James Clark Maxwell, creator of "Maxwell's Demon"

The concept of entropy has arisen periodically during the course of my PhD study, both as an analog to thermodynamics operationalised by Alan Wilson in his ‘family’ of spatial interaction models and to Shannon’s entropy as formalised spatially by several scholars including Mike Batty. Alan Wilson’s models of urban systems work because modelling an entropy function allows for the ‘most probable state of a system’ to be realised; as in thermodynamics’ if you start with an initial condition in which the state of the system is unbalanced, or disorganised, and iteratively maximise this state until an equilibrium is reached, that equilibrium state will represent the most probable state. Similarly, information entropy, pioneered by Claude Shannon, tells a similar story that we might think of as unpredictability, or uncertainty, in the transmission of information; practically information theory in geography has been used to characterise ‘evenness’ in the observed distribution of phenomena.

Entropy is generally regarded as something of a wily concept, frequently managing to avoid clear-cut explanations of what it represents, and acting at times as a mysterious quantification of uncertainty. It is with some delight then that it should crop up far outside of the scientific, or at least social-scientific arena, within the work of Thomas Pynchon, the famous American novelist and acknowledged recluse. Although I believe it is a reoccurring theme in his work, I encountered Pynchon’s entropy in the context of his 1966 work “The Crying of Lot 49″. This was a curious moment of coincidence as I had only recently discovered from Peter Baudains that some work on ‘complexity science’ he had been involved with was supported by English novelist Giles Foden, famous for his 1998 novel “the Last King of Scotland”. Such coincidences aside, the aim of this post is simply to consider how Pynchon invoke’s entropy, and what he means by it.

The Crying of Lot 49 is concerned with the story of Oedipa Maas as she struggles to come to terms with the practicalities of executing the estate of her deceased tycoon ex-boyfriend Pierce Inverarity. The task that Oedipa faces is complicated by the apparent realisation that she may be entwined in an historic, and ongoing, global conspiracy between two postal companies: Thurn and Taxis, and Tristero. Pynchon’s direction leads Oedipa through a set increasingly confusing circumstances, which seem to point towards society behaving in an increasingly unpredictable way; echoing the entropic state of a system as inherently disorganised. Oedipa begins to behave like the ordering, maximising, function in a Spatial Interaction Model, attempting to sort and seek out a most probable understanding of just what is happening as the novel unfolds. The core question pertains to the success of Oedipa’s efforts, and is largely unresolved at the books ending, simply put: can we ever overcome the uncertainty of life?

Tellingly, Pynchon makes reference to “Maxwell’s Demon”, a philosophical device that can supposedly overcome entropy, the idea behind Maxwell’s demon is that there exists some “finite being” (as Maxwell put it) to order the disparate elements of a distribution. In thermodynamics this would mean creating an artificial seperation between hot and cold particles, thus avoiding the thermodynamic equilibrium of the 2nd law. Currently it is unclear whether Maxwell’s demon could in fact violate the second law of thermodynamics.

I saw a thought provoking presentation recently, given by Wenwen Li of the University of California Santa Barbara, the talk was a wide ranging insight into Cyber Infrastructure, its uses for geospatial information, and some of the computational techniques that underpinned the project. One element of the project involved zone design for the greater Los Angeles region, and involved the implementation of an algorithm that was intended to aggregate small areal units into larger zones whilst meeting a number of conditions, principle among these conditions was ‘compactness’. The output looked very much like a single hierarchy of Christaller hexagons, and this got me thinking about the nature of space and compactness.

