Tag: Academic

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Generative Landscape Science

A paper from the recent special issue of Professional Geographer (and discussed briefly here) of particular interest to me, as it examines and emphasises an approach and perspective similar to my own, was that by Brown et al. (2006). They suggest that a generative landscape science, one which considers the implications microscale processes for macroscale phenomena, offers a complementary approach to explanation via other methods. Such an approach would combine ‘bottom-up’ models of candidate processes, believed to give rise to observed patterns, with empirical observations, predominantly through individual-based modelling approaches such as agent-based models. There are strong parallels between modelling in a generative landscape science approach and the pattern-oriented modelling of agent-based systems in ecology discussed by Grimm et al. (1995). As a result of the theory-ladeness of data (Oreskes et al. 1994) and issues of equifinality (Beven 2002) landscape modellers often find themselves encountering an ‘interesting’ issue (as Brown et al. put it):


“we may understand well the processes that operate on a landscape, but still be unable to make accurate predictions about the outcomes of those processes.”

Thus, whilst pattern-matching of (model and observed) system-level properties from models of microscale interactions may be useful for examining and explaining system structure, it does not imply prediction is necessarily possible. There is a distinction between pattern-matching for validation (sensu Oreskes and Beven) and pattern-matching for understanding (via strong inference), but it is a fine line. If we say, “Model 1 uses structure A and Model 2 uses structure B, Model 1 reproduces observed patterns at multiple scales more accurately than Model 2, so Model 1 is more like reality, and therefore we understand reality better”, we’re still left with the problems of equifinality.

And so (rightly IMHO) in turn, Brown et al. suggest that whilst the use of pattern-matching exercises to evaluate and interpret models will be useful, we should wary of an over-emphasis on these techniques at the expense of intuition and deduction. This perspective partly contributed to my investigation of the use of ‘stakeholder assessment’ to evaluate the landscape change model I’ve been developing as part of my PhD.

In conclusion Brown et al. suggest a generative component (i.e. exploiting individual- and process-based modelling approaches) in landscape science will help;

  • develop and encode explanations that combine multiple scales

  • evaluate the implicaitons of theory

  • identify and structure needs for empirical investigation

  • deal with uncertainty

  • highlight when prediction may not be a reasonable goal

This modelling approach adopts perspective that is characteristic of recent attitudes toward the uses and interpretation of models arising recently in other areas of simulation modelling (e.g. Beven in hydrology and Moss and Edmonds in social science) and is also resonant with perspectives arising from critical realism (without explicitly discussing ontology). As such their discussion is illustrative of recent trends environmental and social simulation with some good modelling examples from Elk-Wolf population dynamics in Yellowstone National Park, and places the discussion in a context and forum in which individuals with backgrounds in Geography, GIScience and Landscape Ecology can all associate.

Reference
Daniel G. Brown, Richard Aspinall, David A. Bennett (2006)
Landscape Models and Explanation in Landscape Ecology—A Space for Generative Landscape Science?
The Professional Geographer 58 (4), 369–382.
doi:10.1111/j.1467-9272.2006.00575.x

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Spring Conferences

The preliminary program and schedule of sessions for the 2007 AAG (Association of American Geographers) National Meeting in San Francisco, April 17-21, is now available online.

It looks like I should have some time during April, and several colleagues from King’s Geography Dept. are going to San Francisco, so it might be good to go. Unfortunately, I wasn’t banking on having the opportunity so I haven’t submitted anything to present.

The alternative would be to go to the EGU (European Geophysics Union) General Assembly 2007 in Vienna, Austria, 15 – 20 April. I’m second author on a poster due to be displayed there:

Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling
Romero-Calcerrada, R. and Millington, J.D.A
Session NH8.04/BG1.04: Spatial and temporal patterns of wildfires: models, theory, and reality (co-organized by BG & NH)

I’ll have a think about it…

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RGS-IBG 2007 Call for papers

The on-line registration procedure for abstract submission for the Royal Geographical Society (with IBG) Annual International Conference 2007 is now ready and the call for papers has begin.

The conference has the theme “Sustainability and Quality of Life” and is to be held at the RGS-IBG and Imperial College in London, from 28-31 August 2007 with the first papers being presented on 29 August.

