferior products don’t excel in the marketplace.

There were several additional interesting items in the data collected for this study, but this blog is already too long and I have some Christmas shopping waiting for me. So with that, I send my warmest wishes for a Happy Holiday Season and may 2015 be your best year yet!

Dr. Mike

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Posted in Authority and mapping, business listings, Categorization, Google, google map updates, Google maps, Local Search, map updating, Mapping, mapping business listings, Mike Dobson, updating business listings | Comments Off on Google Maps and Business Listings – Better, but not quite there

Rumors Run Rampant – MapQuest on the Outside – another map engine on the inside?

September 10th, 2014 by admin

I heard what I will call a “rumor” this morning, but suspect that it was a statement of fact. If I am wrong, I apologize in advance. As you know, there are shades of rumors, so I will add some color where I have additional information.

It appears that AOL has quietly begun the shutdown of parts of the MapQuest operation. A few weeks ago the announcement was made at MapQuest’s back-end operation hub for MapQuest located in Lancaster, PA. Some members of the engineering staff were let go then, more will be released in November, and the operation in Lancaster is scheduled to close in March of 2015. Perhaps most important here is the fact that Lancaster is the back-end mapping operation for MapQuest. One would think that if they were moving the engineering operation to Denver (where the rest of the MapQuest group operates) that they would have moved the engineering team there, as well. It is my opinion that AOL intends to contract with another service to provide the mapping engine for MapQuest. Well, whatever the case, this is a rumor, but, I think, specific timing notwithstanding, the strategy of the story has been in the oven at AOL for some quite some time.

As some of you may know, MapQuest was once the leading provider of online maps and routes. Its historical trail involved a number of companies headed by Barry Glick and culminated in the property that eventually became MapQuest being acquired by Donnelley Cartographic Services, an organization that made maps for print publishers, but was wise enough to see the future of online mapping. Though the future was unclear, Barry and his successors navigated the road ahead and took MapQuest to a successful IPO, followed by the acquisition of the company by AOL.

MapQuest was the King of the Road in online mapping until it began to encounter a headwind from Google Maps. Of course, there were other earlier competitors than Google, but one-by-one these pretenders became irrelevant, fell into decline and ceased operations. The few that survived continue in business, but remain minor footnotes in the market.

It is somewhat interesting to note that the demographic that was attracted to MapQuest on the Internet was an older than average, mature audience. It is thought by many that the original audience for MapQuest continues with the service even today, with some of those loyal customers still printing out routing instructions rather than using route guidance through smart phones or other personal navigation devices.

The problem that nagged MapQuest’s planned IPO was a lack of revenue. Suffering from a real world case of the “Innovator’s Dilemma” MapQuest was a product that no one requested. When launched it was a “give-away”, a status that it could not escape once the genie was out of the bottle. Indeed, the numerous map-making companies littering the roadsides today are a result of “free-Internet maps.” Unfortunately, while MapQuest was able to overcome the perception of the “operating at a loss,” problem by up-selling its popularity when the stock market for Internet properties was “incandescent”, the problem did not disappear. The lack of revenue issue was knowingly acquired by AOL, whose executives were sure they could monetize the product line. Unfortunately, the strategies they implemented to cure the revenue problem failed and, when altered, failed again.

Those of you who read this blog may have noted that on several occasions I have indicated that Google is in the advertising business and that mapping, a side-line, was integrated into their strategy as a method of selling more advertising, especially location-based advertising. While MapQuest tried the advertising gambit, even Google advertising, it could not generate enough revenue to cover operating expenses. It’s likely that even Google has made its investment in mapping with little hope of recovering its map compilation and serving expenses. However, its map base provides advantages to the company in advertising and beyond that prompt it to continue its massive investment, at least for a while.

The important note here is that online maps have produced a state of disequilibrium in the market for online maps– one in which the revenue results not from the sale or the use of maps, but results from the advantages that maps can bring to other product lines over long periods of time. I think you all know the budget battles that must ensue at Google about who is paying for what and why this new mapping initiative deserves to be funded. If Google has not had these arguments yet, I guarantee you that they will in the future

What we are left with is an unbalanced market where Google, HERE (Nokia) and Apple will remain the major players. I suspect that AOL will replace MapQuest with either HERE maps or Google Maps, but in any event, MapQuest, an American original, will be soon be no more than a shell of its past glories.

My hat is off to the stalwarts that created, popularized and polished MapQuest. You did your profession and your company a great service. AOL? Well, they never seemed to understand mapping, or the use of spatial data. Perhaps more importantly, it appears that the executives did not understand how to manage MapQuest to success. I understand that both MapQuest and AOL Search report to the AOL Chief Analytics Officer. I am sure that monetizing spatial data is not one of his competencies. After all, map data and map engines are generic – at least to those who know nothing about either!

It is likely that switching the MapQuest engine is “merely” a matter of expense for AOL. Too bad they did not see the promise of MapQuest, but “buyer’s remorse” is a terrible thing and usually leads, as it did in this case, to limited investment for new product development. Speaking of “buyer’s remorse” it is an issue that may be endemic in the mapping industry, as HERE continues to muddle making a success of the former Navteq and TomTom is rumored to be in a dither about mapping expenses from the former Tele Atlas.

One more thing – there are some highly talented software engineers from MapQuest now available. I found a few of them on LinkedIn – take a look if interested.

I hope your Labor Day Holiday was relaxing and rewarding,

Dr. Mike

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Posted in Apple, Geospatial, Google, Google maps, HERE Maps, local search advertising, map compilation, Mapping, MapQuest, Mike Dobson, Navteq, Nokia, Personal Navigation, Tele Atlas, TomTom | 2 Comments »

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