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Blog Thoughts

Why Uber is not Geo-Taxi!

For years, the geospatial industry has grappled with an identity crisis, often resorting to a rather peculiar linguistic habit: slapping “Geo-” in front of any technology term remotely related to location. “Geo-analytics,” “geo-fencing,” “geo-marketing”—the list goes on.

And don’t get be started on Geo-AI !

This pervasive prefixing, I’d argue, is a desperate attempt to cling to the myth that Geo or “Spatial is special,” a self-defeating endeavour that ultimately hinders the industry’s true integration into the broader technological landscape.

Let’s be clear: location is of course fundamental. It’s an essential component of countless modern applications, from logistics to social media. But the notion that simply incorporating spatial data elevates a technology to a unique “geo” category is, frankly, outdated and counterproductive. It creates an artificial barrier, suggesting that anyone outside the “geo” bubble can’t fully grasp or innovate within this space.

Let’s be clear: location is of course fundamental.

Consider the ubiquitous example of Uber. It’s a prime example of a company whose entire business model is built upon location intelligence. Without real-time tracking, optimised routing, and dynamic pricing based on geographic demand, Uber simply wouldn’t exist. So, by the geospatial industry’s own logic, shouldn’t we be calling it “Geo-Taxi”?

Of course not.

Calling Uber “Geo-Taxi” sounds ridiculous precisely because it highlights the absurdity of this “geo-prefixing” habit. Uber is a transportation platform. It leverages a multitude of technologies—mobile computing, payment processing, data analytics, and yes, geospatial data—to deliver a service. The “geo” aspect is an enabler, not the defining characteristic of the entire enterprise.

The problem with constantly adding “Geo-” is that it inadvertently reinforces the idea that geospatial technology operates in a silo, distinct from mainstream tech. It implies a level of complexity or niche expertise that, while sometimes true for highly specialised applications, isn’t reflective of how location data is now seamlessly integrated into everyday solutions.

The reality is, most groundbreaking innovations that heavily rely on location data are coming from companies that don’t brand themselves as “geo” companies. They are logistics companies, e-commerce giants, social media platforms, and autonomous vehicle developers. They embed geospatial capabilities deeply within their systems without needing a special prefix to validate their use of spatial information. They simply solve problems, and location data is a critical tool in their arsenal.

So, what’s the alternative? Instead of clinging to the “Geo-” prefix, the geospatial industry needs to pivot its narrative. We should focus on the value that location intelligence brings to various domains, rather than trying to carve out a separate, “special” category for ourselves.

We should be highlighting:

  • The power of spatial analytics: How understanding patterns in location data can drive business decisions, optimise resource allocation, and improve urban planning.
  • The transformative impact of location-aware applications: How real-time positioning is enabling everything from personalised experiences to more efficient supply chains.
  • The integration of geospatial data into mainstream platforms: Emphasising how location information is now an integral part of databases, cloud services, and AI models.

By shedding the “Geo-” crutch, the industry can better position itself as an indispensable enabler of innovation across all sectors. We can move beyond the myth of “Spatial is special” and instead demonstrate that “Spatial is essential“—an embedded, foundational layer within the vast and interconnected world of modern technology. 

Let’s stop trying to make “Geo-Taxi” a thing, and instead celebrate how location intelligence is quietly and profoundly shaping the world around us.

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Blog

Snap Map the maps app you probably don’t use.. and that’s the point !

Snap Map - Local Search

When Snap Map first launched within the Snapchat app, it was met with a mix of curiosity and privacy concerns. Yet, this integrated feature has quietly become a significant success story, particularly among its core demographic – Millennials. While perhaps not always the focus of mainstream tech commentary, Snap Map has proven to be far more than just a simple location-sharing tool; it’s a dynamic social and discovery platform that resonates strongly with this generation.

Snap Map’s genius lies in how it caters to Millennials, a generation that grew up alongside the internet and the rise of social media. It transforms the static concept of a map into a vibrant, real-time representation of their social world. Users can see cartoon-like “Actionmojis” of their friends’ Bitmojis placed on a map, indicating their general location and what they might be doing (based on factors like speed or time of day).

