Is what you see what you get?: Post-development auditing of methods used for predicting the zone of visual influence in EIA

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Environmental Impact Assessment Review20 (2000) 537–556

www.elsevier.com/locate/eiar

Feature article

Is what you see what you get?Post-development auditing of methodsused for predicting the zone of visual

influence in EIAGraham Wood*

Oxford Brookes University, Impacts Assessment Unit, School of Planning, Gipsy LaneCampus, Headington, Oxford OX3 0BP, United Kingdom

Received 1 June 1999; accepted 31 January 2000

Abstract

Post-development auditing has been widely acknowledged as a means throughwhich EIA could fulfill its potential to “learn from experience.” However, thereremains a paucity of EIA audit research that focuses on the evaluation of specificpredictive methods, and to date advances in the development of audit methodologieshave been limited. In this paper a spatial approach to auditing techniques used forpredicting the Zone of Visual Influence (ZVI) of projects is developed. For a seriesof four audit case studies, relevant ZVI predictions are tested to evaluate theiraccuracy and to identify the extent of impact over-prediction, under-prediction,and the occurrence of no-error (i.e., the prediction is correct). Statistical modelsof the residual errors (over- and under-prediction) and the no-error classificationare then developed and interpreted to explore factors that explain the performanceof the predictive techniques examined. Drawing on the audit findings, a frameworkfor determining the likely accuracy of ZVI predictions is then developed based onthe relationship between the precision of the predictive method and the complexityof the landscape setting. Finally, conclusions are drawn and limitations of the auditapproach are highlighted. 2000 Elsevier Science Inc. All rights reserved.

Keywords: Post-development auditing; Zone of visual influence (ZVI); Predictive tech-nique evaluation

* Tel.: 100 (44) 01865-483942; fax: 100 (44) 01865-483559.E-mail address: [email protected]

0195-9255/00/$ – see front matter 2000 Elsevier Science Inc. All rights reserved.PII: S0195-9255(00)00055-X

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538 G. Wood / Environ. Impact Assessment Rev. 20 (2000) 537–556

1. Introduction

EIA is principally a predictive exercise that aims to identify and estimatethe characteristics and significance of impacts associated with a proposeddevelopment action or plan. The supply of such information to the decision-making process is intended to result in better project planning, more envi-ronmentally sensitive development, and ultimately enhanced environmen-tal protection [1]. However, the current emphasis on pre-decision analysisin statutory procedures means that EIA is failing to maximise its potentialto “learn from experience,” and despite the central role of prediction withinEIA, it remains the case that little is known about the actual performanceof predictive techniques. As Tomlinson and Atkinson [2] state “. . . it ispossible for predictive techniques to be transferred from one EIS to anotherwithout authors ever knowing how well the techniques perform, or howappropriate they are for the intended application.”

Monitoring and auditing the actual impacts that occur as a consequenceof development has been widely acknowledged as a means by which a“feedback mechanism” could be established in EIA, which would not onlyincrease knowledge on the effects of development in various environmentalsettings, but would also provide a basis for improving predictive techniquesand their use [1]. However, despite recent renewed interest and activityin EIA follow-up (e.g., [3–5]), there remains a shortage of research thatspecifically relates to the evaluation of EIA predictive techniques or thedevelopment of audit methodologies.

The typical subdivision of the environment into impact “themes” withinEIA (e.g., noise, air quality, water, flora and fauna, etc.) indicates thatauditing could potentially be undertaken for a variety of predictive tech-niques across a range of impacts. This paper focuses on post-developmentauditing of techniques used for predicting the Zone of Visual Influence(ZVI) associated with projects that have undergone EIA in the UnitedKingdom. The approach involves testing impact predictions made using arange of predictive methods, followed by the quantitative analysis of theoccurrence of impact over-prediction, under-prediction, and correct predic-tion. Thus, for a series of audit case studies impact prediction accuracy isassessed and the spatial distribution of errors (i.e., over-prediction, under-prediction, and no-error) is identified using a Geographical InformationSystem (GIS). (Clearly, the “no-error” class does not represent a true error,but is considered as a classification for simplicity and to facilitate subsequentstatistical modelling). Statistical models of the errors are then developedto examine factors that may serve to explain the relative performance ofthe techniques used to produce the original predictions. Using insightsgained through auditing and modelling the errors, a framework to explainthe accuracy of visibility impact predictions in EIA is then developed,before drawing conclusions.

