Cornput., Environ. and Uhan Systems, Vol. 14, pp. 223-241, 1990 Printed in the USA. All rights reserved.

0198-9715/90 $3.00 + .oo Copyright 0 1990 Pergamon Press plc

INNOVATIONS IN COMPUTER LABS

ADVANCES AND APPLICATIONS IN GEOGRAPHIC INFORMATION SYSTEMS IN THE UNITED KINGDOM :

THE CONTRIBUTION OF THE SOUTH EAST REGIONAL RESEARCH LABORATORY

John Shepherd

South East Regional Research Laboratory Department of Geography

ABSTRACT. One of the main differences between the NCGIA initiative in the USA and the Regional Research Laboratories (RRLs) in the United Kingdom is that the latter are more oriented towards a regional community of GIS and database users. This means, among other things, that individual RRLS have developed research foci which have emerged from specific institutional circumstances, as well as from the skills and interests of individuals. Among RRLs the “laboratory” concept is also a key concept in resource management and as the South East Regional Research Laboratory (SERRL), based at Birkbeck College, this is interpreted as providing an environment in which research and applications in GIS are brought together in a ml!tually beneficial relationship. Three case studies of SERRL work-on the development of a regional database, the linkage of various data types for subregional planning and the integration of remotely sensed data with town planning data4llustrate the nature of this relationship.

INTRODUCTION

In contrast to the National Centre for Geographic Information and Analysis (NCGIA), the Regional Research Laboratories in the UK-which have a similar broad remit to advance research in spatial data handling methodology-are less centrally coordinated, regionally rather than nationally focused, and (perhaps), more oriented towards an external client community (Shepherd et al., 1989). The South East Regional Research Laboratory (SERRL), which is the focus of this paper, is an example of an RRL which has directed a good deal of its energies towards a special type of user needs, namely high-quality applied research which is also judged to have significant implications for basic research.

This paper describes how and why SERRL has come to operate in this way, though it is emphasized that it is not typical of all RRLs in the United Kingdom. At the base of this modus

Requests for reprints should be sent lo John Shepherd, South East Regional Research Laboratory Department of Geography, 7115 Gresse Street. LONDON WlP 1 PA.JANET: j.shepherd @ UK.ac.bbk.ge. Note: SERRL Working reports may be obtained from the SERRL Administrator, The Department of Geography, Birkbeck College, 7-15 Gresse Street, LONDON WlP 1PA. Tel 0716316483; fax 0716316498; E-Mail via JANET: j.shepherd @ UK.ac.bbk.ge

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224 J. Shepherd

operandi is a belief that, particularly in the case of GIS technologies, there is a real research need to monitor real applications in the real world. In order to give an indication of the range of SERRL research and to provide some detail of SERRL activities, the paper focuses on three case studies: the building and application of a regional settlement and infrastructure database, the use of a GIS to derive practical solutions to problems of data coded at different levels of spatial resolution, and the integration of remotely sensed information with planning data. These three projects, whilst indicative of the mode of SERRL working, are by no means exhaustive of its activities. As a conclusion to each case study, therefore, we indicate examples of current extensions to that particular area of work.

SERRL: THE SOUTH EAST REGIONAL RESEARCH LABORATORY

SERRL, which is situated in the Department of Geography at Birkbeck College, University of London, is one of eight RRLs established by the Economic and Social Research Council (ESRC) in October 1988. Prior to that, staff at Birkbeck (in conjunction with colleagues at the London School of Economics), had taken part in the modestly funded trial phase of the RRL initiative launched by ESRC in 1986 (Masser, 1987). Currently, SERRL consists of two direc- tors and core grant-holders (Professor David Rhind and Dr John Shepherd), six full-time research staff, four research associates based at Birkbeck (an urban planner, a GIS theoretician, a statistician and an expert in software teaching), and a number of more or less well attached associates in other universities (including other RRLs). A novel feature of SERRL among all the RRLs is its collaboration on applied GIS work with Halcrow-Fox Associates, a highly respected firm of economic development consultants and town planners.

