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Ecological urban dynamics: theconvergence of spatial modelling andsustainabilityBrian DealPublished online: 18 Oct 2010.

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Ecological urban dynamics:the convergence of spatial

modelling and sustainability

Brian Deal

Construction Engineering Research Laboratory and the Building Research Council,University of Illinois, 1 E. St. Mary’s Rd, Champaign, IL 61820, USA

E-mail: deal@uiuc.edu

Planning professions have focused their attention on continued technological solutions to environmental problems despitediscussions relating to the sustainability of the built human community. However, ecological sustainability involves theidenti� cation of ecologically sound alternatives to current practices, not necessarily involving technological intervention. Inthe urban ecosystem this means the identi� cation of the processes of urban change. New theory, tools, and researchmethods in ecological systems have the potential to improve the dynamics of change in urban environments. A varietyof sophisticated computational and theoretical tools exist for characterizing urban systems at a conceptual level, andfor visualizing and understanding these characterizations. The theoretical underpinnings and practical application of theintegrated, multi-disciplinary, ecological and engineering approach to modelling urbanland use transformation is presented.A new tool, LEAM (Land use Evolution and Impact Assessment Model), has been developed to provide researchersand professionals to address urban dynamics in greater detail at a variety of scales and interfaces. The conceptual frame-work of the LEAM model is provided, followed by an application of the model to a ‘collar county’ in the metro Chicagoarea. The model can visually examine the dynamic spread of human development in the county and the associatedenvironmental impacts of the urban growth and development patterns.

Keywords: ecology, environmental assessment, planning, simulation modelling, sustainability, sustainability indicators,tools, United States, urban development

Les professionnels de la plani� cation ont concentré leur attention sur les solutions technologiques continues aux problèmesécologiques, malgré les discussions concernant la pérennité de la communauté humaine construite. Cependant, la pérennitéécologique implique d’identi� er les bonnes alternatives écologiques aux pratiques actuelles, sans impliquer nécessairementune intervention technologique. Dans l’écosystème urbain, cela signi� e l’identi� cation des processus de changement urbain.De nouvelles théories, de nouveaux outils et de nouvelles méthodes de recherche dans les systèmes écologiques peuventaméliorer la dynamique de changement des environnements urbains. Un certain nombre d’outils informatiques etthéoriques existent, permettant de caractériser les systèmes urbains au niveau conceptuel et de visualiser et comprendreleurs caractérisations. Les renforcements théoriques et l’application pratique de l’approche intégrée, pluridisciplinaire,écologique et de mise en oeuvre de la modélisation de la transformation de l’utilisation du paysage urbain sont présentées.Un nouvel outil, nommé LEAM (Land use Evolution and Impact Assessment Model, Modèle d’évaluation de l’évolution etde l’impact de l’utilisation de la terre), a été développé a� n de fournir aux chercheurs et aux professionnels un moyend’affronter la dynamique urbaine de manière plus détaillée sur des échelles et des interfaces variées. Le cadre conceptuel dumodèle LEAM est fourni, suivi d’une application du modèle à une région test de la zone métropolitaine de Chicago. Lemodèle peut examiner de manière visuelle l’étendue dynamique du développement humain dans cette région et les impactsécologiques de la croissance urbaine et des modèles de développement qui lui sont associés.

Mots clés : écologie, évaluation écologique, plani� cation, modélisation de simulation, pérennité, indicateurs de pérennité,outils, États-Unis, développement urbain

Building Research & Information ISSN 0961-3218 print/ISSN 1466-4321 online © 2001 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals

DOI: 10.1080/09613210110064295

Building Research & Information (2001) 29(5), 381–393

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Introduction

Wherever I go I see buildings imposed. Imposedbecause they are inappropriate, insensitive. They arecrystallized monologues which do not meet the needsof people or place. In the days of hand-power it waseasier to go around a tree root or a boulder or fol-low a contour than go straight through. The linesthat resulted – for path, � eld boundary or buildingplacement were for pragmatic reasons if no other, inconversation with the landscape.

(Day, 1990)

Historically, ecological stimuli shaped the developmentpatterns of the human community. Traditional design solu-tions were the products of parochial regionality, based inlarge part on available building and landscaping materials,energy sources, climatic conditions and navigable trans-portation routes. These inherent ecological conditions dir-ectly in� uenced the community or building design outcomeand were important for the development of a communitythat derived its identity from the ecology of place. But theecology of place is rapidly being replaced by the inventionand machine age, systematically removing our naturalconnections and ecological decision making processes (Orr,1994). Advancements in mass communications, mass trans-portation and building science technology, have begun tospread universal planning solutions that remove the regionaldistinctions exhibited in the past. These technologicalimprovements permit urban densities that were oncethought impossible (in locations previously determined to beun-buildable), causing a proliferation of environmentallyrelated problems (Van der Ryne and Cowan, 1996).

