Computers, Environment and Urban Systems 33 (2009) 269–277

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Computers, Environment and Urban Systems

journal homepage: www.elsevier .com/locate /compenvurbsys

The impact of class resolution in land use change models

T.M. Conway *

Department of Geography, University of Toronto at Mississauga, 3359 Mississauga Rd N Mississauga, ON, Canada L5L 1C6

a r t i c l e i n f o

Article history:Received 3 July 2008Received in revised form 2 February 2009Accepted 10 February 2009

Keywords:Land useLandscape changeModelingClass resolutionUrbanization

0198-9715/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.compenvurbsys.2009.02.001

* Tel.: +1 905 828 3928; fax: +1 905 828 5273.E-mail address: tenley.conway@utoronto.ca

a b s t r a c t

A recent focus of land use/land cover research is the design and validation of spatially explicit predictivemodels. An often overlooked aspect of model development is the role of class resolution. The objective ofthis paper is to evaluate the impact of changing the number and breadth of land use classes on a model’scalibration and predictions. To address the objective, three model specifications were developed repre-senting urban development in the Barnegat Bay watershed (New Jersey, USA). The models range fromone based on a single broad resolution class to one using six fine resolution classes. The results of theanalysis indicated that changing the level of class resolution impacts the model’s calibration parametersand predicted outcomes, but more finely defined urban conversion classes did not uniformly improve theaccuracy of the model. If fine resolution classes are identified, however, the specific types of conversionsthat are not well captured by the model are revealed. The paper ends with a discussion of the broaderimplications of class resolution decisions in land use/land cover models.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Unprecedented expansion of urban development is occurringworldwide, radically changing local environments and impactinglarger regions through socioeconomic and biophysical linkages(Lambin et al., 2001; Riebsame, Meyer, & Turner, 1994). A greaterunderstanding of urbanization patterns and effective approachesfor controlling such development is necessary to address environ-mental change. Land use/land cover (LULC) models provide oneway to explore the causes and patterns of urban conversions, whilegenerating scenarios of future conditions. Through this approach,the environmental consequences of future land use and effective-ness of alternative management approaches can be identified be-fore irreversible transformations occur (Lee et al., 1998).

Significant attention has been given to developing spatially ex-plicit LULC change models in recent years, with a variety of modeltypes applied to urbanizing landscapes (e.g. An & Brown, 2008;Clarke, Hoppen, & Gaydos, 1997; Conway, 2005; López, Bocco,Mendoza, & Duhau, 2001; Schneider & Pontius, 2001; Tang, Wang,& Yao, 2007; Wear & Bolstad, 1998; White & Engelen, 2000; Xian,Crane, & Steinwand, 2005). These models vary in terms of broadstructure (Markov, statistical, cellular automata, agent-based,etc.), spatial and temporal resolution, LULC class definitions, andthe types of conversions allowed. The influence of model structurehas begun to be explored, with considerable variations in predictedoutcomes resulting from alternative structures applied to the samecase (Geoghegan et al., 2001; López et al., 2001; Overmars & Ver-

ll rights reserved.

burg, 2005; Pontius & Malanson, 2005; Theobald & Hobbs, 1998).The impact of addition aspects of model design, including spatialresolution (Jantz & Goetz, 2005; Verburg & Veldkamp, 2004) andinput data structures (Flamm & Turner, 1994), have also receivedconsideration in relation to predicted results. However, almost noattention has been paid to the influence of LULC class resolution(i.e. breadth and number of classes), even though most modelsare based on a discrete set of predetermined classes.

How classes are defined may have a potentially large effect onmodel outcome, particularly in models that explicitly try to cap-ture the process behind change. For example, in many modelsfocusing on urban conversions, all urban uses are combined intoone broad class even though separate processes are likely drivingresidential, commercial, and other types of urban development.Thus, using a single broad class to represent all urban conversionsmay lead to a model that is not well calibrated to some types ofchange. On the other hand, having numerous, narrowly definedclasses may reduce the accuracy of a model due to the difficultyof capturing relatively rare occurrences and the chance that thelocation of a conversion is correctly predicted but the specificend state is incorrect.

This paper explores the influence of changing class resolutionusing a spatially explicit model focused on urban expansion insouthern New Jersey, USA. Class resolution is defined as thebreadth of each land use change class used in the model, with a fi-ner resolution model having more narrowly defined classes (fol-lowing Dungan et al., 2002). Three questions are specificallyaddressed: (1) How does model calibration differ when the classresolution changes; (2) What class resolution is best able toreproduce actual land use change; and (3) What is the impact of

270 T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277

alternative class resolutions when the model is used to make fu-ture predictions? The model is calibrated using logistic regression,to define the relationship between location of conversion and cor-related locating factors, and then applied to predict future conver-sions. The next section provides background on LULC classdefinitions in existing models.

