Social Network Analysis

As I mentioned in a tweet earlier this week, Prof. Ken Frank was ‘visiting’ CSIS this week. Ken studies organizational change and innovation using, amongst other methods, Social Network Analysis (SNA). SNA examines how the structure of ties between people affects individuals’ behaviour, at how social network structure and composition influences the social norms of a group, and how resources (for example, of information) flow through a social network. This week Ken organised a couple of seminars on the use of SNA to investigate natural resource decision-making (for example, in small-scale fisheries) and I joined a workshop he ran on how we actually go about doing SNA, learning about software like p2 and KliqueFinder. Ken showed us the two main models; the selection model and the influence model. The former addresses network formation and examines individuals’ networks and how they chose it. The latter examines how individuals are influenced by the people in their network and the consequences for their behaviour. As an example of how SNA might be used, take a look at this executive summary [pdf] of the thesis of a recent graduate students from MSU Fisheries and Wildlife.

On Friday, after having been introduced through the week to what SNA is, I got to chat with Ken about how it might relate to the agricultural decision-making modelling I did during my PhD. In my agent-based model I used a spatial neighbourhood rule to represent the influence of social norms (i.e. whether a farmer is ‘traditional’ or ‘commercial’ in my categories). However, the social network of farmers is not solely determined by spatial relationshps – farmers have kinship ties and might meet other individuals at the market or in the local cerveceria. We discussed how I might be able to use SNA to better represent the influences of other farmers on an indiviuals’ decision-making in my model. I don’t have the network data needed to do this right now but it’s something to think about for the future.

If I’d been more aware of SNA previously I may have incorporated some discussion of it into the book chapter I re-wrote recently for Environmental Modelling. In that chapter I focused on the increasing importance of behavioural economics for investigating and modelling the relationships between human activity and the environment. SNA is certainy something to add to the toolbox and seems to be on the rise in natural resources research. Something else I missed whilst working on re-writing that that chapter was the importance of behavioural economics to David Cameron‘s ‘Big Society’ idea. He seems to be aware of the lessons we’ve started learning from things like social network analysis and behavioural economics – now he’s in charge maybe we’ll start seeing some direct application of those lessons to UK public policy.

Incendio en un Paisaje Mediterráneo

Our recent paper describing and testing the Mediterranean Landscape Fire Succession Model I developed during my PhD has caught the eye of some folks in Spain. sinc (Servicio de Informacion y Noticias Cientificas), a Spanish scientific news website <a href="; class=”regular”, target=”_blank”>has posted details of the paper (in Spanish) – hopefully it will generate some interest in our work and that some find it useful for their own.

Update 18th August 2009
Several other websites have picked up on the sinc summary and re-published an English version:

Environmental Modelling and Software paper In Press

It took a while (first submitted late February 2008) but the manuscript I submitted with colleagues to Environmental Modelling and Software has now been accepted for publication. The paper describes the bio-physical component of the integrated socio-ecological simulation model I developed during my PhD. I don’t envision it changing it much so the abstract is copied below. When it’s in print I’ll holler again…

Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model
James D.A. Millington, John Wainwright, George L.W. Perry, Raul Romero-Calcerrada and Bruce D. Malamud

We present a spatially explicit Landscape Fire Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation ‘climax’ states. Wildfire behaviour is represented using a cellular automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land cover change and the wildfire regime. Such sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a ‘pattern-oriented modelling’ approach in conjunction with wildfire power-law frequency-area scaling exponent beta to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.

PEST or Panacea?

Although some may say blogging is dead, the editors at Nature think it’s good to blog. The Nature editors discuss the place of blogging in scientific discourse, focusing on the reporting of results from papers in press (i.e. accepted by a journal for publication but not actually in print yet). They suggest that if the results of an article in press are reported at a conference then they are fair game for discussion and blogging. And they argue that “[m]ore researchers should engage with the blogosphere, including authors of papers in press”.