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G. Wood / Environ. Impact Assessment Rev. 20 (2000) 537–556 539

2. Post-development auditing in EIA: An overview

An array of distinct but complementary types of post-development audithave been identified in the EIA literature (e.g., [2,6,7]). Implementationaudits emphasise the role of follow-up in checking project compliance withplanning and mitigation conditions. Project impact and predictive techniqueaudits stress the scientific approach to follow-up, where the intention is toidentify the actual impacts associated with a development, and then tocompare the observed impacts with those originally predicted to generatefeedback on the accuracy of the predictions and the utility of the predictivetechniques employed. In contrast, procedures audits focus on the performanceand effectiveness of the EIA process and can include scientific and policyissues and the findings of audits relevant to pre-development appraisal.

Much of the existing EIA audit literature focuses on macro-level studiesthat seek to assess the accuracy of impact predictions in specific EIA systems(e.g., Culhane et al. [8] in the United States, Buckley [9] in Australia, andmore recently Dipper et al. [3] in the United Kingdom). The results ofthese broadscale audits are useful in three ways: first, in providing anindication of how well impact predictions are actually performing in EIAsystems at a macro level; second, in identifying some of the problems andlimitations in conducting audits; and third, in advancing the developmentof audit methodologies (e.g., [6]).

However, such macro-level studies can be criticised in that they do notfully meet the potential of auditing to provide feedback in EIA which willactually serve to improve future practice, since sparse attention is paid tothe evaluation of predictive techniques. Tomlinson and Atkinson [10] pro-vide an overview of examples of audit studies where findings are moredirectly related to predictive methods, and other studies, specific to individ-ual predictive techniques, can be found in Konikow [11], and more recentlyin Thistle et al. [12] and Wu-Seng Lung [13]. However, characteristicallythese studies do not attempt to establish the underlying reasons for anyerrors identified, and no attempt is made to develop audit methodologiesthat might be applied more generally within EIA systems.

In summary, despite the appealing logic behind EIA auditing, to datethere has been a lack of research that provides a detailed explanatoryassessment of the accuracy of particular predictive techniques, and so thepotential to improve predictions in future EISs remains largely unfulfilled.This paper presents an analysis of the actual impacts and errors revealedby auditing techniques of visibility impact assessment employed to predictthe ZVI of development projects. As such, it serves to provide much-needed case study material of audits that supply feedback on specific ZVIpredictive techniques. In addition, the research illustrates the use of aspatial methodological approach to auditing, which emphasises the empiri-cal analysis of predictive errors.

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3. Visibility impact assessment and audit in EIA

Most EISs address the landscape and visual impacts of development;indeed a recent survey of U.K. environmental statements indicated that25% of all the predictions identified relate to this impact category [3]. InEIA the landscape and visual impacts of a development proposal willtypically be addressed using a combination of landscape evaluation andvisibility impact assessment. Methods of landscape evaluation focus onthe subjective assessment and perception of landscape [14]. In contrast,techniques of visibility impact assessment are largely concerned with theextent to which a development can be seen from the surrounding area andgenerally do not attempt to quantify human reaction or perception of theintrusion [15].

As stated, this paper focuses on visibility analysis, specifically the ZVI(also known as the “viewshed” or “visual envelope”), which delineates thearea around a proposed development from where the project will be visible.The ZVI can be an important element of visual impact assessment sinceit serves not only to define from where a development is visible, or morespecifically to whom or what the development is visible [16], but alsobecause it can be used as a precursor to the selection of individual viewsfor more detailed visual and landscape impact assessment (e.g., using photo-montages or techniques of landscape appraisal).