2. Background

Most LULC change models are discrete state-space models, witheach cell representing a single categorical state (or class) at a giventime. Class definitions are often based on the Anderson, Hardy,Roach, and Witmer (1976) or similar classification scheme, withspecific classes reflecting modeling goals, prior knowledge of thestudy area, and/or available input data. LULC models applied tourbanizing landscapes commonly define urban uses in four broadways: a single class, functional classes, density-based classes, orspatially-defined classes.

In many cases all urban development is lumped into a singlebroad class, reflecting either urban land cover or urban land use(Conway, 2005; Hu & Lo, 2007; Machemer, Simmons, & Walker,2006; Pijanowski, Brown, Shellito, & Manik, 2002; Tang et al.,2007; Turner, 1987; Wang & Zhang, 2001). In other models, a singleclass like unvegetated or deforested is used to represent conver-sions primarily to urban development (Berry, Hazen, MacIntyre, &Flamm, 1996; Schneider & Pontius, 2001). Models that use a singlebroad urban class implicitly assume that all urban conversions canbe represented though a single conversion relationship. In addition,if the environmental, social and/or economic impacts of urbaniza-tion are considered, these models also assume that the impacts ofinterest will be homogenous across all urban land use types. Thislast assumption is clearly limited, given recent work focusing onenvironmental heterogeneity in urban areas (Alberti, 2005).

Some models use several fine resolution urban classes based onfunctional differences, recognizing that the process of conversionlikely varies across urban activities. In most models following thisapproach, residential, commercial and industrial classes are trea-ted separately (de Almeida et al., 2005; Lau & Kam, 2005; Verburg,Ritsema van Eck, de Nijs, Dijst, & Schot, 2004), or a model may con-centrate on only one relatively finely defined urban function or use.For example, Bockstael (1996) only examines conversions to resi-dential uses when modeling changes in the Chesapeake drainagebasin.

In other models focusing on finer resolution urban classes, thereis an emphasis on the density or intensity of urban activities. Manyof these models are limited to residential conversions (De Nijs, deNiet, & Crommentuijn, 2004; Theobald, 2005), using two or morerelationships to differentiate higher and lower housing densities(An & Brown, 2008; Logsdon, Bell, & Westerlund, 1996). Wearand Bolstad (1998) defined an unusual set of classes in their modelof landscape change in the southeastern US. They not only speci-fied density, defined by the number of buildings in one hectarecells, but also incorporate information on the land cover associatedwith the different densities. This acknowledges that there may behigh, medium or low density development with or without treecover, and that these types of differences significantly impact localconditions.

A final approach used to more narrowly define urban uses with-in LULC models is to differentiate between the spatial locations ofconversions. In this situation, contiguous urban expansion is differ-entiated from disconnected or isolated development (Barredo, Ka-sanko, McCormick, & Lavalle, 2003; López et al., 2001; Theobald &Hobbs, 1998). While urban uses may still be lumped into one cat-egory, such an approach acknowledges that different processesmay be driving continuous versus discontinuous development.

Classifying urban development into several spatial processes ismost commonly used in applications of the SLUETH model (Clarke& Gaydos, 1998; Clarke et al., 1997; Herold, Goldstein, & Clarke,2003; Solecki & Oliveri, 2004; Xian et al., 2005). In this model, ur-ban growth is typically defined as either continuous expansion,discontinuous creation of new urban centres, growth along roads,or spontaneous growth that is not related to surrounding land uses.

While little space is often given to justifying the definition orresolution of LULC classes in a model, these issues have receivedmore attention in discussions surrounding the classification of re-motely sensed imagery, where most model’s LULC data is derived.Within the remote sensing literature, efforts have focused on pre-versus post-classification class definitions, hard versus soft classi-fication schemes, and level of resolution in relation to accuracyassessments (Pontius & Cheuk, 2006; Robbins, 2001; Tso & Mather,2001). Pontius and Malizia (2004) explicitly explore the effect ofclass resolution when examining LULC change over time usingtwo land cover maps. Not surprisingly, the number of classeswas shown to impact the amount and type of change seen.

