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Archive for the ‘Publications’ Category
Monday, July 2nd, 2007
This week sees the Annual Conference of the British Society for Geomorphology (BSG – formerly the British Geomorphological Research Group, BGRG). Running from Wednesday 4th to Friday 6th, the conference is being held at the University of Birmingham in the UK. With the theme Geomorphology: A 2020 Vision, recent developments and advances in the field, such as models and modelling approaches, will be explored and debated, and the potential to exploit emerging approaches to solve key challenges throughout pure and applied Geomorphology will be discussed.
With these recent and future advances in mind, one of my PhD advisors, Prof. John Wainwright, will present a paper entitled Modelling Human Impacts on Geomorphic Processes which contains work originating from my thesis. He’ll be presenting it in the first session of Wednesday afternoon, Process Modelling: Cross-Cutting Session. I’m sure it will turn out to be an interesting session, and one that continues the recent thirst for inter- and cross-disciplinary research. Here’s the abstract:
Modelling Human Impacts on Geomorphic Processes
John Wainwright and James Millington
Despite the recognition that human impacts play a strong – if not now predominant – rôle in vegetation and landscape evolution, there has been little work to date to integrate these effects into geomorphic models. This inertia has been the result partly of philosophical considerations and partly due to practical issues.
We consider different ways of integrating human behaviour into numerical models and their limitations, drawing on existing work in artificial intelligence. Practical computing issues have typically meant that most work has been very simplistic. The difficulty of estimating time-varying human impacts has commonly led to the use of relatively basic scenario-based models, particularly over the longer term. Scenario-based approaches suffer from two major problems. They are typically static, so that there is no feedback between the impact and its consequences, even though the latter might often lead to major behavioural modifications. Secondly, there is an element of circularity in the arguments used to generate scenarios for understanding past landform change, in that changes are known to have happened, so that scenarios big enough to produce them are often generated without considering the range of possible alternatives.
In this paper we take examples from two systems operating in different contexts and timescales, but employing a similar overall approach. First, we consider human occupations in prehistoric Europe, in particular in relation to the transition from hunter-gatherer to simple agricultural strategies. The consequences of this transition for patterns of soil erosion are investigated. Secondly, an example from modern Spain will be used to evaluate the effects of farmers’ decision-making processes on land use and vegetation cover, with subsequent impacts on fire régime. From these agent-based models and from other examples in the literature, conclusions will be drawn as to future progress in developing these models, especially in relation to model definition, parameterization and testing.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Environmental, Modelling, Publications | Comments Off
Wednesday, April 18th, 2007
I’m not attending the European Geophysics Union General Assembly this year as I have done the past couple. However, I do have a poster there (today, thanks to Bruce Malamud for posting it) on some work I have been doing with Raul Romero Calcerrada at Universidad Rey Juan Carlos in Madrid, Spain. We have been using various spatial statistical modelling techniques to examine the spatial patterns and causes (including both socioeconomic and biophysical) of wildfire ignition probabilities in central Spain. The poster abstract is presented below and we’re working on writing a couple of papers related to this right now.
Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling
R. Romero-Calcerrada, J.D.A. Millington
In countries where more than 95% of wildfires are caused by direct or indirect human activity, such as those in the Iberian Peninsula, ignition risk estimation must consider anthropic influences. However, the importance of human factors has been given scant regard when compared to biophysical factors (topography, vegetation and meteorology) in quantitative analyses of risk. This disregard for the primary cause of wildfires in the Iberian Peninsula is owed to the difficulties in evaluating, modelling and representing spatially the human component of both fire ignition and spread. We use logistic regression and weights-of-evidence based GIS modelling to examine the relative influence of biophysical and socio-economic variables on the spatial distribution of wildfire ignition risk for a six year time series of 508 fires in the south west of the Autonomous Community of Madrid, Spain. We find that socioeconomic variables are more important than biophysical to understand spatial wildfire ignition risk, and that models using socioeconomic data have a greater accuracy than those using biophysical data alone. Our findings suggest the importance of socioeconomic variables for the explanation and prediction of the spatial distribution of wildfire ignition risk in the study area. Socioeconomic variables need to be included in models of wildfire ignition risk in the Mediterranean and will likely be very important in wildfire prevention and planning in this region.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Economic, Environmental, Geographic, Modelling, Publications, Social, Wildfire | 1 Comment »
Tuesday, March 13th, 2007
So, finally, it is done. As I write, three copies of my PhD Thesis are being bound ready for submission tomorrow! I’ve posted a short abstract below. If you want a more complete picture of what I’ve done you can look at the Table of Contents and read the online versions of the Introduction and Discussion and Conclusions. Email me if you want a copy of the whole thesis (all 81,000 words, 277 pages of it).