Details of accepted sessions can be found here (scroll to bottom to view .pdf version of all sessions) and details of how to submit an abstract found here. All abstracts need to be received on-line by the revised deadline of Thursday 1 March 2007 (previously advertised as 14 February 2007).

The RGS Postgraduate Forum will be sponsoring the following sessions:

  • Postgraduate Research
    (Sponsored by the PGF)

  • Postgraduate Research in Urban Geography
    (Jointly sponsored by the Urban Geography Research Group and the PGF)

  • Postgraduate Research in Applied Quantitative Geography
    (Jointly sponsored by the Quantitative methods Research Group and the PGF)

  • Postgraduate Research on Planning, the environment and sustainable development
    (Jointly sponsored by the Planning and Environment Research Group and the PGF)

Each of these session is designed to provide postgraduate students at any stage of their career, the opportunity to present their research and obtain constructive feedback in a supportive environment.

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Landscapes as Kaleidoscopes

A recent special issue of The Professional Geographer focuses specifically on the integration of theory and methods from Landscape Ecology and Geographic Information Science (GIScience). Entitled “Landscape Form, Process and Function: Coallescing Geographic Frontiers”, the six papers arose from the Centennial meeting of the 2004 Association of American Geographers and span the application-theory (e.g. Mast and Chambers – Malanson et al. respectively) and the GIScience-Landscape Ecology (e.g. Southworth et al. – Young and Aspinall respectively) spectra.

The general message is that the integration of method and theory GIScience and Landscape Ecology offers the opportunity to better examine and understand the interactions of pattern, process and landscape change. Concluding the special issue, Young and Aspinall use the metaphor of landscapes as kaleidoscopes;


“… a kaleidoscope serves as an engaging metaphor in this context because of its visualization of fragments, shreds, patches, and filaments that create a host of mosaics. A kaleidoscope creates these and other patterns and then shifts them, changing one set of forms into another, by altering colors and the locations of edges, thereby changing the appearances, sizes, and spatial distributions of the fragments. This device captures some of the complexity and shifting dynamics of the forms that characterize the Earth’s land surfaces. It would be difficult, but feasible, to record, measure, and otherwise describe those changes taking place within a kaleidoscope. It might even be possible to predict future patterns, or at least bracket the possible forms and patterns that could occur by tracking changes through time. Rather than a person creating these patterns by rotating a colorful tube, however, it is the landscape-forming and landscape-transforming processes that do so in reality.”

But in the majority of contemporary landscapes it is people rotating the landscape. Those landscape-forming and landscape-transforming processes are people-driven. The emphasis in this special issue is still largely presented from a formal (spatial) scientific perspective in the tradition of American Landscape Ecology, emphasising technical and philosophical approaches for examining patterns and processes. Given Professional Geographer is the forum and journal of the Association of American Geographers this is understandable and these approaches will surely improve and enhance our ability to examine and understand landscape change. However, landscape is an intrinsically holistic concept and change is often due to the interplay of both biophysical and human causes. Alongside furthering our technical abilities to study changing landscapes we need to continue to develop innovative approaches that consider the more humanistic side of landscape change and integrate them with the technical tools. Computer models, satellite imagery and the tools of GIScience are and will continue to be useful to monitor, evaluate and project change in their own right, but increasingly we need to find and develop ways that incorporate and include the humans turning the kaleidoscope.

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Volcano Modelling with Google Earth

One of my former colleagues (and good mate) at King’s, Dr. Peter Webley, is now working at The University of Alaska, Fairbanks. Pete is a volcanologist, with a particular interest in the remote monitoring and modelling of volcanic phenomena. Recently, he’s been working on the integration of Puff, a computer model of ash cloud formations, with Google Earth to improve communication between scientists and the public at large. Pretty cool stuff – checkout videos and animations here or even run your own volcano model here.

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Ecosystems Paper

In an effort not to become one of the estimated 200 million blogs that have now been abandoned, I thought it about time I let the blogosphere know that the paper I submitted to Ecosystems with Dr. George Perry and Dr. Raul Romero-Calcerrada has been accepted for publication. The paper arose out of the initial statistical modelling of the SPA I did for my PhD thesis (also used in Millington 2005) and examines the use of statistical techniques for explaining causes of land use and cover changes versus techniques for projecting change.