This real-time social layer is incredibly powerful for Millennials. It facilitates spontaneous meetups (“Oh, you’re nearby? Let’s grab coffee!”), provides a sense of connection to friends scattered across a city or even the globe (“Just checking in on what everyone’s up to”), and subtly fuels that ever-present fear of missing out (FOMO) in a visually engaging way. It’s not just about knowing where someone is, but feeling connected to their immediate reality and potential activities, aligning with the Millennial desire for authentic, in-the-moment experiences.

Beyond the social aspect, Snap Map has evolved into a powerful discovery engine. Heatmaps appear on the map, indicating areas where a lot of Snapchat activity is happening – be it a major concert, a local festival, or just a popular hangout spot. Millennials can tap on these areas or specific locations to view public Snaps submitted by people at that place, offering a ground-level view of events and environments as they unfold. This transforms the map into a living, breathing news feed of local happenings, curated by the community itself, perfect for a generation keen on exploring and experiencing their surroundings.

For Millennials, Snap Map integrates seamlessly into their existing Snapchat habits. Checking the map becomes as natural as viewing stories or sending snaps. It leverages the platform’s visual language and social graph to create a unique, engaging experience that keeps users within the app longer and reinforces their connection to their friends and their local environment.

A Different Kind of Local Search: Competing with Giants?

While Google Maps remains the undisputed king of navigation and comprehensive business listings, Snap Map presents an interesting, albeit different, form of local search and discovery, particularly for its core Millennial audience. Traditional platforms excel at providing structured information: business hours, reviews, directions, and contact details. Snap Map, on the other hand, competes by offering something more experiential and immediate.

Instead of searching for a restaurant’s phone number, a Millennial user might check Snap Map’s heatmaps to see what’s buzzing in a particular neighborhood, or tap on a location to see real-time Snaps from inside a bar or event. This provides an authentic, user-generated glimpse into the current vibe, crowd, and activities happening at a place – information that static listings often can’t convey. It’s less about finding information about a place and more about experiencing what’s happening there right now.

For a generation that values experiences and peer validation, seeing friends or other users actively sharing moments from a location can be a powerful motivator to visit. Snap Map’s local discovery is inherently social and visually driven, offering a complementary, and sometimes even preferred, way for Millennials to decide where to go and what to do, especially for social outings and events. While it won’t replace the need for navigation or detailed business info, Snap Map carves out its niche by offering a dynamic, community-powered lens on the local world, posing a subtle, experience-focused challenge to traditional local search paradigms.

While it may fly under the radar for those outside the typical Snapchat demographic, Snap Map’s ability to blend real-time location data with social interaction and content discovery has made it a sticky, successful feature that significantly contributes to the overall appeal and utility of the Snapchat platform for millions of Millennials worldwide. It’s a prime example of how location services, when designed with a specific user base and their social behaviours in mind, can become incredibly compelling.

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Blog

3D Tiswas style

For people of my generation the term Splat will always be associated with the Saturday Morning Children’s TV show ‘Tiswas” and was to the result of someone getting a “custard pie” in their face…

Now the hot topic in 3D Geospatial rendering are Gaussian Splats..

In Geospatial 3D, Gaussian splats are used as for rendering and representing surfaces, particularly in point-based rendering techniques. Unlike traditional polygon-based methods, which use vertices and edges to define a surface, Gaussian splats represent the surface as a collection of points or particles, each with an associated Gaussian function.

This function is typically a bell-shaped curve, which spreads out in space, creating a smooth, continuous representation of the surface.

The advantage of Gaussian splats is that they can represent complex, organic shapes and soft surfaces without the need for explicit mesh structures, as a result this is an alternative to most of the 3D techniques used in Geospatial Technology so far..

Moreover, Gaussian splats are valuable in managing large datasets, such as those found when working with large point clouds. Since each splat is a smooth approximation of the surface at that point, it allows for efficient rendering, even with millions of points.

Overall, Gaussian splats provide a flexible and efficient approach to 3D rendering, offering smooth approximations of surfaces with minimal computational overhead.