So just the small matter of defending the thesis at my viva voce in May. But before that I think it’s time for a celebratory beer on the South Bank of the Thames in the evening sunshine…
Modelling Land-Use/Cover Change and Wildfire Regimes in a Mediterranean Landscape
James D.A. Millington
March 2007
Department of Geography
King’s College, London
Abstract
This interdisciplinary thesis examines the potential impacts of human land-use/cover change upon wildfire regimes in a Mediterranean landscape using empirical and simulation models that consider both social and ecological processes and phenomena. Such an examination is pertinent given contemporary agricultural land-use decline in some areas of the northern Mediterranean Basin due to social and economic trends, and the ecological uncertainties in the consequent feedbacks between landscape-level patterns and processes of vegetation- and wildfire-dynamics.
The shortcomings of empirical modelling of these processes are highlighted, leading to the development of an integrated socio-ecological simulation model (SESM). A grid-based landscape fire succession model is integrated with an agent-based model of agricultural land-use decision-making. The agent-based component considers non-economic alongside economic influences on actors’ land-use decision-making. The explicit representation of human influence on wildfire frequency and ignition in the model is a novel approach and highlights biases in the areas of land-covers burned according to ignition cause. Model results suggest if agricultural change (i.e. abandonment) continues as it has recently, the risk of large wildfires will increase and greater total area will be burned.
The epistemological problems of representation encountered when attempting to simulate ‘open’, middle numbered systems – as is the case for many ‘real world’ geographical and ecological systems – are discussed. Consequently, and in light of recent calls for increased engagement between science and the public, a shift in emphasis is suggested for SESMs away from establishing the truth of a model’s structure via the mimetic accuracy of its results and toward ensuring trust in a model’s results via practical adequacy. A ‘stakeholder model evaluation’ exercise is undertaken to examine this contention and to evaluate, with the intent of improving, the SESM developed in this thesis. A narrative approach is then adopted to reflect on what has been learnt.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Geographic, Landscapes, Modelling, MyPhD, Publications, Wildfire | Comments Off
Thursday, February 8th, 2007
This post is my fourth contribution to JustScience week.
Multiple regression is an empirical, data-driven approach for modelling the response of a single (dependent) variable from a suite of predictor (independent) variables. Mac Nally (2002) suggests that multiple regression is generally used for two purposes by ecologists and biologists; 1) to assess the amount of variance exhibited by the dependent variable that can be attributed to each predictor variable, and 2) to find the ‘best’ predictive model (the model that explains most total variance). Yesterday I discussed the use of logistic regression (a form of multiple regression) models for predictive purposes in Land Use/Cover Change (LUCC) studies. Today I’ll present some work on an explanatory use of these methods.
Finding a multivariate model that uses the ‘best’ set of predictors does not imply that those predictors will remain the ‘best’ when used independently of one another. Multi-collinearity between predictor variables means that the use of the ‘best’ subset of variables (i.e. model) to infer causality between independent and dependent variables provides little valid ‘explanatory power’ (Mac Nally, 2002). The individual coefficients of a multiple regression model can only be interpreted for direct effects on the response variable when the other predictor variables are held constant (James & McCulloch, 1990). The use of a model to explain versus its use to predict must therefore be considered (Mac Nally, 2000).
Hierarchical partitioning (HP) is a statistical method that provides explanatory power, rather than predictive. It allows the contribution of each predictor to the total explained variance of a model, both independently and in conjunction with the other predictors, to be calculated for all possible candidate models. The use of the HP method developed by Chevan and Sutherland (1991) by ecologists and biologists in their multivariate analyses was first suggested by Mac Nally (1996). More recently, the method has been extended to help provide the ability to statistically choose which variables to retain once they have been ranked for their predictive use (Mac Nally, 2002). Details of how HP works can be found here.
