ConsultingWhere was recently commissioned by GeoPlace, a public sector limited liability partnership between the LGA and Ordnance Survey, to research the cost/benefit evaluation of the impact of address and street data across England and Wales. An article written for Geoconnexion detailing the project outline and methodology can be found here.
In 2008 Satoshi Nakamoto published a paper on the cryptography mailing list metzdowd.com describing a method for preventing double-spend of digital currencies using digital signatures, peer-to-peer networks and a concept called ‘proof-of-work’. His idea resulted in the first cryptocurrency Bitcoin, market capitalisation at time of writing $4.2 Billion USD.
The success of Bitcoin generated a slew of imitators and interest in the technology is now extending beyond the field of cryptocurrencies. The distributed public ledger concept central to Bitcoin has become known as The Blockchain and is being been touted as a potential replacement for any existing centralised database. Just as Bitcoin is now seen by some as a viable alternative to banks some see The Blockchain as a potential alternative to other centrally administered civic institutions, such as, in the field of land administration, land registries.
Although not mainstream this idea is slowly gaining traction in land administration circles. In May 2015 the Young Surveyors Network of the International Federation of Surveyors (FIG) Working Week ran a workshop entitled ‘Surveying the Future: Mapping the Bitcoins’, the presentation was prepared by Paula Dijkstra, Christiaan Lemmen, Annet de Boer from the cadastral agency in the Netherlands and by Eva-Maria Unger, chair of the Young Surveyors Network (from the Austrain cadastre)..’ Around the same time Reuters reported that Honduras is piloting a land registry using Bitcoin technology, while in northern Ghana an organisation called Bitland has begun implementing a land registry and titling protocol using the Bitcoin’s Blockchain to timestamp land titles.
To understand how Blockchain technology might be applied to land administration a good starting point is to examine some of the principles that underlie the initial application Bitcoin.
How does Bitcoin work?
Bitcoin is traded over and administered by a distributed network of nodes connected via the internet. Anyone wishing to buy or sell Bitcoin must first become a node on this distributed network. This is done by downloading a peer-to-peer networking application known in Bitcoin parlance as a Wallet. It is also possible to sign up for an online third party Wallet service.
The Wallet software generates private and public keys used to digitally sign your transactions. It is common practice in Bitcoin to generate a new private and public key for every transaction to maximise privacy, but in a fully transparent system your public key could also act as your unique identifier.
The Wallet software initially downloads, and then continually syncs with, The Blockchain. At the time of writing approximately 20GB of data. This is the definitive record of every transaction in the history of the Bitcoin cryptocurrency.
To make a Bitcoin transaction a user must broadcast the details of the transaction to the network using their Wallet application. The transaction is signed digitally and contains the unique key of both parties. Specialist nodes called “miners” collect hundreds of transactions together into a bundle called a “block”. Miners validate the transactions (check The Blockchain to check it has not been already spent – described as a double spend) and then complete a ‘proof of work’; a computationally difficult, but easily verifiable, calculation. The first miner to complete the proof of work (as verified by the other nodes) adds their block of transactions to The Blockchain and receives a reward, currently 25 bitcoin (£3,880.51 at time of writing), plus any additional transaction fees.
The Blockchain is not technically immutable, but it is computationally impractical for an attacker to alter it, provided honest nodes control a majority of CPU power in the network. Currently the distributed computational power in the Bitcoin network is thought to be 280 times more powerful than the top five supercomputers in the world.
In addition to being difficult to corrupt, the incentives provided to Bitcoin miners mean that it is more financially advantageous for actors with access to large amounts of computing power to use it to mine Bitcoin, thereby administering the system, than attempt to corrupt The Blockchain.