To try out both the capture and rendering abilities of Gaussian Splats I used the recently developed iPhone App Scaniverse from Niantic (yes the Pokemon GO people!) on a riverside walk to capture a large sculpture at Runnymede on the banks of the River Thames called “The Jurors”. The scan using my iPhone took about 5 minutes and the results are very impressive.

This is still very early days for the technology, but it’s nice to see some innovation in the 3D visualisation space, Niantic have a vision to map the world using Scaniverse, that’s quite a challenge but then so was Google Earth !

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Blog

What’s My Map?

It was a great pleasure to spend half a hour talking with my friend Jerry Brotton on his excellent History of Maps podcast, “What’s Your Map“. This excellent podcast series cover the history of cartography by getting people to select a single map to place it’s significance in the development of mapping.

The podcast is supported by the wonderful Oculi Mundi a digital heritage website which is the home of The Sunderland Collection of world maps, celestial maps, atlases, globes and books of knowledge.

My map is he ‘Christian Knight Map’, produced by Jodocus Hondius in 1597, and the first map to use Mercator’s Projection after the death of its inventor, Gerard Mercator.

Now today the Mercator projection is the subject of much criticism, mostly as a result of it’s misuse and no doubt the impact of a episode of the much loved “West Wing” TV show which featured a group of cartographers lobbying the President to ban it’s use !

I make the point in the podcast that beyond any issues of “Social Equality” the projection certainly had many advantages in the early days of web mapping when the size of Greenland did not seem to matter…

Well…

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Blog Data Policy

So an AI walks into a Pub..

There is a joke/useful analogy that in very simple terms explains despite all the complexities and technicalities, how modern AI systems work at a fundamental level..

An AI walks into a pub and goes up to the bar, the bartender greets the newcomer and wants to know what they would like to drink..

“What’s everyone else drinking…”

Good is it not…

An AI or specifically a LLM is a reflection of it’s training data and is looking for the most statistical relevant or in simple terms “most common” response to any question you give it.. what most people are drinking in the bar analogy..

“What’s everyone else drinking…”

The reason I bring this up is a trip I made to Dublin last week, and a visit to one or two bars in that fine city.

What is everyone else drinking… well in the Temple Bar area of Dublin, is going to be a pint of Guinness.. and perhaps in most of the city that is going to be the case.

But how representative is this.. the bartender in the joke / analogy is of course the training data used to train our model so while Guinness have the statistical significance in a Temple Bar pub, is it the case for Dublin, or indeed the rest of Ireland.

If we expanded out sample of bartenders to include all or Ireland Guinness may have less significance on the other hand if we focused on some of Dublins more up market bars we might find a lot of expresso martinis consumed..

A bar in Dallas, Sydney, or Bangkok are all lightly to produce different responses for our imaginary bartender..

The moral of this is clearly that models are very sensitive to their training data and how representative the training data is of the subject of interest, in almost all cases in may not be as representative and we might like and an important question for the industry is what to do in those circumstances.

How we alter the response (weight) of a system based on a foundation model to take into account limitations of data is the real “Question for our Times”, and indeed it’s also important to remember that sometimes the data is actually an accurate reflection of reality even if we might not like it..

In AI data is the code

In AI data is the code, so we need to really understand all aspects of it, not just how representative it is but its antecedence, who created it and for what original purpose.

More thinking along this lines to follow…

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Blog Thoughts

Why Concorde is the only airliner at Heathrow on Google Maps…

It was 20 years ago today…

It was 20 years ago today

BA002 on finals returning from New York JFK on the last commercial operation by Concorde on the 24th October 2003 marking the end of the era of supersonic air travel. Filled with celebrities G-BOAF touched down at 4:05pm and I was lucky enough to be there amongst the many thousands of Concorde fans to see it and the two previous consecutive Concorde landings.. 

Today G-BOAF also the last Concorde ever to fly is on display at the excellent Aerospace Museum in Bristol, while at Heathrow her sister aircraft G-BOAB resides outside at British Airways Maintenance Facility, seemingly unloved and forgotten.

However all is not lost, if you look at Heathrow Airport of Google Maps you will find that Concorde is the only airliner visible !

Heathrow the airport without airliners ?

This is not the result of a Satellite or Aerial photograph captured during a very quiet period or during the COVID lock-down when of course there were many aircraft parked at the airport, instead it is the result of image processing and the use of AI techniques to remove moving objects..

