and modeling of urban land use change

questions for future research are nally put forward to help strengthen the potential of the

Dynamic urban change processes, especially the tremendous worldwide expansion

of urban population and urbanized area, aect natural and human systems at all

geographic scales (Brockhero, 2000; United Nations Population Division, 2000).

Computers, Environment and Urban Systems

29 (2005) 369399* Corresponding author. Tel.: +1-805-893-4196; fax: +1-805-893-3703.proposed framework, especially regarding the further exploration of urban dynamics at dif-

ferent geographic scales.

2003 Elsevier Ltd. All rights reserved.

Keywords: Spatial metrics; Urban growth; IKONOS; Land use change; Urban modeling; Remote sensing

1. IntroductionMartin Herold *, Helen Couclelis, Keith C. Clarke

Department of Geography, University of California Santa Barbara, Ellison Hall, Santa Barbara,

CA 93106, USA

Received 18 February 2003; accepted 3 December 2003

Abstract

The paper explores a framework combining remote sensing and spatial metrics aimed at

improving the analysis and modeling of urban growth and land use change. While remote

sensing data have been used in urban modeling and analysis for some time, the proposed

combination of remote sensing and spatial metrics for that purpose is quite novel. Starting

with a review of recent developments in each of these elds, we show how the systematic,

combined use of these tools can contribute an important new level of information to urban

modeling and urban analysis in general. We claim that the proposed approach leads to an

improved understanding and representation of urban dynamics and helps to develop alter-

native conceptions of urban spatial structure and change. The theoretical argument is then

illustrated with actual examples from the urban area of Santa Barbara, California. SomeThe role of spatial metrics in the analysis

www.elsevier.com/locate/compenvurbsysE-mail address: martin@geog.ucsb.edu (M. Herold).

0198-9715/$ - see front matter 2003 Elsevier Ltd. All rights reserved.doi:10.1016/j.compenvurbsys.2003.12.001

Worsening conditions of crowding, housing shortages, and insucient or obsolete

infrastructure, as well as increasing urban climatological and ecological problems

and the issue of urban security underline a greater than ever need for eectivemanagement and planning of urban regions (OMeara, 1999). Recently, innovative

approaches to urban land use planning and management such as sustainable

development and smart growth have been proposed and widely discussed (Kaiser,

Godschalk, & Chapin Jr., 2003; American Planning Association, 2002). However,

their implementation relies strongly upon available information and knowledge

about the causes, chronology, and eects of urban change processes. Despite the

recent proliferation of new sources of data and tools for data processing and anal-

ysis, these have not directly led to an improved understanding of urban phenomena.This paper explores both conceptually and with practical examples how using remote

370 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399sensing technology in combination with spatial metrics can improve the under-

standing of urban spatial structure and change processes, and can support the

modeling of these processes. Fig. 1 illustrates the simple conceptual framework

developed in the paper, consisting of three main components: remote sensing, spatial

metrics and urban modeling, and their interrelations. While the potential direct

contribution of remote sensing to urban modeling is fairly well understood (rela-

tionship 1 in Fig. 1), we argue that the combined use of remote sensing and spatialmetrics will lead to new levels of understanding of how urban areas grow and change

(relationships 2 and 3 in Fig. 1).

In recent years, the use of computer-based models of land use change and urban

growth has greatly increased, and they have the potential to become important tools

in support of urban planning and management. This development was mainly driven

by increased data resources, improved usability of multiple spatial datasets and tools

for their processing, as well as an increased acceptance of models in local collabo-

rative decision making environments (Klosterman, 1999; Sui, 1998; Wegener, 1994).However, the application and performance of urban models strongly depend on the

quality and scope of the data available for parameterization, calibration and vali-

dation, as well as the level of understanding built into the representation of the

processes being modeled (Batty & Howes, 2001; Longley & Mesev, 2000). Remote

sensing data products have often been incorporated into urban modeling applica-

Fig. 1. General framework for analysis and modeling of spatial urban dynamics.

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 371tions as additional sources of spatial data (relationship 1 in Fig. 1), primarily for

historical land use information (Acevedo, Foresman, & Buchanan, 1996; Clarke,

Parks, & Crane, 2002; Meaille & Wald, 1990). Relationship 3 in Fig. 1 correspondsto the use of spatial metrics in urban modeling. This path has been proposed in a few

studies that use spatial metrics to rene and improve remote sensing data for urban

models, for model calibration and validation, or in studies of urban landscape

heterogeneity and dynamic change processes (Alberti & Waddell, 2000; Herold,

Goldstein, & Clarke, 2003; Parker, Evans, & Meretsky, 2001).

Remote sensing represents a major though still under-used source of urban data,

providing spatially consistent coverage of large areas with both high geometric detail

and high temporal frequency, including historical time series. Remote sensingmethods have been widely applied in mapping land surface features in urban areas

(e.g. Haack et al., 1997; Jensen & Cowen, 1999). Several recent developments in

remote sensing have the potential to signicantly improve the mapping of

urban areas. These relate to the availability of data from new remote sensing systems

such as the IKONOS-satellite (Tanaka & Sugimura, 2001), hyper-spectral sen-

sors (Ben-Dor, Levin, & Saaroni, 2001; Herold, Gardner, & Roberts, 2003)

and MODIS (Schneider, McIver, Friedl, & Woodcock, 2001), all of which can

support detailed and accurate urban area mapping at dierent spatio-temporalscales.

Much less widely known than remote sensing, spatial metrics can be a useful tool

for quantifying structure and pattern in thematic maps. Spatial metrics are com-

monly used in landscape ecology, where they are known as landscape metrics

(Gustafson, 1998). Recently there has been an increasing interest in applying spatial

metrics techniques in urban environments because these help bring out the spatial

component in urban structure (both intra- and inter-city) and in the dynamics of

change and growth processes (Alberti & Waddell, 2000; Barnsley & Barr, 1997;Herold, Clarke, & Scepan, 2002). We argue that the combined application of remote

sensing and spatial metrics can provide more spatially consistent and detailed

information on urban structure and change than either of these approaches used

independently. Indeed, coupling these two approaches can improve the thematic

detail and accuracy of remote sensing mapping products and facilitate their analysis

for specic urban applications.

In Sections 24 we review current issues in urban remote sensing, spatial metrics

and urban modeling respectively, discussing the relatively new area of spatial metricsin more detail. We emphasize in particular the combined use of spatial metrics with

remote sensing techniques and their potential contribution to urban modeling (Fig.

1, relationships 23). In Section 5 we illustrate these points with some concrete

examples. Section 5 highlights ve major areas where urban modeling could be

improved using the suggested framework. The examples incorporate the use of

IKONOS satellite data to study spatial urban pattern, specic spatial model appli-

cations, and the analysis of spatio-temporal urban dynamics at dierent scales.

Important areas of future research are outlined along with these examples. Finally,in the concluding Section 6, we summarize what we believe to be the proposed

approachs contribution to urban analysis and modeling.

372 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 3693992. Remote sensing of urban areas

For decades the visual interpretation of aerial photography of urban areas hasbeen based on the hierarchical relationships of basic image elements. The spatial

arrangement and conguration of the basic elements (tone and color) combine to

give higher order interpretation features of greater complexity such as size, shape

and texture, or pattern and association, that are signicant and characteristic for

urban areas and urban land use (Bowden, 1975; Haack et al., 1997). A number of

urban remote sensing applications to date have shown the potential to map and

monitor urban land use and infrastructure (Barnsley et al., 1993; Jensen & Cowen,

1999) and to help estimate a variety of socio-economic data (Henderson & Xia, 1997;Imho, Lawrence, Stutzer, & Elvidge, 1997). However, much of the expert knowl-

edge of the human image interpreter was lost in the transition from air photo

interpretations to digital analysis of satellite imagery.

