They know you're about to put the kettle on before you do

One of the major technological innovations that defines how advanced civilizations are is their ability to control the flow of water. For examples one need not look far. The birth of sedentary communities and growth of agriculture came out of the innovation of irrigation. The Roman Empire's aqueducts allowed for unseen urban growth and standards of living. Today as renewable resources are being explored and instigated, hydroelectric power is an exciting component of modern infrastructure. In this post, I am going to explore an outline for another example of complete computational integration and operational management. From the 'upstream' [i.e. monitoring electricity requirements (current and potential) and environmental monitoring], to 'at the coal-face' [i.e. understanding how a river is running and a dam is working in real-time] and 'downstream' [i.e. looking at the effects of a system of this type].

Here as before, by operational management I'm talking about the real-time operations on a timeframe of minutes to hours, as opposed to tactical management (several days to weeks).

I'm going to explore this in reference to 2 case studies, the 2 largest hydroelectric dams on Earth:

Itaipu dam on the border between Brazil and Paraguay is the world's largest producer of hydro-electrical power. In 2012 it produced 98,287 GWh of electricity. Villwock et al. (2007) report that it opened in 1984 and in 1986 2200 monitoring instruments (e.g. peizometers, entensometers, pendulums, and flow gauges) were installed. These have been periodically collecting information about how the dam is functioning and the data is now a vast store of useful information. In 2007, the Itaipu company converted 210 of these to automatic data monitors collecting information in real-time around the clock.

Itaipu dam

 
The second case study is the Three Gorges over the Yangtze River in China. Although conceived in the 1920s, the dam opened in 2008. It stretches more than 2 km over the river and is 200 meters high. The reservoir behind the dam extends back 600 km. In 2012, the dam produced 91,100 GWh of electricity. It is one of the most monumental pieces of infrastructure ever built.

Three Gorges Dam

Producing such a vast amount of electricity both of these dams have undeniable benefits to society. However, both projects are controversial in regard to their effect on both the environment and society. Operating dams and infrastructure like these in the past has been done largely manually and based on periodically collected data. By integrating automatic data collectors and modelling they can be operated to a much higher degree of efficiency and these impacts can be reduced.

The first question to explore is how, in real and tangible terms, is automating the observation and operation of these dams beneficial. Well, to start with it would provide the ability to accurately forecast system behavior and requirements. Information relating to social variables such as the usage figures of the electricity allows the dam's turbines to be activated when required to avoid blackouts or unnecessary usage of water stored in the reservoir. Usage can be predicted too with models that can be fed back into the dams operation system. For example, imagine the Chinese Meteorological Agency's weather model predicts a snow-storm is coming, this can be fed into a model of the behavior of the Chinese population which might (again for example only) imply that this means people will be staying in, watching television, boiling lots of water for mugs of tea and turn on their heaters. Having this information, the dam can open and close its turbines to produced just the right amount of electricity at just the right time.

Secondly, these automated data monitors and models can notice faults and problems in the system much faster, on average, than would be recognized by human operators. So costs and system closures are reduced as they are fixed before they become major problems.

Thirdly, all this information is assembled in huge databases. These can then be mined, using machine learning algorithms and statistics, to identify the driving mechanics behind system behavior. For example, it might be identified that one of the major variables driving electricity requirement in the Itaipu dam is football match times.

The type of system I'm describing is called Knowledge Discovery in Databases (KDD) by engineers. Maimon and Rokach (2010) describe them as consisting of a constant cycle of data collection processing, mining and result interpretation and evaluation. The effects of applying this type of system are profound. Electricity costs can be drastically reduced. Considering that energy cost are one of the largest single expenses (10 - 50%) in industrial processing, chemical plants, agriculture, pharmaceuticals, plastics, pulp, metal, mining and manufacturing, changes in energy costs will have a profound societal effect.