How could Blockchain technology be applied to land administration
Factom, the company appointed by the government of Honduras to apply Bitcoin technology to its land registry has developed a technology that inserts references to data stored within its own peer-to-peer network into the Bitcoin Blockchain thereby piggy backing on Bitcoin’s trust and immutability. The references are inserted using Bitcoin’s OP_RETURN protocol, introduced in 2013, that allows users to insert up to 40 bytes of data into any transaction. It seems likely (from a reading of Factom’s whitepaper) that the system will be a method of recording deed transaction and will only provide a record that the transaction has occurred. The system will not attempt to validate the transaction. This is analogous to a registry of deeds, or title registry.
Bitland’s method is similar, but is more open and uses a suite of already available technologies. GPS, or aerial imagery is used to delineate plots which are added to the cadastral layer in Open Street Map. Title deeds are drawn up using the standard land title contract including references to the mapping. The land titles are signed using the land owner’s Public Key and the Public Keys of neighbouring landowners to indicate consensus. The existence of the title deeds is then time stamped and immortalised by inserting it into the Bitcoin Blockchain using a service such as btproof or proofofexistence. This services allows you to create a cryptographic hash unique to your document and to insert it into a Bitcoin transaction. When this transaction is inserted into the Blockchain, the block it is within is timestamped. The hash stored in The Blockchain alongside an original copy of the title deed is proof that the title deed existed at a point in time, the Blockchain acts as a public notary.
Inserting references to title deeds into The Blockchain proves that the deed existed, but not that it was valid. A system that could also validate a transaction would have to include a method of linking ownership of land, or a land right, to a cryptocoin so that when the coin is transferred the right is transferred with it. The FIG Working Week suggested linking land units with Bitcoins on a one coin per unit, or part of one coin per unit basis see Fig 1. Bitland also suggests a similar approach using the Bitcoin Meta Protocol Colored Coins.
An alternative to linking transactions to Bitcoin would be to set up an entirely new protocol based on Bitcoin/Blockchain technology, but for the express purpose of recording land transactions. The advantage of this method is that each coin could be specifically linked to a geographic entity such as a grid square, a link that could be hard coded into the coin’s ID. The validation mechanism built into the system would ensure that it would be impossible for two entities to hold the rights to the same piece of land simultaneously. You could feasibly also programme the protocol to include a mechanism for validating real world identity. The problem is that the system would require a mechanism to incentivise the miners needed to validate the transactions and maintain The Blockchain ledger. Because land is finite it is not possible to grant land as an incentive to the miners. The system would have to rely on transaction fees, which would have to be maintained at a sufficiently high level to attract enough miners to ensure the integrity of your new Blockchain.
Another advantage of setting up an entirely new protocol is that control over how the protocol develops over time can be secured. Bitcoin is an open source project, currently there are five developers with push access to the Github project for the Bitcoin core protocol. In August 2015 a dispute among this group over the correct size for a block led to the development of a rival protocol. Making a protocol critical to a country’s land transactions and then ceding control of that protocol is risky because changes to the protocol could disrupt the land administration process. For example the timestamping services used by Bitland to insert hashes into The Blockchain alters the public keys used in the transaction in a way that prevents the Bitcoin in that transaction ever being spent again. Some within the Bitcoin community consider this a corruption of The Blockchain and want the protocol changed to prevent it. Similarly the OP_RETURN protocol used by Factcom was reduced to 40 bytes from 80 bytes by the core developers, the developers retain the power to reduce it further or remove it altogether.
The advantages of Blockchain for land administration
Existing land information systems are typically centralised ledgers containing records of a nation’s land transactions. Where they function well they underpin the value of the country’s land assets by guaranteeing title, or facilitating a system of title insurance. Where they are dysfunctional they inhibit economic growth by reducing security of tenure, provoking ownership disputes and concealing corruption. A Blockchain land registry would have the same function (and potential problems and benefits) as a traditional land registry, but with one key difference, the ledger (the record of the nation’s land transactions) would be decentralised. Theoretically this could have a number of advantages over a centralised system.
Resilience: every node on the network would have a full copy of the ledger. This level of redundancy would make the registry almost indestructible. Flood, fire, electrical storm, invading army; The Blockchain persists.
