GIS and Evidence-Based Policy Making - Chapter 9 pot

It is very different to one in which housingdemand—driven by population growth—would inevitably be met by theconstruction of family housing immediately recognizable by remote sensingand

Residential Property Utilization: Monitoring the Government Intensification Agenda

Peter Bibby

CONTENTS

9.1 Introduction 177

9.1.1 Policy, Evidence, and GIS 178

9.2 Patterns of New Construction: Accommodating Housebuilding within Urban Areas 181

9.3 Accommodating Housebuilding: Urban Areas and Beyond 185

9.4 Using Grids to Characterize Dispersal of Housebuilding 191

9.5 Using Grids to Explore Structural Effects and Market Relations 194

9.6 Within the Urban Areas: Intensification of Units of Occupation 1998—Reconstructing a Grid Using PAF 214

9.7 Within the Urban Areas: Intensification of Utilization of Existing Property 217

9.8 Constructing a Fine-Grained Settlement Geography to Identify Development Contexts 220

9.9 Conclusions 229

9.9.1 Development Patterns and Policy Objectives 230

9.9.2 Methods and Representations 230

9.9.3 Relation between Policy, Evidence, and GIS 233

References 235

The Government is committed to promoting more sustainable patterns

of development, by:

. concentrating most additional housing development within urban areas; . making more efficient use of land by maximising the reuse of previously developed land and the conversion and reuse of existing buildings;

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. assessing the capacity of urban areas to accommodate more housing;. adopting a sequential approach to the allocation of land for housingdevelopment;

. managing the release of housing land; and

. reviewing existing allocations of housing land in plans, and planningpermissions when they come up for renewal

(Department of Environment, Transport and the Regions;

DETR, 2000c, para 21)

It seems peculiar to her suddenly that they should be living in this space:

a hundred years ago it would have been a garment factory, whereimmigrants from eastern Europe stitched fabric into human shapesand practised getting their tongues around the muted diphthongs ofEnglish This is what Lily loves about London, that every building,street, common and square has had different uses, that everything wasonce something else, that the present is only the past amended

(Maggie O’Farrell, My Lover’s Lover, London, Review 2002, p 41)

9.1.1 Policy, Evidence, and GIS

In the opening years of the twenty-first century, planning policy in Englandand Wales was clearly directed to conserving undeveloped land and tothe intensification of use of urban areas DETR’s Planning Policy GuidanceNote 3 of 2000 (PPG3) encapsulated this emphasis The term intensificationdenotes ‘‘a combination of changes in built form and activity’’ and focusesattention on the capacity of urban areas both to accommodate extra dwell-ings and to adapt to new economic roles At the microscale, the term impliesdevelopment of previously undeveloped pores within cities; the redeve-lopment of existing buildings and previously developed sites at higherdensities; and the subdivision, conversion, and extension of existing build-ings All contribute to the intensification of use of existing buildings or sitesand changes of use allowing increases in the numbers of people living in, orworking in an area (Williams, 1999, p 168) Policy has focused on amendingthe past in a manner which provides for more sustainable development andwhich celebrates—perhaps in the manner of O’Farrells’s Lily—the values

of urban living Over the same period, across government, there was areinvigorated interest in founding policy upon evidence It thereforeseems plausible that there might be some potential role for GIS (and indeedfor Geographic Information Science (GISc)) in developing and monitoringpolicy for reshaping of the physical environment

This chapter explores some of that potential Its focus is on monitoringurban growth and the conservation of undeveloped land, on monitor-ing the mediating influence of urban land recycling, and on the reuse ofexisting buildings It attempts to contribute to debate at three levels.Most immediately, it attempts to use GIS to draw some inferences aboutdevelopment patterns in England and Wales which might be pertinent to

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the assessment of policy Second, it considers how particular techniques,including the use of natural language processing (NLP) with GIS, cancontribute to the exploitation of data for policy purposes Third and mostfundamentally, it is concerned with the overall relationship between policy,evidence, and GIS and with the manner in which GIS use is and might beembedded within policy processes.

A prerequisite of addressing the first of these concerns is a broad standing of aspects of relevant government policy in 2000 and immediatelyafterwards, while engagement with the third concern demands some explicitconsideration of how the term policy itself is to be understood The emphases

under-of the 2000 revision under-of PPG3 reflect a commitment to regeneration andintensification, which suffuses popular planning thought and rests in turn

on underlying concerns about sustainable urban living and broader notions

of environmental sustainability The 2000 revision of PPG3 must, therefore,

be understood alongside a welter of other documents (including, forexample, the urban and rural white papers of DETR, 2000a,b) and Prescott’s(2003) statement on sustainable communities which depend upon thebroader discourse of sustainable development It must be emphasized, how-ever, that other discursive currents influence present policy set out in theCommunities and Local Government’s Planning Policy Statement 3 (PPS3;CLG, 2006) CLG is the successor department to the Office of the DeputyPrime Minister (ODPM), DETR, Department of Transport, Local Governmentand the Regions (DTLR), and the Department of Environment (DoE).The concept of policy pertinent to this chapter should neither be reduced

to the text of PPG3 (or PPS3) nor bloated to include the sum of concernsabout sustainability In the tradition of Heclo, policy might be regarded as a

‘‘course of action or inaction’’ (Heclo, 1972, p 85) The policy process mightthus be seen as centering on the articulation of commitments intended toguide subsequent action From this perspective, the prime significance oftexts such as PPG3 is that they potentially allow such commitments to bindactors such as local authority planners who may be distant from centralgovernment policy making both in space and time The policy processinvolves ensuring such attenuation, so that policy becomes a ‘‘stancewhich once articulated, contributes to the context within which a succession

of future decisions will be made’’ (Hill, 1997, p 7 ascribed to Friend et al.,

1974, p 40) The context reproduced by the policy process is sometimesreferred to as the policy setting and includes an assumptive world ofvalues, metaphors, and core narratives reflected in bureaucratic practices,operational definitions, and procedural rules

Evidence is always used to support or supplant a story Policy rests uponparticular understandings of the nature of the world Given the nature ofpolicy, its relation to evidence is less straightforward than might firstappear Context denies the possibility of transparent empiricism, therebycomplicating the role of GIS in monitoring its effectiveness Sustainability,moreover, should perhaps be seen as an ‘‘essentially contested’’ concept inthe spirit of Gallie (1955–1956) Without elaboration of a particular narrative,