In the basic visibility model, sight lines pass from the viewer throughthe local environment and the atmosphere to be intersected by surfacefeatures and/or terrain. The physical limits of this process of intersectionare recorded in two dimensions in the form of a map of the ZVI and areinfluenced by elements of the macro- and micro-environment. The macro-environment includes terrain (including landforms), surface features thatproject above the terrain (e.g., vegetation and buildings), and the atmo-sphere, where conditions related to visibility include clarity and the intensityand direction of illumination. The micro-environment in the model refersto the nature of the environment in the immediate vicinity of the observerand includes features such as buildings, vegetation, or windows, whicheffectively serve either to block, filter, or frame a view. In addition to thescreening influences of the macro- and micro-environment, the extent ofthe ZVI can be influenced by the curvature of the earth and the loss ofvisual sharpness that occurs with increasing distance from a project [17].

The simplest and oldest analytical methods for determining the ZVI inEIA may be traced back to the early 1970s and are summarised by Kent[14]. The basic desk-based approach developed by Weddle [18] involvesthe plotting of topographic sections and sight lines at 108 intervals. Althoughinevitably crude (due to the limited number of sections used), slow, andlaborious, this approach enables the identification of so-called “deadground” (i.e., areas from which a development is not visible) and providesan explicitly spatial indication of the ZVI. A faster and more refined manual

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technique for determining the ZVI was developed by Hebblethwaite [19,20]and involved the use of a standard contour map and two special plastictemplates. This technique is fully described by the Department of theEnvironment [21] and Clark et al. [22]. The laborious nature of manualmethods for determining the ZVI has led to a demand for faster, computer-based techniques. Early advances in this area involved the development ofcomputerised versions of the techniques of Weddle and Hebblethwaite.More advanced methods have since been developed, which make use of“digital terrain models” (DTMs; i.e., three-dimensional computerised mod-els of terrain). Functionality for generating DTMs and applying them for“viewshed” determination is now standard in most GIS software and hasbeen the subject of considerable investigation by Fisher [23].

Despite moves to disseminate best practice in techniques of visual assess-ment in EIA (e.g., in the United Kingdom, the Institute of EnvironmentalAssessment [24] and the Countryside Commission [25]), little attention hasbeen given to the post-development evaluation of predictive techniques com-monly employed in EISs. It remains the case that “accuracy standards formost visibility work are currently non-existent” [26] and that “empiricalresearch has often not paralleled the evaluations made by professionals” [27].

Some limited research examining four different techniques of visibilitymapping is provided by Felleman [28], who contrasted the ZVIs producedon the basis of field observation, topographic maps and vertical air photo-graphs, physical topographic models, and computerised digital terrain mod-els. The study found that each of the techniques generated a different ZVIand that sensitivity analysis produced areas of ambiguity. While represent-ing a useful start in contrasting different approaches to ZVI prediction, theresearch did not assess the absolute accuracy of the techniques throughcomparison with actual visibility. Consequently, it does little to help identifywhich techniques are the most accurate or to suggest reasons why this maybe so. More recently a study in the United Kingdom by Chris BlandfordAssociates [29] audited the visibility of completed wind farms and foundthat the ZVI was greater than predicted, although the findings were basedlargely on the results from one site and the research did not seek to relatethe findings to the method originally employed for prediction [30].

Clearly there has been a paucity of research that serves to audit methodsused to estimate the ZVI. The research documented in this paper presentsan attempt to conduct a systematic and empirical evaluation of ZVI pre-dictive techniques used in EIA.

4. Auditing ZVI predictive methods used in EIA in the United Kingdom

4.1. Case study selection

Using the database and collection of U.K. EIS submissions held at theSchool of Planning, Oxford Brookes University, visibility impact predictions

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and associated predictive techniques were identified that were potentiallysuited for audit. Of the 96 EISs for which it is known that the developmenthas been implemented, 16 (approximately 20%) were found to containZVI predictions with potential for audit using a spatial approach. Furtherscreening was undertaken to identify case studies for which:

• there had been no significant design changes to the project in the post-development period that would serve to invalidate the original pre-diction;

• the development was at an appropriate life cycle stage for auditing,to ensure that the actual ZVI is directly comparable with that origi-nally predicted;

• there had been no significant changes in baseline conditions in thepost-prediction period (e.g., the construction of substantial buildingsor the clearance of major visibility screening structures such as denseblocks of woodland); and

• access to land surrounding developments was possible to completemonitoring fieldwork to determine the actual ZVI.

