a technique for rapidly forecasting regional urban growth ben… · developing useful modeling...

1 IntroductionIn 2007, for the first time in history, more than half the world's population resided inurban areas (United Nations Population Fund, 2007). In the United States the transi-tion from dense urban centers and dispersed rural populations to expansive suburbanlandscapes has been rapid (Calthorpe and Fulton, 2001). When compared with USpopulation growth, this development history has led to a disproportionately high rateof land-use change and the creation of concentrated stretches of impervious surfaces(Ewing et al, 2002).

This style of urban growth has concerned researchers and planners, and it hasspawned many new tools for studying the effects of large-scale urbanization, includingnumerous computer-based simulation models of urban growth. Several metastudieshave surveyed these models, describing their advantages, disadvantages, and intendeduses (Agarwal et al, 2002; EPA, 2000). Agarwal et al (2002) analyzed these modelsand their capabilities based on three dimensions: spatial detail, human decision

A technique for rapidly forecasting regional urban growth

James WesterveltUS Army Engineer Research and Development Center, Construction Engineering ResearchLaboratory, PO Box 9005, Champaign, IL 61826, USA;e-mail: [email protected]

Todd BenDor ôDepartment of City and Regional Planning, University of North Carolina at Chapel Hill,CB #3140, New East Building, Chapel Hill, NC 27599-3140, USA; e-mail: [email protected]

Joseph SextonNASA Goddard Spaceflight Center, Greenbelt, MD 20771, USA;e-mail: [email protected] 8 March 2009; in revised form 24 January 2010

Environment and Planning B: Planning and Design 2011, volume 38, pages 61 ^ 81

Abstract. Recent technological and theoretical advances have helped produce a wide variety ofcomputer models for simulating future urban land-use change. However, implementing these modelsis often cost prohibitive due to intensive data-collection requirements and complex technical imple-mentation. There is a growing need for a rapid, inexpensive method to project regional urban growthfor the purposes of assessing environmental impacts and implementing long-term growth-managementplans. We present the Regional Urban Growth (RUG) model, an extensible mechanism for assessingthe relative attractiveness of a given location for urban growth within a region. This model estimatesdevelopment attraction for every location in a rasterized landscape on the basis of proximityto development attractors, such as existing dense development, roads, highways, and natural amen-ities. RUG can be rapidly installed, parameterized, calibrated, and run on almost any several-countyregion within the USA. We implement the RUG model for a twelve-county region surroundingthe Jordan Lake Reservoir, an impoundment of the Haw River Watershed (North Carolina, USA).This reservoir is experiencing major water-quality problems due to increased runoff from rapid urbangrowth. We demonstrate the RUG model by testing three scenarios that assume (1) `business-as-usual'growth levels, (2) enforcement of state-mandated riparian buffer regulations, and (3) riparian bufferregulations augmented with forecast conservation measures. Our findings suggest that the RUGmodel can be useful not only for environmental assessments, stakeholder engagement, and regionalplanning purposes, but also for studying specific state and regional policy interventions on thedirection and location of future growth pressure.

doi:10.1068/b36029

ôCorresponding author.

making capability, and dynamic implementation. However, from the perspective ofresearchers or planners interested in implementing these models, complex technicalneeds, high implementation costs, and extensive data and time requirements may bemore significant factors affecting model adoption.

Our study arises from a lack of attention given in the literature to these practicalimplementation issues, which can make urban modeling cost prohibitive and limit thepropagation of these beneficial technologies. We are also concerned that the advancedtechnical knowledge required to run even the simplest simulation models createsanother barrier to the use of these tools. In this paper we introduce the RegionalUrban Growth (RUG) model, a software tool intended to be rapidly implemented bya planner with reasonable geographic information system (GIS) knowledge, or a GIStechnician with little requisite simulation expertise.

We implement RUG over a twelve county region encompassing the Haw RiverWatershed and Jordan Lake Reservoir of North Carolina (NC) an area in the USconcerned with the effects of future growth on water quality and recreation resources(figure 1). We used the RUG model to address several questions concerning environ-mental impacts of new urban development upstream of the Jordan Lake Reservoir (seecolor plate 1) including: (1) where is future growth in the watershed likely to take place,and (2) what are the effects of local growth restrictions and other policies on thedirection and location of new development?

0 10 20 40 km

Study area extent

Hydrological bodies

Municipal boundaries

Haw River Watershed

Triangle regionöRaleigh/Durham/Chapel Hill

Triad regionÐWinston-Salem/Greensboro/Highpoint

Figure 1. North Carolina study area.

62 J Westervelt, T BenDor, J Sexton

2 Background2.1 Review of urban land-use-change modelingTools for modeling urban environments and land-use change are useful for predictingand understanding urban, social, and ecological problems (Agarwal et al, 2002;Barredo et al, 2003; Grimm and Railsback, 2005), and for presenting environmentalcomplexities to wide audiences (Costanza and Voinov, 2001; 2004; Ford, 1999).

Waddell and Ulfarsson (2004) provide an introduction to the design and develop-ment of operational urban simulation models, recommending a number of steps fordeveloping useful modeling tools. These steps begin with assessments of (1) institu-tional, political, and technical context, and (2) stakeholders value conflicts, and publicpolicy objectives. Next, measurable benchmarks for objectives are developed, alongwith inventories of policies to be tested, maps of policy inputs to outcomes, andassessment of model requirements. Finally, input data are prepared, followed by modelspecification, estimation, calibration, validation, and finally, model usage.