Trust: land registries trade on trust. As described above, the Bitcoin Blockchain, as it is currently operates, is effectively immutable. Most centralised land registries also cannot be altered without due process and a fully documented audit trail, but there have been cases of countries with weak institutions where corrupt and powerful interests have used influence to disrupt due process.
Transparency: every transaction is in the open for anyone to see. Every node on the network has all the information they need for an audit of every transaction. However for full auditability there would need to be a mechanism for identifying the parties involved in the transactions, currently this is not possible with Bitcoin.
Efficiency: by removing third parties you can theoretically increase the efficiency of the system. The land registry in England and Wales had an operating cost of £239.9 million in 2013/14, this is only part of the total cost to the economy of land transactions, with a full picture including the fees charged by solicitors.
Automation: property transactions often rely on chains, a buyer needs to sell their property before they can buy and so on. It would be possible to automate such chains with a transaction being programmed to proceed once a defined transaction appears as complete in The Blockchain.
Many of the benefits listed above are also benefits of any digital land registry, not exclusive to The Blockchain. Any fully digital e-conveyancing system could be set up to eliminate or minimise expensive third parties and could even be programmed to carry out automated chains of transactions. The UK Land Registry experimented with exactly this type of system over five years ago but shelved it citing a lack of interest. The fact that so few fully digital land registries exist is not due to a lack of technology, but more to do with a lack of political will and resistance from vested interests.
There are also many examples of digital land registries failing to deliver promised improvements in land administration processes. Rarely is it the technology that is ultimately at fault, more often it is tricky real world issues, such as correctly identifying the initial rights holder and documenting the geographic boundary of a claim. These issues would be equally difficult to handle regardless of the particular land registry’s underlying technology.
The benefit of transparency provided by The Blockchain is also already available to a centralised land registry if its data is made open and freely available. Although The Blockchain concept has built in transparency it can still be used in an anonymous way by generating a new unique key for every transaction. For any system of land registration using Blockchain technology to work there would need to be a mechanism for linking the transactions recorded in The Blockchain with real world identities. If this identity database is centralised you are effectively replacing a single centralised database with a decentralised database plus an adjunct centralised database.
The Blockchain used for Bitcoin is very resilient and secure, but the resilience and security of the network depends on its users (the integrity of The Blockchain relies on it being computationally impractical for an attacker to alter provided honest nodes control a majority of CPU power in the network). The website Bitnodes recorded the number of reachable nodes in the Bitcoin network as 6139 at the time of writing. This offers a huge amount of redundancy (copies of The Blockchain), plus a huge amount of distributed computational power. But will the network always be this healthy? What if the value of the currency collapses below a level where it is uneconomical for miners to participate? If a country had linked its land registry to Bitcoin would the government have to step in and provide its own nodes? In addition should a nation rely on a distributed network of unknown actors for the integrity of its land registry? Would this not expose them to the risk of cyber attack from malicious foreign powers?
Ultimately it may come down to a question of trust, or more accurately (since proponents of Bitcoin describe The Blockchain as a trust-less system) reputation. The value of a land administration system lies not in its efficiency, so long as it functions adequately. The value is in the level of trust that is placed in it based on its reputation. Property systems in the UK are considered so trustworthy that billions of pounds a year flow into them from hundreds of other countries where assets are not considered to be as secure. Will a technology that is currently synonymous with drug dealers provide the same level as assurance and trust as such respected and esteemed institutions as the Land Registry, the Law Society and the Royal Institution of Chartered Surveyors, who, for a healthy, but reasonable fee, currently administer the system of land transactions in the UK? If the answer is “no” then the cost to the UK economy of instituting a Blockchain land registry would be considerably greater than any efficiency savings such a system could offer.
Published 19th August 2015 by Philip Knight, Senior Consultant, ConsultingWhere
Correction: a previous version of this article omitted to mention that ‘Surveying the Future: Mapping the Bitcoins’ was a workshop organised by the Young Surveyors Network outside of the main programme of the FIG Working Week.