The following case studies were finally selected for use in auditing andstatistical modelling, and in each case the date of the relevant EIS is in-dicated:

• the Rye Hill opencast coal site, Houghton-Le-Spring, County Dur-ham (1991);

• the Knostrop clinical waste incinerator near Leeds (1991);• the Ovenden Moor wind farm near Halifax (1991); and• the power-station component of the North Yorkshire Power Project

(NYPP), near Yeddingham, in the Ryedale district of North York-shire (1990).

With all four case studies the basic data source used for impact predictioncomprises topographic information drawn from maps, while at Rye Hill,Knostrop, and the NYPP, the ZVI was further refined using land-use infor-mation and through site visits and photographs.

Although the case studies use similar input data, the way in which theZVI predictions are displayed in the EISs indicates that quite differentunderlying predictive techniques have been applied. Thus, the ZVI for theRye Hill opencast coal site and the Knostrop clinical waste incinerator(Figs. 1a and 2a) are presented as a continuous function with no evidenceof dead ground within the boundaries of the ZVI, implying that the effectsof visibility screening structures have not been considered. For the OvendenMoor wind farm the ZVI was determined solely using a desktop studybased on topography and was determined for a limited number of sightlines that have been adapted to provide a continuous coverage (Fig. 3a).In contrast, the ZVI for the NYPP has been developed on the basis of

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Fig. 1. Tye Hill opencast coal site.

extensive field survey work, and the prediction goes so far as to make thedistinction between full and filter views of the site (Fig. 4 a). In all casesthe predictions relate to project visibility at eye level, (approximately 1.5 mabove the ground), and atmospheric conditions that enable maximum visi-bility have been assumed. None of the case studies have employed the useof topographic models or DTMs to predict the ZVI.

4.2. Determining the actual ZVI

The first step in the process of auditing the ZVI predictive techniquesemployed in the case studies involved determining the actual extent of projectvisibility. Extensive field observation was undertaken during the summerseason under conditions of maximum visibility. During fieldwork the surveylocation was constantly monitored and project visibility at eye level wascontinually assessed. In some instances the complete spatial extent of visibil-ity could not be determined through direct observation (e.g., where thepresence of a crop prevented access to the centre of a large field). On suchoccasions a best estimate of visibility was made from field boundaries, takinginto consideration topography and the effect of any potential screening frombuildings or vegetation. In addition, for each case study a random spatialsample of over 300 point locations from within the study area was definedand visibility was determined, giving particular attention to locational accu-racy. Since visibility and location were precisely determined for these points,these sample data are used in the subsequent statistical modelling.

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Fig. 2. Knostrop incinerator: (a) predicted ZVI (views of stacks); (b) actual ZVI (views ofstacks).

4.3. Assessing the accuracy of impact predictions

The next stage in the audit involved a comparison of the predicted andobserved ZVI. A GIS was used to map both the impact predictions andthe spatial extent of actual project visibility as revealed through monitoringin the post-development period (see Figs. 1 to 4). A visual inspection ofthese maps reveals that in the case of the Rye Hill opencast coal siteand the Knostrop clinical waste incinerator, the impact predictions havesignificantly overestimated the degree of project visibility, although a closermatch is apparent between the predicted and actual ZVI for the NYPPand the Ovenden Moor wind farm.

To provide a quantitative indication of impact prediction accuracy, Jac-card’s and kappa coefficients ([31]; Jaccard’s and kappa coefficients aredefined in the Technical Appendix) were employed to measure the degree

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Fig. 3. Ovenden Moor Wind farm: (a) predicted ZVI; (b) actual ZVI.

of correlation between the maps of predicted and actual ZVI. Jaccard’scoefficient is a simple similarity coefficient, the value of which can rangebetween 0 (indicating complete dissimilarity between the two maps) and1 (indicating complete similarity). The magnitude of Jaccard’s coefficientis not affected by areas where project visibility is absent in both maps. Thekappa coefficient is commonly used for the evaluation of remote sensingclassifications where “ground truth” data are available and, in contrast toJaccard’s, a correction is made for the expected degree of matching dueto chance. The value for kappa can range from 21 (perfect disagreement)to 11 (perfect agreement), with a value of 0 indicating that any agreementbetween the maps is no better than that what might be expected by chance.Finally the classification of the sample of over 300 point locations at eachsite as either “under-prediction,” “over-prediction,” or “no-error” (i.e.,the impact prediction is correct) provides another means of assessing theaccuracy of the impact predictions.