It could result in a much better service and resource allocation and a huge reduction in the environmental impact of large scale energy infrastructure projects. Both Itaipu and the Three Gorges dam have been the subject of much environmental criticism. Sun et al. (2012) describe the monumental consequences the Three Gorges has had on the downstream hydrological regime, wetlands and nearby lake basins. The Itaipu, for example, has been shown by Santos et al. (2008) to cause a huge problem for the migration of fish, and habitats for aquatic life. Of course, healthy fish depend upon habitat connectivity and suitable habitat feature. It is unavoidable that hydroelectric dams, especially on the scale of the Itaipu will effect the river flow regimes and thus the environmental system it operates within. However, by utilizing computational technology dam operation can be optimized to have the minimum environmental impact possible.

This sort of system can and should be integrated into all sorts of applications from power stations, to irrigation schemes, to transport infrastructure. And they are! It is now in vogue to invoke the term 'big data' and this refers to optimizing operations in exactly the way I'm exploring in these posts. However, one of the major issues to implementing these type of schemes to being able to act of what the data is telling you. The 'big data' trope came out of technology and internet companies that provide products with short life-cycles (mobile phones) and services that are easily changed (a website). Making adjustments of infrastructure is MUCH MORE difficult and working out how best to implement the lessons learnt from 'big data' is one of the major operational and managerial challenges of the coming decades.

I'm keenly aware that I have been proposing the benefits of no-holes-barred computation integration without really yet looking into potential adverse effects. In my next post, I will endeavor to resolve this.

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As always, please leave comments, criticisms and suggestions below.

The Transportable Array: difficult data observation

Last week marked an interesting point in the history of geology. It was announced in Nature that scientists had almost finished completely mapping the whole of the United States seismology in high resolution. 'The Transportable Array', a set of 400 seismometers (see below), has, since 2004, been moving across the country. Elements (stations) in the array are arranged at 70-kilometer intervals. Each one stays in the ground for 2 years before being dug up and moved to the easterly edge. In this way it has moved, over the last 9 years, across from the Pacific to the Atlantic recording observations 40 times per second. This data has provided the scientific community with an unparalleled opportunity to look at the Earth's crust. It has allowed scientists to construct images of the deep earth and trace big earthquakes' movements over the globe.

  [Source: Nature]

Comparing this project to the real-time monitoring of infrastructure or continuous monitoring to the planet's atmosphere by satellites, one could easily be underwhelmed. However, the point to be made through this example is that not every useful variable can be measured easily but that doesn't mean the data is less useful. This might seem like a facile observation but in reality financial resources tend to follow the path of least resistance and projects like this can easily be overlooked. Observing tectonic activity is notoriously difficult. It is also very expensive (the Transportable Array cost US$90 million). However, despite these constraints, long-term data collection projects like this one can be conceived and implemented to huge benefit to society at large not just the Ivory Tower.

Earthquake's continue to plague civilization. They are almost impossible to predict bar vague recurrence statistics. Projects like this can help us drastically improve our knowledge about what's going on under our feet. Similar projects, recording high-resolution data of difficult-to-pin-down variables, should not be swept under the rug.

Thinking roads part 2. (..... in waiting .....)

I'm still playing around with using the real-time data to look at the road network in the UK. As I wrote that I would put something of this sort up by last friday I though I would direct those interested to a (albeit more technical) post doing the same thing while I fix the bugs in my code. I'm using Rich Wareham's code as a starting point for my things.

See this link here. 


******* Tuesday, 14th of January 2014 Note *********

I've just come back to this. I've been trying to get this code to work now for ages and have been emailing Rich Wareham but unfortunately I haven't been able to do it because some code needed has depreciated. I'm really sorry. The point I was trying to make is that you can plot the state of the road network at any given time in the UK as the government puts all the data online. It calculates this by looking at long long it takes car to travel certain distances. They do this using the cameras reading number plates! People clever than me can plot this data, here what Rich Wareham's looked like when he did it last summer: 

    

Again the link to his post is here.

And the link to where the data is put is here.

Thinking roads part 1.

Gone are these days ....

The first time a road was paved was in Egypt sometime around 2600 and 2200 BC. Since then they have played a central part in civilization. However, this current decade is witnessing a major milestone for this form of public infrastructure. Going are the days when the road was a passive tool, something that we operate on. Soon, roads will be something we operate with.

The degree to which road have aided society and changed history and geography is almost immeasurable and is not the main subject of this post -  how we are making them better is.