 Bitcoin: A Peer-to-Peer Electronic Cash System https://bitcoin.org/bitcoin.pdf
 Archive of Satoshi Nakamoto’s posts at metzdowd.com http://email@example.com&q=from:%22Satoshi+Nakamoto%22
 Market capitalisation as of 29th July 2015: https://blockchain.info/charts/market-cap
 P Dijkstra, C Lemmen, A De-Boer (Kadaster) E M Unger (BEV). FIG Working Week, Young Surveyors Session. ‘Surveying the Future: Mapping the Bitcoins’, available at: http://www.fig.net/resources/proceedings/fig_proceedings/fig2015/ppt/ys/ys_bitcoins.pdf
 Reuters, ‘Honduras to build land title registry using bitcoin technology’, available at: http://in.reuters.com/article/2015/05/15/usa-honduras-technology-idINKBN0O01V720150515
 Bitland Homepage, available at: http://bitland.adsactly.com/blog/bitland-overview/
 Wired Retail, ‘Bitcoin might fail but the blockchain is here to stay’, available at: https://www.youtube.com/watch?v=jbu6I-8mNUo&t=8m24s
 Factom, Whitepaper, available at: https://github.com/FactomProject/FactomDocs/blob/master/Factom_Whitepaper.pdf?raw=true
 BTProof, homepage, available at: https://www.btproof.com/
 Proofofexistence, homepage, available at: http://www.proofofexistence.com/
 The Guardian: ‘Bitcoin’s forked: chief scientist launches alternative proposal for the currency’, available at: http://www.theguardian.com/technology/2015/aug/17/bitcoin-xt-alternative-cryptocurrency-chief-scientist
 Coindesk: ‘Developers Battle Over Bitcoin Block Chain’, available at: http://www.coindesk.com/developers-battle-bitcoin-block-chain/
 HM Land Registry, ‘Annual Report and Accounts 2013/14’,
available at: https://www.gov.uk/government/uploads/system/uploads/at
 The Guardian, ‘Web plan for property sales shelved after lack of interest in pilot’, available at: http://www.theguardian.com/technology/2008/apr/10/property
 Reachable nodes as of Fri Jul 31 2015 17:13:42, sourced via: https://getaddr.bitnodes.io/
The development of autonomous vehicles has received considerable attention in the technology press lately. So far this hasn’t translated into a lot of interest from the geospatial sector, despite this emerging technology’s reliance on geographic data. This article examines self-driving cars from the perspective of the geospatial industry, it examines the technology’s requirements for hugely detailed geographic data and suggests novel methods that technology firms could use to meet these demands, some of which could have a potentially disruptive impact on the geographic information industry.
As this advert from 1957 demonstrates self-driving cars are a long held dream. A dream that, until now, has repeatedly failed to materialise. This is set to change. Catalysed by the rapid advancements in, and falling costs of, computing power and sensor technology. Along with a big push from DARPA (Defence Advanced Research Projects)1, autonomous vehicles are no longer a futuristic concept, they are a reality. Google’s autonomous cars have driven safely for more than 700,000 miles2; Nissan have revealed they are planning to release a driverless car by 20203 and the newest Tesla will include a feature that will allow drivers to summon their cars to meet them at a prearranged spot4 (for use only on private property, for now). The technology is attracting interest from government’s worldwide; the Chinese government has sponsored six driverless car competitions5; while the UK government has also offered funding and regulatory reform6. Meanwhile the list of major companies in the autonomous vehicle race is ever growing. At the time of writing Wikipedia listed 12 major companies and research organizations that have developed working prototypes7.