Christaller’s hexagons are the defining illustration of something called ‘central place theory’, a geographical abstraction that idealises settlement pattern based upon an underlying space which is assumed to be isotropic. The assumption of spatial isotropy is the big leap in this model, it assumes that the ‘friction of distance’ from any given point increases at an equal rate whichever way you go from that point. Clearly such a suggestion is not applicable to Los Angeles, where huge freeways and interchanges can make adjacent parcels of land remote neighbours, and increase the connection between advantageously placed non-adjacent sites? Surely a city in which sprawl and ribbon development, as well as segregated communities should be modeled differently? Why then do many of our zoning algorithms favour compact ‘circular’ shapes, very much in the christaller mould, and why do we reject uncompact areal features as ugly slivers? In short, how did the circle come to be the ideal shape of zone in regional studies? Certainly, it is easier, both implementationally and conceptually, to model circles than to consider optimising a zone system over an n zone by n zone similarity matrix pertaining to variables which may be important to aggregating any set of areal units. However, as we explore more and more the complex systems defined by cities and regions, surely there is a need to start integrating a more realistic anisotropic view of space, one in which the friction of distance from any given point in any given direction is defined by the underlying demography, built environment and/or infrastructure.

One such attempt at this, AMOEBA (A Multidirectional Optimum Ecotope-Based Algorithm), developed by Aldstadt and Getis, is worth noting. In this algorithm, zones are defined via the Getis-Ord Gi* statistic, which is a local statistic for identifying clustering, thus zones are defined by local conditions, which are free to vary anistropically across space, rather than by a predefined preference for circles. Spectacularly this algorithm is implemented in the superb clusterpy python module for spatially constrained clustering.

The government is set to release a bill detailing how it is they expect the proposed GP Consortia to work. GP Consortia, groups of GPs working together, are set to replace the current structure of Primary Care Trusts (PCTs) and Strategic Health Authorities (SHAs) as the mechanism through which primary healthcare is provided to the public, and services are commissioned. Recently, the planned wholesale changes to the NHS have come under a sustained attack from the media, professional bodies and MPs, meanwhile the plans for GP consortia have moved into a trial phase in which different setups are being tested for their effectiveness. The trial consortia demonstrate the extent to which the plans represent a completely new venture, with a broad spectrum of possibilities being tested in terms of consortia templates, from a ‘consortia’ of a mere 3 GP practices, to a vast group of 83 GP practices. There seems little reasoning behind how Consortia are allowed to form at the moment, thus I saw an interesting opportunity to consider the ‘GP Consortia Problem’ as a geographic question. This is most evident in the fact that the NHS is mandated to provide an equitable and universal service, and an unmetered potential for GPs to ‘consort’ may well lead to increasing inequities in healthcare provision.

I see the ‘GP Consortia Problem’ as solvable through a zone-design approach. To do this, I identify contiguity between all English GPs and employ spatially constrained clustering. The following assumptions are made:

• Distance is important, GP consortia should be space covering without holes or islands, therefore a ‘neighbour’ approach to contiguity is advocated using graphs.
• As a preliminary test, GPs are considered to be equal, although there is scope in the future to develop measures of dissimilarity and homegeneity which will provide better, or more appropriate solutions to the GP Consortia problem.
• Based on the trials, I assume that Consortia must consist of at least 35 GPs, the average number of GPs per consortia in the trial phase.

I have used two approaches to creating contiguity amongst the English GP practices, both of them graph theoretical concepts based upon geometric analyses: the delaunay triangulation, and the gabriel graph. I believe that the gabriel graph is a sub graph of the delaunay triangulation, as such it is sparser than the delaunay graph. The two graphs are defined as:

• Delaunay Triangulation – for a set of nodes (GP practices) the delaunay triangulation is the set of triangles created by drawing a circle with 3 nodes (which define the triangles edges) on the circle’s perimeter, in which the circle does not contain any other points- iterated for all sets of 3-points.
• Gabriel Graph- 2 nodes are connected if they form the start and end-point of the diameter of a circle, and the circle does not contain any other points – iterated for all pairs of points.

In this sense, both the Delaunay triangulation and the Gabriel graph are nearest proximity measures. Having obtained the graph, the differences can be seen below. Note both graphs have been constrained for the English boundary.