The annual cost of traffic congestion to the US economy is estimated to be in excess of $100 billion. That's almost as much as the GDP of Morocco. On top of this, in the US more than 40,000 people are killed in road traffic accidents annually and 5 million are injured. This is in a country that is not dealing with the rapid urbanisation that is being experienced in the developing world.

Evidently then there is huge scope for improvement for the operation of road infrastructure systems. Computational technology can help us solve this problem. We can create and deploy technology at a range of spatial and temporal scales to drastically improve the capacity of existing infrastructure through improvements to its operational efficiency. In doing this we can reduce the burden on the environment and energy sources. We can also make the roads safer. 

Lots of cities the world over are employing systems of this type. The market leader in implementing them is IBM which has or is building them in lots of cities and countries including in Dublin, Boston, Vietnam, the Ivory Coast and Los Angeles. As well as this lots of companies are now developing systems and programs to allow individuals to directly access and contribute data for this. One such company is Waze, bought by Google in June for $1.3 billion.

One of the first adopters of an integrated, real-time traffic coordination system was the U.S.. In 1991 Congress launched their Intelligent Transportation Systems (ITS) program to consolidate information, and automate highway technology. It consists of an extensive network of sensors and communication devices lining the roads. Interestingly this system was initially developed well before the internet took off. Today vehicles too communicate information to the network. With this information the system spews out pre-emptive signalling control and electronic route guidance. As this systems becomes increasingly advanced the highways are becoming essentially automated. Thus the roads will know their own operational status.

More recently Dublin have adopted a similar system. The council here commission the IBM road and traffic department to employ their system to assemble real-time data coming from a wide range of sources including road sensors, video camera and GPS updates from the city's 1000 buses. Naveen Lamba of IBM's Smarter Government team had this to say:

"A big part of our solution is the predictive element. You can influence the outcomes as opposed to reacting to what's happening out there".

This type of thing though is not restricted to the realms of developed countries and wealth city councils. Da Nang, a city in Vietnam, has just signed an agreement with IBM to intelligently manage its water and transportation infrastructure.

Da Nang is the country's biggest port and 4th largest city with a population of 890,000. It is also the area of the country's fastest growing urban sprawl. It provides a useful comparison to similar cities in neighbouring China that have experienced so much growth over the last decade. The contract with IBM is great evidence that the local council doesn't want to mirror that environmental impact urban growth has had in China. IBM is being paid to make sure the city turns into one of its smarter cities.

In both Dublin and Da Nang IBM's work is characterised by a deep integration of computational technology with the environment and the legacy infrastructure. A huge amount of software and a huge number of sensors are being embedded into public transport, roads and motorways. Using this information, computers can then optimise traffic flow and this minimise car congestion. Da Nang has only 100 buses but the council believes that by employing IBM's system and other effective management schemes this will be enough for its almost 1 million inhabitants.

The technology required to do all this is significant. The optimization problems needing to be solved pose great analytical challenges. Nevertheless, computers are getting more powerful, and most importantly cheaper, so councils and government of all shapes and sizes should be able to afford them. This should revolutionise public infrastructure.

Interestingly, very recent software and hardware advances integrated into the vehicles themselves have progressed to such an extent that they are now driving themselves better than they are by humans. The idea that a more advance version of autopilot would be developed is not a new idea but it is now becoming a reality. However, its interesting to compare all these developments with air traffic control. Despite all these huge advantage in technology a significant component of air traffic control is still done by human employees. 
Famously too give computational technology in life-controlling situations has ended badly, video here.

This is a really interesting time for the development of these technologies in the context of climate change and the rapid urbanisation that much of the world is experiencing.

This is the first part of this post. In the second part (which hopefully I'll put up on Friday) I'll use real-time, publicly available data from the UK government's data.gov.uk website to plot real-time conditions of road in the British Isles. This data is derived from automated number plate recognition systems that track cars driving on the British road network. 

 Coming are these days......

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As always, please leave comments, suggestions, questions etc.

Data assimilation

This post is going to be about 'fleshing out' data we have. What I want to illustrate by writing it is that once we've collected information we can use it to get better information. This is really vital to using it effectively to draw meaningful conclusions. There are two ways to do this: 1) Looking between data we know and 2) Using what we know about the relationship between variables to infer more information.