Table 1 summarises the values obtained for the quantitative indicatorsused to describe impact prediction accuracy, where Jaccard’s and kappacoefficients relate to the complete spatial coverage and the figures for under-prediction, no-error, and over-prediction refer to the sample of data points.

The values for Jaccard’s coefficient and kappa in Table 1 confirm thatthe ZVI predictions for the Ovenden Moor wind farm and the NYPP arefar more accurate than the prediction relating to the Knostrop clinical wasteincinerator or the Rye Hill opencast coal site. Indeed, for Knostrop thevalue for kappa suggests that the agreement between the predicted and

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Fig. 4. North Yorkshire power project: (a) predicted ZVI; (b) actual ZVI.

audited ZVI is no better than might be expected due to chance. Table 1also shows that the NYPP has the highest proportion of points classed asno-error (over 85%), indicating that for the sample of data points it is themost accurate of the predictions tested.

4.4. Impact prediction error analysis

Testing impact predictions in the manner described also facilitates theidentification of errors, and in the analysis GIS overlay was used to deter-mine their spatial distribution. Using the random sample of data points,statistical modelling was then undertaken for each case study to explorefactors that might explain the occurrence of the errors and hence provideinsights on the relative performance of the underlying ZVI predictive meth-ods. A series of explanatory variables were identified for use in the statisticalanalysis, based on a consideration of the project setting, an examination

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Tab

le1

Qua

ntit

ativ

ein

dica

tors

ofZ

VI

impa

ctpr

edic

tion

accu

racy

Jacc

ard’

sK

appa

Und

er-p

redi

ctio

nN

oer

ror

(%)

Ove

r-pr

edic

tion

(%)

(%)

Rye

Hill

open

cast

coal

site

0.45

0.16

83.

458

38.6

Kno

stro

pin

cine

rato

r0.

290.

026

4.6

53.6

41.7

Ove

nden

moo

rw

ind

farm

0.74

0.65

526

.062

.011

.0N

orth

Yor

kshi

repo

wer

proj

ect

0.72

0.64

05.

585

.49.

1

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of the predictive technique employed for each case study, and by referenceto underlying factors that influence visibility. Table 2 describes and explainsthe variables investigated in the statistical modelling and also providesdetails of the data source used to describe each variable. To summarise,three categories of explanatory variables have been identified: first, vari-ables that relate to the existence of visibility screening structures in themacro- and micro-environment (BG, BG100, VEG, VEG100); second, vari-ables reflecting topographic conditions (RELHT, SLOPE); and third, vari-ables that measure distance (DIST, LOGDIST). Other variables that mayinfluence project visibility such as the colour of the development relativeto the background or the direction of the view in relation to the sun, werenot examined. This was considered appropriate because while such variablescan determine the degree to which a development project is noticeable ina landscape, they do not affect the actual extent of the ZVI when it isunder close scrutiny, as in this study.

For each case study, using samples of over 300 point locations, explor-atory modelling of the errors identified was undertaken using discriminantanalysis. In this technique, linear combinations of explanatory variablesare used to distinguish between categories or classes of data, which inthis instance relate to the occurrence of either over-prediction, under-prediction, or no-error. The variables are then used to discriminate betweengroups of cases and to predict into which category a particular locationwill fall, based on the values of the variables employed. Using Fisher’s lineardiscriminant functions, one set of classification coefficients is produced foreach of the error classification groups. The equation for each group issimilar to that for multiple regression and is given as Eq. (1):

Ci 5 a 1 b1X1 1 b2X2 1 . . . bnXn (1)

Where Ci is the classification score for the group i, bn represents theclassification coefficients, and a is the constant. With three groups, threelinear combinations are produced for each case in the target sample, andthe case is assigned to the class that produces the highest classificationscore. The overall performance of a model generated using discriminantanalysis can be assessed by looking at the percentage of points correctlyclassified by the model (similar to the notion of “goodness of fit” in regres-sion analysis).