I wish I had more papers in the in press pile. Unfortunately I’ve got more in the under review pile (see my previous post), but at least I’m adding to it. Earlier this week David Demeritt, Sarah Dyer and I submitted a manuscript to Transactions of the Institute of British Geographers. The paper discusses public engagement in science and technology and examines some of the practical challenges such a collaboration entails. One of the examples we use is the work I did during my PhD examining the communication of my model results with local stakeholders. It’s only just submitted so I’ll just post the abstract for now. As we get further along the review process toward the in press stage (with this and other papers) I’ll return to see if we can spark some debate.

David Demeritt, Sarah Dyer and James Millington
PEST or Panacea? Science, Democracy, and the Promise of Public Participation
Submitted Abstract
This paper explores what is entailed by the emerging UK consensus on the need for increased public engagement in science and technology, or PEST as we call it. Common to otherwise incompatible instrumental and de-ontological arguments for PEST is an associated claim that increased public engagement will also somehow make for ‘better’ science and science-based policy. We distinguish two different ways in which PEST might make such a substantive contribution, which we term ‘normative steering’ and ‘epistemic checking’. Achieving those different aims involves engaging with different publics in different ways to different ends. Accordingly, we review a number of recent experiments in PEST to assess the practical challenges in delivering on its various substantive promises. The paper concludes with some wider reflections on whether public engagement in science is actually the best way of resolving the democratic dilemmas to which PEST is addressed.

US-IALE 2009: Abstracts

The two abstracts I submitted to US-IALE 2009 have been accepted for (oral) presentation at the meeting. I’ll be presenting both on the work I’ve been doing here at CSIS and from my PhD. I’ve copied the initial abstracts below (these may change slightly) and I’ll post a full list of what everyone in CSIS is up to at the conference nearer the time. See you in Snowbird!

Modeling Interactions of Human and Natural Disturbances in a Managed Forest Landscape

James D.A. Millington, Michael B. Walters, Megan S. Matonis, Frank Lupi, Susan Chen, Kimberly R. Hall, Edward J. Laurent, Jianguo (Jack) Liu

As is often the case for coupled human and natural systems, the interactions between human and natural forest disturbances have the potential to produce complex system behavior. Spatially-explicit ecological-economic modeling provides a useful tool to investigate these phenomena in an integrated manner, revealing patterns and processes not observable by investigating the social and natural components separately. We present the development and initial results from such a model that examines the complex interactions among timber harvest, white-tailed deer browse and vegetation dynamics in a managed forest landscape in Michigan’s Upper Peninsula. This landscape has been experiencing low tree regeneration due to overabundant white-tailed deer, and changes in habitat for songbirds of conservation concern due to deer impacts and timber harvesting.

The multi-scale model uses input data on deer population, forest stand structure, tree regeneration, forest cover, habitat type and land ownership data collected at plot, stand, and landscape levels. Vegetation establishment, regeneration and growth are simulated using the USFS Forest Vegetation Simulator (FVS). Deer browse impacts are represented in FVS and parameterized by data we have collected on deer density and forest gap regeneration. As is common for many studies, our stand-level data for model initialization are incomplete across the 4,000 km2 study area. We show how we impute our stand-level data across the remainder of the study area using auxiliary variables including topography and remotely-sensed land cover.

Results show that distance to nearest lowland conifer stand, mean stand tree diameter-at-breast-height and the proportion of hardwood species in the surrounding local area are statistically significant predictors of deer density across the landscape (p < 0.01). These variables alone explain 40% of variance in deer density. Our initial model simulation results indicate complex spatial interactions between deer densities, stand structure and timber values across the managed forest landscape.