Imagine we took a walk. We recorded the altitude along the way: at the start, in the middle and at the end. At the start of the walk (A) we measured the altitude to be 5 units. In the middle of the walk (B) we measured it to be 0 units and at the end (C) we measured it to be -5 units. We have some useful data about the path we've travelled. What if we want to know what the shape of the land surface we walked through was(without taking a measurement at every point)? To do this we can use some maths. We can interpolate between and around these points to get an idea of what it might look like. We can the take an informed guess at the value we would have measured at some other point, say D. Here's a cartoon of the walk we just took:

 

In the plot below I've used an interpolation method to do just this. The colour of the dots A, B, and C represents the altitudes we measured on our walk. The colour of the surface is the interpolated landscape. It uses the information we know, i.e. the altitude at points A, B, and C. and the distance between them to fill-in the gaps. The point in the middle is our unknown-altitude point D. Our model here suggests that if we had walked to point D and taken a reading the altitude would most likely be close to 0 units (shown by its colour).  

 

The picture will be different if we collect more points along the away. For example if we had taken a detour between A and B and found the altitude at this new point to be 3 units. Then it we interpolate through these points we get a change in the landscape but here it is still likely that our point D is around 0 units. 

The picture though can change dramatically if we take more points. Say we took the altitude at home just before we started the walk (the point in the top-left-hand corner below) and measured it as -5 units. Our interpolated, general picture of the landscape changes a lot very quickly. Now point D is more like 4 units than 0.

This general method for using the data we have for interpolating for data we don't have is crucial. With it we can make much better use of the information available to us. It does however assume that we know nothing about the context of the data. Say, for example we know that any land at an altitude of -3 units is always unstable so cannot exist. Then we know something about the system we are collecting data in. Having this information often allows us to make much better guesses at the values we haven't recorded. Using this information is the process of modelling.

For an example let's take Isaac Newton's second law of motion with acceleration as the subject:

Acceleration = Force/Mass

             

This relation says something about the way changes in the forces acting on an object and the object's mass will effect its acceleration. We can use this to predict what the acceleration will be for any given force. So we can use information we collect, say about the acceleration of a car and its mass, to infer the force acting on it. This is a very simple example of a model.

When we build up very detailed models they can be extremely useful. For example, complicated models have been developed to predict the weather. They work on exactly the same principles as the simple example above. The most powerful versions of these models are called General Circulation Models and are many tens of thousand lines of computer code. These are what weather forecasts and climate change research use.

The most advanced method of data assimilation involves one more step. It takes into consideration one of the most important and fascinating discovery of maths of the last 100 years. In fact it was discovered by a meteorologist! Edward Lorenz. He noted that the equations that govern climate, i.e. the known relations that we are using in our models, are incredibly sensitive to the data we are putting in at the start. Even a minor change will result in a dramatically different outcome. In fact the uncertainties inherent to our data collection process make this a really significant consideration. Not only is recording data difficult, making a note of the exact value of some condition (for example temperature at the top of Gower Street) is impossible! The equations we are using are so sensitive that the number of decimal places required to get the exact value is infinite. We can't even measure it exactly let alone write it down! So, to take account for this an ingenious method has been developed. This is the last thing I going to write about in the context of data assembling.

Instead of giving up saying that we can't predict anything because of Lorenz's chaos we collect our data as accurately as possible and then make lots of little changes to it. These are called perturbations. This way we have a better idea at what the actual value (of say temperature at the top of Gower Street actually was). We put all these sets of data through our model to look at the range of outcomes and how much these differences actually matter. If we want we can then take the average of all these results to get a probability forecast rather than the deterministic one we would have if we used only one set of data.

The plot below, taken from some research I did over the summer, shows exactly this! The navy blue line starting off the East coast of Africa is the actual path of Hurricane Bill in 2009. All the other lines are forecast tracks. That is they are informed guesses at where Hurricane Bill might have gone based on information collected when the Hurricane was just north of the Caribbean. As much data as possible is collected and then it is changed a little bit, lots of times, exactly as described above, until there are 51 sets of perturbed data and the model is run for each one. The profound effect of all these changes, and the sensitivity of the model to the input data, is shown really nicely in this plot by the spread in the hurricane tracks. Some find Bill going towards Spain and some find Bill going north of Scandinavia.