Table 3 lists the explanatory variables that proved significant in the finalstatistical model for each case study and also indicates the performance ofeach model in terms of the percentage of sample points assigned tothe correct error classification group. For Rye Hill the model shows animpressive performance, with nearly 70% of the sample points correctlyassigned to the relevant class. It is clear that in this case the existence oflocalised visibility screening structures (represented in the statistical modelby the variables VEG100 and BG100) are important in determining the

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Tab

le2

Des

crip

tion

/exp

lana

tion

ofva

riab

les

inve

stig

ated

inth

eer

ror

mod

ellin

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Var

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Pro

ject

visi

bilit

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ISIB

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Act

ual

audi

ted

exte

ntof

the

ZV

IA

udit

field

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kR

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RE

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with

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nat

the

view

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tion

grea

ter

rela

tive

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(Cet

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Arc

/Inf

oG

ISSl

ope

(%)

atth

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glo

cati

onSL

OP

EP

roje

ctvi

sibi

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ism

ostl

ikel

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ons

Det

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(Cet

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Table 3Model performance and explanatory variables incorporated

Case study Points correctly Explanatory variablesclassified (%)

Rye Hill 68.3 BG100, VEG100Knostrop 58.3 RELHT, BG, BG100, SLOPE, VEG, LOGDISTOvenden Moor 52.2 DIST, SLOPE, RELHTNYPP 43.2 SLOPE, LOGDIST

nature of the errors identified in the audit. The original impact predictionmethod employed at this site produced a continuous ZVI (Fig. 1a), whichdoes not consider the screening effects of vegetation or buildings, and themodel shows considerable success in estimating the likely error classificationthat results as a consequence of this assumption. The ZVI prediction forthe Knostrop case study was produced using an identical technique andagain the statistical model incorporates a combination of variables thatrepresent the screening effects of vegetation and buildings (BG, BG100,and VEG) in addition to variables that describe distance and terrain.

In contrast to Rye Hill and Knostrop, the error models for NYPP andOvenden Moor show a poorer performance in terms of correctly classifyingthe errors observed. Therefore, it seems that where impact prediction accu-racy is poor, the discriminant analysis modelling of the errors proves to beeffective, but where predictions are more accurate, the approach is lesssuccessful. In addition it is interesting to note that unlike Rye Hill orKnostrop, the explanatory variables incorporated in the models for Oven-den Moor and the NYPP do not relate to the visibility screening effects(i.e., variables such as BG, BG100, VEG, etc.) but are associated with theeffects of slope, relative height, and distance.

While the overall degree of matching for all error classes is lowest withthe NYPP and Ovenden Moor case studies, the correlation for the no-errorsubsample is far higher (72% and 65% for NYPP and Ovenden Moor,respectively). This suggests that the terrain- and distance-related variablesincorporated in the discriminant analysis models are useful in terms ofidentifying locations where a ZVI prediction will be correct. The effective-ness of these variables in determining the no-error class is also logical asvisibility tends to be enhanced from higher viewpoints, on steep slopes,and at locations close to a development, and so predictions for locationswith these characteristics are most likely to be correct when comparedwith audited visibility, whatever the precision of the original predictivetechnique employed.

It is also possible that with the most accurate predictions the poorerperformance of the statistical models may occur, since the impact predictionerrors are either random in nature or because they relate to other variablesthat were not accounted for in the statistical analysis. For instance, during

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audit fieldwork at the NYPP it was noticeable that in some locations thenature of the vegetation had obviously changed with time, either growingto enhance screening effects or having died or been cut back, leading to areduction in screening. This raises the problem that the dimension of timehas for the practice of auditing impact predictions. With the Ovenden Moorwind farm it was found that under-predictions appear to systematicallyoccur at the fringes of the audited ZVI. One possible reason for this system-atic under-prediction is that the visibility of the wind turbines was deter-mined using the height of the turbine column, as opposed to the height ofthe column plus the turbine blades.

5. A framework for interpreting ZVI predictive accuracy

The previous section has served to provide a quantitative and empiricalanalysis of

1. the accuracy of ZVI impact predictions for a series of selected casestudies; and

2. factors that serve to account for the error classifications observed.