The architectural professions in general, are failing to makesigni� cant contributions to the ecology of place; focusingalmost exclusively on the structural entity of form andfunction, while the context in which these entities have beenconstructed lacks any real analysis (Ford, 1994). Environ-mental awareness programmes have broadened the scope toinclude ‘green’ concepts and principles including the intro-duction of a variety of green rating tools and similarmechanisms for evaluating the probable environmentalperformance of a structure. These principles and mechanismin general, are related only to built structures and fail totake into account the entire landscape and socio-culturalecosystem in which they function. According to Orr (1994)focus can no longer be on individual buildings withoutconsideration for the impacts that these buildings haveon the community, the environment and the ecology ofplace. The built environment should be viewed in terms ofthe quantitative (energy, disposal, material cycling etc.) andqualitative impressions that the structures make on thebroader community.

Campbell (1996) described the planners’ triangle of con-� icting goals in which the sustainable trichotomy betweeneconomics, ecology and social justice are described:

Planners need better tools to understand their citiesand regions not just as economic systems, or as staticinventories of natural recourses, but also as environ-mental systems that are part of regional and globalnetworks.

(Campbell, 1996)

This requires an integrated approach to analysing urbansystems that realizes some of the consequences of ourcurrent actions are far removed from their origination. It alsorequires a shift from measuring probable causes to measuringprobable effects at larger and broader scales. This impliesthe use of an integrated, ecosystem approach to discerningthe environmental implications of urban planning actions,an approach that is well grounded in the planning literature.

An ecological approachIn 1905 Clements proposed the theory of dynamic ecology;association, invasion, and succession were the dynamic pro-cesses used to describe change in a given landscape. Clementssuggests that these processes move all biotic communitiestoward an ultimate or climax state (McIntosh, 1985). Thesesame arguments are used as the basis for the study of UrbanEcology that began in the 1920s. Urban ecologists de� nedurban change as ‘. . . an orderly sequence of changesthrough which a community passes in the course of itsdevelopment from a primary and relatively unstable, to arelatively permanent or climax stage . . .’ (Park et al., 1925).The idea was initially broached in ‘An Ecological Approachto the Study of the Human Community’, by McKenzie inNovember of 1925 (McKenzie, 1925). The article emphasizedthat methods used in the study of ecology may be ‘pro� tablyapplied’ to the analysis of the human community. Theseearly Urban Ecology models applied Clements’ sucessionaltheories to the study of urban land use patterns. The con-ceptual model portrayed the city as a series of concentriczones; one zone succeeded another through the processof invasion and competition. Every community is seen asexpanding from a central core due to pressure from thebusiness and industrial community and from residential‘pull’ (Burgess, 1924; Burgess, 1928).

Modern theoretical ecologists have begun to focus on spatio-temporal scales and hierarchical patch theory to describe thedynamic phenomena observed in biotic systems. Ecologistnow view broad scaled landscapes as a mosaic of small-scalesystems with individual components or ‘patches’ that arepart of a larger integrated network (Levin and Paine, 1974).Based on the theory of Island bio-geography, the hierarchicalpatch theory of ecology describes the dynamic relationshipbetween the landscape and the mosaic of smaller ecosystempatches that de� ne the landscape (MacArthur and Wilson,1967). Any speci� c patch in the landscape is constantlychanging and evolving, apparently in complete chaos at thesmaller scale. Yet at the landscape level, the overall age andspecies distribution remains relatively stable. In the midstwhat might seem chaotic at one scale, is found to be constant

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at another. An example of this idea can be found in newforest management policies. They require the cutting of timberto simulate the formation of patches (similar to the way dis-turbances – � re etc., form patches, as opposed to clear cut-ting the entire landscape). Areas adjacent to the cut patchesare not disturbed until it is most bene� cial to the survivaland stability of the entire landscape to cut them. A buildingin an urban community, as equated to a tree in a forestedlandscape, should be evaluated using the same conceptualframework; decisions regarding its replacement or removalshould not be made until its effect on its patch, and thepatch’s impact on the community can be evaluated. Newfringe neighbourhoods and fringe commercial developmentsshould be scrutinized for the possible redistribution ofresources from existing neighbourhood patches to these edgedevelopments and the possible impacts this redistribution hason other neighbourhood patches. Describing these complexdynamic relationships and interactions is a dif� cult task,requiring computationally and visually pro� cient tools.

Spatial modellingAdvances in dynamic spatial modelling techniques currentlyused to analyse ecologically based systems, (Hannon, 1991;Westervelt et al., 1995; Hannon and Ruth, 1997; Deal et al.,2000) are being developed to advance a modern urban ecologyapproach to urban systems modelling (Dendrinos, 1992; Deal,1997; Deal and Hannon, 2001). The technique allows for theincorporation of modern ecological theory (speci� cally thehierarchical patch theory) to be incorporated into dynamicurban systems modelling. A patch theory model recognizesthat different time scales exist in a mosaic of patch locationsacross a landscape. This suggests that landscape dynamicsare not uniform; that the dynamics in one patch of a land-scape impact other, seemingly disparate patches. The popula-tion of a speci� c urban community changes slowly, eventhough each neighbourhood may be undergoing rapid anddynamic change. When one neighbourhood is in decline,another may be succeeding toward an equilibrium state, eventhough the larger landscape remains seemingly unchanged.This idea has been used in the ecological economics � eldto describe the problem of time discounting. Individual dis-count rates are high (10–20%); individuals typically put alarger value on short-term returns. Societal discount rateshowever, can be as low as 1–2%; placing more value ondesigning for the long term (Hannon, 1990b; Hannon, 1994;Ventolo, 1998). To the community, the future can be an orderof magnitude more important than it is to the individual.The community can be characterized as comprised of amosaic of varying discounting ideals (individually high), eventhough the communal structure itself is more stable andconsiderate of the spatial reach and impacts of policy relateddecisions (Hannon, 1995; Perrings and Hannon, 1996).