The great strength of remote sensing is that it can provide spatially consistent data

sets that cover large areas with both high detail and high temporal frequency,

including historical time series. Mapping of urban areas has been accomplished at

dierent spatial scales, e.g. with dierent spatial resolutions, varying coverage or

extent of mapping area and varying denitions of thematic mapping objects. Globaland regional scale studies are often focused on mapping just the extent of urban

areas (e.g. Meaille & Wald, 1990; Schneider et al., 2001). A basic diculty these

eorts encounter relates to the indistinct demarcation between urban and rural areas

on the edges of cities. Remote sensing provides an additional source of information

that more closely respects the actual physical extent of a city based on land cover

characteristics (Weber, 2001). However, the denition of urban extent still remains

problematic and individual studies must determine their own rules for dierentiating

urban from rural land (Herold, Goldstein & Clarke, 2003).Most local scale remote sensing applications require intra-urban discrimination of

land cover and land use types. Considering the land cover heterogeneity of the urban

environment several studies have shown that a spatial sensor resolution of at least 5

m is necessary to accurately acquire the land cover objects (especially the built

structures) in urban areas (Welch, 1982; Woodcock & Strahler, 1987). Since 2000,

data from new, very high spatial resolution space borne satellite systems have been

commercially available. For example, IKONOS and QUICKBIRD may be con-

sidered the beginning of a new era of civilian space borne remote sensing withparticular potential for application in the study of urban areas (Ridley, Atkinson,

Aplin, Muller, & Dowman, 1997; Tanaka & Sugimura, 2001).

Investigations in local scale mapping of urban land use have shown that analysis

on a per-pixel basis provides only urban land cover characterization rather than

urban land use information (Gong, Marceau, & Howarth, 1992; Steinnocher, 1996).

Based on the experience with visual air photo interpretation (Haack et al., 1997) it is

known that the most important information for a more detailed mapping of urban

land use and socioeconomic characteristics may be derived from image context,pattern and texture, also described as urban morphology (Barnsley et al., 1993;

Mesev, Batty, Longley, & Xie, 1995). There are several versatile approaches for

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 373including structural, textural and contextual image information in land use mapping.

Some studies have used textural measures derived from spectral images to include

this information in the classication process (Baraldi & Parmiggiani, 1990; Forster,1993; Gong & Howarth, 1990; Gong et al., 1992). Others have applied spatial post-

classication to estimate urban land use information from remote- sensing derived

land cover maps (Barnsley et al., 1993; Steinnocher, 1996). A few studies have used

remote- sensing derived discrete land cover objects or segments and described their

morphology and spatial relationships in a detailed mapping of urban areas (Barnsley

et al., 1993; Mehldau & Schowengerdt, 1990; Moller-Jensen, 1990). Barnsley and

Barr (1997) further developed these ideas and presented a complex GIS-based sys-

tem for detailed contextual urban mapping on an illustrative dataset. Manyresearchers believe that detailed spatial and contextual characterization of urban

land cover has high potential to result in detailed and accurate mappings of urban

land uses and socioeconomic characteristics (Barr & Barnsley, 1997; Herold et al.,

2002).

An emerging agenda in urban applications of remote sensing calls for a new

orientation in related research (Longley, Barnsley, & Donnay, 2001). The traditional

remote sensing objectives emphasizing the technical aspects of data assembly

and physical image classication should be augmented by more inter-disciplinaryand application-oriented approaches. Research should focus on the description and

analysis of spatial and temporal distributions and dynamics of urban phenomena, in

particular urban land use changes. However, there is still a lot of resistance, espe-

cially among social scientists, against using remote sensing techniques in urban

studies. Rindfuss and Stern (1998) mention several reasons. First, there is a general

concern about pixelizing the social environment, i.e., focusing too much on thephysical aspects of urban areas at the expense of social issues. Indeed, the socio-

economic variables of interest are usually not directly visible from measurementstaken from remote sensing observations. Secondly, the social sciences outside of

geography and planning are generally more concerned with why things happen ra-

ther than where they happen, and accordingly, most social scientists tend to

underestimate the value of the detailed spatial data that remote sensing provides. It is

not yet widely appreciated that remote sensing can provide useful additional data

and information for social science oriented studies, e.g., by quantifying the spatial

context of social phenomena and by measuring socially induced spatial phenomena

as these evolve over time. For example, by helping make connections across levels ofanalysis and between dierent spatial and temporal scales, remote sensing has the

potential to provide additional levels of information about the links between land

use and infrastructure change and a variety of social, economic and demographic

processes (Rindfuss & Stern, 1998). In terms of analyzing urban growth patterns,

Batty and Howes (2001) believe that remote sensing technology, especially consid-

ering the recent improvements mentioned above, can provide a unique perspective

on growth and land use change processes. Datasets obtained through remote sensing

are consistent over great areas and over time, and provide information at a greatvariety of geographic scales. The information derived from remote sensing can help

describe and model the urban environment, leading to an improved understanding

scale, determined by the spatial resolution, the extent of spatial domain, and the

374 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399thematic denition of the map categories at a given point in time. When applied to

multi-scale or multi-temporal datasets, spatial metrics can be used to analyze and

describe change in the degree of spatial heterogeneity (Dunn, Sharpe, Guntensber-that benets applied urban planning and management (Banister, Watson, & Wood,

1997; Longley & Mesev, 2000; Longley et al., 2001).

3. Spatial metrics

The analysis of spatial structures and patterns are central to geographic research.Spatial primitives such as location, distance, direction, orientation, linkage, and

pattern have been discussed as general spatial concepts in geography (Golledge,

1995). In geography these concepts have been implemented in a variety of dierent

ways. Here these basic spatial concepts and the analysis of spatial structure and

pattern will be approached from the perspective of spatial metrics.

Under the name of landscape metrics, spatial metrics are already commonly used

to quantify the shape and pattern of vegetation in natural landscapes (Gustafson,

1998; Hargis, Bissonette, & David, 1998; McGarigal, Cushman, & Neel, 2002;ONeill et al., 1988). Landscape metrics were developed in the late 1980s and

incorporated measures from both information theory and fractal geometry (Man-

delbrot, 1983; Shannon & Weaver, 1964) based on a categorical, patch-based rep-

resentation of a landscape. Patches are dened as homogenous regions for a specic

landscape property of interest, such as industrial land, park or high-density

residential zone. There is no inherent spatial scale to a patch, nor is there an

inherent level of classication such as an Anderson level (Anderson, Hardy, Roach,

& Witmer, 1976). The landscape perspective usually assumes abrupt transitionsbetween individual patches that result in distinct polygons, as opposed to the con-

tinuous eld perspective. Patches are therefore maximally externally and mini-

mally internally variable. Landscape metrics are used to quantify the spatial

heterogeneity of individual patches, of all patches belonging to a common class, and

of the landscape as a collection of patches. The metrics can be spatially non-explicit,

aggregate measures but still reect important spatial properties. Spatially explicit

metrics can be computed as patch-based indices (e.g. size, shape, edge length, patch

density, fractal dimension) or as pixel-based indices (e.g. contagion, lacunarity)computed for all pixels in a patch (Gustafson, 1998).

Applied to elds of research outside landscape ecology and across dierent kinds

of environments (in particular, urban areas), the approaches and assumptions of

landscape metrics may be more generally referred to as spatial metrics. In general,

spatial metrics can be dened as measurements derived from the digital analysis of

thematic-categorical maps exhibiting spatial heterogeneity at a specic scale and

resolution. This denition emphasizes the quantitative and aggregate nature of the

metrics, since they provide global summary descriptors of individual measured ormapped features of the landscape (patches, patch classes, or the whole map). Fur-

thermore, the metrics always represent spatial heterogeneity at a specic spatial

In summary, the application of spatial metrics for both mapping and modeling

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 375the urban environment is just beginning, but has already focused on a variety ofdierent applications. Most case studies point out the importance of these methods

in urban analysis and urge further systematic investigations in this area (Barr &

Barnsley, 1997; Geoghegan et al., 1997; Parker et al., 2001). An important, thus far

little explored potential lies in the combined application of remote sensing and

spatial metrics. Indeed, remote sensing can provide the spatially consistent,gen, Stearns, & Yang, 1991; Wu, Jelinski, Luck, & Tueller, 2000). Based on the work

of ONeill et al. (1988), sets of dierent metrics have been developed, modied and

tested (Hargis et al., 1998; McGarigal et al., 2002; Ritters et al., 1995). Many of thesequantitative measures are implemented in the public domain statistical package

FRAGSTATS (McGarigal et al., 2002).