The implications of changing class resolution in a LULC modelare unclear. Fewer classes at a broader resolution may mean fewererrors associated with predicting specific sites converting to thewrong class, but trying to predict the location of future changesusing more broadly defined classes may lead to errors associatedwith heterogeneous conversion processes being represented byone homogeneous relationship. Thus, potentially important pro-cess-oriented information is absent. However, if numerous fineresolution classes are included there may be problems associatedwith limited calibration data, conversion mis-specification, andthe challenge of predicting very rare cases. The next section detailsthe methods used in this study to better understand the influenceof changing class resolution on model calibration and predictions.

3. Methods

3.1. Study area

The study area is the Barnegat Bay watershed, located on theouter coastal plain in southeastern New Jersey, USA (Fig. 1). Thewatershed drains 1720 km2 into the Barnegat Bay, a shallow la-goon-type estuary. The year round population was very low and in-land urban development limited to a few small towns scatteredthroughout the region until the middle of the 20th century (BBEP,2001). However, the region has long been valued for its recrea-tional opportunities, with tourism beginning as early as the1700s on the barrier islands (Lloyd, 1990). The rate of developmentthroughout the mainland exploded after WWII, associated with apopulation increase of more than 800% between 1950 and 2000(US Census, 1990, 2001). This growth primarily took the form oflow density suburban residential development.

By 2002, 28% of the study area was urban and 27% mapped asavailable for future development (i.e. not already urbanized or per-manently protected as open space). The majority of non-urbanland is forest (42%) or wetlands (26%), while one percent was usedfor agriculture and three percent was classified as barren land(NJDEP, 2007). Though the absolute level of urban developmentin the watershed is relatively low (as compared to the rest of theBoston-Washington corridor), existing urban development hasbeen associated with an increase in pollutants entering the estuary,increased water withdraws reducing stream base flow and contrib-uting to localized salt water intrusion, destruction of coastal habi-tats, and shifts in aquatic biota ( BBEP, 2001; Kennish, 2001;Zampella et al., 2002).

The majority of recent urban development in the region is forcommercial uses or single family home construction ( McKeon,

Long Island

Atlantic Ocean

20 0 40 Km

BarnegatBay Watershed

PinelandsManagement

Areas

NewJersey

Pennsylvania

New York

Delaware

Maryland

Connecticut

Fig. 1. The Barnegat Bay watershed (New Jersey, USA).

ResidentialOther UrbanCommerical

Fig. 2. Urban land in 2002 based on the NJDEP (2007) classification.

T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277 271

2003; Fig. 2). Most commercial development is in the form of stripdevelopment, occurring along roads on previously forested land.Residential development is occurring at a variety of densities, fromlow density rural residences to more intensive developments thatinclude townhouses or multi-family dwellings. However, themajority of residential conversions involve the sub-division oflarge lots into building parcels between 0.05 ha (1/8th of an acre)and 0.4 ha (1 acre) used for single family homes. Future urbandevelopment is expected to continue in the study area due toexpansion associated with the Atlantic City, Philadelphia, andNew York City metropolitan areas, as well as people drawn tothe area due to the natural amenities of the region.

3.2. LULC data

The modeling approach uses logistic regression to define therelationship between LULC and conversion locating factors. LULCdatasets created by the NJDEP (2001) for 1986 and 1995 were usedto for the calibration process, while a 2002 NJDEP dataset was usedto validate predictions (NJDEP, 2007). All three datasets were ini-tially derived from digital orthophotographs using a head’s up dig-itizing approach. The images were classified based on a modifiedAnderson et al. (1976) scheme, with a minimum mapping unit of0.4 ha. The original classes were used to create three levels of classresolution capturing various types of urban conversions (Table 1).The exception is the other urban class which represents severalsmall Level 2 urban classes in the original classification. For theLULC model, the datasets were converted to raster images with acell size of 63 m, creating 0.4 ha cells.

3.3. Urban conversion locating factors

To calibrate the model, the relationships between the eight clas-ses defined across the three resolutions and hypothesized locating

factors were examined using logistic regression. Potential locatingfactors, or explanatory variables, include accessibility to urbanactivities and other amenity features, site conditions, and spatialland policies (Table 2). These variables reflect factors that are com-monly included in LULC models emphasizing urban conversionlocation (Berry et al., 1996; Verburg et al., 2004).

The first set of variables reflects distance to existing built andnatural amenity features. Three variables were included as mea-sures of distance to natural amenities: Euclidean distance to near-est waterbody (lake, estuary, or ocean); Euclidean distance tonearest protected open space (including federal, state, municipaland privately owned land); and distance to nearest barrier islandbridge, following the road network. This last variable representsa measure of driving distance to the beach from locations on themainland.