No one knows for sure when this technology will replace the average driver but the consensus seems to be for a vehicle market dominated by autonomous vehicles in around 20 years. Of the educated guesses available: ABI Research predict 50% of new vehicles will be autonomous by 2032, Navigant Research predict sales of autonomous vehicles to reach 75% by 2035 and Morgan Stanley predict 100% market penetration by 20468 (global car sales 2013: 68.69 million units9). The safety benefits are the most widely touted at this early stage (Volvo target zero deaths by 202010), but it is the savings in time, money and carbon that a more efficient and less congested transport system would provide that make up the biggest wins. Wins that led Morgan Stanley to predict vehicle autonomy will contribute $5.6 trillion in annual economic savings globally11.
Currently the self-driving car world is split into two camps. Companies that want to use automation to make drivers safer and more efficient through ‘driver assisted technology’ and companies that want to replace human drivers entirely through full vehicle autonomy. The most high profile advocate of full autonomy is the intrinsically disruptive Google. Full autonomy is disruptive because it allows the possibility of Shared Autonomous Vehicles (SAV). Networks of self-driving taxis on demand. University of Texas Austin have produced models of the impacts of SAVs and found with just 5% penetration every SAV on the road would replace 11 conventional vehicles12. Google have shown their SAV ambitions by developing a demonstration fleet of shared autonomous golf carts13; releasing a fully autonomous prototype14 (no steering wheel); and, perhaps most convincingly of all, investing $258 million in the ridesharing app Uber15.
Google’s approach also differs in another key area, they completely reject vehicle to vehicle, or vehicle to infrastructure communication, in favour of fully self-contained vehicles. As Google product lead Anthony Levandowski explained their car needs to be able to operate just fine on the roads as-is, without relying on any new infrastructure, because “the first person that has this should be able to get benefits16.”
It is Google’s solution to the challenge of a fully autonomous and entirely self-contained vehicle that is of significant interest to the Geographic Information industry because to achieve this Google’s cars must be pre-loaded with detailed maps of the areas they will travel in.
“Everywhere the vehicle drives we’ve mapped ahead of time to about 11cm resolution.17”
Chris Urmson, Director, Google Self-Driving Car Project
“We tell it how high the traffic signals are off the ground, the exact position of the curbs, so the car knows where not to drive.18”
Andrew Chatham, Mapping Lead, Google Self-Driving Car Project
The need for a highly detailed map of most of the world’s road network is a formidable challenge for any organisation, but Chris Urmson, Google’s Project Director, has downplayed the difficulty, variously describing it as “work, but not intimidating work17” and stating “we know how to deal with that scale of data12”.
Many in the GI industry who have first-hand experience of the sisyphean task of maintaining up to date spatial data infrastructures, of considerably lower resolutions than those required by self-driving cars, may detect hubris in Google’s attitude. But Google do have impressive form when it comes to data. We’ve seen before with Google’s street view successes (a programme incidentally designed by some of the same people now in charge of the autonomous vehicle programme19) that Google are not shy about undertaking large scale data collection exercises and we know they have the cash to deliver20.
Clearly one implication for the GI industry of the need for such a map is that there will be a new and significant source of demand for high resolution geospatial information, but the way in which this data may be derived will also have implications for the supply of geospatial data.
This image shows the sensors that will be present in Google’s autonomous vehicles. A whole array of sophisticated sensors: LiDAR, radar, cameras and position estimators. Note no GPS receiver is listed, the cars will have them, but won’t rely on them for navigation. This is because GPS requires communication with external infrastructure, which is not always reliable, and violates Google’s design principles. Instead the cars use the information derived from the sensors to track the car’s position on the pre-loaded digital map of the area they are travelling through and to identify features that are not on the map, including crucially other vehicles.
A crucial element of the system that the car uses to identify features that it encounters is called Simultaneous Location and Mapping, or SLAM. This is a technique used in robotics in which a robot builds a map of a foreign environment in order to orientate itself within it. SLAM is already being used for automated surveying. A company called 3D Mapping is currently marketing the ZEB1, a lightweight backpack which uses Lidar and 3D SLAM to automatically map areas without reliable GPS signals such as mine shafts21. Google are also deploying SLAM devices to update their mapping. In September 2014 Google announced the Cartographer. A back pack equipped with 2 multi-echo laser scanners that uses SLAM to map the interiors of buildings such as shopping centres22.