Having created the ‘contiguity’ graphs, I wrote a short python script to extract the realtionships between GPs and write the output as a ‘.gal’ file for use with pySAL. I utilised the pySAL regionalisation module to compute the consortia solutions, I have used this previously in my blog, so I won’t go into detail on it. I paramterised the solution using the contiguity matrices created, assuming equality amongst GP practices, and looking for groups of at least 35 GPs. The regionalisations were then joined to a special areal geography I created for visualisation, this is simply the Voronoi diagram of the English GPs clipped to the English boundary. The results are below:

In these results it is notable that the Gabriel graph gives a cleaner result, the density of the delaunay-based contiguity matrix means that the result is subject to some sliver-like polygons in the regionalisation, and ‘spikier’ regions in general.

Of course, this is just a test, but it does point at the potential to create a rationalised system fo GP Consortia. Naturally, the biggest issue with these maps is that they only establish an areal depiction of consortia, one that is largely irrelevant. This is because the actual service areas of GPs tend to overlap and extend beyond any given GP’s voronoi defined footprint. Therefore the geography of patients requires a subsequent treatment once a geography of COnsortia has been established, and only in the interaction of the two can issues pertaining to equity be understood.

I liked Ben Hennig’s population cartogram of the UK under snow, but I thought it could perhaps show something a little more serious than simply where the people are. To do this I went to the UK Census 2001 (I know, an old data source, but the only thing I was aware of that could help me) and downloaded a dataset of counts by area (LSOA) of households without central heating. Using these counts as a base population, I created the cartogram below.

Whilst very similar to Ben’s cartogram, there are some differences, notably Scotland is not as prominant as in Ben’s. Perhaps the higher frequency of harsh winters in Scotland has made central heating a necessity. This also seems to be true in the far north of England. Likewise, Wales shrinks away in all areas aside from Cardiff which is a notable bulge of people without central heating. It is clear, however, that the people most effected by a lack of central heating are those that live in the south and middle of England in large population centres such as London – perhaps complacency to cold weather, plus a stock of substandard housing, or high levels of deprivation have caused this. Needless to say, it is likely to be these people that disproportionately feel the cold this winter.

A subtitle to this post might also be: Are we all being mislead by the New York Times? In stating this I am referring to the recent maps released by the New York Times looking at ethnic distributions from the US Census Bureau’s American Community Survey.

The most immediate thing we can learn about this project is that it is a spatial ecology, that is, an examination of the spatial patterning of a phenomena, here it’s ethnicity, at a given level of spatial aggregation, in this case “every city, every block”.This much is apparent both when you drag the mouse across the geography of America and the Census areas are highlighted, as well as when you zoom in, and you navigate from the Census tract level to the Census block level, a finer scale areal aggregation.

On the one hand, what has been achieved in this map is tremendous, and the use of dot density mapping allows for a singular look at multivariate data. The sheer level of residential segregation in the US also makes the dot density approach a very persuasive cartographic representation. However, first let us consider what the dot density approach is.

First and foremost, it is important to note that the dot density approach does not represent the real-world locations of individuals, far from it, dot density maps are simply another way of drawing a choropleth map. Choropleth maps show data aggregated into predefined areas (e.g. Census Blocks) and thematically colour these areas based upon some classification of the share of the mapped phenomenon that each area has. In a dot density map, each dot represents an observation, or number of observations, that occur within an area, each dot is then randomly positioned within that area. This means that phenomena do not strictly occur where they were sampled, which can (in increasingly large areas) lead to increasingly large uncertainties and misrepresentations. A higher number of dots within an area indicates a greater number of observations, with density described by the relative spacing of the dots in each area: smaller spacings indicate higher density.

Herein lies the difficulty – most ways of dividing up territory, and census delineations in particular, use a space covering approach. This continuous, spatially extensive way of dividing up land means that all land areas, even areas that have no people living in them, are potentially subject to the random placement of a dot, in the image below this is shown by the placement of dots in water bodies. Dot density can be logically unsound, particularly when two adjoining census blocks have significantly different population densities, shown by the representation of apparently hard ‘edges’ at areal boundaries as in the image below.