Several metastudies have reviewed the urban growth modeling literature (Agarwalet al, 2002; EPA, 2000). Historically, models have addressed growth at two distinctspatial scales: local and regional. City-scale models include METROPILUS (Putman,1983), MEPLAN (Echenique et al, 1990), and UrbanSim (Waddell, 2002). Suchmodels often seek to identify the likely development of city parcels over time. Forexample, the ``What if ?'' model (Klosterman, 1999) represents parcels as entitiesusing GIS vector shape files. These entities change though time according to thegrowth needs of the region, the state of the parcel, and the state of its neighboringparcels.

Hunt et al (2001) compared models that specifically link transportation networks tourban development. Most notably, DRAM-EMPAL allocates employment (EMPAL)and residence location (DRAM), while TRANUS and MEPLAN allocate land-useactivities to zones based on land-use interdependencies and using an iterative operationof transport and land-use models.

Regional models typically cover multiple counties and attempt to forecast thedevelopment of land at the edges of and beyond cities. Examples include SLEUTH(Clarke and Gaydos, 1998; Jantz et al, 2003), the Land Use Evolution and ImpactAssessment Model (LEAM) (Deal and Schunk, 2004; Wang et al, 2005), and CaliforniaUrban Futures 2 (Landis and Zhang, 1998). These are raster GIS-based models thatuse probabilities of land development and land transformation to forecast futureregional urban patterns. All use raster data as inputs that generally include slope,no-growth areas (eg public open space), city boundaries, urbanization quality (orattractiveness), and road networks. Outputs include projections of land-use change,which can include various urban land uses such as residential, commercial, industrial,transportation, and urban open space.

Land-use modeling frameworks typically seek to answer specific questions such as:. Where are people likely to be living within urban regions over the next twentyto fifty years (typical long-term modeling and planning time horizons) (ChicagoMetropolis, 2020, 1999; Lane Council of Governments, 2007)?

. To what extent and where will new impervious surfaces be introduced into thelandscape?

. What percentage of habitats is likely to be damaged and in what patterns?

Rapidly forecasting regional urban growth 63

Urban growth 1985 ^ 2005

Original 1985 urban extent

Haw River Watershed

Hydrological bodies

County boundaries

0 25 50 100 km

Color plate 1. Land-use change in study area.

Color plate 2. Regional urban-growth (RUG) model calibration and scenario results. Attractive-ness maps: probability surfaces where dark areas represent higher attractiveness levels. Individualcell values are averaged into 270m cells for vizualization. Projected growth maps: simulatedgrowth for each scenario. For visualization, growth maps represent the number of 30m cellsdeveloping in each 270m summary cell. During both calibration simulations, we assumed thatthe random growth adjustment coefficient j � 0:3. All maps are shown for study subarea.(a) and (e): attractiveness of each cell based on the RUG model's linear calibration on trainingdata representing urban growth from 1985 to 2005. Growth results for calibration are comparedwith blue training data, which represents the cells that developed between 1985 and 2005. Train-ing and calibrated growth comprise equal cell counts (151375 cells across the whole region;13 623.8 ha) to simulate 1985 ^ 2005 growth values; (b) and (f ): attractiveness and growth mapsfor the base-case scenario; (c) and (g): attractiveness and growth maps for stream-buffer scenario.All streams and riparian buffer cells were added to no-growth map; (d) and (h): attractivenessand growth maps for the stream-buffer and forest-conservation scenario.


Rapidly

forecastingregional

urbangrow

th65

N:/psfiles/epb3801w

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0 5 10 20 km

Attractiveness

High: 100

Low: 0

Haw River Watershed

Jordan Lake

No-growth areas

(a) (b) (c)

(e) (f) (g)

(d)

(e)Actual30m cells

Estimated30m cells

High:81

Low:0

High:81

Low: 0

(d)

(e)

Calibration Base case Stream buffersStream buffer

and forest protection

Projected

growth

maps

Attractivenessmaps

2.2 The high cost of urban growth modelingUrban growth modeling is currently not readily accessible to many small municipalitiesand many local governments have relatively little experience with formally forecastingfuture land-use patterns. Moreover, budget and staff limitations force analyses to beconducted without major financial investment, and often without the support ofregional coordinating entities (Harris, 1994). Many planning offices cannot affordmost currently available regional planning simulation models, which tend to be veryexpensive to localize and run. After evaluating several popular urban models, includingDRAM-EMPAL, MEPLAN, and METROSIM, Oryani and Harris (1996) estimated acost of approximately $600 000 to apply the DRAM-EMPAL model to the DelawareValley Regional Planning Commission area. Consulting firms can charge $500 000 ormore to build, test, and apply a regional model that generates future-land-use scenariosin response to proposed regional plans. However, it cannot be assumed that expensiveand complicated models will be trusted or accepted by community stakeholders.Funtowicz and Ravetz (1991) argue that scientific models are accepted by decisionmakers in situations where stakeholder interest is low and the scientific certainty ishigh. They show that as stakeholder interest decreases and/or scientific certaintyincreases, the opinions of experts (who may themselves be using models) may besufficient. But when scientific certainty is low and/or stakeholder interest is high,the stakeholders prefer to make decisions themselves, often through ad hoc gover-nance. In such situations, models must be transparent and readily understood(Metcalf et al, 2010). In many situations simpler models may be more appropriatefor understanding complex relationships, such as the fate and transport of streampollutants (Jian and Yu, 1998) and other dynamic, nonintuitive systems (Ford, 1999).However, few regional planning models are inexpensive to develop, localize, andcalibrate to a specific location. While several metaanalyses of the state of urbanmodeling have evaluated models on the basis of their capabilities (Agarwal et al,2002; EPA, 2000), planners and researchers who use models must also evaluatemodels on the basis of practicality. From this perspective, key implementation issuesinclude:(1) Accessibility. Are models proprietarily licensed or freely distributed? Is the softwareopen source?(2) Data requirements. How much, and what type, of data are necessary to run themodel? Are proprietary data required or does collection largely involve use of free,universally available data sources (eg from the US Census Bureau).(3) Implementation period and technical prerequisites. Is the simulation software welldocumented, or does implementation require the services of a consultant or complexcoding or software programming skills? How long does the simulation system take toset up and run?(4) Land-type resolution. How many types of land-use or land-cover conversion willthe model allow? Can users specify many land-use types or is land simply considered tobe either developed or nondeveloped?(5) Interface with concurrent modeling efforts. Can the model be integrated withtransportation modeling systems or coupled with data or simulations of ecologicaldegradation, water-quality issues, or other environmental impacts?