Briefly, the aims and objectives of the ESRC RRL initiative can be summarized in the three words of its title: regional, research, and laboratory. First, as one of eight regional centres of expertise in the management of large-scale, spatially referenced databases and GIS (Figure l), SERRL exists to serve and advise a community of users within its own region. However, since

this regional remit takes place in London, the national capital, our potential area of operation is national and even international (especially European), in scope. Second, there is a commit- ment to use ESRC funding to conduct high quality research in database management and appli- cations and to spin this off into GIS teaching programmes. Thirdly, the work of SERRL is organized within a laboratory ethos in which resources of data and machines are shared, per- sonal skills are developed on a complementary basis, and distinctions between projects (both pure and applied), are kept to the absolute minimum required to ensure deadlines are met.

It should also be born in mind that the ESRC commitment to core funding is for three years

only and there is a working assumption that if RRLs wish to remain in existence after that date, they must be self-supporting. Such an assumption also fits in with the requirement that RRLs

exist, in part, to serve a regional client community on at least an “at cost” basis. As a result, SERRL has been extremely active in acquiring additional research grants and contracts notably from the joint ESRC/NERC Programme in Geographic Data Handling (four grants for general- ization in GIS, spatial languages, a database for the Eastern Thames Corridor, and 3-D GIS/remote sensing) and from the Department of Environment (i.e., for a study of new technol- ogy and the 1991 census; linking house condition survey data and future rates of urbanization). The possibility of the need to achieve self-financing status is also leading SERRL managers to consider other forms of external financing, including venture capital.

The purpose of this paper, therefore, is to describe some of the research that has emerged from this mode of working. Broadly, over the last three years, the research effort of SERRL has been a function of four main factors: First, is the starting point in a Department of Geography that had a strong tradition of applied work but which was re-energized in the direc-

Advances and Applications in Geographic information Systems in the UK 225

NORTH EAST

Leicester/Loughborough

SOUTH EAST

FIGURE 1, tocatlon of UK Reglonal Research Laboratorles.

226 J. Shepherd

tion of GIS by the arrival of David Rhind as Head in 1982. Second, the particular interests and expertise of its core and associated academic and research staff; third, a group commitment to “learning by doing” in all branches of GIS work, whether database design and management, database applications, or understanding the institutional implications of the new technology;

and, finally, the particular circumstances of working in an RRL located in the centre of London and South East England. The intellectual, commercial, and urban environment in which SERRL operates is thus in many ways a critical determinant of its development.

SERRL: THE RESEARCH ENVIRONMENT

In its three years of operation, the direction of some of the aspects of the SERRL research programme has changed and new projects have been added, but the core of its research endeav- our remains in the field of urban and regional planning information systems. This includes the

analysis of settlement systems and the consequences of infrastructure change (Congdon & Shepherd, 1986, 1988; Shepherd & Congdon, 1990), the design and application of settlement and infrastructure databases (Green & Shepherd, 1987; Green 1987), and the handling and analysis of census and other data describing and predicting change in settlement and infrastruc- ture systems (McKee, 1990a, 1990b, 1990~; Rhind, 1983, 1989). In pursuing these research specialities, SERRL personnel have worked with a number of organisations responsible for, or advising upon, policy in urban and regional planning, including central and local government, regional bodies, and private consultancies.

It was, however, a deliberate policy objective of SERRL not to become involved in writing new GIS and related software. This is an expensive and complex operation (especially in London) and other RRLs are in a better position to pursue this end-although we have written various “macro-type add-ons” to existing routines. Moreover, the new Apple Macintosh

Mapping Centre at Birkbeck, which is closely associated with SERRL, is currently engaged in developing an intelligent “front-end” to ARC/INFO and the well received “GIST” package is being translated into an IBM Environment with ESRC/SERRL support, (Raper & Green, 1989). Our policy, therefore, is to use proprietorial GIS software where possible. ESRI’s ARC/INFO is our main GIS “work-horse” in most applications and research work. However, four other systems are also in use or under evaluation, including LASERSCAN’s METROPO- LIS system. In this way, we seek to maintain an independent and objective stance in an envi- ronment dominated by the major software houses.