Other applications in urban ecology have used a mathematicalecology approach. Mathematical models of the urbandynamic constructed by Allen et al. (1978) and Wilson (1981)are believed to be among the � rst attempts to adapt anon-linear ecological approach (Allen et al., 1978; Wilson,

1981). In early ecological economic work, Samuelson (1971)recognizes the attractive elements of mathematical ecology(Samuelson, 1971). Use of the Volterra-Lotka predator-preymodel by Curry (1981) describes occupational competition ina spatially structured labour market and by Sonis (1983) inmodelling geographic diffusion processes (Curry, 1981;Sonis, 1983). In terms of explicit urban ecological modellingDendrinos (1979; 1985) take the concept one step further,addressing dynamic non-linear interdependencies, stabilityand equilibrium, conditions that they argue are necessarycomponents (Dendrinos, 1979; 1985).

Urban land use transformation modellingRecent work in urban systems modelling has begun to utilizean arithmetic approach as well. Socioeconomic and geo-graphically based information sets are integrated intodynamic and spatially oriented visualization tools using aCellular Automata (CA) based data acquisition and visual-ization system (Birkin, 1990; Batty, 1992; Clarke et al., 1996;White and Engelen, 1997; Deal and Hannon, 2001). Acellular automaton is a discrete dynamic modelling system.Space, time and the states of the system are represented in aregular spatial grid or lattice, and posses any one of a � nitenumber of states. The states of the cells in the latticeare updated at each time step according to a local rule orprogrammed model. The state of a cell at any given timeis dependent not only on its local rules for change butalso on the states of its nearby neighbours at the previoustime step. All cells on the lattice can be considered inter-dependent relational entities and are updated synchronously,so that the visual state of the entire lattice advances in dis-crete time steps. CA is typically considered an ‘agent-basedapproach’, combining elements of many � elds of researchin the methodology (Langton, 1989; Wolfram, 1994; Epstienand Axtell, 1996).

In urban systems modelling, a cellular automaton is typicallyspeci� ed to give a spatially detailed representation of theevolution of the urban land-use patterns. Cell states repre-sent land uses; transition rules express the likelihood ofa change from one state to another as a function bothof existing land uses and the inherent suitability of the cellfor each possible use. Engelen (1997) used this technique torepresent urban land use dynamics to forecast climate changeon a small island setting (Engelen et al., 1997). Wu (1998)presented a model that also included user decisions to deter-mine model outcomes (Wu and Webster, 1998). White’sSt Lucia model (White and Engelen, 1997), is an exampleof high-resolution CA modelling of urban land-use dynamicsand an attempt to use the standard non-spatial models ofregional economics and demographics, as well as a simplemodel of environmental change for predicting the demandfor future agricultural, residential, and commercial/industrialland uses. An urban growth model of the San FranciscoBay Area (Clarke et al., 1997), is another example of usingrelatively simple rules in the CA environment to simulateurban growth patterns. The four types of growth as de� nedby Clark are:

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� spontaneous growth

� diffusive growth

� organic growth

� road in� uenced growth

Each cell, at each time step has a de� ned propensity tochange from an existing land use to a modi� ed land usebased on the four characteristics of growth.

A disaggregated, ecosystem approach to complexspatial modelsSpatially explicit modelling of complex environmentalproblems is essential for developing realistic descriptionsof past behaviour and the possible impacts of alternativemanagement policies (Risser et al., 1984). The conceptualcomplexity of formulating, building and calibrating intricatemodels and the noted weaknesses with a single modellerparadigm, has lead to a general recognition of the need fora more disaggregated, collaborative modelling system. Toaddress the conceptual complexity and collaborative barriersto spatio-temporal ecosystem model development, an inte-grated environment for high performance spatial modelling,called the Spatial Modelling Environment (SME) has beendeveloped at the University of Maryland. SME transparentlylinks icon-based modelling environments with advancedcomputing resources and Geographic Information Systems(GIS), allowing modellers to develop spatial simulationsin a user-friendly, graphical environment, requiring noknowledge of computer programming (Maxwell et al., 1999).Automatic code generators construct (spatial) simulationsand enable distributed processing over a network of paralleland serial computers, allowing transparent access to state-of-the-art computing facilities. The environment imposes theconstraints of modularity and hierarchy in model design,and supports archiving of reusable model components (SME,1999). The system removes the ‘black box’ complexities andadvances a disaggregated approach to spatial modelling.

