From coconuts to GPS: A brief history of navigation

It's good, but it's no coconut. Image: Getty.

If I ask Google:

It helpfully displays a map of where I used to live:

Google is very good at knowing where I used to be. My phone is constantly keeping track of my location and uploading it to their servers. It has stored my location 579,088 times since September 2013.

Each location stored looks like this:

{
 “timestampMs” : “1431497952458”,
 “latitudeE7” : 513453840,
 “longitudeE7” : -1015043,
 “accuracy” : 27,
}

This isn’t that easy to read. The E7 is an instruction to divide by 10,000,000, to reach a traditional set of latitude and longitude coordinates. “timestampMs” tells us that wherever 51.345384° N -0.1015043° E is, I was there at 1,4314,9795,2458 milliseconds after midnight on the 1st January 1970.

Even knowing what each of those numbers represent, we need to do some work to get these back into a human context. By putting the numbers through mapping software I can find out that “51.345384°, -0.1015043°” is Purley Oaks station in South Croydon. By running the timestamp through a conversion system, I can see I was apparently there at 7:19:12 AM on the 13 May 2015. This makes perfect sense, it was part of my daily commute at the time — I’d have been there most days at that time.

Most of the data stored about my location places me somewhere I lived or somewhere I worked. Just occasionally, I do something interesting and the database gets to store whole new sets of coordinates. If I take several years of this data I can produce maps of the sums of my positions over time:

This is my life as latitude and longitude, expressed in a way that can be easily understood by a human. Where I’ve spent any amount of time the map is redder; journeys appear as snail trails across the country.

Google’s algorithms don’t require any of this “coloured in map” nonsense. After a few weeks, your Android phone can make a reasonable guess at where your work and home are, based on where you spend most of your days and where you spend most of your nights. It doesn’t need to ask — that would be intrusive.

To determine a position on a globe while inconveniently being stuck on that globe you need fixed external references. Fortunately the universe is full of these.

One of simpler means sailors used to work out their relative position from destination was a kamal – a board with a hole in the middle. By putting a string through the hole and holding one end of the string in your teeth, you position the lower edge of the board on the horizon and move it further away until the board obscures your target star (typically Polaris — if visible).

An enthusiastic Wikipedia editor showing how the kamal works. Image: Markus Nielbock/Wikimedia Commons.

The length of the string between your teeth and the board tells you your latitude. By knowing the length of string required for certain ports, you could adjust course to navigate to a place. Using nothing more than your teeth, a string, a plank of wood, a star – and the horizon.

In Polynesia (lacking in a helpful pole star) titiro ‘ētū – “star peekers” – made of nothing but coconuts and seawater were used to navigate to specific islands. To use these, you cut off the top of the coconut and make a ring of holes around the base. You then make a hole near the top for the target star and fill it with water up to the holes (with coconut oil to maintain surface tension). You look through the device at the star at its highest point; if the water inside the device is flat, you are on the same latitude as your destination. The stars will guide you with the simplest of tools, if you know how to use them.


Progression east-west (longitude) can be understood if you know the difference between high-noon on a clock set at a fixed location (Greenwich) and a clock set at the current location. Each hour difference represents 15° of travel longitudinally (1/24 of 360°). Simple enough, if you have a clock that can keep time on the ocean – but that was a complicated problem to solve. Before that, all sailors could really do is line up on the right latitude and go for it.

To make use of more markers than the sun and North Star, you could use nautical almanacs and sextants. These almanacs were essentially large lists of what celestial objects should appear at certain points of the sky, and at what time they can be expected to do so. By using the sextant to compare predicted appearances to actual locations, you can determine the distance to fixed positions.

The Global Positioning System (GPS) has mostly replaced the need for these tables. Reliable but not available on-demand stars have been replaced by artificial celestial bodies that spend their whole lives yelling about where they are and what time they think it is. By comparing signals from several different satellites to the time your GPS device thinks it is, you can triangulate your position on the earth within a few meters.