It is, however, the location of SERRL in central London, at the centre of a metropolitan region of some 18 million people, that is most critical to our mode of working and the nature of much of our research output. A London location has advantages and disadvantages which can- not be ignored if SERRL is to survive beyond the period of ESRC committed funding. The

advantages of the location are obvious: proximity to a large and sophisticated potential market for GIS applications and research in the form of Central Government Departments, public cor- porations, and private consultancies of various kinds; association with a very large metropolitan university with several departments of geography, planning, surveying, and so forth; and a regional context in which major settlement and infrastructure projects are being planned or developed (i.e., the Channel Tunnel, the London-Paris High Speed Rail Link, London Docklands, new housing, and retailing projects). These are potentially ideal circumstances in which to demonstrate the role and value of GIS databases in the planning and decision making process.

The major disadvantage of working in Central London is, quite simply, that the core-funding (ESRC) monies go considerably less far than in other parts of the country. On an age-compara- tive basis an experienced GIS/database technician can earn at least twice the university scale

Advances and Applications in Geographic Information Systems in the UK 227

rate in private industry than she/be can in academia. In these circumstances, SERRL must gen- erate significant amounts of new income from contract research in order to stay at the forefront of the GIS research effort. The aim is to ensure that this applied contract research not only funds more basic investigations but also itself contributes to advances in the applications of GIS on a broad front. The immediate context of our work therefore is that of a laboratory on the scientific model in which corporate objectives are set but where there is ample room for

individual initiative and creativity in research. As the following three examples show, we believe we have made a useful start in nurturing academic research within the embrace of real- world applications.

CASE STUDY I : THE SERRL SETTLEMENT AND INFRASTRUCTURE DATABASE

At the centre of the research and applications activities of SERRL and drawing together many of the technical and substantive interests of the SERRL team is the SERRL Settlement and Infrastructure Database (SSID). This consists of some 500 Mb. of digital cartographic data representing the strategic elements of the settlement patterns and infrastructure network of England. Onto this can be “locked” various other types of data (i.e., the Population Census and other surveys) which describe the main spatial elements (Shepherd & Conway, 1988). The SSID is currently maintained in an ARC/INFO GIS environment running on a VAX 11/750, a VAX II GPX Workstation, and (in PC ARC/INFO) on an IBM PS2/80, although we are gradually evolving the hardware into a fully networked system with a central file-server.

The SSID consists of a dozen or more “layers” of data “tiled” to the administrative counties of England (Green, 1987). Most of the data have been digitized from 1:50,000 scale OS map sheets although certain “data rich” areas those where there are issues of special planning importance such as town centres or the City/Docklands area-have been digitized at 1: 10,000, 1:2,500 and 1: 1,250 scales. The main categories of data layers are as follows :

Settlement: defined by land use as “urban areas” (OPCS, 1984), which are in turn coded as members of a hierarchy of functional urban regions (Coombes et al., 1982); in addition, the 100,000 plus census ED’s are “flagged” as “urban” or “rural.”

Transport Networks: a detailed road network based on the Department of Transport Present Year Network File, the surface and underground rail systems (and stations) of South East England and London (Network South East), and some utilities networks.

Administrative Boundaries: such as wards, local authority districts, counties, and parliamentary constituencies.

Planning Areas: including Green Belts, Areas of Outstanding Natural Beauty, Development Corporations and Sites of Special Scientific Interest.

Database Applications

Although by no means the only focus of SERRL initiative, the SSID has proven to be of considerable value as a research and applications resource and as a source of corporate identity. In fact, in the intensely empirical and practical world of GIS we have found that significant basic research issues have “spun-off’ the project work and that the needs of customers have helped to clarify and “prioritize” our own research work. It has also taught us to be extremely cautious about the claims made for GIS products and the true cost of undertaking GIS- orientated contract work. Three brief examples must suffice to indicate the nature of the applications/research symbiosis.

228 J. Shepherd

One of the first tasks that SERRL carried out on a contract basis was for British Rail Network South East, the division of the national rail carrier concerned with commuter and intercity traffic focused on London. The task was to analyze the demographic shifts taking place around the 900-plus stations in the network (Green, 1987). Such knowledge was of value to the client in predicting demand for bus services or car-parking spaces at stations so that the latter could serve an increasingly scattered population. The study was invaluable in gaining an understanding of the database management requirements in a “one-off’ task context, but it also underscored the artificiality of standard GIS search functionality and the need for the integra- tion of GIS with planning modelling software (Figure 2).

