Ecological Urban Dynamics and Spatial Modeling

Brian Deal, Construction Engineering Research LaboratoryDonald F. Fournier, Construction Engineering Research Laboratory

ABSTRACT

Although the current literature includes discussions relating to the sustainability ofthe built human community, planning professions have focused their attention on continuedtechnological solutions to environmental problems. These interventions attempt to stabilizethe problem at hand through further technological infusion increasing the gap between theorigination of the problem and ecologically sound solutions. Ecological sustainability,however, involves the identification of ecologically sound alternatives to current practices.In the urban ecosystem this means the identification ofthe processes ofurban change.

New theory, tools, and methods ofresearch in ecological systems promise to improveour understanding of the dynamics of change in urban environments. We now have access toa variety of sophisticated computational and theoretical tools for characterizing urbansystems at a conceptual level, and for visualizing and understanding these characterizations.A multi-disciplinary, systems modeling approach, using geographic information systems(GIS) and dynamic computer modeling techniques allows researchers and professionals toaddress urban dynamics in greater detail at a greater variety of scales and interfaces.

This paper focuses on an integrated, ecological and engineering approach to modelingurban dynamics. A spatially explicit computer model is developed to visually examine thedynamic spread of human development across a watershed in the metro Chicago area. Thetechnique allows for the evaluation of environmental and energy related impacts caused byurban growth and development. Results of the model are used to develop scenarios thatsupport policy level changes aimed at softening the ecological strain of human developmentpatterns and increasing resource efficiency.

Introduction

An extensive literature exists in the urban planning and regional science fieldsrelating to large-scale urban models. Historically, computer based simulations of urbanproblems appears to have its origin in the 1950’s metropolitan transportation studies(Klosterman 1994) and the geographic accessibility models that resulted. Theoretical urbansimulation models for locating residential development, and retail centers were then added tothe to previously simple and straightforward transportation models in the 1960’s. Thesesuccesses encouraged a number of ambitious, expensive, and highly visible attempts to buildlarge-scale metropolitan simulation models. In 1973 Douglas Lee wrote a cornerstone articlein the Journal of the American Planning Association that effectively eliminated large-scaleurban modeling research for nearly 20 years. The article titled - A Requiemfor Large-Scale

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Models, depreciated the then ambitious attempts to develop large-scale computer models ofthe metropolis (Lee 1973).

Despite the practical failures of the large-scale modeling efforts, the mathematicalprogramming techniques developed for use in these models were found to be useful forconstrained and well structured problems with a specified number of calculable variables,well-defined goals, and firmly established technical solutions. In 1969 Jay W. Forresterwrote his seminal work —Urban Dynamics (Forrester 1970), in which he develops a computerbased dynamic simulation tool (a precursor to current dynamic modeling software) todescribe the changing fabric of the urban environment. Forrester’s modeling tool wasfocussed on a constrained problem set that enabled planners to introduce a temporal approachinto previously static methodologies. However, these model types still failed to addressanalytic or empirically verifiable solutions (Dendrinos 1992). Forrester bases his work on hisconcepts of ‘industrial dynamics’ and ‘industrial ecology’ (Forrester 1961), that can looselybe tied to earlier work by the Urban Ecology movement in the 1920’s.

An Ecological Approach

The Ecological Approach to the Study of the Human Community, by R. D. McKenzie,was first published in the American Journal of Sociology in November 1925 (McKenzie1925). It was intended to emphasize that methods utilized in the study of ecology may be‘profitably applied’ to the analysis of the human community (Park, Burgess, and McKenzie1925). Along with earlier works by R. E. Park and E. W. Burgess, it developed into thediscipline known as Urban Ecology and became a precursor to one ofthe first ecologicallybased models ofcommunity development.

The earliest Burgess and Park models (ofdominance and successional theories) wereapplied to the study of urban land use patterns. The city was seen as a product ofcompetition and interdependence, and was characterized by the mobility of its population(Burgess 1928). These ideas were incorporated into a conceptual model that portrayed thecity as a series of concentric zones. These zones were considered dynamic elements thatemphasized change and its effect on social organization. The dynamic mechanism for themodel also parallels the successional theory of ecology. One group succeeds another in theuse of an area through the process of invasion and competition. Every community is seen asexpanding from its central core outward due to pressure from the business and industrialcommunity and from residential ‘pull’ (Burgess 1924).