Investigating the Interaction of Land Use/Cover Change and Wildfire using Agent-Based Modelling
(Global Land Project symposium on agent-based modelling of land use effects on ecosystem processes and services)

James D.A. Millington, John Wainwright, Raul Romero-Calcerrada, George L.W. Perry and David Demeritt

Humans have a long history of activity in Mediterranean Basin landscapes. Spatial heterogeneity in these landscapes hinders understanding about the impacts of changes in human activity on ecological processes, such as wildfire. We present an Agent-Based Model (ABM) of agricultural land-use decision-making. This model is integrated with a spatially-explicit, state-and-transition Landscape Fire-Succession Model (LFSM) to investigate the relative importance of anthropic and ecological drivers of the wildfire regime.

The ABM considers two ‘types’ of land-use decision-making agent with differing perspectives; ‘commercial’ agents that are perfectly economically rational, and ‘traditional’ agents that represent part-time or farmers that manage their land because of its cultural, rather than economic, value. Results from the ABM indicate that land tenure configuration influences trajectories of land use change. However, simulations for various initial land-use configurations and compositions converge to similar states when land-tenure structure is held constant. For the scenarios considered, mean wildfire risk increases relative to the observed landscape.

The LFSM uses plant functional types to represent spatial and temporal competition for resources (predominantly water and light) in a rule-based modelling framework. Wildfire behaviour is represented using a cellular-automata approach. Results from the integrated ABM-LFSM indicate that fires ignited by human causes burned greater areas of shrubland than would be expected at random, and modelled lightning fires burned greater areas of forest land-cover types than would be expected at random.

We conclude by discussing our efforts to achieve a form of ‘stakeholder model validation’. This evaluation process involved taking the model and its results back for examination by the agricultural actors and decision-makers that aided our model conceptualization. We put this discussion in the context of recent calls for increased engagement between science and the public, highlighting some of the problems we encountered with this form of model evaluation.

Seeds and Quadtrees

The main reason I haven’t blogged much recently is because all my spare time has been taken up working on revisions to a paper submitted to Environmental Modelling and Software. Provisionally entitled ‘Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model’, the paper describes the biophysical component of the coupled human-natural systems model I started developing during my PhD studies. This biophysical component is a vegetation state-and-transition model combined with a cellular-automata to represent wildfire ignition and spread.

The reviewers of the paper wanted to see some changes to the seed dispersal mechanism in the model. Greene et al. compared three commonly used empirical seed dispersal functions and concluded that the log-normal distribution is generally the most suitable approximation to observed seed dispersal curves. However, dispersal functions using an exponential function have also been used. A good example is the LANDIS forest landscape simulation model that calculates the probability of seed fall (P) in a region between the effective (ED) and maximum (MD) seed distance from the seed source. For distances from the seed source (x) < ED, P = 0.95. For x > MD, P = 0.001. For all other distances P is calculated using the negative exponential distribution function is used as follows:
where b is a shape parameter.

Recently Syphard et al. modified LANDIS for use in the Mediterranean Type Environment of California. The two predominant pine species in our study area in the Mediterran Basin have different seed types: one (Pinus pinaster) has has wings and can fly large distances (~1km), but the other (Pinus pinea) does not. In this case a negative exponential distribution is most appropriate. However, research on the dispersal of acorns (from Quercus ilex) found that the distance distribution of acorns was best modeled by a log-normal distribution. I am currently experimenting with these two alternative seed dispersal distributions and comparing them with spatially random seed dispersal (dependent upon quantity but not locations of seed sources).

The main thing that has kept me occupied the last couple of weeks has been the implementation of these approaches in a manner that is computationally feasible. I need to run and test my model over several hundred (annual) timesteps for a landscape grid of data ~1,000,000 pixels. Keeping computation time down so that model execution does not take hundreds of hours is clearly important if sufficient model executions are to be run to ensure some form of statistical testing is possible. A brute-force iteration method was clearly not the best approach.

One of my co-authors suggested I look into the use of Quadtrees. Quadtrees are a tree data structure that are often used to partition a two dimensional space by recursively subdividing regions into quadrants (nodes). A region Quadtree partitions a region of interest into four equal quadrants. Each of these quadrants is subdivided into four subquadrants, each of which is subdivided and so on to the finest level of spatial resolution required. The University of Maryland have a nice Java applet example that helps illustrate the concept.