Two other applications of the SSID pinpointed the need for further work on GIS functionali- ty. In a pilot study for the Department of the Environment (DOE) all 1:1,250 scale OS map sheet boundaries were “overlain” on OS/OPCS urban area boundaries to devise a sampling frame for land-use change analysis. In a study of urban area growth the problem of the opti- mum size for “buffering” urban areas (where “buffers” could overlap), assumed considerable importance and remains to be resolved satisfactorily. Secondly, the well-known problems of linking census enumeration district data to unit Post Code areas (Oppenshaw, 1989) are further complicated when, as in a study for a major organisation in market research recently carried out by SERRL, there was a requirement to relate their spatially linked data to other OPCS/OS

urban areas and OS topographical data.

Settlement and Infrastructure Systems and GIS

The issues raised in these applications point ultimately to the need for a better specification of the relationship between “settlement” and “infrastructure” in GIS databases. That is, between the population centres at which demand for services are generated and the physical infrastructure (roads, railways, hospitals, water treatment works, etc.), which is the means for satisfying demand (Eberhard & Bernstein, 1984). This is an essential requirement if GIS are to

be more than storehouses of vast amounts of spatially referenced data in search of substantive meaning. It is especially important if GIS is to play a major part in improved public and pri- vate decision-making and policy analysis (Worrall, 1989).

In the meantime, GIS methodology itself can be used to clarify some underlying conceptual and empirical issues. On the “settlement” side of the settlement-infrastructure equation there are numerous different concepts and representations of population concentration, some of which are more suited to specific applications than others. Tyler (1988), in a valuable review of settlement databases at the locality level (OS Gazetteer, Postcode Directory, OPCS Index of Place Names), suggests that a major constraint on GIS in this field stems from the absence of any standard geographical division of Britain. However, it is more likely that users will adopt

one (or even more) of many such divisions and that, in such circumstances, GIS will play an indispensable role in evaluating the differences between each approach, both cartographically and in terms of population distribution and structure.

The SSID has also been used in a number of ways to evaluate various definitions of “settle- ment” in digital databases. In a study of small town growth a statistical comparison was made between the OPCS/OS definition of urban areas and the 1971-81 census “change file” defini- tion (Shepherd dz Congdon, 1990). Using a test release of OS 1:50,000 scale digital map data a visual comparison has been made between OPCS/OS urban areas and the “settlement” catego- ry on the topographic sheet (Figure 3). An overlay of a section of CURDS functional urban regions (Coombes et al., 1982) on OPCS/OS urban areas illustrates the overlap between the two concepts in a complex urban region (Figure 4). Finally, in a preliminary study for the Rural Development Commission, SERRL is examining the implications of using Enumeration

Advances and Applications in Geographic Inforrnafion Systems in fhe UK 229

Canier~uru ut, \

FIGURE 2. BrHish Rail Stations Catchment Map, 2 KM Radius.

r r-

230

Advances and Applications in Geographic lnformation Systems in the UK 231

District centroids combined with urban area boundaries in order to create a more satisfactory definition of rural planning areas.

CASE STUDY II: THE USE OF GIS TO LINK PLANNING DATA AT DIFFERENT LEVELS OF SPATIAL RESOLUTION

As a “spin-off’ from a project for the Department of the Environment (DOE) to design and implement a prototype planning GIS (Green, Mackay & Shepherd, 1988), SERRL was asked to examine the practical problems involved in linking and analyzing data sets coded to differ-

ent levels of spatial resolution. The results of the project have implications not only for the techniques of data integration but also for the process of routine data collection and data

geocoding in large multi-unit planning organizations.