Here’s the abstract:

In many areas of the northern Mediterranean Basin the abundance of forest and scrubland vegetation is increasing, commensurate with decreases in agricultural land use(s). Much of the land use/cover change (LUCC) in this region is associated with the marginalisation of traditional agricultural practices due to ongoing socioeconomic shifts and subsequent ecological change. Regression-based models of LUCC have two purposes: (i) to aid explanation of the processes driving change and/or (ii) spatial projection of the changes themselves. The independent variables contained in the single ‘best’ regression model (i.e. that which minimises variation in the dependent variable) cannot be inferred as providing the strongest causal relationship with the dependent variable. Here, we examine the utility of hierarchical partitioning and multinomial regression models for, respectively, explanation and prediction of LUCC in EU Special Protection Area 56, ‘Encinares del río Alberche y Cofio’ (SPA 56) near Madrid, Spain. Hierarchical partitioning estimates the contribution of regression model variables, both independently and in conjunction with other variables in a model, to the total variance explained by that model and is a tool to isolate important causal variables. By using hierarchical partitioning we find that the combined effects of factors driving land cover transitions varies with land cover classification, with a coarser classification reducing explained variance in LUCC. We use multinomial logistic regression models solely for projecting change, finding that accuracies of maps produced vary by land cover classification and are influenced by differing spatial resolutions of socioeconomic and biophysical data. When examining LUCC in human-dominated landscapes such as those of the Mediterranean Basin, the availability and analysis of spatial data at scales that match causal processes is vital to the performance of the statistical modelling techniques used here.

Look out for it during 2007:

MILLINGTON, J.D.A., Perry, G.L.W. and Romero-Calcerrada, R. (In Press) Regression techniques for explanation versus prediction: A case study of Mediterranean land use/cover change Ecosystems

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Critical Mass and Metaphor Models

Bruce Edmonds has reviewed Phillip Ball’s 2005 book Critical Mass: How One Thing Leads to Another for the Journal of Artificial Societies and Social Simulation (JASSS). Providing a popular science account of the history the development of sociophysics and abstract social simulation the book (apparently – I haven’t read it) makes the common mistake of conflating models and their results for the systems they have been built to represent. In Edmonds’ words:

In all of this the book is quite careful as to matters of fact – in detail all its statements are cautiously worded and filled with subtle caveats. However its broad message is very different, implying that abstract physics-style models have been successful at identifying some general laws and tendencies in social phenomena. It does this in two ways: firstly, by slipping between statements about the behaviour of the models and statements about the target social phenomena, so that it is able to make definite pronouncements and establish the success and relevance of its approach; and secondly, by implying that it is as well-validated as any established physics model but, in fact, only establishing that the models can be used as sophisticated analogies – ways of thinking about social phenomena. The book particularly makes play of analogies with the phase transitions observed in fluids since this was the author’s area of expertise.

This book is by no means unique in making these kinds of conflation – they are rife within the world of social simulation.

(from Edmonds 2006, JASSS)

And not only within social simulation. In a previous paper, I highlighted with some colleagues that the name given to the ‘Forest Fire Cellular Automata’ made famous by Per Bak and colleagues, is better treated as a metaphor than an accurate representation of the dynamics of a real world forest fire (Millington et al 2006). This may be a seemingly an obvious point to make, but simulation models can provide an unjustified sense of verisimilitude and the appearance of the reproduction of complex empirical systems’ behaviour by simple models can lead to the false conclusion that those simple mechanisms are the cause of the observed complexity.

In a forthcoming paper with Dr. George Perry in a special issue of Perspectives in Plant Ecology, Evolution and Systematics, we discuss the lure of these ‘metaphor models’ and other issues regarding the approaches to spatial modelling of succession-disturbance dynamics in terrestrial ecological systems. I’ll keep you posted on the paper’s progress…

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Critical Realim in Environmental and Social Sciences

Richards (1990) initiated debate on the possibility of the adoption of a realist perspective toward research in the environmental sciences (specifically geomorphology) by criticising the then emphasis on rationalist (hypothetico-deductive) methods.

The ontology of Critical Realism (CR) theorises that reality exits independently of our knowledge of it or scientific research or theories about it, and that it is structured into three levels:

  1. ‘Real’ natural generating mechanisms
  2. actual events caused by the real mechanisms
  3. empirical observations of the actual events

The separation these three levels impose between real processes and human observation means that whilst reality exists objectively and independently, we cannot observe it. Therefore perception and cognition are important components of our knowledge about the real world. In this way, critical realism sits as an alternative between positivism and relativisms, between the nomothetic and the idiographic, and between determinist and stochastic perspectives (Sayer 2000).