One solution that could work to mitigate the issue of representing areal data using dot density maps would be to apply dasymetric mapping. The dasymetric mapping technique is a method of reallocating a population recorded on a continuous areal basis to one which is a better representation of where people actually are. To do this, more information than simply population counts are usually required, such as landuse classifications, or delineations of developed area. In reallocating population counts from an areal unit created on a continuous basis, to one which aims at a more realistic placing of people in space, the volume of people per area is preserved, this means that you will never end up with more or less people than you started with. David Martin has, in the UK, been responsible for some notable dasysemtric outputs with regard to the UK Census, and provides a software tool, SurfaceBuilder, here.

The overarching goal of dasymetric mapping is to circumnavigate the ecological fallacy, which manifests itself in issues I have suggested exist in the dot density mapping of the US. Whilst dasymetric mapping would resolve some issues, dot density would still be subject to some mislocation of data, which largely stems from the conflicting ontology of representing an areal-based data, such as a population count by census area, as a series of points within that area; it is too easy for the viewer to interpret the points as having some level of significance above and beyond the areal container within which they sit. Therefore it is useful that the New York Times mapping also provides an option to look solely at the thematic choropleths classified by colouring the areas for each individual ethnicity. In this representation the viewer cannot confer the same kind of absolute interpretation upon the meaning or location of points, as they may do for dot density representations.

Naturally, as a Geographer the wikileaks release of facilities that the US believes critical to its security was interesting. Much like the chaps over at Floatingsheep some of us (Martin Austwick, James Cheshire, Peter Baudains, Alex Braithwaite) took it upon ourselves to map out the reported list. Martin came up with the following visualisation that I think is really rather nice.

Naturally, the same sort of caveats that floatingsheep note also applies here – most of these points are only accurate to the city level, such as for locating industries, or at the country-level such as for locating mines, or oil pipelines, where the features were not immediately apparent on maps. The uncertainty inherent means that the pattern of facilities is best understood from the global perspective, and it is from this vantage that I’ll attempt a brief analysis.

Firstly, the most interesting pattern I note is connected to the Spatial Division of Labour (aka new international division of labour) which is a concept extolled by globalisation theorists such as Doreen Massey, Paul Krugman etc. Essentially- look at how globalised a system of critical facilities the US identifies – the kind of bordered protectionism that we might have once expected from a hegemon is missing. Indeed, we could see this globally distributed network of critical sites as evidence of neo-imperialism and the reach of neo-liberalism, this is the corperate extension of the American empire, and in a sense, it maps the USs perceived vulnerabilities. It is the modern reproduction of the trade empires previously established by the European powers, and very much in this tradition it is the developing world countries, on the whole, that are responsible for raw materials production, and natural resources, and the developed ‘western’ world that is responsible for technology and medical products. The apparent primacy of the west is also demonstrated by the vastly larger numbers of critical sites that exist in the west, as opposed to the developing world.

Further to this, there is an interesting balance that is mediated by geopolitics and the nation state, on the one hand it is notable that the US privilleges ports as transport hubs (but, perhaps strangely, not railways or airports) demonstrating the global reach of trade and movement, but also heavily emphasises the importance of it’s physical land borders with Canada and Mexico, reiterating the perceived importance of the US as a nation state, a physical, defensible entity. In some sense this approach is reminiscent of Halford MacKinder’s ‘heartland’ hypothesis – the US, in this document is attempting to demonstrate, and encapsulate itself as a heartland state with a global reach and network of operations.

The importance of some resources is emphasised, and tends to relate to energy, such as the “Nadym Gas Pipeline Junction”, which is denoted “the most critical gas facility in the world”, likewise, Yemen’s “Bab al-Mendeb” Shipping lane is seen as “a critical supply chain node”. However, it is neither these remarks, nor the references to certain products being crucial to the Patriot missile production that really define the data here, rather it is the whole, which defines an incredibly diverse set of global interests for the US. The diversity of US interest in this map, both by type of critical facility identified and by where they occur reminds me of the chess-board analogy to geopolitics. I can’t remember who originally cited this analogy, or where I heard it, nonetheless it goes something like this: essentially a hegemonic entity, such as the US, is actually playing at geopolitics on a number of different chessboards. One chessboard relates to what the US do militarily, and certainly there are critical facilities on this list that best fit into this game, secondly there is a diplomatic chessboard, which relates to the persuasive power that the US has, how well it can negotiate and leverage its position as a functional hegemon. The military chessboard is perhaps best articulated by the positioning of military bases, and deployment of military personnel; the diplomatic chessboard is perhaps best understood in terms of the locations of embassies and diplomats. Additionally, with respect to the critical facilities, we might see a corperate chessboard, articulated through the interests of US companies and understood in terms of the locations of multi-nationals, and flows of currency from host countries back to the US. Finally, the US might also play chess on the basis of deterrence, this is the reasoning behind nuclear armaments etc. and to some extent the presence of a US list of facilities considered critical is curious evidence of what the US considers its reach and responsibilities, as well as what it has vested interest in maintaining oversight on. It might suggest that repercussions for challenging these listed site would be harsher that they might otherwise have been.