The Nature of the Problem

The impact of land-use planning can be assessed by tracing policy decisions through to land- use change. Thus, a site which receives a DOE Derelict Land Grant (DLG) should, after a suit- able period, be improved and undergo some degree of land-use change. In order to identify sites where policy decisions have resulted in land-use change it is necessary to “link” this poli- cy information to the land-use change information. In this project attempts were made to link several types of “policy” information, including data on Derelict Land Grant (DLG), the European Regional Development Fund (ERDF), the Land Register (LG), and the Historical

Land Register (HLR), with a single criterion or “outcome” measure-OS Land Use Change (LUC) data (DOE, 1987).

Information for all of these sites refers to an area geocoded by a single x, y coordinate grid reference based on the National Grid. However, this grid reference varies in both accuracy and precision. Here “accuracy” relates to the quality of the locational measurement in relation to a standard and “precision” is concerned with the resolution or exactness with which that measure is expressed (DOE, 1987). It is feasible, therefore, if the geocoding is both accurate and pre- cise, to link different data sets purely on the basis of a grid reference. However, if the geocoding is imprecise (i.e., in relation to some more precise data), or inaccurate, then other ways have to be found to link the data.

Data Characteristics

In addition to the “point” nature of the data sets, a number of other factors (attributes) influ- enced the linking of the policy information with the outcome information. These included non- locational factors (number of sites in the data sets, time scales of the data, and data categorisa- tion methods), and locational factors (spatial resolution, spatial referencing, locational innacu- racies, and size/shape of sites). Any or all of these factors might be used to achieve or aid the

process of data linkage. Among the locational factors two-spatial resolution and spatial referencing-are

particularly important. Thus, the outcome (Land Use Change) data have a spatial resolution of lOOm, that is, sites are stored with a four-figure grid reference, a figure obtained by OS by effectively truncating the actual grid reference. The “true” location of the LUC centroid could therefore occur anywhere in the grid square north and east of the location indicated (Figure Sa). The DOE LR and HLR data sets, on the other hand, are encoded with a five-figure (1Om resolu- tion) grid reference, calculated by rounding the actual grid reference “up or down” to the near- est 1Om grid intersection (Figure 5b).

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Advances and Applications in Geographic Information Systems in the UK 233

382700 - /-------_+-3827(-~-~

100 m GRID SQUARE. * (434186,382663)

382600 - o-------_+-38~

I I

434800 434900

I I

434800 434900

FIGURE 5A. LUC Site Centered on 434816,382663 is a”Truncated” and Allocated theGrid Reference 4348,3826 in the Database. The Shaded Area indicates the Zone in Which Points Will Be Allocated to the Grid intersection 4348,3826.

-+-

I 434810

434810 434820

I I 382670 -+--------+ - 382670

10 m GRID SQUARE.

382660

+--_- ---+

1 t

* (434186,382663)

I a-- 0 -382660

I

434820

+ ----_----3_

FIGURE 58. Historical Land Register Located at 434816, 382663 is “Rounded” to the Nearest Five

Figure Number and Allocated the Grid Reference 4382, 38266 in the Database. The Shaded Area indicates the Zone in which Points Will be Allocated to the Grid intersectfon 43482,38268.

234 J. Shepherd

Finally, the way in which the reference point is located within a site is also of considerable importance. Most of the DOE site centroids, for example, approximate the geometric centre of the site. If the reference point is assigned to the lowest-left comer or randomly located within a site (as happens), then the same site might be differently grid referenced in different databas-

es.

Making the Connection

The study was carried out on the area of South Yorkshire covered by OS 150000 sheet 111. The trial release digital version of this map was used as a backdrop to the matching procedure and for a screen-based demonstration of the procedure involved. (Green & Shepherd, 1988). Four techniques were used to link the data sets:

1. Absolute matching of grid references; 2. Application of thematic criteria to support possible links; 3. Generation of pseudo-area boundaries for selected sites; 4. Comparison of actual site boundaries.

The fist of these is the simplest but it was also the least successful. A marginal increase in

the scope of this me~od~ssentiaily a circular search around given grid references-was a lit- tle more successful. By applying the attribute data associated with a site it was possible to limit the number of sites to which the locational match was applied. Thus, when linking LUC to HLR sites it was possible to reduce the number of LUC sites from 3055 to 1285 simply by selecting those which were changing from a “vacant” land-use category.