Application of this system of collaborative spatial modellingwas � rst made to the Atchafalya Estuary in Louisiana inthe early 1990s. The initial modelling process began with thedivision of the Estuary waterscape into square kilometrecells. Dynamic models of the waterway were then con-structed in each of the cells. The two-dimensional modelswere then compiled and integrated by the SME. The requiredspatial data varied with each location and was supplied bydigital maps from a standard geographical informationsystem. Aerial photographs were used to calibrate and verifythe accuracy of the model (similar models are being used inSouth Florida Water Districts). Later applications of thetechnique were developed at the Geographical ModellingSystems Laboratory at the University of Illinois. The large andrelatively undisturbed landscape of Army training groundswere the focus of efforts to determine the impact of both

civilian and military activities on indigenous endangeredspecies. (Military training landscapes are managed differentlythan contiguous and surrounding lands. While neighbouringlands are often highly managed for agriculture or humanhabitation, the training land is likely to have been maintainedin a more natural state.) The collaborative spatial modellingapproach has also recently been applied to the problem ofthe spread of infectious disease. The dynamic spread of foxrabies across the state of Illinois was modelled to deter-mine the spatial patterns of the spread of the disease and toevaluate possible disease control strategies (Deal et al., 2000).

Land use Evolution and impact Assessment Model(LEAM)The Land use Evolution and impact Assessment Model(LEAM), utilizes the SME collaborative environment adaptedfor the purpose of developing a Spatial Decision SupportSystem (SDSS) to evaluate human development patterns.Developed at the University of Illinois with funding from theNational Science Foundation, LEAM describes land-usechanges across a landscape that result from the spatial anddynamic interaction among economic, ecological, and socialsystems in the region. In the LEAM approach, groups orindividuals who have substantive knowledge relating to aparticular system develop and test separate models of thatsystem. These contextual sub-models are run simultaneouslyin each grid cell of raster based GIS map(s); linked to formthe main framework of the dynamic spatial model (LEAM).The SME collaborative approach enables the model to becreated in an open and distributed manner that bringsdifferent expertise to bear on the problem. Inputs to themodel utilize USGS national land use data sets (at 30x30mresolution), census and economic data (readily available andtransportable to multiple sites), along with variables relatingto impact assessments sub-models (e.g. habitat, ecoregionalinputs, water an energy inputs) to parameterize the model.The products of LEAM model runs will be an analysis ofa series of policy scenarios; GIS maps or movies that showthe transformation of the subject landscape as a productof policy related inputs. These dynamic visual outputsare critical for testing policy scenarios and raising con-cerns regarding the impacts of development, environmentaldegradation, or con� icting land-use policies (George, 1997).The SDSS developed will include a simple user interface andtransportable data sets for application to multiple sites.

Figure 1 describes the fundamental LEAM approach tocapturing land use transformation dynamics. It begins withmodel drivers. Model drivers are considered those forces(typically human) that contribute to urban land use trans-formation decisions. The model drivers describe land usetransformation probabilities. The simulation visually dis-plays the landscape transformation realized at each time stepusing scenario based planning exercises. The resulting visualimages are then analysed for environmental impacts duringthe impact assessment phase. Sustainable indices based onthe derived impacts are then developed to feed back into themodel drivers.

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Figure 1 The LEAM spatial modelling environment

Model driversA simple illustration of the model drivers can be viewed inFigure 2. The LEAM model uses a 30×30m raster basedland use map (LU MAP), based on the USGS NationalLand use classi� cation system. The initial maps are used toparameterize the existing land use conditions (the model usesa 30×30m resolution to simulate the parcel by parceldecision making that in� uences urban growth patterns).The existing land use is then analysed for its developmentprobability (DEV PROBABILITY) at each time step. Theprobability of a cell changing to an alternate land use (ALTLANDUSE) depends on how the conditions for change in theimmediate (as well as global) area of study have changedusing a Markov chain approach.

A Markov chain describes the behaviour of transitionprobabilities between a system’s states. The process considersthe different states that any particular cell in the modelledlandscape can assume and the statistical probabilities thatgovern the transition of the phenomenon from one state to

another. In the LEAM approach, any developable cell in thelandscape has a probability of land use change associatedwith it. The calculation of the cell’s probability is based ona set of criteria that is evaluated by the model at each timestep. Each variable considered in the chain affects the overalldevelopment probability (DPR) of land use change:

DPR = LUex(f Ut + Nr + Ec + Pp +

So + Oc + Rr + Rs + Dm + Gt + Tr . . .)

Where (Figure 3):

� LUex determines the existing land use condition

� Ut de� nes utilities and resources available at the site

� Nr describes the neighbouring land use characteristics

� Ec represents the local economic conditions

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Figure 2 LEAM simple model driver

� Pp represents the gross population projections

� So represents social decision making factors

� Oc describes the probability that the cell will develop asopen space

� Rr determines the presence of roads

� Rs de� nes the random chance of land use change(spontaneous growth)

� Gt describes the spatial growth trends of the region

Figure 3 LEAM model drivers

� Dm describes the geography of the area

� Tr represents traf� c congestion coef� cients

Each driver is developed as a sub-model; de� nitions arecompleted and run independent of the larger LEAMorganization. Variables of interest can be scaled and plottedin formats that help visualize sub-model behaviour and con-textual experts can calibrate and test sub-model behaviourbefore it becomes integrated into the larger model. Usingiconographic modelling techniques for sub-model develop-ment greatly decreases the learning curve for enabling con-textual experts; it also increases the ease with which themodel can be changed and calibrated. The effects of changesmade can be viewed immediately; allowing the user to con-centrate on modeling instead of computational details(Maxwell et al., 1999).