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and of particular definitions, GIS, however useful, cannot provide a tool fordistinguishing sustainable and unsustainable patterns of development It,therefore, cannot somehow ground policy in evidence in an unproblematicmanner The evidence assembled using GIS is constrained by the data which

it has been deemed worthwhile collecting and framed by particular tives and images within the policy setting

narra-Understanding the potential of using GIS in policy monitoring involvesappreciating the character of the traditional narratives One such narrativeprovides an account of urbanization which focuses on the construction ofdwellings, leading from the idea of exogenous household growth to expan-sion of the contiguous urban area and concomitant reduction in undevelopedland The number of dwellings in Great Britain has increased by 80% in thelast 50 years (Matheson and Babb, 2002, p 163) The traditional narrative hasmoved with images such as ‘‘a Bristol a year,’’ directly from increasingnumbers of households to the expansion of the contiguous urban area, andthis provides the imagery by which the press expresses the environmentalconsequences of household growth [see, for example, the transmutation offorecast changes in numbers of households into ‘‘twenty-seven huge newtowns’’ (Daily Telegraph, 1996) or the invocation of ‘‘an area the size ofManchester’’ (Observer, 2003)] They converge with images of urban growth,urban sprawl, and urban spread, which liken cities to organisms, demandingresponses such as CPRE’s Sprawl Patrol Such images are reflected and sup-ported by familiar cartographic devices, which record the expansion ofparticular towns over time, which may be replicated within GIS

More recent narratives, however, qualify this story Growth in numbers ofhouseholds remains at the core Although population growth has been modest

in recent years, household growth—and hence urban growth—has continued(sustained by rising real incomes) This growth is to be understood in relation

to changing lifestyle choices that show themselves statistically as continuingfalls in average household size Variants of the narrative typically questionhow new households or dwellings are to be accommodated, but not thesustainability of those social choices that allow household size to continue tofall (DoE, 1996) Through the 1990s policy discussion became increasinglyconcerned with the extent to which development might be concentrated onbrownfield sites and hence mitigate pressure for urban expansion This in turnprompted GIS development including both small-scale analytic work under-pinning urbanization forecasts (Bibby and Shepherd, 1996) and development

of a National Land Use Database (NLUD)—an inventory of brownfield sites

In the absence of strong population growth, by 2000, householdgrowth had come to coexist alongside crude housing surplus at nationallevel (Matheson and Babb, 2002, p 164) In particular cities and regions,problems of low demand for housing had come to assume prominence (e.g.,Bramley et al., 2000) and these issues had risen high up the policy agenda.Narratives of urban growth thus came to interact with rather different narra-tives of local housing market collapse These emphasized the rapid, extreme,and essentially arbitrary nature of local market adjustment as withdrawal ofkey actors (such as particular social landlords), vandalism against empty

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property, and outbreaks of social disorder might undermine the possibility ofcontinued occupation The specter of urban expansion running apace along-side the dereliction of redundant urban quarters had become evident.Policy, moreover, must be concerned not only with substantive goals butalso to the manner in which they are to be pursued In a climate whereevidence is used to legitimize policy, where there is a lack of confidence inforecasts, and where there is uncertainty over the performance of localhousing markets, monitoring came and remains to the fore (in principle atleast) The 2000 revision of PPG3 introduced a ‘‘plan, monitor, and manage’’approach to planning for housing in preference to the previous regime—somewhat disparagingly dubbed ‘‘predict and provide’’ retrospectively(Prescott, 2000) This provides the context in which this particular series ofGIS applications is set It is very different to one in which housingdemand—driven by population growth—would inevitably be met by theconstruction of family housing immediately recognizable by remote sensingand easily represented on large-scale maps.

Housebuilding within Urban Areas

The introductory quotation from the 2000 revision of PPG3 (DETR, 2000c)focuses on three objectives: concentrating housebuilding on sites withinurban areas, concentrating housebuilding on previously developed sites,and accommodating new dwellings within existing buildings The remain-der of this chapter treats each of these objectives in turn, using GIS toexplore how far patterns of housebuilding in the 1990s proved consistentwith the intentions set out in 2000 and exploring some of the issues arising

In so doing it must have regard to the closely linked intentions to

avoid developments which make inefficient use of land (those of lessthan 30 dwellings per hectare net)

encourage housing development which makes more efficient use of land(between 30 and 50 dwellings per hectare net)

and

seek greater intensity of development at places with good public port accessibility such as city, town, district and local centres oraround major nodes along good quality public transport corridors

trans-(PPG3; DETR, 2000c, para 58)The location of new development in relation to existing urban areas wouldappear to be an issue where there is a clear role for GIS and where the analyticissues are trivial Effective monitoring might appear to depend simply on theavailability of information on the location of new housing sites on the onehand and the boundaries of urban areas on the other recorded with sufficient

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pre cision and accura cy Fortunately, Ordnance Survey (OS)—the nationalmapping agency—generate both sets of data Since 1985 they have collectedLand Use Change Statistics (LUCS) for what is now CLG as an adjunct toupdating the national map base (Sellwood, 1987) This constitutes a tractablesource of very fine-grained information about the location of new housebuild-ing (among other things) OS have also produced for CLG and its predecessorshighly detailed boundaries of physical urban areas for use alongside Censusstatistics for 1981, 1991, and 2001 (for a discussion of these boundaries and theirrelation to other urban definitions, see Shepherd et al., 2002).

LUC S data re fer to the land par cels shown on ba sic-scale maps (1:125 0

in urban areas 1:2500 at the urban fring e and 1:1 0,000 in mo untainand mo orland areas ) Whe re the use of any suc h parcel changes (on thebas is of a 24-categ ory classific ation) a LUC S record is create d It willincl ude a 10-m gri d referen ce for a rep resentat ive poi nt with in the parcel ,

a one charact er code (e.g , R for residen tial) indicati ng the use befo re andanoth er ind icating the use after the change , an estim ate of the year ofchange, an estimate of the area of the site, and (in the case of residentialdevelopment) an estimate of the number of dwellings demolished and thenumbe r of uni ts buil t As shown in Tab le 9.1, these da ta ind icate that in theyears from 1990 to 2000 (inclusive), 1.45 million houses were built in Eng-land on 586 square kilometers of land (i.e., at an average density of 24.7units to the hectare) It is important to note at the outset that the impliedannual rate is historically low, although the scale of development is of thesame order of magnitude as that required to meet household projections (e.g.,DoE, 1995) or that suggested by the Barker (2004) review

Digital boundaries of physical urban areas are generated for CLG by OS

on the basis of a series of rules The rules are used to aggregate parcels onthe basis of their use and the distance between them Any parcel on a basic-scale map is treated as being in either urban or rural use The classificationused is the same as that in LUCS, the individual uses being arranged intothese two divisions Parcels in urban use are then joined with their neigh-bors or other such parcels within 50 m to form areas of urban land Open landtotally surrounded by an area of urban land (such as Hampstead Heath orRichmond Park in London, or Sutton Park in Birmingham) is also treated asforming part of it (Under the 1991 definition, a subset of these areas of urbanland are deemed to be urban areas.)

Simply overlaying LUCS point data on the OS 1991 urban area polygonsreveals that over the 1990s, in the order of 57% of new dwellingswere accommodated within those urban areas (Table 9.1).* Although

* In the case of the boundaries produced by OS for use with the 1991 census, a distinction was made between areas of urban land and urban areas An urban area for this purpose was defined

as an area of urban land that impinged on four or more enumeration districts (the smallest units for which 1991 census data were released) This implied a variable lower limit to the population

of urban areas (between 1000 and 2000 persons) The boundaries produced for the 2001 census encompassed a far larger group of settlements For this study the term urban areas refers to physical settlements treated as urban areas in 1991 and with a 1991 population of 2000 or more.