Current SLAM mapping technology still requires manual input to identify the features mapped by the devices. However this step may also soon be automated. 510 Systems, the start-up that Google acquired to develop the autonomous car project in 2011, had, prior to being acquired by Google, developed an automated system for utility companies to survey overhead cables23. In 2011 when talking about the process of building the first set of maps needed to operate the prototype vehicles Chris Urmson spoke about the algorithmic and machine learning work that the self-driving car project had done to pull features out of the road without having to click through the data by hand18. And Google aren’t the only mapping company working on automatic feature recognition. This slide is taken from an Australian research project looking into the automated identification of road features24.
Whether or not Google will be the first company to master automated surveying they are currently the only mapping data company that has a fleet (which could one day be potentially millions strong) of automated vehicles fitted with a sophisticated array of surveying equipment with a track record of crowdsourcing spatial data.
Google’s record of crowdsourcing spatial data refers to Waze, a $966 million, July 2013, Google acquisition25. Waze uses multiple GPS traces, crowd sourced from GPS receivers used by in-car navigation devices, to map road locations and turn restrictions.
Automation is part of Google’s mapping strategy. This is demonstrated by their recent patent for automatically locating the position of roads using satellite data26 (combined with their acquisition of Skybox imaging, a satellite company27). Crowd sourcing data is also part of the Google strategy, as demonstrated by the Waze acquisition. Meanwhile SLAM enabled devices are likely to have ever increasing applicability in surveying, as demonstrated by the ZEB1 and Cartographer backpacks. Whether autonomous vehicles will combine all these trends into one ubiquitous automated surveying device remains to be seen.
If the cars can be used as surveying devices then they have the potential to be a hugely powerful tool. Not only are they fitted with sophisticated sensors capable of collecting huge amounts of data, but they also have the necessary artificial intelligence to filter that data at source into meaningful topographic information. The prospect is a highly detailed dataset that is current along all navigable routes. The obvious market for such a dataset is the automated vehicle market itself, which will use this data to navigate and for traffic optimisation. Then there are the potential spin off markets, such as transport asset management and transport planning. Such a dataset would also present a threat to the business models of existing data collection agencies, such as survey companies and national mapping agencies, and present a whole host of questions. The safety and efficiency of the transport network would rely on the most up to date and detailed dataset being as widely available as possible. Who will control this dataset? And that’s without mentioning the ever looming privacy implications.
Published 1st December 2014 by Philip Knight, Senior Consultant, ConsultingWhere
1. DARPA Grand Challenge. Wikipedia. Available at: http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
2. Google’s Self-Driving Cars Still Face Many Obstacles. MIT Technology Review, August 2014. Available at: http://www.technologyreview.com/news/530276/hidden-obstacles-for-googles-self-driving-cars/
3. Nissan Plans to Offer Driverless Cars by 2020. Mashable, August 2013. Available at: http://mashable.com/2013/08/27/nissan-plans-to-offer-driverless-cars-by-2020/
4. Tesla Model S gains Autopilot, all-wheel-drive option. CNet, October 2014. Available at: http://www.cnet.com/uk/news/tesla-model-s-gains-autopilot-all-wheel-drive-option/