3.1. Research on urban analysis using spatial metrics

Interest in using spatial metric concepts for the analysis of urban environments is

starting to grow. Based on the few studies published so far, Parker et al. (2001)

summarize the usefulness of spatial metrics with respect to a variety of urban models

and argue for the contribution of spatial metrics in helping link economic processes

and patterns of land use. They investigate their hypothesis using an agent-based

model of economic land use decision-making resulting in specic theoretical land use

patterns. They conclude that urban landscape composition and pattern, as quantiedwith spatial metrics, are critical independent measures of the economic landscape

function and can be used for an improved representation of spatial urban charac-

teristics and for the interpretation and evaluation of modeling results. Alberti and

Waddell (2000) substantiate the importance of spatial metrics in urban modeling.

They proposed specic spatial metrics to model the eects of the complex spatial

pattern of urban land use and cover on social and ecological processes. These metrics

allow for an improved representation of the heterogeneous characteristics of urban

areas, and of the impacts of urban development on the surrounding environment.Geoghegan, Wainger, and Bockstael (1997) explored spatial metrics in modeling

land and housing values. They show that . . . the nature and pattern of land usessurrounding a parcel have an inuence on the price, implying that people care very

much about the patterns of landscapes around them . . ., and recommend the use oflandscape metrics to describe such relationships (urban landscape composition).

Earlier, Batty and Longley (1994) systematically investigated the role of fractals in

representing urban structure, including urban land use morphology. Barr and

Barnsley (1997) explored concepts of graph theory in mapping and representingurban land use structures. Their approach used spatial primitives such as location

and area and spatial relationships such as adjacency, distance, orientation and

containment. They implemented and applied a framework called XRAG, designed

to describe graph relations and characteristics of urban land cover objects

(graphtown) based on digital line vector datasets (Barnsley & Barr, 1997) and

remote sensing data analysis (Barnsley & Barr, 2000).

376 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399high-resolution datasets that are required for the analysis of spatial structure and

pattern through spatial metrics.

3.2. Problems in the application of spatial metrics

The development and evaluation of a framework for spatial metrics analysis of

datasets derived from urban remote sensing must deal with both theoretical and

methodological problems. These relate to issues of scale in the selection and analysis

of appropriate remote sensing data and in the application of the metrics, and to the

selection of the appropriate spatial metrics themselves.

3.2.1. Spatial accuracy

An important consideration is the spatial accuracy and/or spatial resolution of the

remote sensing data used as inputs to the spatial metrics analysis. Data accuracy andresolution directly aect landscape heterogeneity as represented in the mapping

product and determine the appropriate spatial scale of the investigation. This issue is

central to all remote sensing data analysis and has been recognized in related re-

search (Woodcock & Strahler, 1987). The lower the spatial resolution, the more

generalized the structure of the mapped features (e.g. urban land cover objects) and

their spatial heterogeneity will be in both the image data and the metrics. At too low

a spatial resolution, individual objects may appear articially compact or they may

get merged together. The spatial measures are then dominated more by the rectan-gular shape of the pixels than by the actual object patterns of interest (Krummel,

Gardner, Sugihara, ONeill, & Coleman, 1987; Milne, 1991). Furthermore, specic

kinds of structures, especially linear features, may not be represented at all, thus

leading to an overestimation of landscape homogeneity. In some cases it may be

useful to include ancillary digital data, e.g. relating to linear landscape elements, to

improve the remote sensing data product and have these features included in the

spatial metrics analysis (Lausch & Menz, 1999).

3.2.2. Thematic accuracy

The thematic accuracy of the remote sensing data product relates to the denition

of the thematic mapping classes and the classication accuracy. Thematic accuracy

obviously directly inuences the further analysis of the map with spatial metrics

(Barnsley & Barr, 2000). The thematic mapping capabilities of remote sensing data

mainly depend on the spectral contrast between the classes of interest and the

spectral resolution of the sensor. The lower the spectral separability of mapping

categories, the less accurately the land cover characteristics of an area can be

mapped. An overall classication accuracy of 85% is commonly considered sucientfor a remote sensing data product (Anderson et al., 1976). However, the denition of

the classes should represent all thematic objects and structures in the landscape that

are of interest in a specic investigation. A generalized class denition may result in a

representation of the landscape that is too homogenous, and as a result important

structural features may not be detectable with spatial metrics. On the other hand, if

the landscape classication is too detailed, relevant structures may get lost in a highly

yields a higher fractal dimension.

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 377Before any kind of application, these metrics have to be interpreted, analyzed and

evaluated as to their ability to capture the thematic information of interest (Gus-

tafson, 1998). The few studies published so far on spatial metrics analysis in urban

areas have applied and suggested dierent sets of metrics. Geoghegan et al. (1997),

Alberti and Waddell (2000), Parker et al. (2001) and Herold, Liu, and Clarke (2003)

suggest and compare a wide variety of dierent metrics. Their results show the role

each of these plays in representing the composition, spatial conguration and spatialneighborhood of the urban landscape as represented in urban models. These studies

were especially interested in analyzing land cover/land use pattern and economic

landscape function (Parker et al., 2001) and in explaining land values (Geoghegan

et al., 1997). So far, there is no standard set of metrics best suited for use in urban

environments as the signicance of specic metrics varies with the objective of theheterogeneous pattern. Furthermore, the classication accuracy of the remote

sensing data usually decreases as more classes are derived. Accordingly, the thematic

denition of the classes should consider both the spectral mapping capabilities of thesensor and the user requirements concerning thematic map accuracy for spatial

metrics analysis. The analysis of urban land cover and land use must consider at the

very least the two main land cover categories built up (buildings and transportation

surfaces) and non-built up (vegetation, bare soil, water). A renement of this clas-

sication, e.g. the discrimination of dierent built up and land use categories, may be

useful in an analysis of spatial urban structure but should take into account the

separability of land cover mapping categories and related quality characteristics of

the land cover map.

3.2.3. Selection of metrics

A number of dierent approaches in representing spatial concepts have resulted in

the development of various spatial metrics or metric categories as descriptive sta-tistical measurements of spatial structures and patterns. Commonly applied metrics

are patch size, dominance, number of patches and density, edge length and density,

nearest neighbor distance, fractal dimension, contagion, lacunarity, etc. (see

McGarigal et al., 2002). Some of these names are self-explanatory. The contagion

index measures the probability of neighborhood pixels being of the same class and

describes to what extent landscapes are aggregated or clumped (ONeill et al., 1988).

Landscapes consisting of patches of relatively large, contiguous landscape classes are

described by a high contagion index. If a landscape is dominated by a relativelygreater number of small or highly fragmented patches, the contagion index is low.

For example if an urbanized area is represented by one large compact blob the

contagion index will be high. The more heterogeneous the urbanized area becomes as

a result of higher fragmentation or a larger number of individual urban units, the

lower the contagion index will be. The fractal dimension describes the complexity and

fragmentation of a patch as a perimeter-to-area ratio. Low values are derived when a

patch has a compact rectangular form with a relatively small perimeter relative to the

area. If the patches are more complex and fragmented, the perimeter increases and

378 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399study and the characteristics of the urban landscape under investigation (Parker

et al., 2001).

3.2.4. Denition of the spatial domain

A basic problem in the application of spatial metrics is the denition and spatial

discrimination of spatial entities for metrics calculation. In general, metrics can

characterize structures or features of an individual patch as a spatially and themat-ically consistent area representing an elementary landscape element (McGarigal

et al., 2002). Metrics can also describe properties of patch classes (e.g. as sums or

mean values of individual patch metrics), and some (e.g. the contagion metric) can

summarize properties of the entire landscape or spatial domain of the analysis. It is

always important to dene the spatial domain of the study as it directly inuences

the spatial metrics. In some studies the extent of the study area will determine the

spatial domain. For other investigations, in particular in the comparative evaluation

of intra-urban structures, it is essential to decompose the urban environment intorelatively homogenous units that will serve as the spatial domains of the metric

analysis.

The spatial discrimination and thematic denition of the spatial units must con-

sider the characteristics of the landscape, the objectives of the study, and the use of

the metrics in further analysis that may require a specic spatial subdivision of the

urban area. There are many dierent ways of spatially subdividing an urban region

based on administrative boundaries, remote sensing and/or map analysis, or on

urban modeling considerations. Another common way is through the use of a reg-ular grid as used in many urban models (Landis & Zhang, 1998; Pijankowski, Long,

Gage, & Cooper, 1997). A similar concept in remote sensing data analysis is the

quadratic window or kernel used to analyze features in the neighborhood of a pixel.