One of the maps that I liberated from the great LSE clearout sometime ago was this visually pleasing and well balanced representation of the ratio of Females to Males from the 1961 Census.

This kind of map has a specific story to tell, announcing that for a long time the women and men in the country have been unevenly distributed, placing emphasis on the demand for different types of labour. The dissaggregation by age and sex also demonstrates that in 1961 more boys are born than girls, but that more men die from occupational hazards resulting in a gender balance during adolescence and a surplus of women in old age.

Using Oliver O’Brien and Pablo Mateos’ superb Census Profiler it is possible to recreate this map using data from the most recent census.

Admittedly this map doesn’t allow you to map rates yet, although a percentage is fundamentally doing a similar job, but it really demonstrates the emerging power of online tools for creating sophisticated visualisations and analysis. What is more, the web-basis for this means that many users have access to this functionality so it reaches more people than the basic paper map, further the adaptable colour scheme and classes allow tailoring to your needs, and the multiple themes allow comparison with other data sets.

Where the paper map succeeds is in the expertise that created it, and the ability for the map to be annotated, this allows for an expert narrative to describe and explain the visualisation. The web map leaves this narrative up for discussion which is both good and bad – those who need help with interpretation don’t get it, but equally the reader is not constrained simply to the given interpretation.

It doesn’t seem like a great leap for the 2011 census to have an web-based geovisualisation site which could do these additional aspects, such as rates, perhaps wiki-based annotations by users and even create output graphs from the datatables such as the 1961 map has for age and sex. Nonetheless, it seems incredible to demonstrate this progression from paper to the web over time using this simple example.

In computational complexity theory the biggest unanswered question is P ≟ NP, that is whether or not a set of problems that fall into a certain complexity class P also fall into the class NP. Basically, this question asks: can all decision-based problems of the class NP in computing be solved in feasible length of time? This is a timely topic because a mathematician named Vinay Deolalikar claims to have found a proof that P ≠ NP, meaning that there are problems for which solutions cannot be found computationally.

First, let’s consider what the terms P and NP mean:

The complexity class NP contains all problems for which a known solution can be verified, this means that if you have a decision problem and you know the answer to be yes, you can write a program to check that that is the case. What it does not contains is all problems for which lacking an answer, a solution can be generated in a feasible period of time.

A good geographical example of an NP problem, albeit an optimisation problem, is the ‘travelling salesman problem’ (TSP). The TSP says, given a list of cities and the pairwise distances between them, what is the shortest route that a travelling salesman could take so that he visits each city only once? This is a complex problem because the more cities there are the longer it takes to solve, after only a few cities the set of possible solutions becomes so large as to make the problem unsolvable in a realistic time frame. Thus for large numbers of cities the only solutions available are created by heuristics, in which a solution is assumed to be near-optimal but unprovable as a ‘best’ solution. This is an example of an NP problem which it is believed does not belong to the set of problems known as P, because the TSP does not currently have a solution which can compute the ‘best’ solution.

The complexity class P is a subset of NP and contains all decision-based problems that can be solved by a computer in a feasible amount of time. A decision problem is essentially a yes-no question that can be solved by an algorithm on a single computer running in a sequential manner (i.e. step-by-step). Decision problems, and the complexity class P are linked to other types of problems, such as search problems (i.e. find a particular element/structure in some set of data), function problems (like decision problems, but returning more complex answer than yes/no), and optimisation problems (for given starting conditions, find the best solution to a question from a set of feasible solutions).