With colleagues, I examined the use of hierarchical partitioning for understanding LUCC in my PhD study area, leading to a recent publication in Ecosystems. We examined the difference in using two different land-cover (LC) classifications for the same landscape, one classification with 10 LC classes, another with four. Using HP we found that more coarse LC classifications (i.e. fewer LC classes) causes the joint effects of variables to suppress total variance explained in LUCC. That is, the combined effect of explanatory variables increases the total explained variance (in LUCC) in regression models using the 10-class LC classification, but reduces total explained variance in the dependent variable for four-class models.
We suggested that (in our case at least) this was because the aggregated nature of the four-class models means broad observed changes (for example from agricultural land to forested land) masks specific changes within the classes (for example from pasture to pine forest or from arable land to oak forest). These specific transitions may have explanatory variables (causes) that oppose one another for the different specific transitions, decreasing the explanatory power of models that use both variables to explain a single broader shift. By considering more specific transitions, the utility of HP for elucidating important causal factors will increase.
We concluded that a systematic examination of specific LUCC transitions is important for elucidating drivers of change, and is one that has been under-used in the literature. Specifically, we suggested hierarchical partitioning should be useful for assessing the importance of causal mechanisms in LUCC studies in many regions around the world.
Technorati Tags: regression, modelling, LandUse
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, Environmental, Modelling, MyPhD, Publications, Statistical | Comments Off
Tuesday, February 6th, 2007
This post is my second contribution to JustScience week, and follows on from the first post yesterday.
During my Master’s Thesis I worked with Dr. Bruce Malamud to examine wildfire frequency-area statistics and their ecological and anthropogenic drivers. Work resulting from this thesis led to the publication of Malamud et al. 2005
We examined wildfires statistics for the conterminous United States (U.S.) in a spatially and temporally explicit manner. Using a high-resolution data set of 88,916 U.S. Department of Agriculture Forest Service wildfires over the time period 1970-2000 to consider wildfire occurrence as a function of biophysical landscape characteristics. We used Bailey’s ecoregions as shown by Figure 1A below.
Figure 1.
In Bailey’s classification, the conterminous U.S. is divided into ecoregion divisions according to common characteristics of climate, vegetation, and soils. Mountainous areas within specific divisions are also classified. In the paper, we used ecoregion divisions to geographically subdivide the wildfire database for statistical analyses as a function of ecoregion division. Figure 1B above shows the location of USFS lands in the conterminous U.S.
We found that wildfires exhibit robust frequency-area power-law behaviour in the 18 different ecoregions and used power-law exponents (normalized by ecoregion area and the temporal extent of the wildfire database) to compare the scaling of wildfire-burned areas between ecoregions. Normalizing the relationships allowed us to map the frequency-area relationships, as shown in Figure 2A below.
Figure 2.
This mapping exercise shows a systematic change east-to-west gradient in power-law exponent beta values. This gradient suggests that the ratio of the number of large to small wildfires decreases from east to west across the conterminous U.S. Controls on the wildfire regime (for example, climate and fuels) vary temporally, spatially, and at different scales, so it is difficult to attribute specific causes to this east-to-west gradient. We suggested that the reduced contribution of large wildfires to total burned area in eastern ecoregion divisions might be due to greater human population densities that have increased forest fragmentation compared with western ecoregions. Alternatively, the gradient may have natural drivers, with climate and vegetation producing conditions more conducive to large wildfires in some ecoregions compared with others.
Finally, this method allowed us to calculate recurrence intervals for wildfires of a given burned area or larger for each ecoregion (Figure 2B above). In turn this allowed for the classification of wildfire regimes for probabilistic hazard estimation in the same vein as is now used for earthquakes.
Read the full paper here.
Technorati Tags: wildfire, statistics, mapping, risk, hazard
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Environmental, Geographic, Modelling, Publications, Statistical, Wildfire | Comments Off
Monday, February 5th, 2007
This post is the first of my contribution to JustScience week.