The neighborhood is determined by the size of the moving kernel and its spectral or

thematic characteristics are derived statistically. Barnsley and Barr (2000) discuss

several problems related to kernel-based approaches in urban area analysis. For

example: grid-based approaches tend to smooth the boundaries between discrete

land cover/land use parcels; it is dicult to determine a priori the optimum kernelsize; and, a rectangular window represents an articial area that does not conform to

real parcels or land use units, which tend to have irregular shapes and their own

distinct spatial boundaries. In contrast, region-based approaches allow the discrete

characterization of thematically and functionally dened areas that are generally

irregularly shaped (Barnsley & Barr, 2000; Barr & Barnsley, 1997; Gong et al., 1992).

Regional subdivisions of urban space vary extensively in size, shape and purpose.

Governmental and planning organizations use systems such as census tracts and

blocks or zoning districts, based on the characteristics of the built environment,socioeconomic variables, administrative boundaries and other considerations (Knox,

1994). Urban models have also used a wide variety of spatial units, including indi-

vidual parcels associated with key human agents such as landowners participating in

micro-economic processes (Irwin & Geoghegan, 2001; Waddell, 1998), and uniform

analysis zones dened by the multiple intersections of polygons on dierent data

layers representing natural and socioeconomic variables of interest (Klosterman,

region-based methods are likely to provide a better segmentation of urban space for

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 379most applications.

Besides spatial resolution, the internal discrimination or subdivision of the study

area is one of the central issues of spatial scale in metric analysis. The available

approaches can overcome the averaging nature of metrics over an entire study area

that may lead to incorrect interpretations of the dynamics in the region. For

example, changes reected in the metrics cannot usually be related to specic loca-tions within the urban area without visual spatial interpretation or some more de-

tailed analysis at the patch level. Furthermore, temporal variations in the spatial

metrics may result from the aggregate or cumulative eects of dierent dynamic

processes. Spatial disaggregation allows the study area to be considered as a set of

smaller individual landscapes and regionalizes the metric analysis to an appropri-

ate scale. Even so, it may be impossible to directly relate the metric changes to

specic urban change processes. For studies of urban land cover and land use

structure change, a denition of more or less homogenous urban land use unitswill usually have to be developed before the analysis can begin. These have to

be dened and spatially dierentiated using the available data sources (e.g.

remote sensing or/and census data) and any other relevant information and local

knowledge.

4. Models of urban growth and land use change

Socioeconomic, natural, and technological processes both drive and are pro-

foundly aected by the evolving urban spatial structures within which they operate.

Research into understanding, representing and modeling urban systems has a long

tradition in geography and planning (Batty, 1994; Knox, 1994). In recent years,models of land use change and urban growth have become important tools for city

planners, economists, ecologists and resource managers (Agarwal, Green, Grove,

Evans, & Schweik, 2000; EPA, 2000; Klosterman, 1999; Wegener, 1994). This

development was mainly driven by an increased availability and usability of multiple

spatial datasets and tools for their processing (e.g. GIS). Community-based col-

laborative planning and consensus-building eorts in urban development have also1999). The denition of regions based on remote sensing uses automated, semi-

automated or supervised approaches. Automated techniques are usually based on

pattern recognition or image segmentation that result in areas with similar spectraland textural pattern. A traditional approach in region-based remote sensing analysis

is the concept of photomorphic region developed for aerial photographic interpre-

tation (Peplies, 1974). Photomorphic regions are dened as image segments with

similar properties of size, shape, tone/color, texture and pattern. Barr and Barnsley

(1997) following Barnsley, Barr, and Sadler (1995) discuss a combined remote

sensing and GIS approach for deriving urban morphological zones that describes the

physical extent of the built up area based on remote sensing data, modied by cri-

teria of minimum size and spatial contiguity based on GIS data. In general, all theseapproaches are appropriate for spatial metrics analysis in urban environments, but

380 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399been strengthened by the new data and tools at the local level (Klosterman, 1999;

Sui, 1998; Wegener, 1994).

Several problems have been identied in building, calibrating and applyingmodels of urban growth and urban land use change. These relate to the issues of

data availability and to the need for improved methods and theory in modeling

urban dynamics (Irwin & Geoghegan, 2001; Longley & Mesev, 2000; Wegener,

1994). In general, the quality of modeling results strongly depends on the quality

and scope of the data used for parameterization, calibration and validation (Batty

& Howes, 2001; Longley & Mesev, 2000). Since many land use change models

simulate both human and environmental systems, the requirements placed on the

data are fairly complex and range from natural and ecological variables tosocioeconomic information and detailed land use/cover data with appropriate

spatial and temporal accuracy. Important socioeconomic data sources include

census and various other types of governmental data as well as data that are

routinely collected by local planning and administrative agencies (Fagan, Meir,

Carroll, & Wo, 2001; Foresman, Pickett, & Zipperer, 1997; Wegener, 1994).

However, these data sources are generally limited in their temporal accuracy and

consistency, in their inclusion of important urban variables, and in their avail-

ability for dierent areas, especially outside the developed countries. Accordingly, anumber of studies have explored alternative sources of data for urban land use

change modeling, in particular data from remote sensing (Acevedo et al., 1996;

Clarke et al., 2002; Meaille & Wald, 1990). These investigations capitalize on the

fact that, as discussed above, remote-sensing techniques can provide spatially

consistent datasets that cover large areas with both high detail and high temporal

frequency, including historical time series. In particular, remotely sensed data can

represent urban characteristics such as spatial extent, pattern and land cover, often

also land use and urban infrastructure, and indirectly, a variety of socioeconomicpatterns (Usher, 2000).

Data issues also underlie, at least in part, the second major problem in urban land

use change modeling, the need for better methods and theory. Longley and Mesev

(2000) argue that our understanding of physical and socioeconomic patterns and

processes through urban modeling is largely limited by the available data. They also

refer to remote sensing as an important and insuciently exploited source of data to

aid not only applications but also theoretical understanding. In the same vein Batty

and Howes (2001) argue that remote sensing data provide a unique view on spatialand temporal urban change patterns and should be further investigated to improve

our understanding and modeling of those processes. Remote sensing may also

contribute to better representations of the spatial heterogeneity of urban land use

structure, landscape features and socioeconomic phenomena, improving on the

traditional models that often tend to reduce urban space to a uni-dimensional

measure of distance (Irwin & Geoghegan, 2001). However, the potential of the

combined application of remote sensing techniques and urban modeling has yet to be

fully explored and evaluated (Batty & Howes, 2001; Longley & Mesev, 2000;Longley et al., 2001).

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 3815. Improving urban modeling with remote sensing and spatial metrics: some case studies

Thus far we presented several arguments for combining remote sensing andspatial metrics to support and improve urban modeling and ultimately, urban

management and planning. In this section we further extend and rene these ideas

and present illustrative case studies based on actual data. We address (1) basic

mapping and data support, (2) model calibration and validation, (3) the interpre-

tation, analysis and presentation of model results, (4) the representation of spatial

heterogeneity in urban areas, and (5) the analysis of spatio-temporal urban growth

pattern. The rst three of these ve points are known and fairly well understood in

current urban modeling. They are included here for completeness as we discuss thenew possibilities resulting from the application of the framework presented in this

paper. The remaining two points are novel and address the core of our argument,

highlighting the potential for improving not only the representation of urban

dynamics but also the theoretical background of urban modeling.

5.1. Basic mapping and data support

Current spatial urban models have specic requirements in terms of data for

parameterization, e.g. data on urban extent, topography, land use, or transportation

networks (Agarwal et al., 2000; EPA, 2000). Remote sensing products are widely

used to provide these datasets or to improve existing databases in terms of spatial

accuracy and temporal consistency (relation 2 in Fig. 1). Researchers must of courseconsider the ever-improving capabilities of sensor systems to provide more detailed

and accurate remote sensing data products. In particular, the land cover hetero-

geneity of urban environments requires special attention in selecting a sensor with

appropriate spatial and spectral characteristics. Next to a more focused sensor

selection, an important potential improvement of current methods relates to the use

of spatial metrics in remote sensing data analysis. For example, the problem of land

cover versus land use in urban areas, as discussed in Section 2, can often be solved by

including a contextual component in the image analysis, which could be providedthrough spatial metrics.