Urban growth simulation models are often designed with powerful stochasticcomponents because, typically, they are not able to capture local details such aslandownership patterns, owner motivations, and local politics. Such details can bevery important, especially over a ten to twenty year planning horizon. Uncertaintiesresulting from the lack of such knowledge are often addressed through the applica-tion of development probabilities. There is a demand for an urban growth modeling


capability that is simple in concept, inexpensive and quick to operate, and thereforecost-effectively acceptable to a broad user community.

Our development goals for RUG meant that it had to: (1) have a freely distrib-utable capability, (2) require data that can be readily retrieved from free governmentsources, (3) be relatively quick to set up and run for a new location, (4) use aminimum set of land-use or land-cover categories, (5) generate results that can bereadily analyzed, and (6) support scenario-based simulation for planning (Xiang andClarke, 2003).

3 Analysis methodology3.1 PedigreeRUG is in a family of raster GIS-based urban growth models, that includes SLEUTH(Clarke and Gaydos, 1998) and LEAM (Deal and Schunk, 2004; Wang et al, 2005).RUG shares a close history with LEAM in that a series of Army Corps of Engineersprojects funded development of both models to serve different purposes. Both areraster ^GIS-based urban growth models based on calculating access times to variousattractors.

LEAM was developed as a flexible process that would tailor urban growth modelsto specific applications according to the needs of a target community. The processrelies on substantial interactions with a community through stakeholder charettes andworkshops. Each LEAM application creates custom, proprietary software developed toaddress the specific concerns and needs of each user community.

RUG was originally developed to help military installation regional planners toforecast future urban residential or commercial development quickly and consistently.It was developed completely within the open source GRASS software environment(GRASS Development Team, 2008) to create the new r.rug program, which generatesa time series of forecasted urban growth over a study time frame. The RUG equationsand processes used to generate overall development attractiveness are documented inthis paper.

LEAM employs a process for local governments to create a tailor-made urbangrowth and impact analysis that is developed to involve local expertise intimately inthe calibration of the model, to connect with other local models (eg transportation),and to connect with a variety of transportation, hydrology, climate change, ecolog-ical, and other models. RUG, on the other hand, is most suitable for conductingrapid and consistent urban-growth forecasts to give local governments (1) a fast andinexpensive look at future consequences of proposed actions, and (2) a consistent,national-level view of growth across a number of locations.

3.2 Hedonic logicThe goal of the RUG process is to generate residential attractiveness maps andgrowth projections that are based on nationally available datasets and require littlehuman intervention to produce. Such maps can then help to inform policy decisionsand elicit stakeholder input about growth and its effects on a range of processes,including impervious surface expansion and aquatic resource degradation in the studyarea. Our approach involves a hedonic framework for modeling the relative attractive-ness values for new urban development in all locations within a study area. In thisstudy, we only consider a generalized form of urban development as constrained byavailable data. Future work will allow users to integrate other development types intothis model.

Hedonic modeling is a statistical approach for identifying the relative importanceof factors considered that set the price or value of a property, including those that


contribute to the interior of the property and those that define its location (Sirmanset al, 2005). The linear form of the hedonic regression function is:

A �Xki� 1

bi Vi , (1)

whereA is the overall development attractiveness of a parcel,Vi is the value of attractor i (k attractors),bi is the coefficient for attractor i.Since we are comparing urbanizing versus nonurbanizing cells, we chose a simplebinary logistic regression model to combine the hedonic attractors to urban growthfor the RUG calibration:

A � b0 �Xki� 1

bi Vi , P � expA

1� expA, (2)

whereA is the overall development attractiveness of a cell,bi is the regression coefficient for attractor i, fit by maximum likelihood (b0 � y

intercept),Vi is the value of attractor i (k values),P is the probability of urban growth occurring on the cell.

Although any number of land characteristics can be considered, here we focus on arelatively small number of site characteristics, most of which involve travel time toattractors. For this analysis, attractiveness considerations include the density of thesurrounding urban neighborhood, distance to surrounding forest resources, drivingtimes to urban centers, interstate highways, intersections, and state and county roads.These potential development attractors are used because they are, typically, viewed asamenities attracting new growth. Other attractors could easily be integrated into thisframework if they are believed to affect the location of new development.(1)

RUG assumes, in the structure of equation (2), that the drivers of growth are staticover time in our analysis. However, we acknowledge that the relative strength of thesedrivers changes over time as the costs and values of transportation, housing, andcommunication change. We anticipate, for example, that significant advances in com-munication bandwidth will result in increased telecommuting, resulting in furthersettlement sprawl. This limits the utility of our model to growth occurring in the nextcouple of decades.