Wildfire is considered an integral component of ecosystem functioning, but often comes into conflict with human interests. Thus, understanding and managing relationship between wildfire, ecology and human activity is of particular interest to both ecologists and wildfire managers. Quantifying the wildfire regime is useful in this regard. The wildfire regime is the name given to the combination of the timing, frequency and magnitude of all fires in a region. The relationship between the frequency and magnitude of fires, the frequency-area distribution, is one particular aspect of the wildfire regime that has become of interest recently.
Malamud et al. 1998 examined ‘Forest Fire Cellular Automata‘ finding a power-law relationship between the frequency and size of events. The power-law relationship takes the form:
where  is the frequency of fires with size  , and  is a constant. is a measure of the ratio of small to medium to large size fires and how frequently they occur. The smaller the value of , the greater the contribution of large fires (compared to smaller fires) to the total burned area of a region. The greater the value, the smaller the contribution. Such a power-law relation is represented on a log-log plot as straight line, as the example from Malamud et al. 2005 shows:
Shown circles are number of wildfires per “unit bin” of 1 km^2 (in this case normalized by database length in years and area in km^2) plotted as a function of wildfire area. Also shown is a solid line (best least-squares fit) with coefficient of determination r^2. Dashed lines represent lower/upper 95% confidence intervals, calculated from the standard error. Horizontal error bars on burned area are due to measurement and size binning of individual wildfires. Vertical error bars represent two standard deviations of the normalized frequency densities and are approximately the same as the lower and upper 95% confidence interval.
As a result of their work on the forest fire cellular automata Malamud et al. 1998 wondered whether the same relation would hold for empirical wildfire data. They found the power-law relationship did indeed hold for observed wildfire data for parts of the US and Australia. As Millington et al. 2006 discuss, since this seminal publication several other studies have suggested a power-law relationship is the best descriptor of the frequency-size distribution of wildfires around the world.
During my Master’s Thesis I worked with Dr. Bruce Malamud to examine wildfire frequency-area statistics and their ecological and anthropogenic drivers. Work resulting from this thesis led to the publication of Malamud et al. 2005 which I’ll discuss in more detail tomorrow.
Technorati Tags: wildfire, statistics, JustScience,
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Environmental, Modelling, Publications, Statistical, Wildfire | Comments Off
Sunday, January 21st, 2007
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…
Technorati Tags: AAG, EGU, academic, conference, wildfire, modelling, statistics
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Geographic, Modelling, Publications, Wildfire | Comments Off
Friday, December 29th, 2006
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
Categories: science, article, modelling, statistics, LUCC, Ecosystems, landuse
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Modelling, MyPhD, Publications | Comments Off
Friday, December 15th, 2006
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…
Categories: forestfire, wildfire, model, social, simulation, modelling, metaphors, article, cellular, automata
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Modelling, Publications, Wildfire | Comments Off
Wednesday, September 13th, 2006
I’ve just received the offprint from the book chapter I wrote with George Perry and Bruce Malamud and have posted it on my website.
MILLINGTON, J.D.A, Perry, G.L.W. and Malamud, B.D. (2006) Models, data and mechanisms: quantifying wildfire regimes In: Cello G. & Malamud B. D. (Eds.) Fractal Analysis for Natural Hazards. Geological Society, London, Special Publications
Abstract
The quantification of wildfire regimes, especially the relationship between the frequency with which events occur and their size, is of particular interest to both ecologists and wildfire managers. Recent studies in cellular automata (CA) and the fractal nature of the frequency–area relationship they produce has led some authors to ask whether the power-law frequency–area statistics seen in the CA might also be present in empirical wildfire data. Here, we outline the history of the debate regarding the statistical wildfire frequency–area models suggested by the CA and their confrontation with empirical data. In particular, the extent to which the utility of these approaches is dependent on being placed in the context of self-organized criticality (SOC) is examined. We also consider some of the other heavy-tailed statistical distributions used to describe these data. Taking a broadly ecological perspective we suggest that this debate needs to take more interest in the mechanisms underlying the observed power-law (or other) statistics. From this perspective, future studies utilizing the techniques associated with CA and statistical physics will be better able to contribute to the understanding of ecological processes and systems.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Modelling, Publications, Wildfire | Comments Off
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