For our seed dispersal purposes, a region quadtree with n levels of may be used to represent an landscape of 2n × 2n pixels, where each pixel is assigned a value of 0 or 1 depending upon whether it contains a seed source of the given type or not. The distance of all landscape pixels to a seed source can then be quickly calculated using this data structure – staring at the top level we work our way down the tree querying whether each quadrant contains a pixel(s) that is a seed source. In this way, large areas of the grid can be discounted as not containing a seed source, thereby speeding the distance calculation.

Now that I have my QuadTree structure in place model execution time is much reduced and a reasonable number of model executions should be possible over the next month or so of model testing, calibration and use. My next steps are concerned with pinning down the appropriate values for ED and MD in the seed dispersal functions. This process of parameterization will take into account values previously used by similar models in similar situations (e.g. Syphard et al.) and empirical research and data on species found within our study area (e.g. Pons and Pausas). The key thing to keep in mind with these latter studies is their focus on the distribution of individual seeds from individual trees – the spatial resolution of my model is 30m (i.e. each pixel is 30m square). Some translation of values for individuals versus aggregated representation of individuals (in pixels) will likely be required. Hopefully, you’ll see the results in print early next year.

ABM of Mediterranean LUCC Paper Published in JASSS

Apparently blogging is just soooo 2004 and we should just leave it to the pros. The blog you’re reading may not be dead, but has been anaemic of late. Although this may not be the place to catch breaking news and cutting edge analysis in the 24-hour current affairs news cycle, it is a place where I can highlight some of my recent thoughts and activities. Maybe others will benefit from these notes, maybe they won’t. But in writing things down for public view it forces me to refine my thoughts so that I can express them concisely. Hopefully this blog has some life it yet and I will try to write soon about what has been taking up all my spare time recently – QuadTrees, seed dispersal and fire.

For now I will just let you know that the paper describing the agent-based model of Mediterranean agricultural Land-Use/Cover Change that I began developing as part of my PhD studies has now officially been published in the latest issue of JASSS.

Millington, J.D.A., Romero-Calcerrada, R., Wainwright, J. and Perry, G.L.W. (2008) An Agent-Based Model of Mediterranean Agricultural Land-Use/Cover Change for Examining Wildfire Risk. Journal of Artificial Societies and Social Simulation 11(4)4

JASSS Paper Accepted

This week one of the papers I have been working on as a result of my PhD research has been accepted for publication in the Journal of Artificial Societies and Social Simulation (JASSS). The paper, written with Raúl Romero-Calcerrada, John Wainwright and George Perry, describes the agent-based model of agricultural land-use decision-making we constructed to represent SPA 56 in Madrid, Spain. We then present results from our use of the model to examine the importance of land tenure and land use on future land cover and the potential consequences for wildfire risk. The abstract is below, and I’ll post again here when the paper is published and online.

An Agent-Based Model of Mediterranean Agricultural Land-Use/Cover Change for examining Wildfire Risk

James D. A. Millington, Raúl Romero-Calcerrada, John Wainwright, George L.W. Perry
(Forthcoming) Journal of Artificial Societies and Social Simulation

Humans have a long history of activity in Mediterranean Basin landscapes. Spatial heterogeneity in these landscapes hinders our understanding about the impacts of changes in human activity on ecological processes, such as wildfire. Use of spatially-explicit models that simulate processes at fine scales should aid the investigation of spatial patterns at the broader, landscape scale. Here, we present an agent-based model of agricultural land-use decision-making to examine the importance of land tenure and land use on future land cover. The model considers two ‘types’ of land-use decision-making agent with differing perspectives; ‘commercial’ agents that are perfectly economically rational, and ‘traditional’ agents that represent part-time or ‘traditional’ farmers that manage their land because of its cultural, rather than economic, value. The structure of the model is described and results are presented for various scenarios of initial landscape configuration. Land use/cover maps produced by the model are used to examine how wildfire risk changes for each scenario. Results indicate land tenure configuration influences trajectories of land use change. However, simulations for various initial land-use configurations and compositions converge to similar states when land-tenure structure is held constant. For the scenarios considered, mean wildfire risk increases relative to the observed landscape. Increases in wildfire risk are not spatially uniform however, varying according to the composition and configuration of land use types. These unexpected spatial variations in wildfire risk highlight the advantages of using a spatially-explicit ABM/LUCC.