3.3 Modeling process overviewThe basic RUG modeling process is accomplished through six primary steps:(1) Acquire GIS map data and project into a common coordinate system.(2) Identify a set of locations for each attractor believed to influence development.(3) Calculate travel times to each attractor forming a map.(4) Convert the travel-time maps to attractiveness maps.(5) Combine attractiveness maps to generate a single, comprehensive urban developmentattractiveness map.(6) Forecast future urban development patterns by specifying growth rates and othergrowth scenario parameters. These growth rates are based on economic growth andpopulation growth, which we assume to be exogenously determined.Each step is discussed in detail in the subsections below.

(1) The model currently only simulates land-use change based on road-based transportation. It would,however, be straightforward to add a similar layer for rail travel or other transportation modes.


3.3.1 Data acquisitionThe first step is accomplished using basic GIS and data-management skills and,typically, requires no more than several hours to locate and download maps frompublicly available databases. The required maps are: (1) a digital elevation model,(2) a transportation map (roads and highways),(2) (3) a land-cover map, and (4) amap of areas where growth is unlikely or prohibited (ie a no-growth map that includes,for example, preserved areas, publicly owned recreation areas, or military lands). Theseare all available from the US Geological Survey (USGS) SEAMLESS data downloadsite (http://seamless.usgs.gov) for a US region of interest, but they may be augmentedwith more precise state or local data.

Nationally available land-use and land-cover maps include the 1992 and 2001versions of the USGS National Land Cover Database (NLCD) (Homer et al, 2004;Vogelmann et al, 2001). A variety of similar maps have been created at the state levelas well, including Illinois and North Carolina Land Cover Datasets (EarthSat Corp.,1998; Luman et al, 2003; US Census, 2008a). These datasets classify raster grid cellsinto cover or use types, delineating urban typologies into categories of land use(eg commercial, industrial) or intensity of urban cover. Due to inconsistencies in theNLCD classification scheme between the 1992 and 2001 datasets (Sexton, 2009) weemployed a multitemporal dataset that captured urban patterns in a consistent manner(Donohue, 2008).

When land-use data that distinguish the intended use of land are not available, it isoften possible to use land-cover information to help determine land use. Several studieshave investigated using the percentage of impervious surface cover as a proxy fordistinguishing residential uses from commercial or industrial areas (Donohue, 2008;Yang et al, 2003). While this is not an optimal approach, it is a strategy for initiatingthe modeling process, particularly in areas where localized digital land-use datasets arenot available.

3.3.2 Identify attractor locationsTo develop travel-time maps we must first identify the locations of attractors of newdevelopment, which are derived from original elevation, no-growth, road-network, andland-cover maps. The locations of roads, intersections, highways, and interstate ramps(all major attractors of development) are straightforward to identify as they can beextracted readily from the base transportation map.

Identifying the exact locations of urban centers (also major attractors) poses agreater challenge. Options include identifying the location of (1) all businesses andtheir associated employment figures, (2) all retail areas and the number of consumersthey attract, (3) the city office-building complex or local courthouse, or (4) thecentroids of known municipal areas. However, our desire for an inexpensive rapidanalysis required us to reject each of these options. In most cases, business locations(and employment figures) and business statistics on retail complexes are not publiclyavailable. Additionally, calculating urban area centroids can misplace activity centersinto city edges or bodies of water from which transportation cost cannot be calculated.To avoid these problems, we developed a procedure to allow rapid identification ofurban attractiveness centers within the regional urban patterns defined in a land-usemap. Our goal was to identify and select sites at the center of locally dense urbanareas.

To select sites at the centers of locally dense urban areas, we located peaks in urbandensity derived from a land-cover map. First, we assigned nonurban areas a value of 0,

(2) National road maps such as the US Census Tiger/LineÕ road-network maps (US Census, 2008a)are available although more localized road data will be more accurate.


low-density urban areas a value of 1, and high-density areas a value of 2 [figure 2(a)] (3)

and then passed the resulting map through an inverse-distance weighted neighborhoodfilter using the GRASS r.mfilter program, specifying a 450m (15-cell) radius (GRASSDevelopment Team, 2008). This process smoothed the spatial pattern of urban cover,removing minor local variations to focus the model on broader patterns [figure 2(b)].Peaks in the smoothed urban density surface were then located by profile curvatureanalysis with the GRASS r.slope.aspect function, which identifies peaks by calculatingthe slope and rate of change in slope (ie curvature) of urban density (GRASSDevelopment Team, 2008). We selected map cells whose value exceeded a threshold[figure 2(c)] and then clumped selected cells together into contiguous patches, assign-ing each centroid of a patch a distinct value using the function r.clump (GrassDevelopment Team, 2008). This process provides a map indicating the centers (andsubcenters) of urban areas throughout the study region.

Ideally, we would then calculate the effect of every parcel's (or cell's) attractivenesson every other parcel. However, the computational power necessary to accomplish thatwould be prohibitive with respect to our current goals.(4) Our urban center identifica-tion procedure selects a set of locations to use for calculating the total attractiveness ofall locations across the map.

(3) Alternatively if density values are not given in available land-use data, all urban land can,optionally, be treated as if it has uniform density.(4) For n cells, this would require generating and summing n 2 maps, with each identifying theattractiveness of a single cell to all surrounding cells. While not intractable with today's super-computers, we are limiting our computing power to that generally available in a planning office.

(a) (b)

(c)

Figure 2. Regional urban growth (RUG) modeling process. (a) A sample urban-pattern map.Dark and light grey represent high density and low-density areas, respectively. From RUG steps 1and 2 (see subsections 3.3.1 and 3.3.2); (b) results of passing the urban-pattern map through adistance-weighted neighborhood filter from RUG step 2 (see subsection 3.3.2); (c) identifiedcenters of urban residential attraction. This 3-dimensional `topographic' map shows the relativeshape of urban density throughout a region. The black dots show the local peaks of this map(centroids and urban clusters). This automated procedure for locating urban centers results in amap containing four levels of centroids: small, medium, large, and extra large. These levels arebased on the height of the `peak' on top of which each centroid is found. From RUG step 2 (seesubsection 3.3.2).