As Concorde G-BOAB has not moved in many years it is the only airliner at Heathrow..

A last laugh for Alpha Bravo !

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Blog Thoughts

Beware Mechanical Turks..

The mechanical turk is a recurring warning to technologists and perhaps wider society in the need to have a healthy degree of scepticism when it comes to demos of what appears to be game changing advances in technology.

The original Mechanical Turk or Automaton Chess Player, was a fraudulent chess-playing machine demonstrated in the late 18th century. The automaton, took the form of a box on top of which was of a life-sized model of a human head and torso, with a black beard dressed in Ottoman robes and a turban. The automaton was able to “Play” games of Chess with some considerable skill, playing notable figures of the time including Napoleon Bonaparte and Benjamin Franklin!

Of course the Turk was in fact a mechanical illusion the box allowed a human chess master hiding inside to operate the machine.

In December 2022 OpenAI released ChatGPT, a demonstration artificial intelligence chatbot to highlight advances made in dialogue based user interfaces. Behind the chatbot is a very large language model fine-tuned with both supervised and reinforcement learning techniques.

ChatGPT has been lauded as making AI mainstream and indeed on first impressions seems to produce very credible results answering specific fact based questions, creating detailed and consistent articles, generating scripts in the style of particular authors and generating working code to carry out defined tasks in many computing programming languages.

But..

ChatGPT is a very accessible interface to OpenAI’s GPT 3.5 language model trained on a wide corpus of internet content containing 175 billion elements, but of course that is both it’s strength and it’s weakness. To be clear I am in no way critical of OpenAI’s work with GPT, but perhaps the nature of a chatbot interface and way in which the chatbot answers questions with confidence can be problematic…

Let me demonstrate..

I asked ChatGPT to write a critical review of what3words well known in the UK as geocoding system using shortcodes of three discrete words.

ChatGPT dialogue

Lets look at the response, which at first seems quite impressive…

what3words is a geocoding system that divides the world into a grid of 3m x 3m squares and assigns each square a unique three-word address. While the idea of using three words to identify a specific location is innovative and has potential, there are several limitations to the system.

This is an accurate summary of how what3words works very much in the style on a standard essay response, as I have asked for a critical review chatGPT has found some limitations…

First, the use of three words as an address can be confusing and difficult to remember, especially for people who are not familiar with the system. In contrast, traditional street addresses and geographic coordinates are typically easier to remember and communicate.

This is really interesting, now I am in no way a massive fan of what3words but this statement would seem to be the direct opposite of the basic proposition of what3words… traditional geographic cooridnates are hard to remember, what3words are not ? There may be a more nuanced arguement here that traditonal address are social constructs that are widely used and understood where they are widely used, but this is not exposed by GPT?

Second, the what3words system is not universally adopted, so not all locations have a three-word address. This can make it difficult to use the system to communicate the location of a particular place, especially if the recipient is not familiar with the system.

Here this is a example of the so-called hallucination, while what3words is not universally adopted of course, it is complexity wrong to suggest that not all locations have a three-word address as the system was designed to provided a global reference system –

Hallucination is perhaps not the best term here the anthropomorphic reference to a false perception is not quite right as, and this is a key point, there is no understanding or intelligence displayed by GPT instead the terms with the highest probability of use within the context of the previous or similar content are selected.

Third, the what3words system is proprietary and owned by a private company, which means that it is not an open, standardized system that can be freely used by anyone. This lack of openness and standardization can limit the potential uses of the system and create barriers to its adoption.

Good points re openness and a well argued point, although of course there are many highly successful proprietary services online.

In conclusion, while the idea of using three words to identify a specific location is interesting, the limitations of the what3words system make it difficult to use and not as effective as other methods of identifying locations. As a result, it may not be the best solution for many applications.

A good concluding paragraph you might expect from an undergraduate essay on the topic, and indeed for many answers provided by all the current language model “AI’s” especially concerning specialised subjects, there is the unmistakable influence of the academic literature which forms an important consistent part of the LLM themselves!

So maybe an important question assessing the usefulness of LLM based AI’s may be.. “Where did you go to college ?”