Five aspects of site conditions were also examined. First, dis-tance to the nearest road, a simple measure representing the easeof gaining access to the site during construction (Turner, Wear, &Flamm, 1996), was calculated. The second site condition includedwas view. Because the area has little topographic relief, view is de-fined as the 100 m neighborhood surrounding each cell, followingGeoghegan et al. (2001). The third site variable was location insideor outside the floodplain, based on NOAA’s 100 years flood line.While development is regulated adjacent to streams, wetlands,and other waterbodies, residential development is often still al-lowed within the broad floodplains in the watershed. Fourth, exist-ing land cover is often an important locating factor of new urbandevelopment, so binary variables were specified to represent theexisting land cover classes of forest, agriculture, wetlands, and bar-

Table 1The nested classification scheme used to represent aspects of urban land use at three levels of class resolutions. Class definitions from NJDEP (2001). The number of hectares thatconverted to each class between 1986 and 1995 is given in parentheses.

Resolution Class Description Definition

Level 1 All urban (5957) Land uses are associated lands with buildings, parking lots, access roads, and other appurtenancesLevel 2 All residential (4231) Single-family residences, multiple-unit dwellings, mobile homes, and mixed residential

Level 3 High density, single or multi – unit(901)

High-density single units or multiple dwelling units on 1/8–1/5 acre lots

Level 3 Medium density, single unit (1674) Single family, 1/8 acre and up to and including ½ acre lotsLevel 3 Low density, single unit (1013) Single family, greater than ½ acre up to and including 1 acre lotsLevel 3 Rural (643) Rural residential, greater than 1 acre and up to and including 2 acre lots

Level 2 Commercial (576) Structures predominantly used for the sale of products and servicesLevel 2 Other urban (1151) A combined class representing Industrial; Transportation, Communication, Utilities; Industrial and Commercial

Complexes; and Mixed Urban uses

Table 2Potential locating factors considered in the analysis.

Variables Source Date(s) representeda

Dist to road (m) Derived from road data created by the New Jersey Department of Transportation (NJDOT) 1995Dist to open (m) Derived from open space data created by the NJDEP, Pinelands Commission, and local municipalities 1986, 1995, 2000/2002Dist to urban (m) Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Dist to water (m) Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Dist to bridge (m) Derived from road data created by the NJDOT 1995Road w/in 100 m Derived from road data created by the NJDOT 1995Open w/in 100 m Derived from open space data created by the NJDEP, Pinelands Commission, and local municipalities 1986, 1995, 2000/2002Urban w/in 100 m Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Water w/in 100 m Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Floodplain (binary) FEMA floodplain dataset UnknownPinelands (binary) NJDEP, reflecting boundary designated in 1972 Created in 1994 from 1986 air photosResidential zone (binary) Derived from most recent zoning map from local municipalities Various dates (1970s–2000)Sewer service (binary) NJDEP, shows current and planned method of wastewater disposal for specific areas Unknown; created 2006Forest (binary) Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Agriculture (binary) Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Barren (binary) Derived from land use/land cover datasets created by the NJDEP 1986, 1995, 2002Wetlands (binary) Derived from land use/land cover datasets created by the State of New Jersey 1986, 1995, 2002

a For variables with multiple dates listed, the data corresponding to the time period of the specific model run were used (i.e. 1980 or 1986 data were incorporated into thecalibration model looking at urban conversions between 1986 and 1995). ‘Various dates’ indicates that the dataset combined data representing more than one point in time.

272 T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277

ren land. Fifth, a variable representing current access to sewer ser-vice was included.

Finally, two variables representing spatial land use policies thatconstrain or enable development in certain areas were included inthe analysis. The first is a binary representation of residential zon-ing designations. Digital and paper zoning maps for the 36 munic-ipalities in the study area were obtained and combined into onedigital dataset. Zoning boundaries from the paper maps wereadded to the dataset using a head-up digitizing approach and dig-ital orthophotos. Areas that were zoned residential were then ex-tracted and converted to a raster file, with each cell representedas inside (1) or outside (0) a residential development zone. Thedensity of the zone was not considered as many municipalitiesuse a default one acre (0.4 ha) lot size in residential zoning dis-tricts. The other spatial policy variable reflects cells inside or out-side the Pinelands Management Areas (Fig. 1), which is regulatedby the restrictive Pinelands Management Comprehensive Plan.

Table 2 gives the dates of the available data representing thepotential locating factors. In some cases, data corresponding tothe dates of the three land use datasets exist (i.e. distance to pro-tected open space), while only one point in time is available forother variables. Some variables do not change over the study per-iod (i.e. Pinelands boundary), so having only a single time period isnot a problem. However, the variables related to features that havelikely changed for which only one date in time is available, maypositively bias the model. For example, several locating variablesare derived from a dataset representing road locations in 1995,the end point of the calibration period. However, in this regionthe majority of new road construction since the mid-1980s has

been local roads inside new sub-divisions, so most changes tothe road network should not affect regional accessibility.