The following example illustrates the application of spatial metrics in the analysis

of urban characteristics, using an IKONOS image mosaic of the Santa Barbara

South Coast region. The IKONOS image analysis includes a land cover classication

(3 classes: buildings, vegetation and rest) using the eCognition software, which

segments the image and allows for the incorporation of spatial and contextual

information of object features in the image classication process (Baatz et al., 2001;

Herold, Liu & Clarke, 2003). The spatial metrics for each land use region (derivedfrom remote sensing data interpretations) were derived using the public domain

program Fragstats (McGarigal et al., 2002). Given a land cover discrimination of

the urban environment in the three main classes: buildings, vegetation, and the rest

(soil, water, and transportation areas) the question becomes: What characterizes the

spatial land cover heterogeneity of urban areas and how can it be described with

metrics? For example, the heterogeneity of the class buildings can be related to the

size of structures (small versus large buildings), their shape (compact versus complex

and fragmented), and the spatial conguration (regular versus irregular). Size is

measured by the mean patch size; the variation in size by the patch size standarddeviation metric. Shape can be quantied by the fractal dimension metric, an

area/perimeter ratio that increases as spatial forms get more complex, and by the

number of edges or edge length of a patch. Spatial building patterns are described by

the mean nearest neighbor distance and the nearest neighbor distance standard

deviation metrics, with the latter metric increasing as the spatial pattern of build-

ings gets more irregular. Similar measures can be applied to explore the heteroge-

neity of the vegetation class.

Characteristic examples of metrics calculations for regions that encompass dis-tinct urban land uses are shown in Fig. 2. The contagion is lowest for single unit

high-density residential, multi-unit residential and commercial/industrial areas.

These land uses represent the most heterogeneous, fragmented type of urban land-

scape. High contagion is found for forest, wetlands, agriculture, and rangelands.

These natural or non-urban environments are clearly identied as such by the

landscape contagion metric. Furthermore, a distinct residential gradient exists, with

lower contagion for higher residential density. The fractal dimension of the vege-

382 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399Fig. 2. Density graphs of four spatial metrics for nine types of land uses found within urban areas from

IKONOS data. The metrics represent dierent spatial features noted on top of each graph, e.g. contagion

describes the whole land use region, the fractal dimension all vegetation patches within each area, the

patch density and nearest neighbor standard deviation the building pattern.

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 383tation areas reects high fragmentation for all residential land uses, that is, resi-

dential development results in characteristic disperse vegetation structures. Although

having less vegetation coverage overall, urban land uses like commercial or publicinstitutions show more compact vegetated areas, e.g. urban parks or ornamental

landscaping. The high values of the patch density metric reect the diverse patterns

of high and medium density residential land uses with high numbers of individual

buildings per area unit. The open space and rural land uses show low values. The

nearest neighbor standard deviation describes the regularity of the building pattern.

The values for forest, wetlands, agriculture, recreational and open spaces are

indistinct as these kinds of areas have no inherent spatial regularities. By contrast,

the actual urban land uses reect the characteristic human ngerprint of a regularspatial conguration. High-density single unit residential areas have the most dis-

tinct spatially ordered building pattern. Commercial/industrial, multi-unit residen-

tial, and medium density single unit residential also indicate a high degree of

regularity. The building congurations in low-density residential area are signi-

cantly less regular.

This thematic exploration of commonly applied spatial metrics emphasizes that

most metrics are in themselves fairly simple statistical measurements. They require,

however, a comprehensive interpretation and translation from the language oflandscape ecology, their domain of origin, to the concepts describing intra-urban

environments. For the purposes of urban model parameterization and remote

sensing data analysis, the metrics provide valuable second-order image information

to help distinguish and map dierent types of urban land use (Herold, Liu & Clarke,

2003). Hence, the combined use of remote sensing and spatial metrics can improve

the data products used to parameterize current models of urban growth and land use

change.

5.2. Model calibration and validation

Model calibration and validation are possibly the most challenging of the prac-

tical aspects of urban modeling. In dynamic models historical datasets of urbandevelopment usually form the empirical basis of these steps. Spatial metrics have

been used to evaluate and assess the local, small-scale performance of models in

addition to the summary statistics addressing total amounts of change or growth.

These metrics help assess the goodness of t in terms of spatial structure and

highlight specic problems, uncertainties or limitations of the model results (Can-

dau, 2002; Clarke, Hoppen, & Gaydos, 1996; Herold, Goldstein & Clarke, 2003;

Manson, 2000; Messina, Crews-Meyer, & Walsh, 2000). The type and number of

metrics used vary among studies, and dierent metrics have been found useful indescribing dierent characteristics of model performance and results.

An example of the use of spatial metrics in the evaluation of a dynamic models

performance is shown in Fig. 3. The model used is the SLEUTH Cellular Automaton

urban growth model (Clarke, Hoppen, & Gaydos, 1997) applied to the Goleta, CA

urban area. The Goleta urban area has experienced intensive urbanization since the

1960s as indicated by the growth in total urban area increasing from 0.6 km2 to more

384 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399than 50 km2 by 2000 (Fig. 3(a)). Interesting features in the diagrams are the sig-

nicant jumps in the metrics graphs for the calibration years, reecting the dis-

crepancies between the independent remote sensing validation measurements and the

SLEUTH model results (annual). The model generally represents well the total

amount of growth (total urban area metric) despite a tendency to underpredict. Thecontagion and fractal dimension metrics also indicate good model performance in

terms of the general spatial form of urban development. Most substantial dis-

Fig. 3. Five spatial metrics are used to evaluate SLEUTHs performance in reproducing urban devel-

opment patterns in the Goleta area. The model calibration and metrics calculation years from remote

sensing data are 1929, 1943, 1954, 1967, 1976, 1986, 1998 and 2001. The jumps in the metric graphs that

appear for the calibration years highlight the disagreements between model and observations as reected

in the metrics.agreements are recorded for the calibration year 1967. That period is associated with

signicant urban sprawl rst appearing in the area and representing a new form of

growth that causes some problems in the models performance. That is, the model

produces less accurate results as the urbanization pattern shows signicant changes

relative to the historical calibration time frame.

The metrics shown in Fig. 3(b) together provide an evaluation of a dierent aspectof the models performance. The metric describing the number of individual urban

patches shows quite signicant discrepancies between the measured and modeled

data. Although the model produces broadly correct results in simulating the amount

and spatial form of urban development, it tends to systematically underestimate the

number of individual urban patches. The model may err either by not generating

enough individual new areas of development (urban diusion) or by not retaining

existing disconnected urban areas. Both these errors would decrease the predicted

number of urban patches. However, the metric describing the percentage of urbanarea in the largest urban patch only marginally reects the major jumps in the

number of individual patches. This observation leads to the conclusion that the

discrepancies are due pre-dominantly to the insucient generation of new devel-

opment units and not to the spatial aggregation and connection of individual urban

patches to the urban core.

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 385This detailed exploration and discussion of problems in the models performance

helps suggest possible improvements. In the case presented here dierent reasons

were identied for the observed disagreements between metric measurements andmodel results. These relate to the general nature of the modeling approaches using

cellular automata, the SLEUTH models calibration method, and the specic

threshold values models that were used in the Monte Carlo simulations to decide

whether a grid cell will be considered urbanized or not. These thresholds were

eective in getting the model to simulate fairly accurately the growth of existing

urban areas but less so when it came to the allocation of new individual areas of

urban development (Herold, Goldstein & Clarke, 2003). It seems indeed that the

spatial metrics, individually or in combination, do reect the multiple facets of theSLEUTH models performance. These results suggest that it would be useful to

explore and evaluate systematically the use of dierent types of metrics in order to

develop more standardized, transparent and ecient model calibration and valida-

tion procedures.

5.3. Interpretation, analysis and presentation of model results

The results of spatial modeling need to be thoroughly interpreted and assessed in

order to derive useful information for specic applications. Remote sensing imagery

can greatly enhance the interpretation, visualization and presentation of model

outcomes, e.g., by providing a recognizable background to the spatial patterns

produced by the model. Realistic visualization is of special importance if the resultsare to be presented to the public. Further renement of the model results and

assessment of the impacts of urban development can be supported by spatial metrics

analysis (Alberti & Waddell, 2000). Berry, Flamm, Hazen, and MacIntyre (1996) use

landscape metrics in their LUCAS model to assess the impact of urban expansion on

the surrounding natural areas. Generally, this approach can be applied in a variety

of investigations relating to urban dynamics and the resulting spatial structures. For

example, spatial metrics can be used to interpret the localized implications of dif-

ferent model scenarios. They can provide a better understanding of how dierentpolicies or weightings of growth factors might impact dierent parts of the urban or

natural areas. Metrics can also be used to dene, rather than just interpret growth

scenarios, as they can help represent locally detailed alternative spatial congura-

tions.