Model drivers represent the dynamic interactions betweenthe urban system and the surrounding landscape. Scenariomaps visually represent the resulting land use changes.Altering input parameters (policies), change the spatial out-come of the scenario being studied. This enables what-ifplanning scenarios that can be visually examined and inter-preted for each simulation exercise. Results of a preliminaryLEAM simulation can be seen on the landscape sample inFigure 4. The landscape shown is in digital elevation formatand the cell transformations are shown in the cell coloursas the cells develop over time (darker (purple) representsa waterway). The newly developed cells represent one ofthree driver types: spontaneous growth, diffusive growth andorganic growth.

An alternative scenario may include the construction ofa new road in the area being studied. Results of the pre-liminary LEAM Model with a new road system added(Figure 5) shows how the land use transformation patternsmay vary with the road system added. In this case theplanning decision to revise the transportation network inthe area has dramatically changed the way the region hasdeveloped. As previously noted, many CA based toolsexist to identify probable patterns of development. Whatis generally lacking is the identi� cation of the societalimpacts that these may probably exert. The identi� cation ofthese impacts is an important component of the LEAMmodelling system.

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Figure 4 A preliminary LEAM scenario of four sample growth characteristics: initial urban land uses, spontaneous growth, diffusive

growth and organic growth

Figure 5 An alternative LEAM scenario that incorporates a new roads network. In contrast to Figure 4, note the increase in devel-

opment and the changing patterns of development due to addition of roads

Impact assessmentAn equally important component for the LEAM effort is thedevelopment of the fundamental criteria and methodologyneeded for determining environmental impacts indices. Thequanti� able impact of the built environment on available

natural resources is signi� cant. Approximately 50% of theSO2, 25% of the NOx, and 35% of the CO2 producedare attributable to buildings. The environmental bene� tsand societal costs savings of land use ef� ciency improve-ments within the US has been well researched and modelled

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(Fournier 1995; Fournier et al., 1999). Attempts to minimizethe coincidental environmental impacts of the built environ-ment to help promote a more sustainable future must focuson the dynamics of change.

Indices of the environmental impacts related to communityeconomic, environmental and social interactions can bederived using the LEAM collaborative approach. Sub modelsthat address the substantive issues are currently beingdeveloped using conceptual stock aggregation and spatialevaluation techniques. Existing methodologies – UrbanEnvironmental Management Systems (EMS) and UrbanForecasting Information Systems (FIS) (SmartPlaces, Toolkitfor Integrated Resource Accounting (TIRA), and SocialCosts of Alternate Land Development Scenarios (SCALDS) )are used to develop these sub-models. The sub-modelsare then compiled into the LEAM framework to evaluatethe impacts of land use changes. Using a scenario basedplanning approach, a critical analysis of each policy beinganalysed and simulated can be qualitatively evaluated forthe emergent land use patterns and quanti� ably evaluatedfor its associated environmental/societal impacts. This canin� uence policy strategies that promote more sustainableland use scenarios. Areas of explicit concern are impactassessments of: water quality, water quantity, resourcedepletion, energy use patterns, air emissions, solid waste,threatened and endangered species, and habitat destructionand fragmentation. Impact indices developed that relate tothese environmental factors will be used to develop regionalindicators of sustainability.

Sustainability indicatorsThe LEAM project has adopted an aggregate approach fordetermining sustainability indicators as they relate to urbanrisk assessment. The method uses a force/state/response sys-tem of indicator development with the criteria as developedby the US Environmental Protection Agency (EPA).Generated environmental impact indices drive the creationof indicators of regional sustainability; obstacles to using theindicators are the key to determining their improvement. Theinformation will be used to inform policy and communityland managers for possible land use modi� cations andco-operative planning efforts that promote more sustainablesolutions by attempting to measure land use sustainabilityindicators.

ApplicationAn application of the LEAM approach was proposedrecently to the planning and development departmentsin Kane County, Illinois a fast-growing ‘collar county’ ofChicago. Growth management, environmental protection,open space preservation, environmental protection and inter-governmental co-operation all are critical issues that con-front Kane County in the 1990s. Consequently, Kane Countyissued its 2020 Land Resource Management Plan in 1996in order to deal with these issues. In this plan, the County isdivided into three strategic areas: urban corridor, critical

growth area, and agricultural/village area (from east to west)based on the county’s historical land use pattern. Thecritical growth area, acts as a buffer between urban areasand agricultural areas and is a mixture of land uses (e.g.countryside residential, farmland and small villages). Theapplied LEAM simulation is intended to evaluate the planand to model urban growth scenarios in the critical growtharea through the year 2020. More speci� cally, the main goalof the simulation is to evaluate the in� uence of urban growthin the critical area on water quality and the conservation ofagricultural lands.