3.4. Analysis

A regression analysis was conducted to identify significant rela-tionships between conversion to the eight urban class variablesand the possible locating factors from 1986 to 1995. Logisticregression was used because the dependent variables were binary,defined as no LULC conversion (0) or conversion from a non-urbanLULC to a specific urban class (1). The logistic function is based on acurvilinear response between the dependent and explanatory vari-ables, defined as:

EðYjxÞ ¼ eðb0þbixiÞ

1þ eðb0þbixiÞ

where the expected value of Y for a given x (E(Y|x)) is bounded be-tween 0 and 1 in the binomial model. Using a binomial specifica-tion, rather than the multinomial form or a nested decision treeapproach, assumes that the urban conversion process is based ondecisions to build specific land uses (i.e. where should high residen-tial development be located) rather than an initial decision to con-vert a piece of land to urban, followed by a second decisionregarding the specific type of development. The advantage of sepa-rately considering each of the urban variables is that differences be-tween significant explanatory variables for a given type of urbandevelopment are highlight.

A simple random sample of 10% for each urban variable wasused. A random sample of non-converting cells equal to the sample

T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277 273

of cells for the Level 1 urban variable was included in all logisticanalyses. This sampling approach takes advantage of a propertyof logistic regression where samples do not need to reflect the lar-ger population proportion, but having roughly equal representa-tion of categories for the dependant variables leads to morerobust analyses (Hosmer & Lemeshow, 2000).

A step-wise method was used to identify significant explana-tory variables for each of the eight urban classes. The fit of theequations were compared using Nagelkerke’s (1991) max-rescaledR2, which tries to mirror the R2 statistic in linear regression, and theROC statistic (Pontius & Schneider, 2001).

The equations derived from the step-wise analysis were thenused as the basis of the LULC change models. Three models werecreated based on the three levels of class resolution: (1) Model 1,broad class resolution, with the Level 1 all urban class representedby one regression equation; (2) Model 2, medium class resolution,with the Level 2 classes (commercial, residential, and other urban)represented by three separate regression equations; and (3) Model3, fine class resolution, with two Level 2 classes (commercial, otherurban) and the four Level 3 residential classes represented by sixseparate regression equations. Each model specification was runfrom 1995 to 2002 (the validation period) and 2002 through2018, using an 8 years time step.

The number of cells predicted to convert for the validation timeperiod was defined as the number that actually changed. Using thisapproach, one or more likelihood maps were created from the lo-gistic equations and the cells with the highest likelihood of changewere ‘converted’ until the actual number of conversions for thespecific urban class was reached. For the models where multipleclasses were used (Models 2 and 3), the separate urban class con-version locations were then combined to create a map showing allpredicted conversions to urban. This process was repeated for 2010and 2018 predictions, updating input layers as needed, with a lin-ear extrapolation used to determine the number converting from2002 through 2018.

The predictions from the three model specifications were as-sessed by determining the degree of correspondence to referencedata and each other. First, kappa statistics were used to comparepredicted change maps with reference data for 2002. This wasdone for each of the eight urban classes (i.e. predicted versus actualhigh density residential conversions) as well as the three models(i.e. Model 1 versus all actual urban conversions). Two kappa sta-tistics were employed: (1) Kstandard, the basic kappa statistic ofagreement that indicates the observed portion that is correct inlight of the expected portion correct due to chance and (2) Kloca-tion, which specifies the level of locational agreement ( Pontius,

Table 3Beta coefficients from the final regression equations for each urban dependent variable. Onlthe table.

Level 1 Level 2

All Urban All residential Commercial Othe

Intercept �0.5474 �1.6064 �2.2266 �0.8Dist to road �0.00243 �0.00649Dist to urban �0.00160 �0.0Dist to bridge �0.00030 �0.00012 �0.0Dist to openRoads w/in 100 0.13290 0.13580Urban w/in 100 m 0.14780Outside Pinelands 0.44690 0.50330Outside residential zone �0.43120 1.14520 0.5No sewer service �1.08240 �0.5Not agricultureNot Wetlands 0.83480 0.69870 1.2Max-rescaled R2 0.57 0.61 0.60 0.4ROC 0.839 0.856 0.757 0.6

2000). Both statistics were calculated on a single cell basis andusing aggregate eight by eight cell blocks that reflects the portionof the neighborhood in each category based on original cell values.The statistics for the aggregate blocks were used to determine ifthe models were predicting conversions in the same neighborhood,but not exactly the same cell.