Fig. 4 shows the results of a case study evaluating and assessing a set of alternative

paths of future urban growth. The comparative analysis and assessment of dierent

possible urban growth trajectories is one of the most important purposes of mod-

eling in connection with urban management and planning (Xiang & Clarke, 2003).The application of the SLEUTH urban growth model (Clarke et al., 1997) to the

Santa Barbara urban region focuses on ve dierent sets of assumptions corre-

sponding to alternative growth trajectories over the next 30 years (Candau &

Goldstein, 2002). The rst of these (MSQ) assumes that the status quo will be

maintained, and future growth will be allowed to continue in a manner similar to

what had occurred in the past. The second alternative (ER) uses the same

386 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399assumptions as MSQ but includes an expanded road network. The third alternative

(EEP) reects maximum protection of environmentally sensitive lands, while the

fourth (MEP) reects a lesser degree of environmental protection. Finally, the fth

alternative (UB) uses an urban boundary to dene and constrain the maximum

extent of urban growth.

The graphs in Fig. 4 compare the ve growth alternatives using four dierent

spatial metrics. The changes in total urban area, length of urban boundary, numberof urban patches, and degree of contagion are for the year 2030 relative to 2001, with

the year 2001 being the last calibration year that was derived from IKONOS satellite

remote sensing data. The metrics analysis shows very similar results for the MSQ and

ER cases. The most urban growth appears for MSQ (continuation of current trends)

and ER (expanded road network), the least for maximum environmental protection

(EEP). The number of individual urban patches signicantly decreases for MSQ and

ER as a result of the large expansion in urbanized area within the physically con-

strained South Coast region, a narrow plain lying between the ocean and a steep

Fig. 4. Spatial metric comparison of ve dierent growth scenarios for the Santa Barbara South Coast

region forecasted for the year 2030 using the SLEUTH urban growth model.coastal range. These patterns reect the build-out of the limited amounts of existing

intra-urban vacant land and the general loss of open space and natural corridors. In

the cases of maximum environmental protection (EEP) and the enforcement of an

urban growth boundary (UB), the decrease in the number of individual urban pat-

ches is signicantly lower due to the spatially regulated and lower total amount of

growth. Both alternatives maintain similar, comparatively high degrees of spatial

landscape homogeneity as indicated by the contagion metric. An interesting dier-

ence between the EEP and UB alternatives is summarized in the change in urbanboundary length that reects the complexity of the urban/rural interface. The EEP

case shows an increase in total boundary length due to the need to avoid ecologically

sensitive areas and retain natural corridors, open spaces and specic habitats. The

UB alternative on the other hand, as would be expected, represents the most com-

pact growth pattern. Finally, the MEP case shows intermediate values for all metrics

with distinct dierences with the EEP case (extreme environmental protection). This

alternative compromises between environmental considerations and the pressures for

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 387further urban development, as reected in its intermediate position on all four

metrics. These examples illustrate the value of metrics in providing additional

information for the interpretation, analysis and presentation of model results.However, further research is needed in order to understand more systematically the

role that particular metrics and combinations of metrics can play in this complex

task.

5.4. Representation of spatial heterogeneity in urban areas

Urban spatial form, especially at the local scale, poses major challenges for urban

modeling. Its correct representation, let alone prediction, are very dicult yet nec-

essary for understanding and managing urban function (Alberti, 1999; Geoghegan

et al., 1997; Irwin & Geoghegan, 2001). Urban form is the focus of urban mor-

phology, described as . . . a specic branch of urban geography (. . . with) its own,largely descriptive, language of discourse. It attempts to nd more precise mathe-matical descriptions of cities or parts of cities. (Webster, 1995). There is potential

for new ways of representing urban form and structure through a combined appli-

cation of remote sensing and spatial metrics. This could lead to much improved

modeling of the spatial heterogeneity of urban form and land use. The structures and

patterns identied with spatial metrics may constitute critical independent measures

of the urban socioeconomic landscape and can be used for an improved represen-

tation of a variety of urban spatial characteristics (Geoghegan et al., 1997; Parker

et al., 2001). Beyond socioeconomic functions, spatial metrics can also help highlightthe relationships between urban spatial form (including its three-dimensional

building structure: Adolphe, 2001), and various dimensions of urban environmental

quality and performance (Alberti, 1999).

More specically, it has been shown that fairly reliable relationships exist between

the spatial conguration of build up areas, as mapped with the help of remote

sensing and spatial metrics, and land use and socioeconomic characteristics (Barr &

Barnsley, 1997; Herold et al., 2002; Liu, 2003). For example, the metric information

in Fig. 2 shows the spatial ngerprints of dierent types of urban land use and theirrepresentation in dierent metrics. To further explore these dierences, the corre-

sponding intra-urban patterns of the Santa Barbara South Coast region are shown in

Fig. 5. The land use characteristics reect the three urban cores and a nearly con-

centric pattern of decreasing residential density away from these. The contagion

metric follows the concentric pattern with heterogeneous urban environments near

the central urban (low contagion) and a gradient of increasing contagion towards the

peripheral rural areas. Contagion reects both the level of human impact or

urbanization and the environmental and ecological signicance of these areas. Thevegetation fragmentation metrics yield high values for residential areas, in particular

for areas surrounding the central urban cores. The cores themselves are character-

ized by lower values since the vegetation is conned to a few small compact patches

(e.g. parks). The fragmentation of vegetation decreases near the rural/urban inter-

face, reecting the more natural character and higher ecological value of these

areas. This example emphasizes the potential of spatial metrics to highlight the

388 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399relationships between urban spatial form and various dimensions of urbanization

and environmental quality (Alberti, 1999).

In Fig. 6, a comparison of the spatial distribution of patch density and nearest

neighbor distance standard deviation with the distribution of population densityreects their similar pattern. Higher population density corresponds to areas of

higher patch density. The relationship is intuitive: if you have more houses per unit

area you expect to have more people living there. The similarity in spatial pattern

between the nearest neighbor distance and the population density is not quite as

obvious but still clear. High-density residential areas are characterized by more

regular building patterns, hence lower nearest neighbor standard deviation measures

(see also Fig. 2). These examples show the potential of representing specic socio-

economic characteristics in urban areas with the metrics. The work of Liu (2003) has

Fig. 5. Spatial urban characteristics of land use and two spatial metrics in the Santa Barbara South Coast

urban area derived from IKONOS data. The land use distribution was derived from spatial metric/texture

based classication. The metrics describe the spatial heterogeneity for each land use region (see Herold,

Liu & Clarke, 2003). The land use map highlights the three urban core areas in the region of Santa

Barbara, Goleta, and Carpinteria.

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 389indicated a direct relationship between IKONOS derived texture measures and

spatial metrics on the one hand, and census population data on the other. The

quality of the correlation, however, was not sucient to use spatial metrics as a

direct predictor of urban population densities. A very close relationship was not

expected since urban spatial patterns are not uniquely associated with land uses (forexample, commercial buildings are hard to distinguish from residences in the patch

density metric though they do not contribute to the urban population count).

Generally the metrics reect combinations of dierent spatial characteristics. From

an urban modeling perspective, it is more important to think about urban land cover

pattern (and consequently related spatial metrics) as the cumulative outcome of

urban development processes. An evolving urban environment will result in distinct

spatial congurations reecting socio-economic characteristics as well as a variety of

other factors inuencing growth (e.g. topography, road networks, planning eorts).

Fig. 6. Spatial urban characteristics of population density from CENSUS data (people per mile2) and two

spatial metrics in the Santa Barbara South Coast urban area derived from IKONOS data. The spatial

distributions can only be compared qualitatively due to the dierent spatial domains they are based on

(a case of the modiable area unit problem).

390 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399In this context, spatial metrics can be used to explore alternative representations of

the urban environment in urban models (Parker et al., 2001). While many urban

models rst categorize urban space into land use classes in order to derive specicspatial characteristics of interest, spatial metrics can provide a more continuous

representation of land use and socioeconomic and demographic characteristics based

on actual, detailed land cover structure.

One of the central research questions certainly concerns the selection of appro-

priate metrics. The metrics used so far are directly adopted from landscape ecology.