The initial phase of the model emphasizes urban landscapetransformation as a result of human activities. The spatio-temporal land transformation processes are examinedusing a variety of indicator parameters and variables. Themodel also evaluates the in� uence of land use changes onsurface water quality using land use imperviousness factorsand average annual rainfall events. The output describespollutant levels for nitrogen, phosphorus and suspendedsolids based on simple land use characteristics and theirassociated multipliers.

Existing land useExisting land use characteristics (Figure 6) are read from aUSGS land use map of the watershed modelled. This simpli-� ed example map includes seven land uses: agricultural,residential, commercial, open space, protected agriculturallands, waterways and roads.

Model resultsThe spatial dynamics of the changing land use patterns inthe example watershed provides an interesting visual of thespread of development among the original mostly agrarianlandscape. The following (Figures 7–8) is a scenario outputof the LEAM watershed model run for 25 years (starting ata 1995 data point). Displayed time steps include t = 5, t = 10,t = 15 and t = 25.

Moving from left to right; the initial map depicts the state ofthe system at time � ve – that is the patterns of developmentin the example watershed anticipated at the � ve year interval.The initial map also gives the reader an indication of thescale of a typical cell (each pixel in the picture represents the30-metre square cell size). The second map shows the forma-tion of new minor roadways and the attractive in� uence ofroads and other development. Map 2 (Figure 7) also showshow the open space in this scenario is attracted to the low-land areas of the waterway and to other open space cells.Careful examination also shows the spontaneous growthcharacteristic of both developed cells and open space cells.Commercial development can also be noted along the majorroadways.

After 15 years (map 3 – Figure 8) the advance of developedcells are clearly recognized, although the patterns are moresparsely populated than expected. (This may be attributed

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Figure 6 The initial land use patterns in the Mill Creek watershed area. Agricultural ± light, residential ± dark grey, commercial ±

lighter grey in developed areas, open space ± medium dark grey, protected agricultural lands ± medium light grey, waterways ±

darker, and roads ± black

to the high spread coef� cient that enables developmentto ‘jump’ more than one cell beyond already developedcells. This is important in areas with minimal geographicrestrictions – e.g. the relatively � at Illinois landscape). Openspace is now clearly encircling the creek and other bodiesof water and the additional subdivision roadways can beseen extended to the south. Spotty development in thesubdivision areas may also be a re� ection on the way higher-end subdivisions are built out; incrementally, instead ofall at once. Although one would expect the subdivisions

seen at time 5 to be entirely built out and occupied by time10–15.

The fourth panel (25 years – Figure 8, map 4) describes amature movement of development across the watershed. Thismature growth re� ects the County’s 2020 plan populationprojections for household growth in the area. It also re� ectsan open space policy scenario that directs a 40% open space/development ratio and the collection of that open space atareas of most bene� t to water quality indicators, generally

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Figure 7 Time 5 and time 10 in the Mill Creek watershed model run example. Note the gravitation of open space to the low areas at

the creek and way that existing development (dark) and roads (black) attract new development. Existing agriculture development

is the base grey and protected agricultural uses a lighter shade

Figure 8 Time 15 and Time 25 depictions of the Mill Creek watershed model. Note the density of the development pattern change at

the major intersection and the proliferation of residential starts in the northeastern portion of the study area

at lowlands and sensitive water edges. As expected growthappears to cluster at the intersection of busy roads and atbuilt subdivided areas. Some clusters have developed spon-taneously and others have been closely linked to existingdevelopment patterns.

ImpactsWater quality assessments were derived as part of the scenarioto discern some of the impacts that the developed land usepatterns will have on the example watershed. Nitrogen,phosphorous, and suspended solids were modelled as indi-

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Figure 9 LEAM water quality assessment output

cator pollutants. Nitrogen and phosphorous were chosenbecause they are the main nutrients in agricultural fertilizers,but are also important in urban runoff assessments.Suspended solids concentrations are a good indication of anincrease in soil and surface erosion. Model output displays atheoretical value of the amount of pollutant that is releasedinto a stream per year in graphic distributions that follow thespatial patterns in a time step format (Figure 9).

Pollutant values are used for comparison in a scenario basedplanning approach. Comparing pollutant loads before andafter a speci� c number of years, testing for policies that varythe amounts and locations of open space preserved in thewatershed, and testing scenarios for the in� uence of housingdensity types were tested.

DiscussionMapped images are extremely powerful for displayingthe spatial interactions and dynamic movement of humandevelopment patterns. Although dif� cult to represent instatic format, the animations of these images provide astrong case for the use of spatial simulation modelling formore intensive land-use change applications. The mappedimages are also important for establishing the most ef� cientdevelopment control strategies, the quanti� cation of environ-mental impacts and the subsequent relation to sustainability.

The Mill Creek watershed application presents LEAM scenariobased planning capabilities in their early form. The 30×30mresolution and the dynamic nature of the visualizations

represents some of the social dynamics present in land usedecision-making. The model application is currently beingre� ned and run for township level and entire county-scaledscenarios. Several more impact assessment sub-models arealso being re� ned and nearing completion, along with theassociated indicators for sustainability that are so critical fora robust analysis. The Kane County planning department,already involved in overseeing the project, is looking forwardto presentation and review of LEAM scenario reviews withCounty board members and interested planning organiza-tions in the summer of 2001.