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TABLE 9.1

New Dwellings Built and Housing Land Developed, 1990–2000, England: Urban Areas (UAs) and Elsewhere

Year Units Hectares Density Units Hectares Density Units Hectares Density Units Land

2004 Definitions

Total(4) 698,284 H(RU4) 34,752 L(RU4) 20.1 750,005.9 H(UA4) 23,880.2 L(UA4) 31.4 1,448,290 58,631.9 24.7 51.8 40.7 Note: The year-by-year values and Total(1) values have been calculated by treating LUCS data as points and overlaying them on urban area polygons The values for Total(2) have been obtained by treating LUCS data as points and overlaying them on a 100-m grid derived from the urban area polygons The values for Total(3) have been obtained by spreading LUCS data across a 100-m grid as described in Section 9.4 and overlaying the derived values on a 100- m grid derived from the urban area polygons.

there are no quantitative targets for the proportion of housebuilding to beaccommodated within existing urban areas, it appears that these areaswere able to absorb well in excess of 800,000 new dwellings in the period.The table appears to provide a substantial degree of comfort to thoseanxious to realize the government’s goal of ensuring that by 2008, 60%

of new housebuilding is accommodated on previously developed sites.(Note that this table says nothing about previously developed sites per se.)Those practitioners and commentators who remain profoundly skeptical

of the realism of such targets might also find within Table 9.1 some cation for their position They might question how long this pattern ofdevelopment might be sustained, pointing out that while more than two-thirds (68%) of new dwellings appear to have been accommodated inurban areas in 1990, this proportion fell steadily through the decade, sothat only half of all new dwellings were being accommodated in this way

justifi-by 2000 (Figure 9.1) Moreover, it appears that less than half of all building land was found within the confines of urban areas as they hadstood in 1991, and that this proportion too followed a distinct downwardtrend This is consistent with the familiar view that with the passage oftime it becomes progressively more difficult to identify sites within theurban area

house-Accommodating housing with urban areas LUCS 1990–2000

Unravel ing these mixed me ssages and draw ing out their im plication sdemands a mo re thorough examin ation of the evidenc e, questio ning theusual nar ratives more closel y, and depl oying GIS more creativel y Table 9.1moves only a tiny step towards understanding how new dwellings havebeen accommodated or the extent to which they might be accommodated inurban areas in the future The rest of this chapter attempts to move succes-sively closer to definitions that are substantively meaningful in policy terms.This first definition of urban areas will be called UA1 The number ofdwellings accommodated within the 1991 urban areas will be referred to

as H(UA1), and the corresponding area of land developed L(UA1) quent definitions of urban areas will be referred to as UA2 and so on, thegeneral case being termed UAi (and the corresponding rural residual RUi).Development within UAi will be referred to here as urban consolidation(accommodating additional households within existing urban areasthrough either infilling of green pores or recycling of previously developedsites) In the 1990s, debate counterposed such urban consolidation againstrural land conversion in the form of either urban extensions (UXi) or ofnew settlements (NSi) (e.g., Breheny et al., 1993) It is, of course, usuallyassumed that demand can be diverted between these different contextsand so it is impossible to understand the volume of new dwellings beingaccommodated in cities in isolation As a next step we attempt to partitionthe total number of dwellings built over the 1990s, H(TO), into thesecomponents

Areas and Beyond

As the very idea of urban extensions embodies the metaphor of the city aspolygon, elementary GIS operations should in principle allow for theirdirect measurement and for examination of their contribution to the housingland supply Urban extension polygons might be defined as a subset of thedifference polygons created by overlaying the urban area polygons defined

by OS for use with the 2001 census with those for 1991 (defining UX1) Newsettlements (NS1) might be represented by urban area polygons not present

in 1991 but found in 2001 Urban consolidation would occur in the polygonsforming the intersection of the two sets (UA1) This simple geometric logicdemands the recognition of two further types of circumstance which aremore marginal to policy discourse The first is represented by differencepolygons referring to land considered urban in 1991 but not in 2001 Thesemight be thought of as urban contraction polygons The second comprises

an outside remaining rural throughout also represented by a polygon (or inprinciple more than one) This last class of circumstance thus constituteswhat might be termed as an exurban context (specifically XC1) The number

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of new dwellings accommodated in each of these contexts might in principle

be assessed by overlaying LUCS point data on the polygons defined Thus

H(TO)¼ H(UAi) þ H(RUi)or

H(TO)¼ H(UAi) þ H(UXi) þ H(NSi) þ H(XCi)

FIGURE 9.2

Urban area polygons in 1991 and 2001 Detail from part of the Nottinghamshire Coalfield Note: Because of the procedural rules used to define urban area polygons (see text), they are very convoluted Comparison of polygons for 1991 with those for 2001 provides a clear indication of urban expansion (see for example areas of expansion such as those marked ‘‘X’’

on the western fringe of Ravenshead Although they are not consistent with the notion that changes to urban use are fundamentally irreversible, areas of urban contraction are also found (such as those marked ‘‘c’’ above) While some of these appear to reflect change on the ground, other change appears to reflect differences of view This seems particularly clear in the inset which shows the south-western limit of Sutton-in-Ashfield Here differences in the western settlement margin appear to reflect a digitzing decision, and the minor contraction along the southern limit an arbitrary decision that in 1991 the A38 dual carriageway should be included within the urban area although it was excluded in 2001 The apparent contraction (c) seems to reflect a change of view, whereas the expansion (d) seems consistent with change on the ground, though the apparent contraction (e) seems to arise from another change of view.

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In practice, such partitioning proves troublesome, both computationally andconceptually Computational difficulties arise in overlaying two large sets ofhighly convol uted bound aries (Figure 9.2) The conceptu al dif ficulti esbecome apparent in examining the nature of change between the polygonsrepresenting the urban extent in 1991 and 2001 urban area polygons.Broadly speaking, such changes may arise

. Where building operations or changes of use imply an extension

or contraction of the urban area on the basis of the definitionalrules used by OS

. Where there is a difference in view of how the definitional rulesshould be applied

. Where there is a difference in view of the appropriate relationshipbetween polygons and intensional settlements