Second, for each cell correctly predicted to convert to any typeof urban development in one of the models, the type of actualchange was determined. In other words, what percent of cells actu-ally converting to commercial between 1995 and 2002, based onthe reference data, were predicted to convert to any type of urbanuse in each of the models? This enabled a determination of thetypes of conversions that were and were not being captured bythe models. Finally, the degree of divergence between the predic-tions for the three models in 2010 and 2018 was determined, basedon the percent of similar cell locations predicted to convert to anytype of urban development.

4. Results

4.1. Calibration differences

The step-wise regression analysis retained four to six variablesfor each binary class specification (Table 3). As expected, differentexplanatory variables are retained and/or opposite relationshipsare seen when comparing the eight regression equations. Forexample, the binary residential zoning variable is retained for allLevel 2 classes, but residential development is negatively corre-lated (i.e. more likely to occur inside residential zones) and com-mercial and other urban uses are positively correlated (i.e. morelikely to occur outside residential zones). There are similarly mixedresults with the explanatory variable sewer service for Level 3 res-idential classes, and the variable is not retained in the Level 2 allresidential class. Interestingly, the explanatory variables retainedin the broader resolution class specifications were most similarto the largest class(es) at the finer resolution. Thus, Level 1 all ur-ban in very similar to Level 2 all residential, and medium densityand low density residential are the Level 3 classes most similarto Level 2 all residential.

A comparison of max-rescaled R2 and ROC values for the differ-ent urban classes indicates that the fit of the regression equationsvaries, but is not directly related to the resolution of the class (Ta-ble 3). However, the broader resolution classes seem to have good-ness-of-fit levels that reflect the average fit of the related finerresolution classes. The equations with the worse fit are for otherurban, low density residential, and rural residential. The poor re-

y those explanatory variables retained in at least one urban specification are shown in

Level 3

r urban High density Medium density Low density Rural

571 �3.4942 �3.1726 �2.8787 �0.8576�0.01080 �0.01100

0331 �0.002310007 �0.00013 �0.00080 �0.00005

0.000400.24610 0.22080 0.06620�0.15450

0.572502650 �0.548609860 �1.72930 �1.07830 0.42160 0.31630

-0.515801470 1.07120 0.762905 0.67 0.66 0.47 0.3733 0.828 0.840 0.805 0.762

Table 4Kappa values for 1995–2002 predictions as compared to reference data, by dependant variable.

Level 1 Level 2 Level 3

All urban All residential Commercial Other urban High density Medium density Low density Rural

Kstandard1 � 1 Cell 0.15 0.07 0.14 0.03 0.02 0.01 0.05 0.058 � 8 Cells 0.41 0.64 0.43 0.26 0.07 0.07 0.25 0.25

Klocation1 � 1 Cell 0.13 0.07 0.12 0.05 0.02 0.01 0.06 0.058 � 8 Cells 0.44 0.68 0.46 0.31 0.07 0.08 0.27 0.24

274 T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277

sults for other urban are not surprising given the heterogeneity ofthis class, while it may be that the explanatory variables are notwell suited to capture the process of low and rural residential con-versions. However, all equations have acceptable levels of fit, basedon commonly used values (Allison, 1999; Pontius et al., 2001).

4.2. Predictions

The kappa statistics used to asses the prediction maps for eachurban class variable highlight two patterns (Table 4). First, the Le-vel 1 all urban class performs roughly the same as the Level 2 clas-ses, but the Level 3 classes are uniformly worse at predicting thelocation of change. Interestingly, the classes with the worst modelfits (low density residential and rural residential), performed bet-ter than the other two Level 3 classes (high density residentialand medium density residential) in 2002. Second, as expected mostvalues are higher for the eight by eight neighborhood than the sin-gle cell comparison. One notable exception is the Level 3 high den-sity residential class where there is little improvement in theaggregated values, suggesting that cells incorrectly predicted toconvert to high density residential are locationally very differentfrom actual ones. Alternatively, the relatively large improvementfor predicted commercial conversions, suggests that predictedlocations are consistently only slightly off.

The predictive ability of the three models follows the results ofthe specific class performance: Models 1 and 2 have roughly equalpredictive power and Model 3, which includes the finest-resolutionresidential classes, performs worse for the 1995–2002 time period(Table 5). In all three models, urban development predicted to oc-cur between 2002 and 2018 is primary located adjacent to existingurban development (Fig. 3). Model 1 predicted more conversions inthe southern part of the study area, along the existing westernedge of development, while Model 3 predicts relatively more urbanexpansion along rural roads. As expected, the pattern createdthrough Model 2 is somewhere in the middle.