Landscape Ecology paper In Press

We were informed this week that the paper I have been working on with Raul Romero Calcerrada and other colleagues at Universidad Rey Juan Carlos has been accepted by Landscape Ecology. I’ve copied the abstract below. It should be out later in 2008, but email me if you want a pre-print.

Currently I’m working on two paper with colleagues describing the construction and initial results of the model I constructed during my PhD research. We’re also submitting abstracts to the European Geophysics Union General Assembly 2008 on this and work related to the Landscape Ecology paper.

The abstract submitted with colleagues at CSIS has been accepted for poster presentation at the US-IALE meeting in Madison in April. Should be a good meeting. Also, the doi for Perry and Millington (2008) in PPEES now works.

Tomorrow I’m heading back to Europe for a couple of weeks. I have my PhD graduation ceremony next week (maybe I’ll post some photos of me looking scholarly/awkward in my academic dress/get-up), a couple days snowboarding in the Swiss Alps, and a couple of days working with Bruce Malamud at King’s following up on the work we published on US wildfire regimes in PNAS. Should be a fun couple of weeks!

GIS analysis of spatial patterns of wildfire human-caused ignition risk in the SW of Madrid (Central Spain) (In Press) Landscape Ecology

Raul Romero Calcerrada; Carlos J. Novillo Camacho; James DA Millington; Inmaculada Gomez-Jimenez

Abstract: The majority of wildfires in Spain are caused by human activities. However, much wildfire research has focused on the biological and physical aspects of wildfire, with comparatively less attention given to the importance of socio-economic factors. With recent changes in human activity and settlement patterns in many parts of Spain, potentially contributing to the increases in wildfire occurrence recently observed, the need to consider human activity in models of wildfire risk for this region are apparent. Here we use a method from Bayesian statistics, the Weights of Evidence (WofE) model, to examine the causal factors of wildfires in the south west of the Madrid region for two differently defined wildfire seasons. We also produce predictive maps of wildfire risk. Our results show that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape, with proximity to urban areas and roads found to be the most important causal factors. We suggest these characteristics and recent socio-economic trends in Spain may be producing landscapes and wildfire ignition risk characteristics that are increasingly similar to Mediterranean regions with historically stronger economies, such as California, where the urban-wildland interface is large and recreation in forested areas is high. We also find that the WofE model is useful for estimating future wildfire risk. We suggest the methods presented here will be useful to optimize time,
human resources and fire management funds in areas where urbanization is increasing the urban-forest interface and where human activity is an important cause of wildfire ignition.

Update 06/02/08: This paper is now online here and here.

Stakeholder Participation and Expertise

The problems of equifinality and affirming the consequent suggest alternative criteria by which to validate or evaluate socio-ecological simulation models (SESMs) will be useful. In my last post in this series I suggested that trust and practical adequacy might be useful additional criteria. In light of the ‘risk society’-type problems facing the systems that SESMs represent, and the proposed post-normal science approaches to examine and resolve them, the participation of local stakeholders in the model validation process seems an important and useful approach to ensure and improve model quality. If local stakeholders are to accept decisions and policies made based upon results from simulation models they will need to trust a model and, by consequence, the modeller(s).

Due to a perceived ‘crisis of trust’ in science over the last 20 years, Wilsdon and Willis suggest “scientists have been slowly inching their way towards involving the public in their work” and that we are now on the cusp of a new phase of public engagement that takes it ‘upstream’. This widely used, but somewhat vague term, is used to refer to the early involvement of the lay public in the processes of scientific investigation. As such, engagement is ‘upstream’ nearer the point at which the research and development agenda is set, as opposed to the ‘downstream’ end at which research results are applied and the consequences examined (see Figure 1).