Future research should explore the potential roles of individual metrics in urban

analysis and the development of new metrics specically tailored to urban space.

This should lead to a special set of urban metrics capturing important urban char-acteristics. These are likely to contribute towards much improved and detailed

representations of urban form, function and functionality. Urban metrics should be

transferable and comparable among dierent urban areas. The dynamic range and

statistical characteristics of the metric values should be considered in adjusting for

possible data skewness in the further analysis (e.g. urban land use classication based

on the metrics). Clearly, the development of urban metrics should focus on

describing the spatial characteristics of the built environment and the patterns

formed by the buildings in particular. These are the obvious components of urbandevelopment and urban form. It is important however to also consider the vegetation

and its spatial characteristics, as previous studies have shown. Vegetation can

present an inverse pattern to building heterogeneity if no other land cover classes are

present (such as transportation areas, soil surfaces, water etc.). This is usually not the

case. A separability study of urban land use categories shows that vegetation-based

metrics contribute more information than building-based metrics (Herold, Liu &

Clarke, 2003). One reason is the unique spectral characteristic of vegetation that

usually result in higher mapping accuracy than for built-up land cover types (Herold,Gardner & Roberts, 2003; Sadler, Barnsley, & Barr, 1991). A second reason relates

to the distinct spatial characteristics of vegetation that reveal dierent information

than building patterns. For example, Fig. 2 shows a clear distinction between urban

and rural land uses based on the fragmentation of the vegetated areas. Vegetation

reects important urban and socio-economic characteristics almost as much as the

building patterns do because vegetation patches in urban areas are usually there as a

result of human design, (e.g. gardens, front yards, parks, open spaces, golf courses,

recreational areas or protected urban habitats). Of course, urban vegetation patternswill play a critical role if the metrics are used primarily for environmental and

ecological purposes (Alberti, 1999).

In summary, remote sensing and spatial metrics combined provide an exiting new

source of information and an innovative way to study and represent spatial urban

characteristics in considerable detail. Central to this development are the high spatial

resolution satellite systems such as IKONOS that provide data at a new spatial scale

that is of particular relevance to the study of urban form and morphology. Nearly all

previous work on urban morphology has focused on either the ner architecturaland design scales of 3-dimensional, internal and external building structures

(Steadman et al., 2000), or on the much coarser scales served by spatially aggregated

census data or remote sensing data in coarser spatial resolution (Foresman et al.,

1997; Mesev et al., 1995).

5.5. Analysis of spatio-temporal urban growth pattern

One major advantage of remote sensing data is their availability and consistency

in terms of historic time series. These datasets used in combination with spatialmetrics can provide a unique source of information on how various spatial char-

acteristics of cities change over time. This allows important insight into urban spatial

structure changes and the evolving urban growth dynamics. An example of the

analysis of urban change in the Santa Barbara, CA region is shown in Fig. 7. The

changes in urban structure were mapped from historical air photos. The values of six

dierent metrics are calculated for each point in the time series, yielding corre-

sponding spatial metric growth signatures (Herold, Goldstein & Clarke, 2003).

The growth of Santa Barbara develops outward from the original downtown core.While the largest growth rates occur in the 1960s and 1970, the rapid growth phase

started in the 1940s and 1950s with the appearance of small individual developments

around the core area. These caused a peak in urban patch density, an increase in the

number of urban patches, and a decreasing proportion of the total area being

Fig. 7. Spatial metrics describing the spatial and temporal growth dynamics mapped from multi-temporal

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 391air photos in the Santa Barbara, CA region 19291997 (Note: %LAND percent of landscape (built up),PSSD patch size standard deviation, CONTAG contagion index, PDpatch density, ED edgedensity, AWMPFD area weighted mean patch fractal dimension).

covered by the largest urban patch (the downtown core area). Through 1967 more

individual urban development patches are formed, causing a peak in the number of

individual urban patches and a signicant growth in the total urbanized area (urbansprawl). In the following years the decreasing patch density, the lower proportion of

urban area in the largest urban patches, and the smaller mean nearest neighbour

distance all indicate a much larger area aected by urbanization than in previous

years and the beginning of spatial coalescence of the individual development units.

By 1976 many individual urban patches have grown together, forming larger

urbanized areas with higher fragmentation, as shown by the fractal dimension. This

trend continues to date with decreasing fragmentation and fairly low mean nearest-

neighbour distance, indicating the loss of open space between the urbanized patches.The continuous growth in total area occurs through new development in sur-

rounding rural areas as well as through the expansion of the existing urban area, as

shown by the fairly stable number of both individual patches and the percentage

of urban land in the urban core area.

The example in Fig. 7 analyses urban growth patterns at a regional scale, con-

sidering both urban and rural land. With high spatial resolution remote sensing data

392 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399urban dynamics can also been studied at the intra-urban scale. The example in Fig. 8

shows the evolution of six dierent spatial metrics indicating the change in urbanstructure over a 10-year period. The metrics represent the changing spatial hetero-

geneity of the actual built-up areas as mapped from historical air photos. The La

Cumbre neighborhood, only marginally developed in 1978, experiences new resi-

dential development in all parts of the area. This process is marked by a decrease in

individual built-up patch density, hence a higher level of spatial aggregation of the

built up areas with higher variance in patch size. The complexity of the landscape

increases signicantly, as shown in the decreased contagion and the higher edge

density metrics. The evolution of the fractal dimension metric indicates the larger

Fig. 8. Local scale changes in spatial urban structure mapped from multi-temporal air photos two areas of

the Santa Barbara, CA urban region (Note: %LAND percent of landscape (built up), PSSDpatch sizestandard deviation, CONTAG contagion index, PD patch density, ED edge density, AW-MPFD area weighted mean patch fractal dimension).

vegetated areas within the urban fabric and the dominance of the built-up class,

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 393including the spatial fusion of the built-up areas. Going one step further, one mightventure the educated guess (correct in this case) that average household incomes are

lower in Isla Vista that in the La Cumbre neighborhood, thus linking urban form

and socioeconomic characteristics.

These two case studies illustrate the contribution that remote sensing and spatial

metrics can make to the detailed analysis of urban growth and land use change

patterns. These examples show how dierent patterns of growth are observable at

dierent scales. Individual metrics help track specic spatial and temporal dynamics,

for example, the impact of urban sprawl on landscape structure. Changes in themetrics over time could be read as urban development signatures representing spe-

cic processes of urban growth and land use change, and the impacts of these

processes on large-scale and small-scale urban spatial structure. Although the

examples here represent descriptive analyses of urban growth patterns, this method

could most likely go beyond this descriptive use. Future research needs to explore

how dierent spatial metrics evolve in relation to a variety of spatial and temporal

change patterns and identify the spatial metrics signatures of particular kinds of

processes at dierent geographic scales. More detailed analysis of spatio-temporalgrowth dynamics should reveal both recurring regularities and distinct dierences in

dynamic growth patterns among dierent urban regions. The dierences in spatial

growth patterns should reect local and regional growth characteristics that may be

linked to specic processes and factors of urban development. Associating the

measured spatio-temporal growth patterns with underlying socioeconomic and other

processes will link the empirical metrics observations to urban and regional theory

and modeling.

6. Conclusions

The framework outlined in this paper represents a somewhat dierent philo-sophical approach to the study of urban spatial structure and dynamics than the

ones usually followed. Indeed, most dynamic urban studies adopt a deductive per-

spective, deriving urban structures as the spatial outcomes of pre-specied processes

of urban change (from process to structure). The approach based on a combination

of remote sensing and spatial metrics reverses the procedure by measuring actual

spatial structures in great spatial and temporal detail and linking their changes overspatial complexity of the built up areas as a function of their growth and increasing

spatial aggregation. Over the same period, the Isla Vista neighborhood also shows

signicant change in urban structure caused by further residential inll development.Here the residential land use is signicantly denser than in the La Cumbre neigh-

borhood. The growth patterns show similar trends in the rst three metrics for both

areas. However, the contagion, edge density and fractal dimension metrics indicate

signicant dierences in the impact of continued urban development on the spatial

structure of the two neighborhoods. In Isla Vista, the complexity of the landscape

and the fragmentation of built-up patches decrease due to the disappearance of

394 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399time to specic hypotheses about the processes at work (from structure to process).