While Fig. 3 highlights gross differences between the threemodels, more subtle and localized differences are evident fromcell-by-cell comparisons. When the correctly predicted conversioncells are broken-down by the actual urban conversion class theyrepresent (Table 6), it is clear that conversions to classes with rel-atively poor fitting equations (other urban, low density, rural resi-

Table 5Kappa statistics for 1995–2002 predictions as compared to the reference data, byoverall model. Comparisons did not differentiate between specific types of urban.

Model 1 Model 2 Model 3

Kstandard1 � 1 Cell 0.15 0.15 0.118 � 8 Cell 0.41 0.39 0.22

Klocation1 � 1 Cell 0.13 0.12 0.088 � 8 Cell 0.44 0.41 0.24

dential) are not well captured in any of the models, no matter theclass resolution used. Interestingly, actual medium densitychanges are accurately predicted at half the rate in the fine resolu-tion model as compared to the broader resolution models. This re-sult may be an artifact of Models 1 and 2 having more cells toallocate per class, because broader classes were used, and thatthese models do a good job of predicting likely sites of mediumdensity conversions. Thus, with more chances (i.e. cells to convert)a higher percent of the predicted conversion cells are in the samelocation as actual conversions to medium density development.

When the three models are compared to each other, Models 1and 3 are most different (Table 7), as one would expect, since theyare most divergent in terms of class resolution. Additionally, the le-vel of similarity between the three models’ predictions declinesover time, with the percent of similar cells shifting to less than50% by 2018 for even the most similar models. This suggests thatwith each time step, the divergence will increase.

5. Discussion

The differences in the explanatory locating factors retained inthe regression analysis highlight the fact that variations exist inconversion processes between urban uses in this study. However,the change processes were not necessarily better representedwhen finer resolution classes were used. In this case, the twobroader classes which were also examined at finer resolutions (Le-vel 1 all urban and Level 2 all residential), were not evenly popu-lated by the finer classes, and it is clear that the regressionequations for the broader resolution classes are heavily influencedby the dominant class(es) at the finer resolution level. If a relativelybroad class is composed of evenly represented finer classes (andassociated change processes) it may be harder to develop a wellcalibrated model for the broad class, and incorporation of each ofthe finer classes separately should be considered. This may bethe situation with other urban in the analysis.

Alternatively, splitting residential development into four classeshighlights the types of residential conversions that are not wellcaptured by the broader all residential class, and provides areasof focus for further improvement. Knowledge of the poor modelfit for the low density residential and rural residential classesand the inability to predict change in cells that actually convertedto these land uses by any of the three models, suggests that theexplanatory variables considered in this analysis may not be suit-able to capture these change processes. Thus, consideration ofalternative explanatory variables that could better capture lowdensity and rural residential development would be an obviousway to improve the model’s predictive ability.

If the goal of the modeling exercise is to explore the process ofchange, then ensuring that classes represent a sufficiently fine res-olution to capture homogenous processes is important. In thisstudy, the explanatory variable representing sewer service areaswas not retained in the equation used for Level 2 all residentialchange, which suggests this not a relevant locating factor. How-

Fig. 3. Predicted new urban development between 2002 and 2018 for (A) Model 1, (B) Model 2, and (C) Model 3.

T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277 275

ever, when the Level 3 residential classes are examined, it is clearthat sewer service was significantly related to all four types of res-idential development, but the nature of that relationship wasopposite in some cases. Thus, focusing on the broader resolutionall residential class would have lead to erroneous conclusionsregarding the role of sewer service.

Defaulting to relatively fine resolution classes, however, pre-sents two challenges. First, incorporating more classes increasesthe complexity of the model. For example, a model based on oneempirical relationship is simpler to design and evaluate as com-pared to a model based on six separate empirical relationships.

The second challenge is highlighted by the Level 2 commercialclass. Although the equation associated with the class indicated agood fit, the class did not perform well in the validation phase,likely because there are so few change cells associated with com-mercial development. The difficulty of accurately predicting a rel-atively rare occurrence is highlighted by the much better kappavalues for commercial when an eight by eight neighborhood is con-sider as oppose to the single cell analysis.

If the goal of the model is to reproduce exiting patterns or createreasonable future predictions and broad classes are dominated byonly one or two relatively homogenous conversion processes, then

Table 6Percent of actual change cells correctly predicted to change between 1995 and 2002.Predicted cells were not differentiated by specific type of urban use.