Figure 1 Public participation in the scientific research process. Recently it has been suggested that public engagement with the scientific process needs to move ‘upstream’ nearer the point at which the research agenda is set. After Jackson et al

Whereas previously the theory of the ‘public understanding of science’ was a deficit model suggesting that the public would trust science ‘if only they understood it’, the contemporary shift is towards and engagement and dialogue between science and society. The implication of this new turn is that the public will trust science ‘if only they are involved in the process itself’. Recently, Lane et al. advocated this move upstream for forms of environmental modelling that address issues and concerns of rural populations. This position has been criticised as devaluing the worth of science, for patronising the public, and being a mask for political face-saving or insurance.

Regardless of other areas of science, in the case of developing simulation models for socio-ecological systems the participation of the public does not result in the first two of these criticisms. Engaging with local stakeholders to ensure a model is both built on a logically and factually coherent foundation and to ensure it examines the appropriate questions and scenarios is of great value to the modelling process and should improve representation of the empirical system. Contributing to successful iterations of this process, local stakeholders will gain both trust and understanding. However, the inclusion of local stakeholders in the modelling process does raise the issue of expertise.

With parallels in the three phases Wilsdon and Willis have suggested, Collins and Evans have suggested we are entering a third wave in the sociology of science. This third wave demands a shift from an emphasis on technical decision-making and truth to expertise and experience. Collins and Evans suggest there are three types of expert in technical decision-making (i.e. decision-making at the intersection of science and politics); ‘No Expertise’, ‘Interactional Expertise’, and ‘Contributory Expertise’.

Individuals possessing interactional expertise are able to interact ‘interestingly’ with individuals undertaking the science, but not to contribute to the activities of science itself (contributory expertise). Brian Wynne’s well-known study of the (inadequate) interaction between Cumbrian sheep farmers and UK government scientists investigating the ecological impacts of the Chernobyl disaster is a prime example of a situation in which two parties possessed contributory expertise, but neither interactional expertise. As a result, the ‘certified’ expertise of the government scientists was given vastly more weight than the ‘non-certified’ expertise of the farmers (to the detriment of the accuracy of knowledge produced). Such non-certified expertise might also be termed ‘experience-based’ expertise, arising as it does from the day-to-day experiences of particular individuals.

The importance of considering non-certified, contributory experience is particularly acute for SESMs. Specifically, local stakeholders are likely to be an important, if not the primary, source of knowledge and understanding regarding socio-economic processes and decision-making within the study area. Furthermore, the particular nature of the interactions between human activity and ecological (and other biophysical) processes within the study area will be best understood and incorporated into the simulation model via engagement with stakeholders. This local knowledge will be vital to ensure the logical and factual foundations of the model are as sound as possible.

Furthermore, engagement with local stakeholders will highlight model omissions, areas for improved representation, and guide application of the model. It provides an opportunity to enlighten experts as to the ‘blind spots’ in their knowledge and questions. As such, the local stakeholders become an ‘extended peer community’, lending alternative forms of knowledge and expertise to the model (and research) validation process than that of the scientific peer community. This knowledge and expertise may be less technical and objective than that of the scientific community, but this nature does not necessarily reduce its relevance or utility to the modelling of a system that contains human values and subjects.

I pursued this idea of stakeholder participation in the modelling I undertook for my PhD. Early in the development of my agent-based model of land use decision-making, local stakeholders were interviewed with regards to how they made decisions and their understanding about landscape dynamics. Upon completion of model construction I went to talk with stakeholders about the model as they offered the prime source of criticism about the model representation of their decision-making activities. By engaging with these stakeholders a form of qualitative, reflexive model validation was performed that overcame some of the problems of a more deductive approach.