The first source is, of course, the focus of immediate interest As CLG andits predecessors have taken the view that rural to urban land conversion isessentially irreversible, it is not clear from a policy perspective how urbancontraction polygons should be treated In what follows, development thatoccurs within them is regarded as being within the urban area UA1 Differ-ences of view become apparent when changes in the urban area polygons areexamined in relation to (unchanging) detail of basic-scale maps This secondsource of change is not necessarily conceptually difficult, but although po-tentially resoluble implies that measures of change based on the polygondatasets are not simply attributable to change in the built environment.Consistent with the expedient adopted with respect to urban contraction,areas viewed as urban in 1991 are treated here as remaining urban thereafter.The third source of change raises more fundamental issues deriving fromthe general relationship between bounding and naming, which can onlyreceive the most cursory treatment here (but see Jubien, 1993; Bibby andShepherd, 2000; Bibby, 2005) The term intensional settlement is used here torefer to the places that those involved in the policy process talk about, or(more strictly) have in mind (On intension, see Searle, 1995.) Despite therationale for defining OS urban area polygons set out above, there is not aone-to-one correspondence between them and the named places assigned aunique identifier in the datasets Many intensional settlements (e.g., Lincoln

or Milton Keynes) are represented by more than one polygon Although thepolygons discussed approximate physical urban areas, unstated intensionaldefinitions are in fact privileged Conversely, a single contiguous area ofurban land may be partitioned into several adjoining polygons Hencealthough the physical definition of London does not extend to its adminis-trative boundary in some places (e.g., LB Bromley) and extends beyond it inothers (e.g., LB Hillingdon), boundaries between the boroughs are imposed.The relationship between naming and bounding gives rise to a range ofcuriosa Changes in view of the appropriate relation between places andpolygons may, for example, alter apparent population sizes Critically, the

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add ition of a new urban are a polyg on cann ot signal develo pment of a newsettlem ent without regard to the nam e or urban-area seria l number attache d

to the place Automated application of the city as polygon metaphor withinGIS might potentially lead to interpretations too literal to be valuable for policypurposes (for example, placing too much emphasis on literal physical connec-tion or disconnection and lacking real implications for likely travel patterns).Recourse to the intensional definitions mitigates this, providing a first illustra-tion of how policy monitoring entails the management of metaphor

For pre sent purposes , it wi ll be suf ficient simply to flag each LUCS recordwith an indicator showin g wh ether it fell within any urban area polygo n: (a)

in 1991 and (b) in 2001 Thi s allows dir ect as sessment of the ro le of urbanconso lidation H(UA1) Using lower cas e letters to express the role of par-ticu lar contex ts (e g., UX1) rela tive to Eng land as a wh ole (TO ), it also allowsass essment of pro portion of new dwel lings (h(U X1)) and of hous ebuildin gland (l(UX 1)) within implie d urb an extensio ns wh ile abs tracting from thedet ails of the geome try of these areas The identi fication of dwel lings in newsettlem ents is necessaril y slight ly more compl ex, as it mu st take into accountnot only of the matt ers just discussed , but also must recognize that polyg onsappear ing for the first time in the 2001 dataset may dem arcate preexis tingsettlem ents It is theref ore not possi ble to def ine in practice a new settlem entcontex t on the polygo n logic (NS1 in pri nciple) The approach taken, there-fore, involved first iden tifying candid ate new settlem ents (denote d byurba n area polygons with cod es appear ing for the first time) and thenover laying these areas on concen tration s of new housebui lding evident inLUCS This was accomplished in fact by using a hectare grid representation

of both the OS urban areas and the LUCS data, producing a definition ofnew settlement contexts (NS3) compatible with definitions of other contexts(UA3, UX3, and XC3) introduced below

This implies an imperfect geometrical distinction between urban extensionand new settlement, and so in the summary entries in Table 9.2, building innew settlement has been subsumed within UX1 It is immediately clear, how-ever, that the contribution of new settlement to accommodating dwellings inthe 1990s was minimal (NS3) Only five urban areas identified in the 2001urban areas dataset but not in that for 1991 had concentrations of residentialdevelopment in the 1990s These were Cambourne in Cambridgeshire, DickensHeath within Solihull District in the West Midlands, Whitley in Hampshire,Cotford St Luke near Taunton in Somerset (a new village on a former hospitalsite), and Hatton near Warwick, a village of mediaeval foundation with aformer hospital site identified as a local growth point in the Warwick DistrictLocal Plan (Warwick District Council, 2003).* Using the grid method, it is

* Other locations identified in applying the procedure that strictly fail to meet the criteria are Millisons Wood (in the West Midlands), Dunkeswell near Honiton in Devon, Southfields in Essex Thameside (Thurrock), and Tanfield (a village abutting the urban area of Cheshunt in the Hertsmere District of Hertfordshire).

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TABLE 9.2

New Dwellings Built and Housing Land Developed, 1990–2000, England: by Mode of Accommodation

Units Built, 1991–2000 Land Developed, 1990–2000 Densities Achieved, 1990–2000 Outside

UAs 2001

Inside UAs 2001 Total

Outside UAs 2001

Inside UAs 2001 Total

Outside UAs 2001

Inside UAs 2001 Total

Outside UAs 1991 205,505 422,059 627,564 14,248.67 17,311.17 31,559.84 14.4 24.4 19.9 Inside UAs 1991 5,256 815,613 820,869 261.16 26,816.13 27,077.29 20.1 30.4 30.3 Total 210,761 1,237,672 1,448,433 14,509.83 44,127.3 58,637.13 21.6 25.7 24.7

Outside UAs 1991 14.2 29.1 43.3 24.3 29.5 53.8 Exurban development Urban extension

Inside UAs 1991 0.4 56.3 56.7 0.4 45.7 46.2 Reclassification Urban intensification

Urban consolidation 820,869 56.7 UA1 27,077.3 46.2 30.3

TABLE 9.2 (continued )

New Dwellings Built and Housing Land Developed, 1990–2000, England: by Mode of Accommodation

Units Built, 1991–2000 Land Developed, 1990–2000 Densities Achieved, 1990–2000 Outside

UAs 2001

Inside UAs 2001 Total

Outside UAs 2001

Inside UAs 2001 Total

Outside UAs 2001

Inside UAs 2001 Total

Totals( 2)

Exurban development 170,910 11.8 XC2 11,789.8 20.1 14.5

Urban extension 399,874 27.6 UX2 16,643.6 28.4 24.0

Urban consolidation 877,401 60.6 UA2 30,194.8 51.5 29.1

Overall 1,448,185 100.0 58,628.2 100.0 24.7

Totals( 3)

Exurban development 185,628 12.8 XC3 12,597.9 21.5 14.7

Urban extension 378,888 26.2 UX3 15,530.5 26.5 24.4

Urban consolidation 883,774 61.0 UA3 30,503.5 52.0 29.0

Overall 1,448,290 100.0 58,631.9 100.0 24.7

2004 Rural Definition

Exurban development 398,165 27.5 XC4 22,949.6 39.1 17.3

Urban extension 300,120 20.7 UX4 11,802.1 20.1 25.4

Urban consolidation 750,006 51.8 UA4 23,880.2 40.7 31.4

Overall 1,448,291 100.0 58,631.9 100.0 24.7

Note: The crosstabulated values and Total(1) values have been calculated by treating LUCS data as points and overlaying them on urban area polygons The values for Total(2) have been obtained by treating LUCS data as points and overlaying them on a 100-m grid derived from the urban area polygons The values for Total(3) have been obtained by spreading LUCS data across a 100-m as described in Section 9.4 and overlaying the derived values on a 100-m grid derived from the urban area polygons.

estimated that new settlements accommodated barely more than 2000 unitsover the period [or 0.2% of all dwellings; H(NS3) is 2207; h(NS3) is 0.2%].Planning practitioners and analysts are unlikely to be surprised by thenature of these results (whatever the precise numerical values) Whateverdefinition might be adopted, enthusiasm for prospective new settlementswaned over the decade, given the difficulties of overcoming risk on the onehand and public opposition on the other It should also be clearly under-stood, however, that in contrast to the presumptions of more journalisticcommentators, the 1990s witnessed the accommodation of more than 1.45million new dwellings, without the construction of new settlements Equallyimportant in considering appropriate policy responses to household projec-tions or to the recommendations of the Barker (2004) review, it becomesimportant to examine quite how such an apparently implausible volume ofdevelopment has in fact been accommodated historically.