Few mobile phones contain true GPS: mostly they use aGPS or WPS. aGPS uses the resources of the mobile network to speed up reconciliation based on fragmented signals, but WPS (Wireless Positioning System) is something different altogether. It takes advantage of the fact that we littered our world (especially urban areas, where GPS struggles) with millions of radio location beacons, in the form of Wi-Fi access points.

While the vans with the weird cameras were taking pictures of every road in the world, they were also mapping the radio landscape we have made: each house with a Wi-Fi access point, broadcasting a unique identifier. By mapping these to a true GPS reading, location services can provide a guide to any device with a wifi chip. If you read Device #1053443 with 50 per cent strength and Device #10232321 with 74 per cent strength and Device #24324239 with 60 per cent strength, the chances are you are “here” — the most likely place where those signals converge at that strength.

These vans are no longer necessary: while walking around your phone will pick up on any new or unknown access points. With sufficient logs of these devices, their location can be deduced by comparison to known devices and used for future navigation. As well as recording our every step, our phones are automated radio cartographers. This is still ultimately working on similar principles to the nautical almanac and sextant, it just has a much larger look-up table and uses thousands of man-made stars to light the way.

As navigation has become much easier there is also the risk of becoming too dependent on what might turn out to be fragile technology. The US Navy is currently re-introducing celestial navigation training. so that its sailors can figure out where they are in the event of an attack on the GPS system. After the apocalypse, we might find ourselves getting around by holding a bricked phone up to the horizon and measuring the length of the headphone cord to our teeth. 

 
 
 
 

Tackling toxic air in our cities is also a matter of social justice

Oh, lovely. Image: Getty.

Clean Air Zones are often dismissed by critics as socially unfair. The thinking goes that charging older and more polluting private cars will disproportionately impact lower income households who cannot afford expensive cleaner alternatives such as electric vehicles.

But this argument doesn’t consider who is most affected by polluted air. When comparing the latest deprivation data to nitrogen dioxide background concentration data, the relationship is clear: the most polluted areas are also disproportionately poorer.

In UK cities, 16 per cent of people living in the most polluted areas also live in one of the top 10 per cent most deprived neighbourhoods, against 2 per cent who live in the least deprived areas.

The graph below shows the average background concentration of NO2 compared against neighbourhoods ranked by deprivation. For all English cities in aggregate, pollution levels rise as neighbourhoods become more deprived (although interestingly this pattern doesn’t hold for more rural areas).

Average NO2 concentration and deprivation levels. Source: IMD, MHCLG (2019); background mapping for local authorities, Defra (2019).

The graph also shows the cities in which the gap in pollution concentration between the most and the least deprived areas is the highest, which includes some of the UK’s largest urban areas.  In Sheffield, Leeds and Birmingham, there is a respective 46, 42 and 33 per cent difference in NO2 concentration between the poorest and the wealthiest areas – almost double the national urban average gap, at around 26 per cent.

One possible explanation for these inequalities in exposure to toxic air is that low-income people are more likely to live near busy roads. Our data on roadside pollution suggests that, in London, 50 per cent of roads located in the most deprived areas are above legal limits, against 4 per cent in the least deprived. In a number of large cities (Birmingham, Manchester, Sheffield), none of the roads located in the least deprived areas are estimated to be breaching legal limits.

This has a knock-on impact on health. Poor quality air is known to cause health issues such as cardiovascular disease, lung cancer and asthma. Given the particularly poor quality of air in deprived areas, this is likely to contribute to the gap in health and life expectancy inequalities as well as economic ones between neighbourhoods.


The financial impact of policies such as clean air zones on poorer people is a valid concern. But it is not a justifiable reason for inaction. Mitigating policies such as scrappage schemes, which have been put in place in London, can deal with the former concern while still targeting an issue that disproportionately affects the poor.

As the Centre for Cities’ Cities Outlook report showed, people are dying across the country as a result of the air that they breathe. Clean air zones are one of a number of policies that cities can use to help reduce this, with benefits for their poorer residents in particular.

Valentine Quinio is a researcher at the Centre for Cities, on whose blog this post first appeared.