 Again, so what's the point of me writing about this? The point is that not only do we now have the ability to record and store more information than ever before. We also have the ability get more out of it. These techniques that I've described are fundamental to using data. The first method, interpolation, is really useful but it is limited. We can only look between the data points we already know - we can't extrapolate. The second method of modelling is extremely powerful. Our ability to run good models depends a lot of the computers we have. These are getting more and more powerful. Our models are getting better and better. This should (and it is) be a major area of focus, research and investment.

Hopefully now I've described effectively the process of data collection and assimilation. I also hope I've highlighted how uniquely positioned Society now is in terms of knowing what has happened, what is happening and what might happen in the future. The benefits of integrating and operating systems to monitor the globe, to store and assimilate this information are myriad and profound.
  
For my next post I'm going to look into physical engineering structures that don't passively observe but rather actively engage with, use and control their environment.


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I've posted the code I wrote for the above interpolation examples at: http://herculescyborgcode.blogspot.co.uk/2013/10/data-assimilation-interpolation-example.html

Like always please leave comments, suggestions or criticisms below or email them to me. I really appreciate this.

The eyes and ears of corn

This is a plot of tropical storms. Every tropical storm that occurred in the Northern Hemisphere between 2008 and 2012. On the far left is continental Europe (you can easily make out Italy), in the middle is the Pacific Ocean and on the far right you can make out Greenland, Iceland and the UK. Instead of plotting the storms as tracks as is normally done I've plotted them as points to show the density of storm activity. For some storms its possible to easily make out the tracks as the points line up in clear paths where there aren't many other points, for example over Canada. The different colours show the basins in which the storms started out in, for example red represents all the storms with their genesis in the North Atlantic. Its simple enough to understand this picture however the amount of organization, observation and data processing required to get this data is quite amazing. This post is going to be about human monitoring of the environment and our attempts to model its processes.

Keeping a record of things happening is very useful. This was recognised very early on in human history. Some of the earliest forms of the written word were pictorial representations of animal stocks and plant locations. Unfortunately, technology for a long time did not afford us the opportunity to keep records without huge vulnerabilities. Early archives were kept by the ancient Chinese, Greek, and Roman civilizations but little of these survive today. Thanks to good data collection, archiving, the emergence of computational technology, hard-drives and the application of statistics (in particular the methods of Thomas Bayes) we are now harnessing the huge power of stored information.

Monitoring the natural world has lots of advantages. Doing so allows us to plan for the future, deal with problems as they arise, tune performance, track expectations and study the environment. Collecting all this information in archives makes it even more powerful. The difficulty though is not so much the actual monitoring process but is rather knowing what should be monitored. Planning, implementing and running monitoring systems is expensive and collecting data that is superfluous also results in unnecessary costs and is unwieldy. So, there is an important question of what to monitor and how often it needs to be recorded. 

Physical environmental variables are fairly simple to pin down. We need to record temperatures, pressures, humidities and the like if we want to know how the weather works and interacts with nature and civilization. Other variables are not so easy to pick out. Generally it is the case that a research field or commercial application will require data on a variable, for example information on forest fires, and then collect it themselves or commission some-one to collect it for them. Not the other way around.

It is also the case that what we can measure and what we want to measure is different. For example, satellites orbiting the world can measure all sorts of things one of which is photosynthesis in plants. But there is nothing actually being sent out by the process of photosynthesis that is directly seen by the sensors on the satellite. So we can develop methods to use information that is picked up by the sensors (i.e. photons of electromagnetic radiation) orbiting Earth to infer information that is useful for other purposes (e.g. using photosynthesis rates for ecological research). 

Broadly, environmental monitoring programs can be split into 2 categories: remotely sensed data and in-situ data collection.
  