While the minimal role of new settlement should occasion no surprise, it

is evident that new settlement and urban extension together accounted forbarely more than one house in four [h(UX1) is 29%] Despite the traditionalnarrative, the number of houses accommodated through urban consolida-tion was almost double that in new settlements and urban extensions.Besides urban consolidation, which accounts for 57% of new dwellings,there remains, however, a further component of accommodation, termedhere exurban development Although in GIS-based analyses it is not pos-sible to ignore development in such contexts, it is relatively little discussed

by policy makers and practitioners Exurban development appears toaccount for some 200,000 dwellings in the 1990s [h(XC1) is 0.14] Suchdevelopment seems, moreover, to have proceeded at particularly low dens-ities (14.2 dwellings to the hectare on average) and thus to account for adisproportionate share of all land developed for housing [l(XC1) is 0.243].Examination of the potential for urban consolidation cannot be reducedsimply to assessing the capacity of urban areas (on the one hand) andunderstanding (on the other) the competing attractiveness of sites withinthe urban area and those which would extend it The scale and character ofexurban development may imply a threat to urban consolidation anddemands more sustained analysis It therefore seems important to take care

to see just what sort of development is involved—whether dispersed erties in sparsely populated areas, for example, or simply buildings veryclose to urban areas but deemed outside by imposition of a particular bound-ary It will also be necessary to have regard to the overall level of demand

As a first step in visualizing the pattern of development, and in trying tounderstand the reasons for place-to-place variation in the relative import-ance of the modes of accommodation, it is convenient to transform the data

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to a fine (100 m) gri d (each ce ll rep resenting one hectar e) Mapping the rawdata points for LUCS is not hel pful as som e 223,538 observ ations und erliethe summar ies in Ta bles 9.1 and 9.2 Grid repres entation form s the basis forthe remain der of the analys es in this chapte r Conver ting the 1991 and 2001urba n areas to hectar e gri d rep resentat ions forms the ba sis for a reviseddefin ition of contex ts UA2, UX2, XC2 allowin g identi fication of urbanconso lidation, urb an extensio n, and exurban devel opment, respecti vely.Tab les 9.1 and 9.2 therefo re also sho w along side estimates mad e on apoint -in-poly gon basis (UA1, UX1, etc.) varia nt estim ates suc h as H(UA 2),L(U X2), etc cal culate d havin g conve rted the phys ical ur ban areas to a 100-mgrid wh ile still treating the LUC S data as point s The figures marke d UA3,UX3, and so on are estim ates calculate d havin g co nverted bot h the physicalurba n area and the LUCS da ta to 100 m grids , with the LUCS measure sspre ad as describ ed abov e.

As LUC S data point s refer to parce ls of very dif ferent size, som e pre ces sing is required Whil e with in LUC S the me dian reco rded size of lan dparce ls devel oped for residen tial use is 0.1 ha, 3.8% of point s refer to parcel sgrea ter than 1 ha in ex tent, wh ich accou nt for 23.1% of units Obvio usly, if

pro-a LUCS point re fers to pro-an pro-arepro-a gre pro-ater thpro-an 1 hpro-a, it mus t overflo w its cell

To offset this, areas of develop ment in exces s of 1 ha must be locally spre ad

as the grid is created (imp lemente d here using Prolog) a lthough the conf urati on of the sites is unk nown In the discuss ion of earlier sections , wherethe relation betwee n LUCS records and urban areas has been reduced to thepoint -in-poly gon trope, this prob lem has sim ply been igno red

ig-Exam inatio n of Ta bles 9.1 and 9.2 shows that spre ading out the LUCSdata has only a mo dest ef fect on interpret ation of how gro wth is accomm o-date d [compar e H(XC 2) and H(XC 3), for exampl e] Convers ion of the urbanarea polygo ns does, however , have an impa ct on the overall pict ure Table9.1 sho ws that the propor tion of hou sebuil ding accomm odated with in theurba n areas appear s to rise from 57% [h(UA1) ] to 61% [h(U A2)] as arou nd60,000 dwel lings are reclassi fied This stands as a war ning of the poten tialsens itivity of any partitio ning of mod es of accommo dation to the preciseplacem ent of the urban area bound ary

Figure 9.3a attemp ts to show variatio ns in inte nsity of develop ment athectar e cell leve l, and dem onstra tes the im possibi lity of graspi ng the patternwith out some gen eraliza tion This can be achieved by usin g movi ng spatialave rages, thereb y commuti ng actual housing output in ce ll q [denoted hereH(TO( q,0)) ] as sho wn in Figure 9.3a, to a tendenc y to develo p over aparticular radius r around cell q The tendency to develop within 2000 m

of cell q [denoted here H(TO(q,2000))] is shown in Figure 9.3b Whenrepresented as a 2-km moving average in this way, the pattern of develop-ment in the 1990s becomes immediately obvious Areas with limited orhighly dispersed housing development are shown by the lightest shades(up to 1% of the land area being developed for housing over the period) Intracts with the deepest grays more than 30% of the area was developed forhousing in the period

ß 2007 by Taylor & Francis Group, LLC.

CH—Chafford Hundred IB—Ingleby Barwick Wo—Worcester BS—Bradley Stoke MK—Milton Keynes

0–0.5

Wh Wh

CH CH

MK MK

BS BS

Wo Wo

IB IB

(b) (a)

Images suc h as Figure 9.3b provide the bas is for a more intuiti ve grasp ofthe relati ve impor tance of differe nt mode s of accomm odation Figu re 9.3 bmake s it a little easie r to visuali ze the co ntribution of diffuse exu rbanhou sebuil ding alon gside the rather smal l numbe r of majo r urban extensio ns[inclu ding Milton Keyn es (MK), Bradle y Stoke (BS) on Bri stol’s north fringe,

Ch afford Hundre d (CH) in Kent Tham eside , Worceste r (Wo), or Ingleb yBarw ick (IB) near Stockt on on Tees ] The new settlem ent at Whitl ey (Wh ) inHam pshi re is also evide nt, as are the large st areas of ur ban consol idation ,e.g., London Docklands