ConclusionsEnvironmental quality and the integrity of our communitiesare being progressively degraded by rampant, low density andsprawling urban growth. Essential industries like agricultureare caught in a no man’s land of uncertainty over theirfuture. As competition for land has intensi� ed, so has dis-agreement over how to balance economic use and conserva-tion of natural resources. To date, the controversy has focusedon private property rights and the appropriate role of govern-ment in managing land. The lack of a genuine dialoguebetween advocates of public and private interests has led toa paralysis of effective decision making at every level ofgovernment.

Dynamic models of complex and interconnected ecosystemsenable scientists to experiment with and thus come tounderstand the interactions of dynamic system components.

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Dynamic models have given scientists valuable insights intothe critical and sensitive components of those systems. Whilegood progress has been made in the development of physicaland biological system models, there have been fewer attemptsand less success at developing social system models. One ofthe most important locations for applying dynamic systemmodelling is at the study of urban/rural interface – wheresocial and natural systems directly interact. The applicationof these techniques can help improve our fundamentalunderstanding of the dynamics of the urban form and thecomplex interactions between urban change, climate change,and sustainable systems. These techniques for evaluatingurban change can also incorporate other sustainability relatedconcepts to form the basis for a comprehensive SDSS.

An overall measure of the sustainability of a building cannotbe considered without considering the impacts that thestructure has on the place in which it is constructed orplaced. This requires the identi� cation of ecologically soundalternatives (Orr, 1992) and the identi� cation of the pro-cesses of urban change. New theory, tools and methods ofresearch in ecology can help to improve our understandingof the dynamics of change and its connection to sustain-ability in urban environments. Speci� cally, the LEAM col-laborative and disaggregated spatially dynamic modellingenvironment has been developed to simulate the changingurban fabric and its environmental consequences. LEAMenables the planning professions to characterize urban sys-tems at a conceptual level, and visualize the environmentalrepercussions of the policy variables tested using scenariobased planning and environmental impact assessment model-ling techniques. LEAM allows researchers and planningprofessionals to address the implications of urban designdecisions in greater detail at a greater variety of scales andinterfaces. Improving land use decision making process andenabling the implementation of more sustainable solutions.

ReferencesAllen, P., Deneubourg, J., Sanglier, M. and DePalma, A.

(1978) The dynamics of urban evolution, � nal report tothe US Department of Transportation.

Batty, M. (1992) Urban modelling in computer-graphic andgeographic information system environments. Environ-ment and Planning, 19, 678–708.

Birkin, M. (1990) Elements of a model based geographicinformation systems for the evaluation of urban policy,in L. Worrall (ed.) Geographic Information Systems.Belhaven Press, London.

Burgess, E. (1928) Residential segregation in American cities.Annals of the American Academy of Political and SocialScience, 140, 105–15.

Burgess, E. (1924) The growth of the city: an introductionto a research project. American Sociological SocietyPublications, 18, 85–97.

Campbell, S. (1996) Green cities, growing cities, just cities?:urban planning and the contradictions of sustainabledevelopment. Journal of the American PlanningAssociation, 62(3), 296–312.

Clarke, K., Hoppen, S. and Gaydos, L. (1997) A self-modifying cellular automaton model of historicalurbanization in the San Francisco Bay area. Environmentand Planning B: Planning and Design, 24, 247–61.

Clarke, K., Hoppen, S. and Gaydos, L. (1996) Methods andtechniques for rigorous calibration of a cellular auto-maton model of urban growth. Third InternationalConference/Workshop on Integrating GeographicInformation Systems and Environmental Modelling,Santa Fe, NM, February.

Curry, L. (1981) Division of labour from geographicalcompetition. Annals of the Association of AmericanGeographers, 71(2).

Day, C. (1990) Places of the Soul, The Aquarian Press, SanFrancisco.

Deal, B. (1997) De� ning Ecological Patterns in the UrbanEcosystem, University of Illinois School of Architecture,Champaign, IL.

Deal, B. and Hannon, B. (2001) An ecological approach tomodelling urban environments using cellular automata;the Mill Creek watershed model. The Journal ofEcological Economics (forthcoming).

Deal, B., Farello, C., Lancaster, M., Kompane, T. andHannon, B. (2000) A dynamic model of the spatialspread of an infectious disease: the case of fox rabiesin Illinois. Environment Modelling and Assessment, 5,47–62.

Dendrinos, D. (1979) A Basic Model of Urban DynamicsExpressed as a Set of Volterra-Lotka Equations, USDepartment of Transportation, Washington, DC.

Dendrinos, D. (1985) Urban Evolution: Studies in theMathematical Ecology of Cities, Oxford University PressNew York, NY.

Dendrinos, D. (1992) The Dynamics of Cities: EcologicalDeterminism, Dualism, and Chaos, Routledge, NewYork.