To reduce computation further, the inverse-distance weighted values at eachsubcenter centroid were divided into four levels, where those with the highest valueswere considered to be the most attractive, and the lowest values the least attractive[figure 2(c)]. This procedure allowed us the option of computing just four cumulativecost analysis maps, estimating the fastest travel time to the nearest city center in eachof the four urban size classes. However, for the highest density areas, we computed theimpact of every urban center on the attractiveness of every cell in the study area. Thisallowed RUG to capture the combined impact of two or more nearby centers, whichoften have powerful synergistic effects in attracting new development (as around urbancenters in Cary and Apex, NC which lie directly between the larger cities of Raleighand Durham in our study area).

3.3.3 Calculate travel times to attractorsWe then calculated the minimum travel time for every grid cell (location) to each of thenearest individual attractors of growth (eg highways, on-ramps, lakes). This processused the GRASS r.cost program (GRASS Development Team, 2008), which requiredinputs from our subcenter point map (derived in step 2, see subsection 3.3.2) and atravel-cost map, which provided travel time to cross every 30m grid cell. Note thattraffic loads and the subsequent changes in travel times are not considered in thismodel. The original r.cost program assumes that all cells are connected to theirimmediate neighbors, which is not true in the case of roads or highways that crossbut do not intersect. We modified the program to restrict limited-access highwayconnections to streets with on-ramps. RUG scripts prepared two additional inputsfor the modified r.cost program: a map of travel times across the limited-access high-way network, and a map showing linkage points between the original transportationmap and the limited-access highway map. At the completion of this step, we have a setof minimum-travel-time maps to all sets of attractors.

3.3.4 Convert travel-time maps to attractiveness mapsIn the fourth step we capture the functional relationship between cumulative traveltimes and the probability of locating urban areas. Research on human perceptions of,and responses to, the environment has found that this relationship is often logarithmicrather than linear and is called the Weber ^ Fechner law (Dehaene, 2003). Therefore, welog-transformed cumulative travel times (step 3, see subsection 3.3.3) and then dividedthe results into twenty equal intervals. Using the GRASS r.stats program (GRASSDevelopment Team, 2008) we then calculated the percentage of our study area ineach interval that has developed into urban land use (while subtracting the no-growthareas). When matched with the midpoint of each interval, these values gave a series of(x, y)-coordinates that define our conversion function for translating travel time intodevelopment probability.

Note that this step in the RUG analysis required a map of developed areas, andthese areas were extracted from the land-use map. This assumed that the urban patternsin that map reflect the land's current attractiveness for the development of urban areasand that future development patterns will mimic the historic development patterns of acity. This may be misleading if the costs and opportunities of transportation andcommunication have changed since the first urban areas were developed. Optionally,if consecutive land-use maps are available (or if recent building or construction permitsare easily available), this analysis could be focused only on recent development pat-terns, which would yield a functional relationship that mirrors modern developmentpreferences and patterns.


3.3.5 Combine maps to generate the urban development attractiveness map (calibration)In the fifth step, the GRASS r.mapcalc program (GRASS Development Team, 2008) isused to convert the cumulative travel-time map to a probability-of-occurrence map foreach attractor under consideration. At this point in the procedure, a full set of hedonicattractor maps was generated and a final development probability calculated usingequation (1) (which requires coefficients for each attractor).

Our approach involves performing a logistic regression analysis [equation (2)] toestimate coefficients that reflect the weight or importance of each attractor (Hosmerand Lemeshow, 1989). To do this, RUG interfaced with the R statistical package(Hornik, 2008) to predict whether a cell will develop or remain undeveloped (binarydependent variable from land-use map), on the basis of the attractiveness values ofevery attractor at each cell location (independent variables) using R's logistic regressionmodel module.(5) Finally, RUG generated the final urban growth attractiveness foreach cell using the resulting equation to calculate the logit probability (P ; between 0and 1). This equation was applied using the GRASS raster calculator engine, r.mapcalc,to generate the urban growth attractiveness raster map.

3.3.6 Forecast future urban patternsFuture urban growth patterns were then forecast using information on the relativeattractiveness of each grid cell to new development (the results of step 5, see subsec-tion 3.3.5). We developed a new GRASS-based program called r.rug (raster analysis forRUG) to generate future urban patterns. This program takes the following as inputs:. The urban growth attractiveness map (from step 5, see subsection 3.3.5).. The number of 30m urban cells needed per year, which approximates populationgrowth using assumptions about average dwelling size (US Census, 2008b) andhousehold population size (US Census, 2008c).

. The number of simulation steps (years).

. A coefficient denoting the weight of the attractiveness index (as calculated insteps 1 ^ 5, see subsections 3.3.1 ^ 3.3.5).

. A coefficient denoting the weight of random or spontaneous growth. This reflectsthe uncertainty in the calculated attractiveness. Coefficients can be experimentallybalanced to generate the best-possible forecasts. The uncertainty is caused bylack of consideration of influences such as property-owner goals, parcel sizes, andspecific development needs of various industries. Random growth is representedby artificially adjusting the attractiveness of cells downward by a random value,thereby representing nonoptimal decisions by landowners or developers.

Using this information, each location was assigned a probability of development [seeCarroll and Ruppert (1988) for more information on functional form] based on:

O � A lRj , (3)

whereO is the overall attractiveness,A is the attractiveness calculated in step 5 (a value between 0 and 1),R is the random number between 0 and 1,l and j are input coefficients between the value of 0 and 1. The `random growth

coefficient' j does not represent random growth, per se, but rather representsa random adjustment downward on attractiveness.