An optimisation problem that falls into the P complexity class is the transportation problem of linear programming. This is an optimisation problem that says: for a given set of supply and demand sites, with a set of associated costs which differ for each pair of demand and supply sites, what is the best way of allocating resources from the supply to the demand site so that the cost is minimised. As a geographer this is a useful problem, I’ve used it to find an optimal set of GP service areas, where local areas ‘supply’ a certain number of people who want to enroll with a doctor, to a set of GPs who meet the ‘demand’ based on the fact that people see GPs as a location based service, and want to travel as short a distance as possible to get to a GP, thus reducing the ‘cost’ to them. The virtues of Linear Programming mean that this problem can be solved reasonably efficiently, however it may not be exactly P as the efficiency of the solution may depend on the constraints you use in the problem. One constraint that I used was that each GP can only serve a certain number of people, and when it is full no one else can go there; adding constraints increases the complexity of a problem and may lead to the definition of an unsolvable problem.

So essentially what we have are NP problems, some of which are known to be P. If the question does P = NP is found to be true, it would mean that there exists somewhere a solution for all NP problems that could calculate an exact, best answer in a feasible amount of time. This would mean we could effectively solve problems like the TSP for large numbers of cities. However, if as suggested by Vinay Deolalikar P does not = NP then we are resigned to the fact that we will only be able to guess at the answer to problems such as the TSP.

Computational Geography uses numerous optimisation methods and looks for solutions to problems that are likely to be NP but not P, in all of these cases we are left in the situation of being able to approximate, but not know for sure, and this limits the certainty with which researchers can claim any particular result to be socially useful, or policy relevant. NP problems that are relevant to computational geography but may not now be solvable include problems in network analysis, including routing, flows and spanning trees; data storage; scheduling and optimisation; automata; geometry and mathematical programming.

In a sense if P ≠ NP it only goes to further enhance the mystery of the spatial and the geographical.

A comment I received by a chap called Bob Stott, on a previous post, got me thinking. I want to pick up this part of the comment in particular:

This set of a couple of neurons firing, firstly, I was reminded that the Guardian recently had a piece lamenting the state of NHS websites, and secondly I remembered some critique I wrote a while ago suggesting that NHS Choices wasn’t up to scratch.

What I thought was: community informatics! What a great term! Here is a concept that might actually work under the new NHS structure! However, rather than Bob’s truly ambitious idea about communicating policy and strategy, what if we keep it simple at first and thought about communicating effectively with local communities about their care choices?

Now, the suggestion that the Guardian makes is that the NHS is wasting money on hundreds of websites, many of which are out-of-date, misleading or just wrong. In fact many of these website actually relate to primary care doctors surgeries, who, it could be argued, have better things to do than maintain a website. In fact there are numerous GPs who do not even have a web presence outside of the NHS Choices search page. Likewise, NHS Choices is an improving website – it has added several search filters and patient feedback methods since I last cast a critical eye over it, but it still acts as a centralised inforamtion portal. This is fine on the one hand, because the NHS is a national system of care, and such a system needs a centralised presence to some extent, however it may be limited when dealing with local issues. This is largely the thinking behind proposed changes to the NHS, the Conservative-Liberal government believe that previously too much power was centralised within the NHS system through explicit heirarchies. This, they claim, meant that central government had too much control over health spending, despite the fact that around 80% of funding was left to the lowest level authority- the primary care trust- to spend. The conservative-liberal system remotes the explicit national-regional-local linkage in favour of local consortia, groups of GPs, and instills a shadowy national body – the NHS commisioning board- about which we do not know too much at the moment, to oversee the consortia. Whilst there are numerous critiques one might make, upon reflection this seems like a potentially advantageous position from the vantage of ‘community informatics’.