Actual conversion typea Model 1 Model 2 Model 3

All urban 17 16 13All residential 18 17 13

High density 15 14 10Medium density 25 22 12Low density 9 12 14Rural 11 11 14

Commercial 28 30 28Other urban 13 13 14

a Based on reference data.

Table 7Percent of urban change cells predicted in the same location.

Models 1 and 2 Models 1 and 3 Models 2 and 3

2002–2010 74 55 652010–2018 49 25 48

276 T.M. Conway / Computers, Environment and Urban Systems 33 (2009) 269–277

using finer resolution classes may not be necessary. However, if abroad class includes evenly represented heterogeneous processes,then the use of finer resolution classes representing that varietyshould be considered. Another limitation of choosing a relativelybroad class resolution is the potential confusion between errorsdue to inherent stochasticity of the process and those that are asso-ciated with a particular type of conversion not being representedby the model. In this case, only when the ability of the three mod-els to correctly predict actually converting cells was summarizedby specific change class could the conclusion be drawn that themodels using the broader resolution classes (Models 1 and 2) dida poor job of representing low density and rural residentialconversions.

Examining the validation and future prediction results alsohighlights that models which have similar performance in the val-idation stage, in this case Models 1 and 2, can substantially diverge.This is an important reminder that even models with equal levelsof accuracy may differ in the location of accurate and inaccuratepredictions.

How should a land use/land cover modeler proceed in the selec-tion and definition of classes incorporated into a model? Based onthis analysis, a reasonable approach would be to begin with thebroadest class resolution able to address the question at hand. Dur-ing the validation process, it should be determined if prediction er-rors are associated with inherent stochasticity in the conversionprocess or more systematic errors due to poor representation ofone or more sub-classes. If the later is the case, then the use of finerresolution classes should to be explored.

An appropriately broad starting class resolution is, however,highly dependant on the specific purpose of the model. For exam-ple, a broad class including all urban conversions may be initiallyused if the goal is to simply capture the pattern of urban expansion.If the purpose of the model is to explore the impact of specific pol-icies or the environmental, social or economic impacts of land usechange, then inclusion of finer-scale classes may be necessary fromthe start. For example, if a policy of interest is directed towardshigh density residential uses then this type of residential develop-ment should be specifically defined. At the same time, the policymay have secondary effects on other residential, commercial orindustrial uses, which can only be explored if these classes are alsoseparated. However, even if classes are initially defined at a fineresolution, attention should still be given to class homogeneity.Thus, errors associated with a relatively fine resolution class, like

high density residential conversions, can be examined to deter-mine if such conversions would be better represented by two ormore sub-classes.

This paper explores the impacts of choices made regarding classresolution, and attempts to isolate the effects of such decisions.However, other aspects of the modeling approach likely influencedthe specific results. First, using a different spatial or temporal res-olution potentially would have altered the relative performance ofthe three models. Future works should examine how decisionsregarding these different types of resolution interact with eachother. Second, a particular set of potential locating factors wereused in the modeling exercise, primarily focusing on the distancebetween a given site and other features. While the locating factorsused to make the predictions for different class specifications couldvary in this study, it may be that the initial selection of potentiallocating factors was better suited to represent one level of classresolution over another. Future research should explore issuesaround the use of alternative types of locating factors (i.e. demo-graphic and economic data over spatial location of key features)to represent the process of LULC change at different classresolutions.

Finally, the use of binomial logistic regression likely influencedthe outcome. In particular, employing several separate relation-ships based on a binomial specification (i.e. conversion to highdensity residential versus no conversion) over a multinomial spec-ification or CART approach likely retained different locating factors.Additionally, the bionomical approach did not allow for competi-tion for prime locations between land use classes. A more realisticdepiction of the conversion processes may be a nested model thatfirst addresses the likelihood of conversion to any urban use andthen, in a series of secondary steps, determines the specific typeof urban land use. Further research should explore changing classresolution using other modeling approaches, with emphasis onthose that include interactions associated with fine-scale LULCclasses competing over locations.

In the development of land use/land cover change models, thechoice of class resolution is often given minimal attention. As exist-ing land use/land cover data are regularly employed, a classifica-tion may be imposed on the modeling exercise with littleconsideration for the impact on model development or output.The model of urban expansion in the Barnegat Bay Watershedshows that changing class resolution impacts the calibration andpredicted outcomes of a model. The use of finer resolution classescan highlight the specific types of conversions that were not wellrepresented in the broader resolution models, and provide a focusfor future model improvement.

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