Urban Growth and Landuse Change

Recent technological and computational developments have altered the basicassumptions of spatial analysis as it relates to urban growth. Recent work by Batty (1992),Birkin (1990), White (1997), and Landis (1994) have begun to utilize a cellular automata(CA) approach to integrate socioeconomic and environmentally based information into adynamic and spatially oriented urban analysis and visualization tool (Batty 1992; Birkin1990; Landis 1994; White and Engelen 1997b). Along with advances in dynamic spatialmodeling techniques currently used to analyze ecologically based systems, (Deal et al. 2000;Hannon and Ruth 1994; Hannon and Ruth 1997; Westervelt et a!. 1997), are being developedto advance a modern Urban Ecology approach to urban systems modeling (Deal 1997; Deal

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1 998a; Deal 1 998b). The application ofthese techniques can help improve our fundamentalunderstanding ofthe dynamics ofthe urban form and the complex interactions between urbanchange, climate change, and sustainable systems. - These tools, used with Spatial DecisionSupport Systems (DSS) and Geographic Information Systems (GIS), are making Lee’s urbanmodeling complexity argument obsolete.

Cellular Automata

A cellular automaton (CA) is a discrete dynamic modeling system. Space, time, andthe states ofthe system are discrete; each point in a regular spatial lattice, called a cell, canhave any one of a finite number of states. The states of the cells in the lattice are updatedaccording to a local rule. That is, the state ofa cell at a given time depends only on its ownstate one time step previously and the states ofits nearby neighbors at the previous time step.All cells on the lattice are updated synchronously. Thus the state of the entire latticeadvances in discrete time steps.

The CA approach to modeling dynamic systems has resulted in a variety ofcomputational tools. Modeling languages such as StarLogo, Agentsheets, Cocoa,SugarScape, and Swarm are being used to create and explore models of dynamic systems.These object-based parallel approaches differ from the “aggregate’t modeling languages;instead of specifying stocks, properties of populations of objects or individuals, the modelerspecifies the property of the individual object directly. Instead of specifying flows, that isrelations between stocks, the learner specifies the rules that govern the interactions ofindividual elements of the system. This new approach, sometimes referred to as embodiedmodeling, allows the learner to specify the simulation at a level closer to his or herexperience of the system elements.

White’s St. Lucia model (White and Engelen 1 997a), is an example ofhigh-resolutionCA modeling of urban land-use dynamics and an attempt to use the standard non spatialmodels of regional economics and demographics, as well as a simple model ofenvironmentalchange for predicting the demand for future agricultural, residential, andcommercial/industrial land uses. A cellular automaton is specified to give a spatially detailedrepresentation ofthe evolution ofthe urban land-use patterns. Cell states represent land uses;transition rules express the likelihood ofa change from one state to another as a function bothofexisting landuses and the inherent suitability ofthe cell for each possible use.

A spatial urban growth model of the San Francisco Bay Area (Clarke, Hoppen, andGaydos 1997), is another example ofusing relatively simple rules in the CA environment tosimulate urban growth patterns. The four types of growth as defined by Clark: ‘a)spontaneous neighborhood growth, b) diffusive growth and spread ofa new growth center, c)organic growth, d) road influenced growth. Each cell, at each time step has a definedpropensity to change from an existing landuse to a modified landuse based on the threecharacteristics ofgrowth.

Once the landuse has been defined for a cell, then its impact on a whole host ofenvironmental issues can be estimated. Impact factors can be developed based on cellarchetypes that can define growth in energy and water use, materials requirements, and theirassociated impacts. Also, alternative growth scenarios may be developed based on redefinedcell- archetypes, different social forces, and varying cell impacts.