Remote sensing is 'the science, technology and art of obtaining information about objects or phenomena from a distance (i.e. without being in physical contact with them)' (definition from Dr. Mat Disney). It is an incredibly useful method of observing the Earth (and elsewhere too). The technology of remote sensing has its roots over 150 years ago with the invention of the camera. The first person to take an areal photograph was a Frenchman called Gaspard-Felix "Nadar" Tournachon. He went up in hot-air balloons tied to buildings to take photos of houses. For a long time, information garnered from remotely sensed images was limited to qualitative information only. As digital sensors improved and satellites were launched concrete, quantitative information could be derived from these images. 

Modern remotely sensed images can give us information on meteorological, geo-physical, biological, social, chemical and political variables that can be used for all sorts of things. For example, information collected by the American National Oceanic and Atmospheric Administration's (NOAA's) satellites is used in operational weather forecasts and scientific research globally. I've produced this animation of global temperature maximums for every 6 hours in the year of my birth 1992. The data is from NOAA's 20th century reanalysis.

On the ground however we can build up a different picture. Electronic, automated data loggers can record information for a range of variables from environmental variables such as temperature and pressure to social variables like road traffic and noise levels. These loggers can be integrated into wireless networks pretty simply and information recorded can be stored and transmitted around the world (Akyildiz et al. 2007). For example, there are networks of tethered buoys in the ocean that transmit information on swell heights. This information is used for all sorts of things for example tsunami monitoring to other more trivial pursuits.

Despite these monumental opportunities for data collection that technology affords us, constraints remain. Some times people actually need to write things down.

For example automating the monitoring of fauna remains difficult. Individual animals can be fitted with trackers but observing whole populations this way currently remains unfeasible.  Keeping records of vertebrate populations is vital for all sorts of reasons not least for agriculture and environmental management. However, population dynamics are understood to occur at large spatial and temporal scales. Collecting a large volume of data in as close as possible to real-time is a difficult problem. Compromises will inevitably be made but we would like to minimize them. Traditionally the alternative has been using a team of highly educated and skilled researchers to decide on a subset of the population thought be representative of the population at large and to periodically visit the area for data collection. This is fieldwork. We can do better.

Luzar et al. (2011) discuss large-scale, long-term environmental monitoring by non-researchers. They describe a 3-year study of socioeconomic factors, hunting behaviour, and wildlife populations in a 48,000 square-kilometre (about 2.5 times the size of Wales), predominately indigenous region of Amazonia. They found that despite high rates of illiteracy, innumeracy and unfamiliarity with the scientific method, provided the potential of the data was explained and had a clear purpose to those involved, people are likely to engage long-term and collect high-quality data.

Expectations are changing. The reality is that engineers, scientists and managers (including politicians) expect statistically robust and up-to-date data informing their decisions. They are expecting more and more high-quality data. So, we are transitioning from a time when patchy, out-of-date information was put up with as a fact of life to one where it will not be. So, the designers and implementers of environmental monitoring schemes are going from a time when it was preferable to have high-quality data to one where is it a requirement.

So what's the point of all this. Well, today we live in an era when we can not only collect all sorts of information about the natural world and how we are interacting with it (and on it). We can also store all this information, query it and use it. This is really a unique time in human history. 150 years ago the first photo was taken, 100 years ago magnetic storage (the precursor to modern hard-drives) was first properly developed, and in 2001 Google earth went live for the first time bring people satellite data from around the world. At the end of 2014 it might be possible that Google earth is in near real time. Having this data should change the fundamental mechanisms on which society works. We can now continually test ideas, fix problems earlier and track our progress better. This data should be looked after properly. It is the lifeblood of the integrated, physical engineering structures that I will explore latter in this blog.

What I'm really keen to get across in this post is that we now have a phenomenal ability of observe reality. One that has never existed before for civilization. Existing monitoring schemes need to be looked after and new ones built. The data collected should be accessible to all those that might need to without being wrapped up in bureaucracy and politics. Society should also make sure its is able to not only use the data to draw effective conclusions but also be able to enact on what the data is telling us.  

All sorts of constraints exist though. Implementing data collection schemes is costly. The cheapest satellites belonging to NASA or ESA are constructed for around 80 to 150 million pounds. Employing people too is expensive. Data collection and monitoring is also wrapped up in ethical questions of accessibility (should this data be private or public?) and ethics (Is our experience of life on earth detracted from by knowing what is happening everywhere at all times?).  I hope to think about some of these issues in another post.