Clearly a well maintained website for individual surgeries, or GP consortia, will be highly advantageous to the local users of the service, and as well as providing general information it could provide highly personalised insight that is tailored to the specific issues faced by either the communities, or the individual themselves. These websites were traditionally the responsibility of GPs who may not have kept them updated, as opposed to the PCTs, who had more important things to do, and perhaps were somewhat inefficient with respect to information dissemination and web media. However, a consortium, which is responsible for a group of local GPs, and which has a more marketised responsibility to provide tailored care may gain an advantage from the potential for several GPs to bring together resources and collaborate on providing community-based information and online services. This is simply because the shifting situation will mean that it is increasingly in the interest of the GPs and the consortia to advertise access to care and provide effective local solutions. Of course, whether this is a realistic possibility remains to be seen, I certainly hope that it could be a positive upshot of the NHS plans, but again there seems potential for the system to become increasingly inequitable for patients across the social scale.

Recently, all and sundry had the chance to rummage through LSE Geography’s map library and liberate any maps of their choosing. Naturally some got over excited (cf. James Cheshire) and took numerous maps of all sorts. I was slightly more selective, and whilst being mostly on the look out for maps that represented social areas (cf. Booth Maps) I did find one particularly interesting map.

The map is by Carl Steinitz, from a time when he was at MIT Department of City and Regional Planning, it appears to be made using Symap. The map is entitled “The Principle Local Activity of a Place”. I think this title is both fascinating, and in terms of the development of spatial analysis quite telling. First however, some background.

Carl Steinitz is a Professor at Harvard Graduate School of Design, and has been a regular visitor at CASA for as long as I’ve been at UCL. He trained as an architect and planner, but became known as an early evangelist of Geographic Information Systems (GIS), his ongoing work concerns the design of environments, often urban, and the use of GIS to describe possible development trajectories. I respect him most for his impassioned stance against needless 3d visualisations, particularly if those visualisation have a musical backing.

Symap, aka synergraphic mapping system, is one of the first software packages that could create outputs that actively resemble current desktop GIS outputs. It was developed in the mid-1960s and carried with it a distinctive style of using ascii characters in order to draw map elements. Andrew Crooks has a couple of interesting examples and some background on his blog.

Now the map in question here doesn’t seem to have a date, which is a shame, and it does not give a specific location to the mapped area, although given that the map was made in MIT it becomes apparent that the area in question is Boston, Massachusetts with the Charles River Basin in the north of the map, and Boston Harbour to the east. The legend denotes different kinds of ‘principle local activity’, using different ascii characters to create a colour graduation. Unfortunately some of the particular legend categories are lost due to the low quality of reproduction on this particular map, nevertheless we see that Boston exhibits a distinct spatial patterning with respect to principle activity. This kind of map is not unusual, land use mapping is still an actively researched area that continues to generate copious debate – what interests me actually seems rather minor, it is the description of the map as presenting “The principle local activity of a place” (emphasis added). Initially I wondered whether this phrasing was simply standard boilerplate, but a google search couldn’t find the exact phrase, or variations on it, anywhere else on the web (which is not to say that it isn’t standard, simply that it doesn’t exist on google, I imagine it would have appeared had it been related to statistical reporting at some time or another). What it may mean then is that it marks the way in which the author Carl Steinitz saw the representation at the time: as a representation of the local activity of a place.

This is interesting, first it is easy to assume that by place he meant ‘Boston’, Boston is after all a place. However, scale has a very interesting role to play in how we think about place: we can conceive of many places within Boston centred around communities of all kinds, these places will be at least partially defined by the ‘local activities’ that occur there. As such the gridded representation of this map hints at the possibility of lots of places within Boston each with particular autobiographies, and each engaging people in different ways and offering different opportunities. Subsequent advances in GIS formalised the discourse of ‘space’ and spatial analysis, after all GIS does fundamentally hinge on the euclidian system of representation, and as such the vast, expansive idea of space sits much better than a nuanced, specific, local concept such as place. It would be easy to disregard Steinitz’s map and say that of course it simply assesses land use in Boston by a grid of systematically defined areas, but that designation of ‘place’ – purposeful or not- adds another layer of interpretation. Fundamentally it gives a different sense to what it is being represented here.