The following discussion draws on insights provided by the audit analysisto develop a simple framework that serves to explain the accuracy ofpredictive methods used in different landscape settings. The intention isthat such a framework could be used as a basis for the selection of anappropriate predictive method for future EIAs, hence serving to put intopractice the notion of learning from experience based on auditing.

The audit findings indicate that the ZVI prediction for the NYPP hasproven to be highly accurate and so to learn from its success, it is worthwhileconsidering further the nature of the predictive technique employed at thissite and its relationship to the landscape setting. It is known that thelandscape and visual assessment component of the EIS accompanying thisdevelopment was conducted by a specialist landscape consultancy, and inthe study by Mills [32] it was found to be the highest-quality visual assess-ment of the 70 examples surveyed. For the NYPP case study, the treatmentof visibility screening structures in the impact prediction appears to becrucial in explaining the effectiveness of the predictive technique employed.In terms of the occurrence of landscape structures that serve to screenproject visibility, the area surrounding the NYPP can be described as hetero-geneous, comprising a spatially disaggregated mixture of industrial, agricul-tural, woodland, and residential land use. The range of visibility screeningafforded by the different land uses in the area infers the need for a high-precision approach to ZVI prediction that matches the variety and complex-ity of the landscape (i.e., the predictive technique must be sensitive enoughto account for the spatially dispersed nature of screening structures in the

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Fig. 5. Framework for determining the accuracy of visibility predictive techniques.

landscape). Such an approach has clearly been used in producing the ZVIprediction for the NYPP case study.

The link between the precision of the visibility impact prediction tech-nique and the complexity of the landscape as a means of explaining theaccuracy of predictions is given further strength when the predictive tech-niques underpinning the other case studies are examined. For the OvendenMoor wind farm, a coarse visibility prediction technique was used, basedsolely on terrain elevation. Despite the low precision of this technique,auditing revealed that the prediction was relatively accurate. This can beexplained since in this case the landscape is homogenous, dominated byopen moorland with virtually no screening vegetation or buildings. Thus,the low-precision predictive technique used in the EIS is suited to the simpleor homogenous landscape, and consequently the prediction is accurate.

For both the Rye Hill opencast coal site and the Knostrop clinical wasteincinerator, the landscape surrounding the developments is comprised ofa mixture of residential, industrial, and agricultural land use in addition toparkland and opencast coal sites (i.e., the areas have heterogeneous, com-plex landscapes, with large changes in the extent of screening structuresoccurring over small distances). Contrastingly, a low-precision techniquehas been used to develop the impact prediction for the sites; there is noevidence that the screening effects of buildings and/or vegetation havebeen taken into account. For these two cases it seems likely that the poorperformance of the ZVI predictions can be explained by the mismatchbetween the precision of the predictive technique and the landscape com-plexity, and variables that serve to account for visibility screening effectsthat accompany a high landscape complexity (e.g., BG100, VEG100, etc.)were found to be successful in accounting for the occurrence of impactprediction errors.

Figure 5 presents a simple framework based on the discussion above,which can be used to determine the likely accuracy of ZVI impact predic-tions. In Fig. 5, although an accurate prediction is likely when the precisionof the predictive technique is high and the landscape complexity is low, itshould be noted that in such a situation the use of a high-precision techniquerepresents a waste of resources and effort, since an accurate predictioncould have been achieved through the use of a more basic, low-precisiontechnique. This indicates that the framework outlined above could be used

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during the scoping stage of a visual impact assessment to assist in theselection of a predictive method that is of an appropriate precision to matchthe landscape complexity.

6. Conclusions

Despite widespread recognition of the potential role for post-develop-ment auditing in EIA, there has been a shortage of research activity focusedon the evaluation of predictive techniques, with only limited advances inthe development of audit methodologies. This paper has served to counterthis, at least in part, through the development and application of a spatialapproach to auditing ZVI predictive techniques, which has emphasised thestatistical modelling of the occurrence of under-prediction, over-prediction,or no-error as revealed by testing ZVI impact predictions.