In my next post will be 'fleshing out' collected data. Even in the most thorough data collection schemes gaps will remain or data will need to be of higher resolution than originally collected for. For this, a mathematical process is used called data assimilation. It involves looking for trends in the data and spotting patterns. Then using these to infer what is happening where data wasn't collected.

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Please engage with my writing and leave comments/criticisms/suggestions etc below.

Prometheus stole fire from the gods and gave it to us mortals, or so the story goes. Was this out of kindness, pity, profound genius and foresight or perhaps boredom? We'll never know. It's an emotive analogy for the Industrial Revolution and one often summoned. Most tellings of this story in this context fail to recount what happened after the great gift however. Zeus, the king of the gods, was so infuriated that he strapped Prometheus to a rock and had an eagle come every day to peck out his liver. Overnight the very same liver grew back. This too though is a useful story to tell in the context of the publication of the fifth report on the state of the climate by the Intergovernmental Panel on Climate Change (IPCC). As a result of our collective industrial activities environmental systems the world over are reeling. Atmospheric pollution, environmental degradation and growing populations are starting to be felt. We are both Prometheus and the humans to whom he gave fire. We are at the beginning of our first day on the rock. 

The story then goes that Hercules, a demigod and the greatest of Greek heroes, rescues Prometheus. Integration between the natural environment and computational technology is emerging as a potential solution to the resuscitation of the planet.

This technology has two component parts. The first comes in the form of information. Operating within environmental systems is most efficient when we have as much information as possible about what’s going on. Modern technology affords us this luxury. We can now collect and store all sorts of data. We can then use modern high-level programming tools, machine learning techniques and computational and statistical modeling to understand what’s going on. The second component is geoengineering. This is the building of systems and structures to control, or work better with, the environment. Once ridiculed by the Academy as ludicrous and ignored by government as unfeasible, geoengineering is now firmly on the agenda.  

The possibility of a cybernetic environment, that is one characterised by complete integration between artificial constructions and natural systems, has arisen in phase with its acknowledgment, discussion and exploration in literature. It also coincides with, although is arguably lagging behind, a movement away from the canonical Cartesian understanding of systems to one of complexity first introduced as early back as Vico. This is interesting as for the first time in a long time the theory and praxis of many of the disciplines that influence and control society and the human environment, such as the sciences (social and natural) and engineering, are coalescing in their thoughts.

There's lots of discussion within the geographical literature. I hope to discuss this in amongst the more physically bounded explorations in these posts. For example, Maturana and Varela have developed ideas around the sustainability of systems (their so-called autopoiesis), ‘biological stickiness’ or 'love' whereby any two systems upon encountering one another stay ‘stuck’ together’, and the difficulties surrounding the distinguishing of component parts of any system. In the light of technology these raise questions about sustainability and evolution of any implementation of the integration under discussion. For individuals too, social and political issues of autonomy and privacy are rearing. Donna Harraway (1991) first into introduced the 'cyborg' into the social sciences to escape from the constraints of gender and materialism. With bio- and wearable- technology ever pervading, individual integration with society and the environment at large is becoming more and more a commonplace reality. 

The IPCC states that the atmosphere has the capacity to store a trillion tons of carbon without altering the climate too drastically. Anthropogenic activity has already put half a trillion tons up there. The outstanding half a trillion will be produced in the next 30 years at current rates of economic growth. There are two potential outcomes to this situation. One sees civilization rallying globally and reducing carbon emissions in time to prevent any damage to the climate that inhibits us from living on a planet with any semblance of the world today. The other sees the world changing so dramatically that a complete reworking of the social, geographical, economic and political landscapes of today must ensue. Either way, computational technology will have to play a huge role.

Through this series of posts I want to explore the integration of computational technology and Nature on a range of spatial and temporal scales. I'm going to write about environmental monitoring and modeling, geoengineering, technology changing the constraints of geography, the internet of things, the politics of this movement, barriers to integrations and the spaces of technological exclusion all in the contect of environmental change. It may well be that by the time I've finished writing I'll have changed my mind, but for now my advocation is that this integration of technology with Nature, to produce a cyborg or multiple cyborgs, is one of the major solutions to the problems civilization faces.