Certainly, the patterns obtained from remote sensing data usually represent the

complex aggregate outcomes of many dierent individual processes, making it verydicult to disentangle the eects of the dierent variables and trends of interest. It

seems that both the deductive and inductive approach could benet by being used in

combination, with the deductive perspective helping to narrow down the possibilities

suggested by the detailed analysis of changing urban form.

At a more technical level, the study of urban growth and land use change at all

geographic scales requires detailed and accurate datasets and appropriate methods

for their analysis, modeling and interpretation. The availability of remote sensing

data suitable for urban analysis has signicantly increased in the past few years. Suchdata can provide unique views of urban change dynamics in terms of spatial and

temporal resolution based on highly detailed, consistent mapping products over

large areas and long time periods. This can now be done at all relevant geographic

scales, provided that the appropriate sensors are selected wisely based on their

spatial and spectral characteristics. More generally, the recent developments in re-

mote sensing and in the digital analysis of thematic data layers with spatial metrics,

as well as the increased opportunities for applying urban modeling techniques in

planning and management, invite a systematic evaluation of the potential of com-bining the strengths of these techniques.

We believe that spatial metrics denitely deserve a place in the urban dynamics

research agenda. They can be used for the detailed mapping of urban land use

change at dierent geographic scales and can help infer a number of socioeconomic

characteristics from remote sensing data. Spatial metrics provide sophisticated de-

scriptors of urban spatial heterogeneity based on the distribution of built-up struc-

tures and open areas. Individual metrics reect specic variables of urban land use,

function, and change, and can form the basis for alternative representations of thesefactors in urban models. The analysis of temporal change in urban spatial structure

based on remote sensing and spatial metrics also encourages a new perspective on

these issues. Temporal change signatures that can be related to specic dynamic

processes should be identiable in urban spatial structure. These dierent improved

levels of description and analysis contribute to a better understanding and repre-

sentation of spatial urban structure and change. Thus, quantitative information

derived from spatial metrics can assist all phases of modeling for a wide variety of

urban models. Indeed, spatial metrics can support model parameterization, cali-bration and validation, as well as the analysis, interpretation and presentation of the

model results.

In conclusion, the methodological framework consisting of the combined appli-

cation of remote sensing, spatial metrics and urban modeling promises to support

the analysis of urban growth and land use change in a variety of dierent ways.

However, the research is still at an early stage and relies heavily on metrics and

assumptions originating in landscape ecology. The derivation of a set of urban

metrics tailored to the needs of urban analysis at dierent scales, the study of spatialmetrics signatures corresponding to specic urban processes, as well as the further

improvement of remote sensing mapping products, are the main research issues

Planning and the Management of Change, APAs Planners Book Service. http://www.planning.org/

guidebook/Login.asp (access: September 2003).

Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover

land-use from ne spatial resolution land-cover data. Computers, Environment and Urban Systems,

21(3/4), 209225.

Barnsley, M. J., & Barr, S. L. (2000). Monitoring urban land use by Earth Observation. Surveys in

M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399 395Geophysics, 21, 269289.

Barnsley, M. J., Barr, S. L., Hamid, A., Muller, P. A. L., Sadler, G. J., & Shepherd, J. W. (1993).

Analytical tools to monitor urban areas. In P. M. Mather (Ed.), Geographical information handling-

research and applications (pp. 147184).classication scheme for use with remote sensor data. US Geological Survey Professional Paper 964.

Baatz, M., Heynen, M., Hofmann, P., Lingenfelder, I., Mimier, M., Schape, A., Weber M., & Willhauck,

G. (2001). eCognition User Guide 2.0: Object Oriented Image Analysis. Deniens Imaging GmbH,

Munich, Germany.

Banister, D., Watson, S., & Wood, C. (1997). Sustainable cities: transport, energy, and urban form.

Environment and Planning B: Planning and Design, 24, 125143.

Baraldi, A., & Parmiggiani, F. (1990). Urban area classication by multispectral SPOT images. IEEE

Transactions on Geoscience and Remote Sensing, 28, 674680.

Barnsley, M. J., & Barr, S. L. (1997). A graph based structural pattern recognition system to infer urbansuggested in the paper. Further investigations should confront these problems and

explore the value of the proposed framework in the context of both urban theory and

specic applications.

Acknowledgements

This work was conducted with support from the National Science Foundationunder UCSBs Urban Research Initiatives project UCIME (Award NSF-9817761).

We would like to acknowledge Jeannette Candau, Noah Goldstein, Melissa Kelly,

and Ryan Aubry at the University of California Santa Barbara for their support of

this research.

References

Acevedo, W., Foresman, T. W., & Buchanan, J. T. (1996). Origins and philosophy of building a temporal

database to examine human transformation processes. Proceedings, ASPRS/ACSM annual convention

and exhibition, Baltimore, MD, April 2224.

Adolphe, L. (2001). A simplied model of urban morphology: application to an analysis of the

environment performance of cities. Environment and Planning B: Planning and Design, 28, 183200.

Agarwal, C., Green, G. L., Grove, M., Evans, T., & Schweik, C. (2000). A review and assessment of land-

use change models: dynamics of space, time, and human choice. Published jointly by the US Forest

Service and the Center for the Study of Institutions, Population, and Environmental Change (CIPEC),

Indiana University.

Alberti, M. (1999). Urban patterns and environmental performance: what do we know? Journal of

Planning Education and Research, 19-2, 151163.

Alberti, M., & Waddell, P. (2000). An integrated urban development and ecological simulation model.

Integrated Assessment, 1, 215227.

American Planning Association (2002). Growing SmartLegislative Guidebook: Model Statutes for

396 M. Herold et al. / Comput., Environ. and Urban Systems 29 (2005) 369399Barnsley, M. J., Barr, S. L., & Sadler, G. J. (1995). Mapping the urban morphological zone using satellite

remote sensing and GIS: UK involvement in the Eurostat pilot project on remote sensing and

urban statistics, Remote sensing in action. In Proc. annual conference of the remote sensing society,

University of Southampton, September 1114, 1995 (pp. 209216). Remote Sensing Society (Notting-

ham).

Barr, S., & Barnsley, M. (1997). A region-based, graph-oriented data model for the inference of second

order information from remotely-sensed images. International Journal of Geographical Information

Science, 11(6), 555576.

Batty, M. (1994). A chronicle of scientic planning: the anglo-american modeling experience. Journal of

the American Planning Association, 60(1), 712.

Batty, M., & Howes, D. (2001). Predicting temporal patterns in urban development from remote imagery.

In J. P. Donnay, M. J. Barnsley, & P. A. Longley (Eds.), Remote sensing and urban analysis (pp. 185

204). London and New York: Taylor and Francis.

Batty, M., & Longley, P. A. (1994). Fractal cities: a geometry of form and function. London: Academic

Press.

Ben-Dor, E., Levin, N., & Saaroni, H. (2001). A spectral based recognition of the urban environment

using the visible and near-infrared spectral region (0.41.1 m). A case study over Tel-Aviv.

International Journal of Remote Sensing, 22(11), 21932218.

Berry, M. W., Flamm, R. O., Hazen, B. C., & MacIntyre, R. L. (1996). The land-use change and analysis

system (LUCAS) for evaluating landscape management decisions. IEEE Computational Science &

Engineering, 3(1), 2435.

Bowden, L. W. (1975). Urban environments: inventory and analysis. In L. W. Bowden et al. (Eds.),

Manual of remote sensing, vol. 2 (1st ed., pp. 18151880).

Brockhero, M. P. (2000). An urbanizing world. Population Bulletin, 55(3), 344.

Candau, J. (2002). Temporal calibration sensitivity of the SLEUTH Urban Growth Model. Master thesis,

Geography, University of California at Santa Barbara, 129 p.

Candau, J., & Goldstein, N. (2002). Multiple scenario urban forecasting for the california south coast

region. In Proceedings of the 40th annual conference of the urban and regional information systems

association, October 2630, Chicago, Illinois.

Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical

urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24,

247261.

Clarke, K. C., Hoppen, S., & Gaydos, L. J. (1996). Methods and techniques for rigorous calibration of a

cellular automaton model of urban growth. In Proceedings, third international conference/workshop on

integrating GIS and environmental modeling CD-ROM, Santa Fe, NM, January 2126, 1996. Santa

Barbara, CA: National Center for Geographic Information and Analysis. http://www.ncgia.ucsb.