App lying spat ial averagi ng at the 2-km scal e to the ind ividual mod ation mod es (u rban co nsolidat ion, urb an extension, and exurbandevel opment) allows visuali zation of geogr aphic variatio n in theircontr ibution— see Figure 9.4a–c fo r H(UA 3(2000)), H(UX3( 2000)), and H(XC3( 2000)), respecti vely The mi nimal contr ibution of urban extensio n(Fig ure 9.4b ) arou nd Londo n (mor e clearly evident in Figure 9.5a), despi tethe volu me of uni ts constr ucted (Figure 9.2c) , must be unde rstood primaril y

accom-in relation to Green Belt policy Figure 9.4b illustrates the somewhat largercontribution of urban extension to accommodation of new housebuilding inthe Midlands, highlighting in particular the continuing expansion of Telford(a growth pole), and the expansion of Leicester (a city without a Green Belt)while once again suggesting the influence of Green Belt policy in limitingurban extension (around Birmingham, for example) More generally, the use

of moving spatial averages calculated over different radii allows patterns to

be analyzed and displayed at different scales, demonstrating the overalldispersion of development

and Market Relations

In order to understand these patterns, to assess whether they are in any wayremarkable and most critically to begin to assess the limits to urban con-solidation, a more analytical approach is required The balance betweenurban consolidation and development elsewhere will depend in part onthe capacity of urban areas to intensify (an issue which has been brought tothe fore in policy terms), but also in part on the extent of development atcompeting sites at the urban fringe, or beyond Departing a little from thedominant narrative, this section has regarded not only the geographicstructure of a locality and planning policy considerations, but also themarket for housing land Actually representing markets within GIS is,however, a significant challenge A market might (in the spirit of Cournot,1838) be thought of as a conceptual space in which free communicationensures that identical goods command identical prices, but this begs thequestion of what is to be treated as identical and what is to be considered(geographically) unique Moreover, the basic devices of market analysis are

ß 2007 by Taylor & Francis Group, LLC.

(a) Urban consolidation (b) Urban expansion (c) Exurban development

0–0.05 0.05–0.1 0.1–0.15 0.15–0.2

> 0.2

Additional dwellings per hectare

London Midlands

Harlow

CH

Birmingham Bracknell

concerned with the realm of intension—quantities which actors might wish

to provide or purchase at particular prices, whereas GIS are concernednecessarily with extension; that is, with dateable, placeable parts of thephysical world (Bibby and Chowdry, 2001) So although the current frame-work of government policy refers to spatially delimited housing markets(ODPM, 2004), the structural metaphors of economics do not always easilyfit with those of GIS Resolving these contradictions would constitute aproject of substantial practical and theoretical significance On the relationbetween GISc and econometrics generally, see the work of Anselin and hiscollaborators (Anselin, 2000, 2001; Anselin et al., 2004.) This is beyond thescope of this chapter

To facilitate critical reflection on observed patterns, and to explore therelation between these two styles of thought (and their corresponding struc-tural metaphors), this section pursues a much more modest goal It exploreswhat might be involved in assessing geographic variation in the degree ofurban consolidation that might realistically be expected on the basis both ofgeographic structure and of market conditions Founding expectations onevidence is clearly more complex than merely providing evidence of out-comes The unit of analysis remains the hectare cell Spatial averaging is used

to create variables that generalize various quanta (e.g., housing output andhouse prices) over a 10-km radius around each cell, so that England iseffectively analyzed as 13 million overlapping circles The analysis appeals

to a set of hypothetical circumstances, referred to as the 10-km radial model(10 KRM) Units constructed are assumed to vary in both plot-size andbuilding footprint but to follow standard dwelling types and layouts, inci-dentally implying the development of residential enclaves of fundamentallysimilar character They are assumed to occupy land of homogenous quality,but located alternately within the urban area as at 1991 (UA3) or outside it(RU3) [recall that H(RUi)¼ H(UXi) þ H(XCi)] Under these assumptionshousebuilding thus implies the construction of suburbs, albeit that theymay be discontinuous suburbs Except in the presence of supply constraint,prices and quantities for each cell, as averaged over 10 km, are assumed toreflect market equilibrium Rather than assuming geographically boundedmarkets a priori, conditions of demand and supply, and hence the position ofthe demand and supply curves, are assumed to change continuously fromcell to cell Elasticities are assumed to reflect more fundamental behavioralchoices, conditioned by broader social values and hence to be constant acrossall cells From this perspective, it would be the failure of these relations thatwould necessitate the identification of local markets

The equilibria of the 10 KRM refer solely to two hypothetical homogenousgoods: housing land and housing space Actual outcomes will differ because

of the varying relationship between the imaginary homogeneous good andunits actually traded, and will depend on the specific characteristics ofindividual parcels and of their immediate environment Nevertheless, 10KRM should provide a benchmark identifying variability at the 10-km scale

to which shorter wavelength variation might be added

ß 2007 by Taylor & Francis Group, LLC.

This model embodies a series of more specific assumptions:

(i) That markets for housing land reach equilibrium in such a mannerthat within (overlapping) areas of 10-km radius around each hec-tare cell demand is equal to supply at the reigning price, except inthe presence of supply constraints

(ii) That the demand for additional housing units in each cell stemsfrom the formation of new households, and thus is a function ofthe distribution of existing households (no allowance being madefor the formation of additional households headed by peopleliving further afield)

(iii) That the demand for additional housing units is price and incomeinelastic, but that the demand for housing space per unit decreaseswith its price per square meter and increases with income

(iv) That the supply of urban land for housing is a function of the stock

of urban land and increases with the price of land

(v) That the supply of rural land is a function of the stock of land free

of planning constraint and also increases with the price of land(vi) That the demand for housing land is derived by reference to thedemand for housing units and the demand for housing spaceFollowing this logic, if supply is not constrained (e.g., by the planningsystem), the number of housing units constructed within each overlappingcircle would be fixed by virtue of assumptions (ii) and (iii) Location withinthese circles would not, however, and together with the area of land to bedeveloped for housing would depend on the intersection of a demand func-tion based on assumption (vi) and a supply function based on assumptions (iv)and (v) On the assumption of identical character (and price), the equilibriumbalance between development on urban and rural sites would be given bytheir contribution to supply at the relevant point on the overall supply curve(reflecting their different conditions of supply) The following paragraphswork through the assumptions of 10 KRM, attempting at each step to illustratethe relationships that seem to hold and to consider their implications.Assumption (i) is not directly testable It provides the logical link betweenextension and intension, allowing observable housing output across a circle

of 10-km radius to indicate the demand for housing units and housing space

at a given price—a hypothetical relation that is not directly observable Italso allows housing output to indicate supply in those circles where the flow

of housebuilding land is not constrained.* Spatial averaging at the 10-km

* It is often assumed that the planning system constrains the residential land supply (or even that the supply of residential land is fixed) While it is true that England’s area is roughly static,

it does not follow that the flow of land that owners will wish to make available for a given use at

a particular time is static Neither is it self-evident that planning designations actually constrain supply (in this sense) below its free market level This is a matter to be determined empirically (DoE, 1992).