(5) Generating a regression table for each cell at a 30� 30 m resolution could yield an intractableregression calculation for a personal computer (our study area contains over fourteen millioncells). For computational expediency, we sampled the center of every nine-cell patch (Mooreneighborhood) throughout the map, yielding an 11% random sample of our data to inform thiscalibration.


The values of l and j express the importance of the attractiveness and random values,respectively. With l � 0 and j � 1 the distribution of new urban development isentirely random. The reverse, where l � 1 and j � 0, leads to urban developmentoccurring in cells in the order of their calculated attractiveness. Because the RUGmodel does not take everything that leads to development into account, a level ofrandomness is appropriate.We believe that perhaps the most important missing factorsare (1) knowledge of the size and shape of ownership parcels and (2) the motivation ofowners. For example, an owner of a location that is highly desirable for urban devel-opment may desire to keep the parcel in a natural state. The j value injects a level ofuncertainty that randomly reduces the calculated attractiveness.(6)

4 Study areaBetween 2000 and 2007 the Triangle and Triad regions of NC experienced unprece-dented population growth rates of 24.5% and 8.5%, respectively (US Census, 2008d)and their spatial growth made them the third and second fastest sprawling US regions,respectively (Ewing et al, 2002). Resulting land-use change and new impervious surfa-ces have placed extensive pressure on local water bodies, including the B EverettJordan Reservoir (commonly called Jordan Lake), a 56:41 km2 multiuse impoundmentof the Haw River operated by the US Army Corps of Engineers. The reservoir'swatershed encompasses 4367 km2 across ten counties and several urban areas (seefigure 1). Land-cover change from 1985 to 2005 for a section of our study area directlysurrounding Jordan Lake is shown in detail in color plate 1.

In 1983 the NC Environmental Management Commission (EMC) classified JordanLake and the Haw River as Nutrient Sensitive Waters [as pursuant to NC Adminis-trative Code 15A NCAC 02B .0223, see NC Division of Water Quality (2005)] afterthey began to experience extensive nutrient enrichment, causing algal blooms and tasteand odor problems. In September 2008 the EMC issued the Jordan Water SupplyNutrient Strategy (commonly called the Jordan Rules) a set of strict requirementsintended to restore and maintain nutrient-related water-quality standards in the lake[Administrative Code 15A NCAC 02B (.0262 ^ .0311), see NC Division of Water Quality(2005)]. These rules provide a mechanism for the state to enforce nutrient limits byimplementing a management strategy that includes provisions for fertilizer applica-tion, agricultural runoff reductions, stormwater management for new and existingdevelopment, and riparian buffering, among others. The regulations also require localgovernments to establish stormwater-control programs, which can require a variety ofmethods of stormwater mitigation for both existing and new development.

The Jordan Rules require 50 ft vegetated buffers around surface waters in thewatershed and aim to prevent timber harvesting and tree removal in areas nearsurface waters [Administrative Code 15A NCAC 02B .0267(14)(B), see NC Division ofWater Quality (2005)]. We are interested in how tree removal and mitigation programsimpact development patterns in the region. By implementing ordinances and policyincentives to slow or prevent tree removal in areas bordering the Haw River andits tributaries, these programs could decrease nutrient inputs of new developmentsexpected in the region over the next several decades.

To study the effect of the Jordan Rules' tree removal and mitigation programs, wewill test a scenario for extending our no-growth zone to areas bordering surfacewaters throughout the watershed. Additionally, we artificially increased the difficultyof developing forested areas to simulate a regional effort in which communities

(6) For the analysis runs report in this paper, l and j were set at 1 and no effort was made tooptimize these values.


implement strict tree mitigation ordinances in addition to riparian buffering efforts[tree mitigation ordinances often require avoidance and minimization of tree damage(Swiecki and Bernhardt, 2001)].

5 DataLandcover data for 1985 and 2005 were obtained from a nine-class land-cover map seriesclassified from Landsat satellite images (Donohue, 2008). Digital elevation models andtransportation data were obtained from the USGS SEAMLESS website and from the2008 US Census TIGER/Line road network (US Census, 2008a), respectively. Finally,we obtained data on no-growth areas from the USGS and the NC State GIS datarepository (http://www.nconemap.com), including maps of federal and game lands, lakesand reservoirs, land trust conservation properties, lands managed for conservation andopen, state-owned lands, and high-resolution (1:24 000) stream-network data.

6 Results6.1 CalibrationWe began by taking advantage of our multitemporal land-use data and calibratingRUG on a set of training data, created by focusing on land-use change between 1985and 2005. During this period, 151375 cells (30m) comprising 13 623.8 ha developed intohigh-density urban land uses throughout the region. We calibrated the model using asimple, binary logistic regression procedure for estimating the relative importance ofeach of our attractors (pseudo-R 2 � 0:199, F � 28 463, p < 0:00001� (see table 1). Theresulting attractiveness map for a section of our study area is shown in color plate 2(a)(see pages 64 ^ 65). Since it is difficult to visualize high-resolution data for a study areathis large, we present out results in terms of this substudy area (see color plate 1).

In order to test how well our models can reproduce development patterns seenbetween 1985 and 2005 (our calibration or training period), we simulated the actualamount of development that occurred throughout the region. During these simulations,we assumed that the random growth-adjustment coefficient (j) in equation (3) was 1.0.This means that the computed attractiveness of each cell was adjusted randomly to avalue between 0 and that number, which accounts for human-decision effects that areexogenous to our model. Our results are shown in color plate 2(e) which shows simu-lated growth in red and actual growth in blue for each of our calibration techniques.We can now use our model to simulate future urban growth in the region over a varietyof scenarios constrained by different growth policies.