Whilst mechanisms are time and space invariant, actual events are not because they are realisations of the generating mechanisms acting in particular conditions and contingent circumstances. The history and geography of events matters. Identical generating mechanisms will not produce identical events at different locations in space and time.

CR does not claim absolute truth; rather it understands science is a method to progress towards understanding true reality. A critical realist approach does not require falsification or predictive success – theories are proven through consistency of theory and explanation at multiple time and space scales. Thus, it emphasises looking at systems within their context and undertaking multidisciplinary scientific activity.

CR has been suggested as a useful perspective for examining environmental (and social) systems for several reaons;

  1. It addresses systems and their elements in context. This is very important given the complex (multiple interacting elements), ‘open’ (energy and mass able to flow across system boundaries) nature of many environmental systems (von Bertalanffy 1950).
  2. It does not attempt prediction of time and space dependent environmental events and phenomena, the accuracy of which is logically impossible to verify (Oreskes et al. 1994, Oreskes 2000).
  3. It provides a more holistic and multi-disciplinary approach to studying environmental systems. Such a perspective is consistent with other other theoretical frameworks (e.g. General Systems Theory, Gestalt Systems, Hierarchy Theory) and as advocated elsewhere in the environmental sciences (e.g. Naveh 2000).

As Sayer (2000) notes; “Realists expect concrete open systems and discourses to be much more messy an ambiguous than our theories”. That is, realists don’t expect their model results to match empirical observations. Rather, the key is to develop an understanding of the relevant causal structures and mechanisms. Characteristically realist questions are:

  • What does the existence of this object presuppose?
  • Could object/process A exist without object/process B?
  • What is it about the structure of this object which enables it to do certain things?

Many landscapes are characteristic of the open, complex systems Richards and Sayer are referring to. Multiple interacting actors and elements are combined with flows of energy and mass and, when humans are in the landscape, meaning and value into and out of them. At the human scale, observed and located in the real world, landscapes exist in a unique time and place – the non-ergodic nature of the universe makes individual events within them virtually unreproducible (Kauffman 2000). In these systems history and geography are important. Adopting a realist perspective toward modelling of these systems, whilst not offering predictions of their future states, offers an approach to better understand them and inform debate about their future.

References
von Bertalanffy, L. (1950) The Theory of Open Systems in Physics and Biology Science 111 p.23 – 29

Kauffman, S. (2000) Investigations. Oxford: Oxford University Press

Naveh, Z. (2000) What is Holistic Landscape Ecology? A Conceptual Introduction. Landscape and Urban Planning 50 p.7 – 26.

Oreskes, N., Shrader-Frechette, K. and Belitz, K. (1994) Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences, Nature 263 p.641 – 646.

Oreskes, N. (2000) Why Predict? Historical Perspectives on Prediction in Earth Science In Sarewitz, D., Pielke Jr., R.A., and Byerly, Jr., R. (Eds) Prediction: Science, Decision Making and the Future of Nature. Washington D.C.: Island Press.

Richards, K. (1990) ‘Real Geomorphology’. Earth Surface Processes and Landforms 15 p.195 – 197.

Richards, K., Brooks, S., Clifford, N., Harris, T. and Lane, S. (1997) Theory, Measurement and Testing in ‘Real’ Geomorphology and Physical Geography In Stoddart, D. (Ed.) Process and Form in Geomorphology. London: Routledge.

Sayer, A. (2000) Realism and Social Science. London: Sage

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Stakeholder Model Assessment

This last week I have been undertaking the final piece of fieldwork for my PhD thesis in my study area, EU Special Protection Area number 56, ‘Encinares del rio Alberche y Cofio’ (SPA56). The aim of this fieldwork is what I have been terming ‘Stakeholder Model Assessment’ and involved interviews with several actors and stakeholders within the study area to assess the credibility and potential utility of my integrated socio-ecological simulation model of land use and cover change (LUCC).

Specifically, two questions guiding these meetings were;

  1. from a technical/modelling standpoint, how can we utilise local stakeholder knowledge and understandings of LUCC better in our simulation models?
  2. if we understand that often science does not move fast enough to deal with pressing environmental and political problems, how can we use socio-ecological models (incorporating local knowledge) to speed the process of decision-making and consensus building in the face of incomplete knowledge about a system?