On the basis of four detailed audit case studies involving a variety ofZVI predictive methods, the original impact predictions were found toexhibit a degree of accuracy ranging from a close match between predictedand actual impacts (Ovenden Moor and the NYPP), through to a poordegree of correlation (Rye Hill and the Knostrop clinical waste incinerator).Statistical modelling of the residual errors and no-error classification wasfound to be relatively successful in cases where the ZVI predictions provedto be inaccurate, and it appears that the failure to adequately consider theeffects of physical barriers to visibility (e.g., buildings and vegetation) servedto explain the pattern of errors identified. On the other hand, in cases wherethe ZVI predictions proved to be more accurate, the statistical modellingwas less successful (in terms of the match between predicted and actualerrors), and it seems that distance- and terrain-related variables were usefulin identifying locations where predictions are likely to be correct.

Although only a limited number of case studies have been examined, itappears that the approach developed has been successful, first in terms ofquantifying the accuracy of specific impact predictions drawn from EISsand second in that advances have been made in identifying and quantifyingthe potential causes of error (through the statistical modelling of impactprediction errors), thus helping to provide feedback on specific predictivemethods. In addition, building on the audit findings, a framework for antici-pating the accuracy of impact predictions was developed based around theinterrelationship between the concepts of impact prediction precision andlandscape complexity. This framework could be a useful tool for use duringthe scoping phase of an EIA to assist in the selection of a ZVI predictivemethod that is appropriate given the project setting.

In summary some useful insights have been gained through the auditprocess, although the limitations of the approach should be noted. In partic-ular, the audits essentially relate to a “snapshot” of the impacts at oneperiod of time, and clearly the extent of visual impacts will change with

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the seasons. Second, due to the time lag between impact prediction andproject completion that is required for audit to be possible, all of the casestudies date back to the early 1990s. Progress may have been made inimproving the accuracy of ZVI impact predictions since this time, althoughthe techniques examined are still widely employed in contemporary EIAsin the United Kingdom. A third time-related factor of concern for auditingis the possibility of changes to the baseline environment. While no majorchanges in the baseline landscape were apparent in the case studies exam-ined, with the NYPP it was noted that in some areas minor changes in thenature of screening vegetation had occurred (i.e., some new growth andsome losses of vegetation in places). Some of the differences between thespatial extent of the predicted and actual ZVI arise as a consequence ofthese minor changes to the baseline landscape, as opposed to any fundamen-tal flaw in the basic predictive technique.

Finally it should be recognised that the audits relate only to a limitednumber of case studies and predictive techniques. For EIA to maximiseits potential to learn from experience, it is vital that more post-audit researchcovering a wider range of audit cases studies and other impact themes isundertaken, and that the findings are disseminated to the EIA community.

7. Technical appendix

Jaccard’s coefficient is a simple similarity or matching coefficient [31],which, for an area cross-tabulation matrix, T, may be defined as shown inEq. (A1):

Cj 5T11

T12 1 T21 1 T11

(A1)

Where T11 is the area of “positive match” (i.e., both maps indicate thedevelopment is visible) and T12 and T21 indicate the area of “mismatch.”The magnitude of Jaccard’s coefficient is not affected by areas where projectvisibility is absent in both maps.

To derive kappa, the results of the classification are expressed in a matrixwhere values in the principal diagonal display the amount of agreement,and figures off the diagonal show the extent to which the two maps aremismatched. The kappa coefficient is then defined in terms of the expectedarea proportions [31] using the formula shown in Eq. (A2):

K 5on

i51

Pij 2 on

i51

Qij

1 2 on

i51

Qij(A2)

Where Pij is the observed area proportion, Qij is the expected areaproportion for the ith row and jth column under the assumption of no

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correlation between maps, obtained from the product of the marginal totalsPi and Pj, and n is the number of matched classes (in this case two).

Acknowledgments

The work presented in this article is drawn from a PhD research pro-gramme funded by the Economic and Social Research Council (ESRC),whose support is gratefully acknowledged. Thanks are also due to Dr.Agustin Rodriguez-Bachiller and Professor John Glasson, School of Plan-ning, Oxford Brookes University, and to the journal referees for someuseful comments for improvements to the paper.

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Documents

Is what you see what you get?: Post-development auditing of methods used for predicting the zone of visual influence in EIA