ß 2007 by Taylor & Francis Group, LLC.

scale is intended to acknow ledge substitut ability of sites and hence thepossib ility of diver sion of dem and with in that radius *

Assump tion (ii), that dem and follow s the existing distr ibution of hold s and dwellin gs, appear s vind icated The stock of hou seholds has beenestim ated by using the gri d referen ces on the postc ode addres s file (PAF; avirtual ly comp rehensive reg ister of postal addres ses) to ass ign each address

house-to its corresp onding hectar e cell y The re is a close relati onship betwe en thestock of dwellin gs within 10 km of a ce ll [O(TO (q,100 00))]—t he deriv edstruct ural variable —and the number of addition al units compl eted within

10 km of that cell [H(TO (q,100 00))] This account s for 87.65% of the variabi lity of the volu me of new hou sebuil ding It a ppears that the number of newdwel lings built over the 1990s was typically equivale nt to 5.2% of the stock

-of dwel lings in 2001 (imp lying a 5.47% increase rela tive to the start ingstock)

Regr ession estimate s based simply on geogr aphic struct ure (ignorin gprice effects) highlight concen trat ion in and arou nd the majo r ci ties (Figure9.6) The evident strengt h of this rela tionship sho uld temp er any tende ncy toposit a contrast bet ween uni form decline in the No rth and rapi d urbanexpansi on of the Southeast No immed iately obvio us Nort h–South gradi ent

is appar ent in the reg ression residual s It is clear, moreove r, that dema ndaround som e north ern ci ties (e.g , Liverpool , Man chester , and Leeds) is

in fact higher than might be expecte d on the basis of the exi sting stock

of hou seholds, thoug h this is not true a round others (e.g , Sheffield orBirmingh am)

The findi ngs underscor e the nece ssity of havin g reg ard to both absoluteand relative change in plan ning poli cy analysi s (although this proves dif fi-cult in practice) The relati onship displ ayed in Figure 9.6 underpi ns theabsolute change in numbers of dwe lling units It highli ghts localitie s withsubsta ntial pop ulation s, and if the prime concer n is with accomm odatinggrowt h this shou ld be pr edominant bec ause the effect of place-to -placevariatio n in the stock of hou seholds dwar fs place- to-pl ace va riation inrates of growt h (de parture from the regressi on parameter estim ated as

* This device is intended to capture a situation of local spatial competition, but without imposing the housing market The assumed spatial scale at which equilibrium is reached might be compared with the modal journey to work distance from the 2001 census (10.79 km).

y The generally close relationship between the distribution of households indicated by PAF [O (TO(q,0))] and that evident from the 2001 census is clear from Figure 9.13 This high level of matching confirms the potential value of PAF in monitoring change in land-use intensity Figure 9.13b was constructed by converting an ArcView shape file representing Census Output Area boundaries to a 100-m grid with household density being calculated for each Output Area

on the basis of its original geometry Figure 9.13a was produced 2 years before from the 2001 Quarter II PAF From the grid-analytic perspective of this chapter, it seems appropriate to think

of statistical reporting units such as OAs, or units of administrative significance (such as postcode sectors) as averaging underlying data over an irregular area of typical but varying radius The value of each cell q in Figure 9.13b might be thought of as O(TO(q,c)), c denoting the average radius of a Census Output Area.

ß 2007 by Taylor & Francis Group, LLC.

(a) Predicted units (b) Residual units Dependent grid:

H(TO(q,10000)) Independent grid: O(TO(q,10000))

0.0078 in Figure 9.6) A focus on variation in rates of co nstructi on re lative tothe dwel ling sto ck, by co ntrast, highli ghts a tract of lan d stretchi ng fromDevon to Lincolnsh ire referr ed to a s Hall ’s Golden Belt (Figure 9.7; see alsoBibby and Sheph erd, 1991, 1996) It is impor tant not to be mi sled by hig hrates of gro wth, howe ver intere sting they may be as an indicato r of theleading edge of chang e Cruc ially, areas such as the economi cally buoyan ttract to the west of Londo n, where Hampsh ire, Su rrey, and Berks hire meet,posses sed substa ntial stocks of exi sting hou seholds and exper ienced hi ghgrowt h rates This unde rlies the pattern of positi ve residuals (Fig ure 9.6).Moreo ver, as 10 KRM takes no account of growt h originati ng beyon dthis dista nce, ot her positive residual s ine vitably highli ght the princip alpolicy-dr iven gro wth centers (su ch as Milton Keyn es).

Assump tion (iii) posits that overal l demand for addition al hou sing unitsdoes not vary with the price of uni ts, but that the demand for ho using spaceper unit decreases with its price per square meter and increases withincome This assumption thus redresses the exclusive emphasis on

M(TO(q,10000))

0–350 m 2 350–500 m 2

> 500 m 2

FIGURE 9.7

Inferred plot size (m 2 ).

ß 2007 by Taylor & Francis Group, LLC.

struct ural conditio ns in assump tion (ii) by introduci ng pr ice and incomeeffects in a st raightforw ard way.

The amount of inferred space sough t per un it in cell q is estim ated sim ply

as the recipro cal of the densit y of new housin g im plied by LUC S, that is

M(TO(q ,10000) ) ¼ 1=W(TO(q, 10000) )

wh ere

W(TO(q, 10000) ) ¼ H(TO( q,10000) )=L(TO (q,100 00))

wh ile the price of ho using space is estim ated simply by dividing the appr pri ate estimate of dwe lling pri ce by the appro priate estim ate of housin gspac e:

o-P(M( TO(q,10000 ))) ¼ P(H(T O(q,10000 ))) =M(TO (q,10000))

Per haps the emp hasis within policy circles on leverin g up densities,* andhenc e depressing space per unit, account s to some degr ee for the verylimited discus sion of the (qu ite start ling) geograph ic variations in the ho us-ing space measure, M(TO(q ,10000) ) as evident in Figure 9.7 Throu gh the1990s there was not onl y a shar p co ntrast bet ween the densit y of newdevel opment in Lond on and den sities in the pro vinces, but also a veryclear distin ctio n bet ween what mig ht be termed the generali zed urb anreal m (see Se ction 9.5 below) , and elsewh ere This reflects and rein forcesthe conclu sions draw n in relation to Ta ble 9 1 Most obvious ly, the large st(inferred) plots were found in sparsely populated or remote areas, asdefined below, such as North Devon, the Welsh Marches, north Northum-berland, much of Lincolnshire, and parts of East Anglia, where densitiesachieved were only a quarter of those found in London It also appears thatdensities may be typically lower in Hall’s Golden Belt

Estimation of price effects must rest on estimates of the price of alldwellings, as estimates of average prices for new dwellings are not distin-guished in the published data The available information derives from HerMajesty’s Land Registry, which releases average transaction value by prop-erty type for postcode sectors (areas with typically 2655 households) on aquarterly basis The overall average for each sector was initially assigned toall occupied hectare cells within it to estimate P(O(TO(q,s))), where s refers

to the average radius of a postcode sector These values were then averagedover 10 km to estimate [P(O(TO(q,10000)))] for each cell q

The income measure is based on modelled estimates of average hold income for electoral wards for 1998–1999 due to the Office of NationalStatistics (ONS, 2004) Analogously with house price data, the estimatedvalues were initially assigned to all occupied hectare cells within therespective wards, to estimate y(q,w), where w refers to the average radius

house-* Since the mid 1990s, U.K policy has sought to increase density, depressing space per unit This has been pursued particularly vigorously since publication of revised PPG3 in 2000 and Prescott’s Birmingham statement in October 2002.