Table 1. Calibration of attractors to 1985 ^ 2005 urban growth training data.

Coefficients Standard z-value *error

Urban centersextra large 13.93 0.30 45.96large 6.13 0.63 9.79medium 2.76 0.34 8.14small 13.99 0.38 36.85

Forest 28.93 0.70 41.15Road intersections 12.36 0.99 12.49Ramps 1.50 0.23 6.53Slope 15.07 0.73 20.75Neighbors 1.56 0.23 6.80State roads 23.44 0.95 24.56Intercept ÿ6.79 0.03 ÿ250.24* p < 0:001.

Notes: pseudo-R 2 � 0:199, p < 0:00001.


6.2 Base-case scenarioWe assumed the same rate of population growth for each of our three projections offuture urban expansion (for 2005 ^ 30) within this region. We determined populationgrowth rates by using estimated recent county growth rates (US Census, 2008d;average of 2005 ^ 07) which suggest over 65 000 new people move into the regioneach year. Extrapolated to 2030, we estimated conservatively, since current rates appearto be accelerated (US Census, 2008d), that the population will grow by 68.3% between2005 and 2030 to a total of 3.71 million residents (2007 estimates are 2.33 million). Giventhe land-use pattern recorded in the 2005 land-use maps, we applied this growth rate to thecurrent extent of high-density urban area to estimate that new high-density urban areaswill encompass 139 608 new cells, comprising nearly 12 565 ha of new land conversion.

We generated the attractiveness and growth maps for our study subarea, see colorplates 2(b) and 2(f). The simulated rates of land conversion for each of the major land-use types are shown in table 2. Here, we see that between 2005 and 2030 the modelpredicts that 43.1% of new growth will be channeled into urban forested (7) areas orlow-density developed areas, 19.4% will occur in urban grassy areas, and 23.4% will bedirected into agricultural areas (pastures, cultivated lands, and row-crop areas). In theattractiveness map for this scenario, we see high development probabilities surroundingJordan Lake.When compared with the high-resolution stream map, nearly 6.5% of newdevelopment is projected to occur nearby small streams and along riparian zones.

6.3 Stream-buffering scenarioöthe Jordan RulesBy buffering stream banks (one riparian cell along streams), we effectively added ripariancells into our no-growth zone (figure 3). In the base-case scenario, stream banksand riparian zones attracted 2.45 % and 4.04% of all new development, respectively.By inhibiting growth in these areas, we see changes in the distribution of growth intoagriculture (25.5% of growth, an increase of 2.1%) and urban ^ forested areas (47.6%,an increase of 4.5%) (see table 2). The attractiveness and growth maps for this scenarioare shown in color plates 2(c) and 2(g).

6.4 Forest-conservation scenarioömoving beyond the Jordan RulesFinally, we considered an additional forest conservation and protection scenario wheredeciduous and evergreen forest areas were partially protected through additional treemitigation or protection ordinances at the local and county levels. These ordinancescan be structured to require avoidance and minimization of tree damage duringconstruction, as well as compensation (offsets) for impacted trees,(8) which reducesdevelopment attractiveness. We simulated this by applying a mask layer to reduce theattractiveness (applied to the map used in the stream buffering scenario) of forestedareas by a given percentage (we hypothesize 30%).

This scenario [shown in color plates 2(d) and 2(h)] shows another shift in develop-ment locations and land conversion (table 2). By making it more difficult and costly todevelop in forested areas, we see a drop of 1.5% and 1% in development in deciduousand evergreen forests, respectively. This growth is shifted into urban forested areas(increase of 1.5%), agricultural areas (increase of 0.8%), and wetlands (increase of 0.2%,and 0.8% over the base case). Under this scenario, 24.57 ha more wetlands areconverted into urban areas of wetlands than in the base case scenario (76.77 ha).

(7) The urban ^ forested class is a mix of very low-density urban and forested areas. This couldinclude low-density urban areas or lawns at the urban periphery.(8) Other types of natural resource mitigation ordinances (including wetland and stream mitigation)often have the effect of reducing the attractiveness of a resource are for development (ELI, 2005).


76JWestervelt,T

BenD

or,JSexton

Table 2. Results from urban growth scenarios in substudy area for 2005 ^ 30 showing cell count and hectares of growth in each scenario. Percentage growthrepresents the proportion of total growth in each land-use or land-cover category.

Calibration Base case Stream buffer Stream buffer andforest protection

cell ha % cell ha % cell ha %cell ha %count count countcount

Urban low density or forest 20 731 1865.8 33.7 15 659 1409.3 43.1 16 924 1523.2 47.6 17 391 1565.2 49.1Forest

deciduous 9 865 887.9 16.0 2 661 239.5 7.3 696 62.6 2.0 165 14.9 0.5evergreen 8 870 798.3 14.4 1 610 144.9 4.4 439 39.5 1.2 88 7.9 0.2

Pasture or hay 4 324 389.2 7.0 2 794 251.5 7.7 3 037 273.3 8.5 3 186 286.7 9.0Urban cultivated 2 055 185.0 3.3 2 444 220.0 6.7 2 538 228.4 7.1 2 572 231.5 7.3Wetlands 2 850 256.5 4.6 853 76.8 2.3 1 060 95.4 3.0 1 126 101.3 3.2Urban grass 5 020 451.8 8.2 7 061 635.5 19.4 7 374 663.7 20.7 7 332 659.9 20.7Row crops 7 857 707.1 12.8 3 290 296.1 9.0 3 506 315.5 9.9 3 563 320.7 10.1