The simulation model I have developed is a tangible manifestation of my ‘mental model’ (i.e. understanding) of processes of change in SPA56. This research aimed to develop an understanding of how well this manifestation corresponds with a (hypothetical) simulation model that would be produced using the ‘mental model’ of the stakeholder.

I embarked on this fieldtrip with a certain amount of trepidation as I was laying myself and my model open to a degree of criticism from a source of knowledge not often tapped. That is, whilst LUCC models developed in an academic setting are routinely exposed to academic peer review they are infrequently reviewed by those actors which they attempt to represent. I was quite prepared to be told that the results and model structure I had developed were not realistic or largely irrelevant.

I was pleasantly surprised to be proven wrong as much of the feedback received was positive, both about model results (maps of land cover for 25 years hence – i.e. 2026) and model structure (i.e. model rules and assumptions). I’m just about to start writing this all up for my thesis but the findings can be outlined as follows;

1. Interviewees were very accepting of the results but focused on the results of individual scenarios that fitted most closely with their projections of future change. They did not seem to have any problems with model output for the scenario that matched their perception of future change, suggesting that the model accurately reflects the expected change for that scenario. (Spatial) criticism of results was rather weak however and their analysis was rather broad.

2. Interviewees confirmed model rules and assumptions, with some caveats;

  1. Distance between fields and farmstead were not deemed important for farmer decision-making
  2. Some interviewees suggested land tenure was not important, others that size of land parcels would dictate what land was changed to
  3. Agent types (i.e. ‘Traditional’ vs. ‘Commercial’ farmers) were deemed sensible. Greater variation is present in SPA56 farmer behaviour but generally this dichotomy is accurate

3. All interviewees commented that the model was lacking consideration of urban development and change (i.e. expansion)

4. Individual agricultural actors (i.e. farmers) were generally apathetic towards model (linked I suggest with their generally pessimistic view of future state of agriculture in the study area). Higher-level, institutional stakeholders (i.e. local development officials and planners) were much more interested in potential uses of the model for planning.

5. Interviews suggest the model is realistic/credible enough to act as a focus around which discussion about future change can proceed (‘model as mediator’ or ‘model as discussant’). Interview discussion followed the presentation of model assumptions and allowed the stakeholder to reflect on the processes causing change.

6. Interviewees’ ‘mental models’ were little influenced by the process of model assessment and discussion for two main reasons;

  1. they are apathetic towards the model and sceptical about what it can do for them
  2. presentation of model structure (and the model structure itself) is not as detailed or nuanced as their understanding of processes and change.

7. Related to point six, some interviewees were positive about the model because it confirmed their understanding of future change. That is, they envisaged opportunities to use the model as a rhetorical tool to further their interests. [More thoughts on this important point to follow soon…]

All-in-all a useful and interesting trip. These are my initial thoughts, more in-depth analysis and reflection is ongoing – I’ll post something more permanent on a page on my main website in the near future.

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Fire-Fighting Strategy Software

Some guys at the University of Granada, Spain, have developed software for managing wildfire-fighting efforts. SIADEX is designed to speed decision-making for resource allocation, as an article in New Scientist describes:

“Computerised maps are already used by people in charge of managing the fire-fighting effort. These maps are used to plan which areas to focus on and which resources to deploy, such as fire engines, planes and helicopters.

But working out the details of such a plan involves coordinating thousands of people, hundreds of vehicles and many other resources. SIADEX is able to help by rapidly weighing up different variables.
Shift patterns

For example, it calculates which fire engines could reach an area first, where aircraft could be used, and even how to organise the shift patterns of individual fire fighters. It then very quickly produces several different detailed plans. … One plan might be the cheapest, another the fastest, and a third the least complicated.”

I wonder how Normal Maclean would have felt about this approach to fire-fighting. I imagine like me he’d be interested in how this new tool can be used to aid and protect wildland fire-fighters, but the given the unpredictability of fire behaviour (in the light of current understanding) would still maintain that human experience, gained over many years dealing with unique situations, will be invaluable in managing fire-fighters and their resources. As with much computer software, this should remain as a tool to aid human decision-making, not replace it.

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