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The Mill Creek Watershed Model

An application of the CA urban growth modeling approach was proposed recently tothe planning and development departments in Kane County, Illinois, a fast growing collarcounty of Chicago. An agreement was signed between the County and University ofIllinoisresearchers to develop a prototype urban growth model of one of the critical watersheds inthe County — the Mill creek watershed. Mill Creek is one of twelve primary watersheds inKane County. Growth management, environmental protection, open space, environmentalprotection and intergovernmental cooperation all are critical issues that confront KaneCounty in the 1990’s (Kane County Development Department, 1996). Consequently KaneCounty issued its “2020 Land Resource Management Plan” in 1996 in order to deal withabove issues. In this plan, the County is divided into three distinct strategic areas: urbancorridor, critical growth area, and agricultural/village area (from east to west) based on thecounty’s historical land use pattern. The critical growth area acts as a buffer between urbanarea and agricultural area and is a mixture of land uses such as countryside residential,farmland, and small villages. This simulation is intended to evaluate the plan and to modelurban growth in the critical growth area until year 2020. More specifically, the main goal ofthe simulation is to evaluate the influence of urban growth in the critical area on waterquality of watershed and conservation of agricultural land and to provide the basis for adynamic decision support system. Further research will extent the model’s projectioncapability to other critical environmental issues such as water and energy usage and otherdemands and impacts associated with development.

The initial phase ofthe model emphasizes urban landscape transformation as a resultof human activities. The spatio-temporal land transformation processes are examined from avariety of perspectives and scales using a variety of indicator parameters and variables.These processes work under a set of rules simulated using CA that utilize a growthprobability function. During each simulation time step, predicted results for new households,commercial landuse, and open space requirements are compared with modeled simulationresults. If there is a surplus or shortage, a self-adjusting mechanism of the model is kickedin, slowing or speeding the growth rates to more closely simulate the predicted results.

The model also evaluates the influence of land use changes on surface water qualityusing land use imperviousness factors and average annual rainfall events. The outputdescribes pollutant levels for nitrogen, phosphorus, and suspended solids based on simplelanduse characteristics and their associated multipliers.

Once the spatial analysis has been calibrated and tested, scenario results can be usedto evaluate landuse policy decisions for environmental and societal impacts based on -

ecological sustainability criteria developed. The environmental benefits and societal costssavings of landuse efficiency improvements within the U.S. has been well researched andmodeled (Fournier, Jenicek, and Uzgiris 1999; Nemeth and Fournier 1994). Thedetermination of scenario impacts should be based on the developed scenario inputs and theresults of model analysis as compared to existing conditions and do nothing strategies.Impact assessments can then be developed and related to environmental factors, pollutionrisk, and costs to assess overall impacts.

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The Model

The Mill Creek watershed model follows some of the fundamental principals of theClarke model with some additional work and modification. Most important is the resolutionof the model. The Mill Creek model represents a 30 meter x 30 meter resolution to simulatethe parcel by parcel decision making that influence urban growth patterns. Anothersignificant deviation from the Clark approach is the development of a Markov chainapproach to determine the probabilities of development within any given cell in thelandscape.

A Markov chain describes the behavior of an informationally closed system oftransition probabilities between that system’s states. It is named after Andrei A. Markov(1856-1922), a Russian mathematician who at the turn of the century studied poetry andother texts as stochastic sequences of characters. The process is linked with the differentstates that any particular cell in the modeled landscape can assume, and the statisticalprobabilities that govern the transition ofthe phenomenon from one state to another.

For any developable cell in the landscape there is probability of landuse changeassociated with that cell. This probability is based on a set of criteria that is evaluated by themodel at each time step. Each variable in the chain affects the overall probability (APR) oflanduse change.

APR LUex f U~+ Nr + E~+ Pp + 5o + O~+ Rr + R~

Where:LUex determines the existing landuse conditionU~defines utilities and resources available at the site for development f (E +G + W + S~+ S~

)• electric, gas, water, sanitary sewer, storm sewer

Nr describes the neighboring landuse characteristics = f (Ag +Inc + P0)• landuse, utilities present in cells adjacent

E~represents the local economic condition = f (Ref)• Regional Economic Forecast - economic resources available in the area

P~represents the gross population projection ofthe area= f (Pef /Hh)• population / household projections

S~is an overshoot function• it increases or decreases the probability of change based on the current number of

cells urbanized in the model area compared to the number ofhouseholds projected forthe time step

O~describes the probability that the cell will develop as open space• based on locational attributes (open space gravitates to water) and the percentage of

open space desired. In the Mill creek watershed model Kane County has specified adesire to maintain a 40% urban to open space ratio.