Total 61 572 5541.5 100.0 36 372 3273.5 100.0 35 574 3201.7 100.0 35 423 3188.1 100.0

7 Discussion7.1 The RUG modelIn this paper we have described a method for identifying the overall attractiveness ofland for new urban development. It is our hope that this method will allow plannersand others involved in local or regional governments to test their theories about futuredevelopment patterns in their regions easily and efficiently. The RUG model can beapplied in one or two workdays. The first half of this time involves acquiring data frompublic domain and widely accessible Internet data sources, and processing the datainto a suitable package for use in the RUG analysis. The second half involves auto-mated GIS processing, which requires several hours (depending on the size of the area)on a reasonably powerful desktop computer. Moreover, RUG uses the public domainGRASS GIS software (http://grass.itc.it/), further decreasing implementation costs.

In our study we had access to data that allowed us to calibrate our model andstatistically test our hypotheses about how a variety of landscape features could beused to determine the locations of new urban growth. While many locations do notcurrently have this data, recent efforts by the USGS (2008) and the National Oceanicand Atmospheric Administration (NOAA, 2008) to develop the 2006 NLCD, may help

0 2.5 5 10 km

Haw River Watershed

Jordan Lake

Streams

Haw River Watershed

No-growth areas

Stream buffers

(a) (b)

Figure 3. Stream buffer and new no-growth map. (a) 1:24 000 stream map; (b) buffered streams(30m buffer) are added to the no growth map, preventing development in small streams andriparian zones.


communities map urban growth between 1992, 2001, and 2006. This new data productis a first step towards helping communities across the nation to begin to understandcurrent and future patterns of land-use and land-cover change in their areas.

Although we have not made substantial efforts to validate our model calibrationöstatistically testing the final relationship between our simulation and actual land-usechange between 1985 and 2005öthere is now an emerging set of tools for understand-ing how well two binary (growthöno-growth) maps match in space (Carmel et al,2001; Foody, 2004; Jean and Bouchard, 1991; Pontius and Lippitt, 2006). Future workon RUG will seek to add a further validation step by automating several spatialclustering and analysis tests, made possible by the convenient integration of GRASSwith the open-source R statistical software package.

Additional future work will include a dynamic extension to the RUG process. RUGcurrently allocates growth on the basis of the attractiveness values determined at thebeginning of the simulation, thereby ignoring the complex feedback effects of earlydevelopment on future growth. Iteratively defining attractiveness values at intervalsthroughout the simulation will help to account for these feedback effects. In extendingRUG to account for these dynamic feedback effects, it will be important to developefficient algorithms to ensure low time and computational costs of running this model.

7.2 Availability of time-serial land-cover datasetsThe RUG model is capable of using multitemporal maps to enhance and calibrate themodel. Where these datasets are lacking, the RUG model may also be applied to asingle land-cover dataset. This is useful for education and for generating thoughtfuldiscussion among stakeholders, but the tradeoff of unitemporal calibration is thatprecision and confidence are compromised (Whitehurst et al, 2009).

7.3 Implications for the Jordan Lake WatershedComparing the base-case scenario with both the stream-buffering and forest-conservation scenarios allows us to evaluate quickly efforts on the part of the stateto reduce nutrient inputs into the Haw River and Jordan Lake Reservoir. A simplerepresentation of riparian protection, whereby all streams and their 30m bufferswere added to our no-growth map, allowed us to understand better the tradeoffsrepresented by growth in this region transferring to other areas.

By prohibiting development in riparian zones (figure 3), the state may experiencesubstantial changes in the direction and manner of future growth. As we have shown,riparian zone protections in this case can refocus development pressure into agricul-tural regions, forested areas, and wetlands. In a state that was one 17% wetlands (ELI,2005), and where further systematic wetlands loss could contribute to severe floodingand water-quality problems, redirection of growth in this manner may come withsubstantial costs. Moreover, redirection of growth into areas currently in cultivationmay be at odds with widescale agricultural preservation efforts currently underwaythroughout the state.

Efforts to protect forested areas may be a logical future step towards protectingwater quality in the Haw River Watershed. Forest protection was simulated using asimple manual reduction in attractiveness. This represented the potential effect of localtree-mitigation ordinances that would make it more expensive for developers to removeforested areas or compensate for forested areas impacted during the course of devel-opment. In this scenario, even more growth was channeled into agricultural areas andwetlands. While this tradeoff may be justified, it is important that local plannersand decision makers are able to understand the potential repercussions of policiesthat limit development on particular types of land.


8 ConclusionsUnderstanding the attractiveness of land for future development will become increas-ingly important for understanding local and regional planning scenarios, particularlyas communities attempt to avoid future environmental and land-use problems, includ-ing nutrient overloading in sensitive hydrological systems. RUG's analysis procedure istransparent and generates results that are easily disseminated and can be useful toenvironmental consultants and managers, community planners, and other importantstakeholders. For small municipalities and counties that have few technical resourcesand are experiencing urban expansion into sensitive areas, RUG is an inexpensive andexpedient tool for developing reasonably accurate projections of development patterns.

Acknowledgements. Primary development of the RUG software was facilitated through a projectentitled ``The Evolving Urban Community and Military Installations: A Dynamic Spatial DecisionSupport System for Sustainable Military Communities (SI-1257)'', funded by the Strategic Environ-mental Research and Development Program (SERDP). Funding was also provided by the UNCInstitute for the Environment. We thank Peter Mucha for providing computational resources andPaul Voss, Beverly Wilson, and John Boos for validation assistance. We also thank Nikhil Kazaand Gordon Cohen for their helpful feedback.

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