5. Driverless cars compete in China. BBC News. November 2014. Available at: http://www.bbc.co.uk/news/world-asia-30077682
6. UK Government Fast Tracks Driverless Cars. July 2014. Available at: https://www.gov.uk/government/news/uk-government-fast-tracks-driverless-cars
7. Autonomous Cars. Wikipedia. Available at: http://en.wikipedia.org/wiki/Autonomous_car
8. Everyone Will Have Self-Driving Car By 2026, Analyst Says. Huffington Post, February 2014. Available at: http://www.huffingtonpost.com/2014/02/27/morgan-stanley-autonomous-cars-prediction_n_4867613.html
9. Global Auto Report. Scotia Bank, September 2014. Available at: http://www.gbm.scotiabank.com/English/bns_econ/bns_auto.pdf
10. Volvo Vision 2020. Available at: http://www.unece.org/fileadmin/DAM/trans/roadsafe/unda/Sweden_Volvo_Vision_2020.pdf
11. Morgan Stanley Analysts Believe Autonomous Cars Will Transform the Auto Industry, Boost the Economy. Morgan Stanley Report, Nov 2013. Available at: http://www.morganstanley.com/public/11152013.html
12. Imagine: A World Where Nobody Owns Their Own Car. CityLab, February 2014. Available at: http://www.citylab.com/commute/2014/02/imagine-world-where-nobody-owns-their-own-car/8387/
13. Google’s Self-Driving Golf Carts, IEEE Spectrum Conference. Youtube, October 2011. Available at: https://www.youtube.com/watch?v=rOWhu_aa9kM
14. Just press go: designing a self-driving vehicle. Google Blog, May 2013. Available at: http://googleblog.blogspot.co.uk/2014/05/just-press-go-designing-self-driving.html
15. Google and Uber Could Transform America. Slate, August 2013. Available at: http://www.slate.com/articles/business/moneybox/2013/08/google_s_uber_investment_autonomous_cars_and_smartphone_taxes_are_a_game.html
16. Silicon Valley Autonomous Vehicle Enthusiasts talk by Anthony Levandowski of Google. iTunes Podcast, October 2013. Available at: https://itunes.apple.com/us/podcast/5.-anthony-levandowski-google/id672410323?i=168818258&mt=2
17. The Trick That Makes Google’s Self-Driving Cars Work. The Atlantic, May 2014. Available at: http://www.theatlantic.com/technology/archive/2014/05/all-the-world-a-track-the-trick-that-makes-googles-self-driving-cars-work/370871/
18. How Google’s Self-Driving Car Works, IEEE Spectrum Conference. Youtube, October 2011. Available at: https://www.youtube.com/watch?v=YXylqtEQ0tk
19. Auto Correct. The New Yorker, November 2013. Available at: http://www.newyorker.com/magazine/2013/11/25/auto-correct
20. Google’s Expected $100 Billion Cash Pile Prompts Call for Dividend. WSJ.D Tech. November 2014. Available at: http://blogs.wsj.com/digits/2014/11/01/googles-expected-100-billion-cash-pile-prompts-call-for-dividend/
21. ZEB1: Hand-held Mobile Mapping. Available at: http://www.3dlasermapping.com/products/handheld-mapping
22. Google Unveils The Cartographer. TechCrunch, September 2014. Available at: http://techcrunch.com/2014/09/04/google-unveils-the-cartographer-its-indoor-mapping-backpack/
23. The Unknown Start-up That Built Google’s First Self-Driving Car. IEEE Spectrum. November 2014. Available at: http://spectrum.ieee.org/robotics/artificial-intelligence/the-unknown-startup-that-built-googles-first-selfdriving-car/
24. Current Research Trends from an Australian Perspective. Collier Philip, 2014. Available at: http://www.indiageospatialforum.org/2014/proceedingPDF/Emerging%20trend/Philip%20Collier.pptx.pptx.pdf
25. Google reveals it spent $966 million in Waze acquisition. CNet, July 2013. Available at: http://www.cnet.com/uk/news/google-reveals-it-spent-966-million-in-waze-acquisition/
26. Updating map data using satellite imagery. US Patent US 8731305 B1. Available at: http://www.google.com/patents/US8731305
27. Google Unearths a Deal with Skybox. Wall Street Journal, June 2014. Available at: http://online.wsj.com/articles/amid-stratospheric-valuations-google-unearths-a-deal-with-skybox-1402864823