Rr determines the presence ofroads• roads are important sources ofutilities and resources for each cell and have a major

impact on the landuse change probabilitiesR~defines the random chance oflanduse change

• spontaneous growth

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Existing landuse. Existing landuse characteristics (figure 1) are read from a GIS map —

produced with data supplied by Kane County. The map includes seven land uses:Agricultural, Residential, Commercial, Open space, Protected Agricultural Lands,Waterways, and Roads.

Population projections. Population projections are based on data supplied by the County2020 plan. It gives data for historic and probable population increases in the county. Thisinformation was plotted and a curve developed for household growth projections usingCounty supplied persons per household data. Density scenarios use increased persons per -

household information along with increased units /acre to determine spatial modification ofthe landscape. The developed algorithm supplies the model with its basic growth function.

Overshoot. The overshoot function in the model determines and analyzes how well themodel is conforming to the household population projection curve. During eachtime step ofsimulation, projected results are compared with simulated activity. If there is a surplus ofhouseholds in the simulation, the model corrects by reducing the growth function, slowingthe construction ofnew units. If there is a shortfall ofunits, the model increases the growthfunction to correct the shortfall. This self-modification function keeps the projectedhouseholds in line with County projections. A limitation of this method lies in thecontinuation ofthe model beyond the 2020 time period. The exponential curve that supplies

Figure 1. The initial landuse patterns in the Mill Creek watershed area. Agricultural -

light, Residential — dark gray, Commercial —lighter gray in developed areas, Openspace — medium dark gray, Protected Agricultural Lands — medium light gray,Waterways - darker, and Roads — black.

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information on projected households could not continue at an exponential rate and wouldmost probably correct to a lower and more reasonable rate of increase over time. For tl~purposes ofthis exercise, however, we used the curve given.

Commercial development. We employed probability, road distance, and known residentialto commercial ratios to simulate the development ofcommercial urban growth. The amountand density of residential development and the location and influence of major roadsdetermine the probability ofchange from a residential to a commercial cell.

Model Results

The spatial dynamics of the changing landuse patterns in the Mill creek watershed inKane County Illinois provides an interesting picture. Figures 2 and 3 displays four mapsproduced from the full 25-year run of the model showing the spread of development amongthe original mostly agrarian landscape (starting at a 1995 data point). The outcome fromtime steps taken the simulation at t’S, t= 10, t= 15 and t=25. Roads were inserted (brown) tosimulate subdivision growth.

Figure 2. Time 5 and time 10 in the Mill creek watershed model run example. Note thegravitation of open space to the low areas at the creek and way that existingdevelopment (dark) and roads (black) attractive new development. Existing agriculturedevelopment is the base grey and protected agricultural uses a lighter shade.

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Moving from left to right, the initial map depicts the state of the system at time 5 —

that is the patterns of development in the Mill creek watershed anticipated at the 5-yearinterval. The initial map also gives the reader an indication ofthe scale oftypical cells (eachpixel in the picture represents the 30-meter square cell size in the model) and the complexityofthe computational problems involved.

The second map shows the formation of new minor roadways and the attractiveinfluence of roads and other development. Map 2 also shows how the open space on ourmodel is attracted to the lowland areas and other open space cells. Careful examination alsoshows the spontaneous growth characteristics ofboth developed cells and open space cells.Commercial development can also be noted along the major roadways.

After 15 years the advance ofdeveloped cells are clearly recognized in the third map,although the patterns are perhaps more sparsely populated clusters than expected. This canbe attributed to the high spread coefficient in this model that enables development to ‘jump’more than one cell beyond already developed cells. Open space is now clearly encircling thecreek and other bodies of water and the additional subdivision roadways can be seenextended to the south. Spotty development in the subdivision areas may also be a reflectionon the way higher —end subdivisions are built out; incrementally, instead of all at once.Although one would expect the subdivisions seen at time 5 to be entirely built out andoccupied by time 10-15.

The fourth panel (25 years) describes a mature movement of development across theMill creek watershed. This mature growth reflects the County’s 2020 plan populationprojections for household growth in the area. It also reflects a desire for open space growthat a 40% ratio and the collection ofthat open space at areas of most benefit to water qualityindicators, generally at lowlands and sensitive water edges. As expected growth appears tocluster at the intersection of busy roads and at built subdivided areas. Some clusters havedeveloped spontaneously and others have been closely linked to existing developmentpatterns.