“Where did you go to college ?”

One final point while we are all fixated on Generative AI and especially LLM’s we should not forget that the majority of applications of Machine Learning (Machine Learning not AI – more on this another time) in general and especially when it comes to applications of ML in Geospatial Technology are not Generative and are instead focused on the classification and extract of features from existing datasets.

Google Open Buildings Dataset derived from a deep learning model trained to determine the footprints of buildings from high resolution satellite imagery.
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Blog Thoughts

Mission Complete !

As you may have noticed over the last three months I have been walking to raise funds for the London Air Ambulance. Like all Helicopter Emergency Services in the UK, it relies on charitable donations to operate and I have supported the charity for a few years.

The charity organised “Miles for Missions” over the summer asking people to walk the equivalent of the distance around London, 116 Miles to raise funds via sponsorship.

I was one of 264 people who helped to raise an amazing £86,062 the equivalent of 42 potentially lifesaving missions by walking 41,000 miles!

I walked 744 miles and raised nearly £400 thanks to 13 generous sponsors – Thank you all !

(btw it’s not too late to make a donation here)

Now it’s time to put my feet up (for a day or two anyway!)

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Blog London Loop

Walking around London again, this time for Charity!

Following on from my challenge to actually walk around London on the London Loop Path, I’m now walking around London again to support for London’s Air Ambulance Charity this time by walking the equivalent length of the London border. 

Miles for Missions is a challenge to complete 116 miles between June 21st and midnight 20th September, to help raise vital funds for this lifesaving service.

London’s Air Ambulance is the charity that delivers an advanced trauma team to London’s most seriously injured patients. Your donation will help save more lives by keeping the helicopter in the air and the rapid response cars on the road, ready for when they are needed most. By providing intervention as quickly as possible after injury, London’s Air Ambulance Charity aims to give patients the best chance of survival, and best quality of life, after trauma.

I’d be very grateful for your support, visit my donations page at https://fundraising.londonsairambulance.org.uk/fundraisers/edparsons/miles-for-missions

Thank you !

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Blog London Loop

Home along the Hogsmill

The London Loop Part 13: Ewell to Teddington

The final 10 miles or so, of my circumnavigation of London following the London Loop path for the most part following the route of the Hogsmill River.

For the last 30 years the Hogsmill has always brought to mind the almost canal like section in Kingston, notable for it’s shopping trolleys and other dumped rubbish midstream and less than active surroundings, however at the start of the route in Ewell it’s a beautiful country stream.

The route of the Loop follows the Hogmill north through a series of nature reserves and is surpassingly rural despite passing though Suburban London.

Only on two occasions at Old Malden and at Kingston does the path separate from the river, and to be honest as a result there are few points of interest along the route until you reach Kingston and the finish for me in Bushy Park.

The Hogsmill Path

I began to recognise more and more of the route North of Malden Manor where the path takes you below the busy A3 and into Berrylands.

Passing by the Knights park Campus of Kingston University was very much home territory, I always though this was the part of the institution the “Cool Kids” went to unlike us Geeks at Penrhyn Road !

The Hogsmill and the Knights Park campus of Kingston University

The section of the path though Kingston itself is poorly signposted so you really need to follow the route as best you can from the map, although for some reason the “official” route takes you on the other bank of the Hogsmill from the Coronation Stone – Kingston’s must see point of interest.

The Coronation Stone

The Coronation Stone s believed to have been the used for the coronation of seven Anglo-Saxon kings in the tenth century, although at a different site, the ancient church of St Mary which no longer exists.

Kingston Bridge

A few hundred metres from the Stone, the Hogsmill meets the Thames, and for me it’s only a mile or so across Kingston Bridge to my starting point back on the Chestnut Avenue in Bushy Park.

Finished !

So my lockdown adventure is complete, it took a little longer than expected as a resulting of the last stay at home regulations but I really enjoyed seeing parts of the London Suburbs I had not visited and to really understand just how fortunate we are that London has such a discrete boundary, the result of planning in the 1950’s mean there really is a Green Belt around London.

If you would like a copy of my actual walking route the kml file is here, which was used to produce the following movie in Google Earth for your enjoyment !