ß 2007 by Taylor & Francis Group, LLC.

of a ward These values were then averaged over 10-km to estimate averagehousehold income for an area centered on each hectare cell [y(q,10000)].Regression evidence provides some justification for assumption (iii).Under certain conditions, the observed relation between the amount ofhousing space and price must be interpretable as a demand function Thiswould seem to be the case where (a) the typical sizes of (inferred) plotssought by housebuilders and householders both fall as the price of landrises, and (b) the supply of land for residential development increases withits price per hectare, but does not respond directly to the number of unitssubsequently developed On the basis that such a housing space demandfunction will take a loglinear form

ln(D(M(TO(q,10000))))¼aþh(ln(P(M(TO(q,10000)))))þb(ln(y(q,10000)))þ«and estimating the price of housing space in each cell by reference to theprice of semidetached property we obtain

ln(D (M(TO( q,10000) ))) ¼ 5:132 9  0: 4538(ln (P(M (TO(q,10 000)))) )

þ 0:5690(l n(y(q,1000 0))) þ « (9: 1)The estim ate of the price-el asticity of the dem and for housing spac e derivedusin g 10 KRM is broad ly co nsistent with those obtaine d usin g conven tionalappr oaches (e g., Ermisch et al., 1996 ) The mode lled dem and for spac e isillu strated in Figure 9.9 Estim ation of price -elasti city might be co nsidere d

as a first step in assessing possible limits to intensification The loglinearform of Equation (9.1) implies that elasticity is constant, i.e., that there is aconstant relationship between a percentage change in the price of space andpercentage change in the quantity of space sought Thus the same absoluteincrease in price where price is very high (e.g., in central London) wouldimply a much smaller reduction in the quantity of space sought than itwould where an average price prevailed Consider a household with aver-age income contemplating purchasing 333 m2(i.e., purchasing a dwelling in

a development constructed at 30 units to the hectare; the lowest density nowconsidered in PPG3 as commensurate with the efficient use of land), butfacing the prospect of paying an extra £1000=m2 On the basis of Equation(9.1), it would review its plans so as to seek only 203 m2, i.e., a plot in adevelopment constructed at 49.25 units to the hectare If, on the other hand,that household anticipated densities of 50 units to the hectare, the upperguide to the efficient use of land, it should expect to have to pay £1560=m2and the prospect of paying an extra £1000=m2would have a less markedeffect on its plans If Equation (9.1) held, it would prompt a reduction inspace sought to 160 m2 increasing equilibrium densities, but only to 62.6dwellings to the hectare It might seem appropriate to consider on the basis

of Equation (9.1), whether there is a point at which increases in price imply anegligible decrease in space sought (in absolute terms), thereby defining aneffective maximum density and hence a limit to densification It is perhapssurprising that estimates of price-elasticity based on 10 KRM and otherapproaches are similar given the degree of extrapolation involved, sincethe estimates of housing space in 10 KRM rest on whole houses recorded inLUCS and do not include measures of size for subdivided units which areevidently required to achieve the highest densities (in central London, forexample) Moreover, one might expect spatial averaging at this scale tofurther reduce the possibility of estimating elasticities compatible withthose based on individual survey data

When due account is taken of variation in the price of housing space and

of average incomes, expectations of the likely pattern of change in urbanform and in urban consolidation differ somewhat from those based onstructure alone The principal effects are clear The high price of housingspace in London and along corridors stretching southwards to Brighton onthe coast and westwards to Reading appears to have choked off demand.Income and price effects together combine to reduce expected density andincrease anticipated landtake in Oxfordshire, Berkshire, and Buckingham-shire to the west of London, where pressure from large household numbers

ß 2007 by Taylor & Francis Group, LLC.

0 2,000 4,000 6,000 8,000 10,000 12,000

is compound ed by high incomes Ne vertheless , the low pri ce of space alon gthe M62 corridor in Northern England (stretchin g from Liver pool , throughMan chester and Leeds to Hull) does not appear to have enco uragedincrease d consump tion of hou sing space.

Eq uation (9.1) , howeve r, account s for half the variance of the dem and forhou sing space ( R2 ¼ 0.499856 ) Thus, although the price of housing space[P(M (TO(q,10 000)) )], illu strated in Figure 9.8, and the leve l of hou seholdinco me exert influ ence on variations in density , they fail to acco unt forthem entirel y Even havi ng taken account of observ able variatio ns both

in the price of housin g space and in inco mes, it appear s that over the1990s there remai ned a contin uing tendenc y for ho using to be co nstructe d

at remar kably low densit ies in the rural dom ain In analy tic terms thisbrings into question the assump tion of a sin gle market for a homo genousgood, rai sing the possi bility that housing space in the country side shou ld betreated as a distin ctly differen t commodi ty from spac e at the urb an fringe(con tinui ng to set aside the possibi lity that it mig ht be nece ssary to define aset of geogr aphi cally limited market s with entirel y distin ct dema nd andsupp ly func tions) In policy terms it rai ses the questi on of whethe r thisexu rban develo pment prejudice d and might conceivab ly contin ue to preju-dic e the urb an emp hasis and the stres s on inte nsificat ion

Wi th assum ption (iv), concer n shifts from the dem and for housing space

to the supp ly of hou sebuild ing land It posits firstly a struct ural effect thatthe supp ly of urban hou sebuild ing land depends upo n the stock of urbanland with in 10 km Second it posi ts a pri ce ef fect Figure 9.10 clearly con-firm s the structural effect There is a very cl ose rela tionship a t the 10-kmscal e bet ween the extent of urban land in 1991 [i.e., G(UA3(q,1 0000)) ] andthe area of urban land devel oped for hou sing in the 1990s [S(L (UA3(q,100 00)) )], a ccountin g for 91.3% of the variabi lity of the latte r (Figure9.10) Genera lly, theref ore, it appear s that the exte nt to which hous ebuild inghas been accommodated on urban sites has varied in accordance with theextent of urban land Across England, the flow of housebuilding landcoming forward within urban areas was around 2.3 ha=km2of urban landover the period, or 0.2 ha=km2per annum

Although the flow of urban land for housebuilding responds primarily togeographic structure, assumption (iv) also allows that the rate at whichurban land enters the residential land supply should be expected to increase

as the price of housing land increases.* Any such tendency is important as itpotentially increases the scope for urban residential intensification (otherthings being equal) Exploring the validity of this assumption is difficult fortwo reasons The first is the paucity of available housing land price data Themost detailed information available is due to the Valuation Office Agency,and relates to their local office areas These data are very thin, however, and

* It is often assumed that supply of residential land is fixed While it is true that England’s area

is roughly static, it does not follow that the flow of land that owners will wish to make available for a given use at a particular time is static.

ß 2007 by Taylor & Francis Group, LLC.

Dependent grid: rsluclanin10k Independent grid: engua2k1091 Regression equation:

r2 = 0.9128

0–0.2 0.2–0.8