Engelen, G., White, R. and Uljee, I. (1997) Integratingconstrained cellular automata models, GIS decisionsupport tools for urban planning policy makers, inH. Timmermans (ed.) Decision Support Systems inUrban Planning, E and FN Spon, London, UK.

Epstien, J. and Axtell, R. (1996) Growing Arti� cialSocieties, Brookings Institution Press Washington, DC.

Ford, L. (1994) Cities and Buildings: Skyscrapers, Skid Rowsand Suburbs, The Johns Hopkins University Press,Baltimore.

Fournier, D. (1995) Sustainable development initiatives withinthe DoD. IEA Energy Conservation in Buildings andCommunity Systems – Future Buildings Forum,Ermington/Lake Constance, CH.

Fournier, D., Jenicek, E. and Uzgiris, D. (1999) EstablishingBaselines and Measuring Performance on MilitaryInstallations, International Energy Programme Evalu-ation Conference, Denver, CO.

George, R. (1997) Hyperspace: communicating ideas aboutthe quality of urban space. Journal of Planning Educa-tion and Research, 17, 63–70.

Hannon, B. (1990b). The Forgetting Rate. Landscape Journal,9(1), 16–21.

Hannon, B. (1991) Accounting in ecological systems, inR. Costanza (ed.) Ecological Economics: The Scienceand Management of Sustainability, Columbia UniversityPress, USA, pp. 234–52.

Deal

392

Dow

nloa

ded

by [

Uni

vers

ity o

f W

ashi

ngto

n L

ibra

ries

] at

04:

24 2

5 A

ugus

t 201

4

Hannon, B. (1994) Sense of place: geographic discountingby people, animals and plants. Ecological Economics, 10,157–74.

Hannon, B. (1995) Input-output economics and ecology,in W. Leontieff and F. Duchin (eds) a special editionof Structural Change and Economic Dynamics, 6(3),331–3.

Hannon, B. and Ruth, M. (1997) Modelling Dynamic Bio-logical Systems, Springer-Verlag, New York.

Langton, C. (1989) Arti� cial Life, Addison–Wesley. RedwoodCity, CA.

Levin, S. and Paine, R. (1974) Disturbance, patch formation,and community structure. Proceedings of the NationalAcademy of Sciences USA, 71(7), 2744–7.

MacArthur, R. and Wilson, E. (1967) The Theory of IslandBiogeography, Princeton University Press, Princeton, NJ.

Maxwell, T., Villa, F. and Costanzia, R. (1999) The SpatialModelling Environment (SME), in M.D. Solomons (ed.)International Institute for Ecological Economics, Centrefor Environmental Science, University of MarylandSystem, USA.

McIntosh, R. (1985) The Background of Ecology, CambridgeUniversity Press, Cambridge, MA.

McKenzie, R. (1925) Ecological approach to the study ofthe human community. American Journal of Sociology.

Orr, D. (1992) Ecological Literacy: Education and theTransition to a Postmodern World, State University ofNew York Press, Albany, NY.

Orr, D. (1994) Earth in Mind: On Education, Environment,and the Human Prospect, Island Press, New York, NY.

Park, R. and Burgess, E. (1925) The City, University ofChicago Press, Chicago, IL.

Perrings, C. and Hannon, B. (1996). A Sense of Time andPlace: An Introduction to Spatial Discounting. Ecology,Society, Economy, Universite de Versailles, PlenarySession.

Risser, P., Karr, J. and Forman, B. (1984) Landscape Ecology:Directions and Approaches, Illinois Natural HistorySurvey, Champaign, IL.

Samuelson, P. (1971) Generalized predator prey oscillations inecological and economic equilibrium. Proceedings of theNational Academy of Sciences, 68, 980–3.

SME (1999) http://kabir.cbl.umces.edu/SME3/index.html,International Institute for Ecological Economics, Centrefor Environmental Science, University of MarylandSystem.

Sonis, M. (1983) Competition and environment: a theory oftemporal innovation diffusion, in D. Grif� th and A. Lea(ed.) Evolving Geographic Structures, Martinus Nijhoff,The Hague.

Van der Ryne, S. and Cowan, S. (1996) Ecological Design,Island Press, Washington, DC.

Ventolo, W. (1998) Fundamentals of Real Estate Appraisal,Real Estate Education Co., Chicago, IL.

Westervelt, J., Hannon, B., Sheikh, P., Cheng, A. and Rubin,C. (1995) Dynamic, Spatial, Ecological Modeling:A Demonstrated Simulation of the Sage Grouse Habitatat the Yakima Training Centre, US Army, Corps ofEngineers, CERL Champaign, IL.

White, R. and Engelen, G. (1997) Cellular automata asthe basis of integrated dynamic regional modelling.Environment and Planning B: Planning and Design, 24,235–46.

Wilson, A. (1981) Catastrophe Theory and Bifurcation:Application to Urban and Regional Systems, CroomHelm, London.

Wolfram, S. (1994) Cellular Automata and Complexity,Addison–Wesley, Reading, MA.

Wu, F. and Webster, C. (1998) Simulation of land develop-ment through the integration of cellular automata andmulticriteria evaluation. Environment and Planning B:Planning and Design, 25, 103–26.

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