I ~ ~ 3. Time 15 and Time 25 depictions of the Mill creek watershed model. Note thedensity of the development pattern change at the major intersection and theproliferation of residential starts in the northeastern portion ofthe study area.

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Discussion. Mapped images are extremely powerful for displaying the spatial interactionsand dynamic movement ofhuman development patterns. Although difficult to represent instatic format the animations of these images provide a strong case for the use of spatialsimulation modeling for more intensive landuse change applications. The mapped imageswill also become important in future work regarding the most efficient development controlstrategies and the quantification of environmental impacts.

The Mill creek watershed model presents a novel way of representing CA landusechange models. The 30-meter x 30-meter resolution of the model represents more clearly,we believe, the social dynamic present in landuse change decision making. The use thisresolution enables the introduction ofvariables that can not be represented in larger scaledmodels.

Dynamic spatial modeling is important for the development of a robust landuse decisionsupport system (DSS). The entire landuse DSS should include:

• evaluation criteria for: global climate change impacts, economic, environmental, andsocially based landuse interactions

• landuse policy scenarios and given evaluation criteria to determine futureenvironmental and landuse sustainability impacts

• infrastructure and community based landuse assessment models to assess impacts,resource requirements, and uncover salient linkages

• a set of regional sustainable indices as they relate to community interaction variables,climate change and urban risk assessments

The overall goal of the DSS should be to improve the gaps in our basic understanding of theurban community, resource requirements, environmental impacts, and landscapesustainability. The model is still a work in progress and calibration and validation of themodel is underway. The model provides a probable future scenario based on factors derivedfrom past actions within the region being modeled.

Conclusions

Dynamic models of complex and interconnected ecosystems enable scientists toexperiment with and thus come to understand the interactions of dynamic systemcomponents. Dynamic models give valuable insights into the critical and sensitivecomponents of those systems. While good progress has been made in the development ofphysical and biological system models, there have been fewer attempts and less success atdeveloping urban ecological models. One of the most important locations for applyingdynamic system modeling is at the study of urban/rural interface - where the builtenvironmental and the natural environment directly interact. The application of thesetechniques the fine scale that this model provides can improve our fundamentalunderstanding ofthe dynamics ofthe urban form and the complex interactions betweenurbanchange, landuse change, and the associated environmental impacts that affect sustainability.These techniques for evaluating urban change can also incorporate other sustainability-related concepts to form the basis for a comprehensive landuse DSS. Sustainability impactsand landuse assessment tools already developed can easily be incorporated into the dynamiclandscape change model.

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Landuse assessment tools that utilize conceptual stock aggregation and spatialevaluation techniques quantify the built environment using archetypes and impact profiles. —

Existing methodologies can be classified into two basic categories - Urban EnvironmentalManagement Systems (EMS) and Urban Forecasting Information Systems (FIS) and arerepresented by available tools including SmartPlaces, Toolkit for Integrated ResourceAccounting (TIRA), and Social Costs of Alternate Land Development Scenarios (SCALDS).These EMS and FIS tools can be compiled into a comprehensive land use assessment modelthat incorporates the impacts of land use changes in the built and natural environments byattaching social and economic costs and the tracking ofresources and emissions.

Ecological sustainability requires the identification of ecologically sound alternativesto current practice (Orr 1992). In urban ecology this means the identification of theprocesses of urban change. Applying new theories, tools, and methods of research inecological systems to urban ecological modeling promises to improve our understanding ofthe dynamics of change in urban environments. We now have access to a variety ofsophisticated computational and theoretical tools for characterizing urban systems at aconceptual level and for visualizing and understanding these characterizations in greaterdetail than previously. Using dynamic simulation will allow scenarios based on public policyaffecting growth to be modeled and their impact on the environment assessed. Finer detail,combined with larger scale allows local as well as regional drivers and impacts to beassessed, especially at the urban/rural interface. The Mill creek watershed modelingapproach allows researchers and planning professionals to address urban dynamics in greaterdetail at a greater variety of scales and interfaces. These concepts provide the engine forfurther research combining the knowledge of landuse change with impacts and resourcerequirements. These methods could also be used to develop scenarios for technologyinfusion impacts for both existing infrastructure and new development.

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