The use of pseudo-area boundaries consisted, in our case, of overlaying circles proportionate to the areas of the sites in two data sets (HLR and LUC), although in principle any relevant shape can be used. This operation produced 37 potential matches of which half had other com- patible attributes (Figure 6). Finally, a comparison of digitized DLG and ERDF bodies was undertaken. On The basis of a limited number of such comparisons it was felt that the addition- al cost of digitizing far outweighed the small gains in accuracy of matching over other methods.

Discussion

These experiments indicated that it is possible to link data sets at different levels of spatial resolution using various techniques available in a sophisticated GIS. Although the number of matches was small-and the number of significant matches even smaller-this was primarily due to differences in time scales over which the various data sets were collected. Given the variability in accuracy and precision with which data from a single organization are customari- ly geocoded, the research does, however, point to the need for a standardized solution for spa- tial referencing of policy-related data.

CASE STUDY III: INTEGRATING REMOTELY SENSED IMAGES AND DIGITAL MAP DATA FOR URBAN PLANNING

One prima-facie valuable source of up-to-date information for a GIS is remotely sensed imagery, providing the technical problems of data integration can be overcome. However, the use of conventional, per-pixel, multispectral classification algorithms for mapping urban areas has been shown to yield relatively low levels of accuracy (Toll, 1985). This is, in part, a result of the spatial heterogeneity and, hence the complex spectral signature of urban land-

f

235

236 J. Shepherd

cover/land-use types (Forster, 1985). Given the legal implications of planning decisions, the reticence of town planning departments in adopting remotely sensed imagery as a primary data source is understandable (Bamsley et al., 1988).

In remote sensing science, conventional classification procedures implicitly assume that there is no a priori info~ation about a study area, other than that relating to training and test- ing sites. In a “map-rich” environment such as Great Britain this is clearly an unrealistic and

wasteful assumption since data relating to the extent of the urban area can be derived from standard cartographic sources such as Ordnance Survey maps. The purpose of this study, therefore, was to evaluate the improvements in classification accuracy resulting from the use of data derived from Local Authority plans to divide an RS image into segments,

Study Area

The study area is the London Borough of Bromley in the south east of Greater London. The Borough encompasses several different types of urban land use, ranging from densely occupied “inner urban” areas in the northwest, through major shopping areas and interwar industrial areas in the centre, to low density suburbs in the South East. Surrounding the urbanized area are very large tracts of open country, much of which is statutory green belt land. It should be noted that “green belt” is both a statutory concept involving the delineation of precise bound- aries on the ground and a diverse set of open spaces ranging from very small intra-urb~ parks to large tracts of farmland and woodland (Figure 7).

Study Method

A cloud-free, multispectral (XS) SPOT HRV image of London (scene 32,246;+22.46 ) acquired on June 30, 1981 was used for the investigation. Data relating to statutory planning boundaries were manually digitized from Bromley Borough Plans compiled in 1986 and based on OS 1:lOOOO scale maps. Data processing was carried out on two independent system-an 12s Model 75 Image Processor and the ARC/INFO GIS software-both systems being sup- ported by VAX 111750 minicomputers. In-house software was written to affect the vector to

raster transfer of data from ARC/INFO to 12s using the POLYGRID function to 16-bit accura-

cy (Bamsley et al., 1989). On the basis of visits to the field site and preliminary analysis of the image data, seven can-

didate land-use classes were identified within the test area(see Table 1). A maximum likeli- hood classification algorithm was then used to classify each segment of the SPOT-HRV image. By dividing the image into two parts it is possible to weight the prior probability of each class differently according to the segment into which it falls: pixels falling within the urban segment are allocated a high probability of belonging to an urban land cover class; pixels falling within

the nonurban segment are allocated a low probability of belonging to the urban classes.

Results

Table 1 indicates the very high level of accuracy achieved by using relatively broad land cover classes. The only major errors that have occurred are the confusion between the two urban residential classes (though this could be very significant in planning terms), and the large percentage of unclassified pixels in the pasture class. Table 2 shows an improvement in the accuracy of urban land cover recognition when a higher a priori probability is assigned to these classes within the urban segment.

It is, however, even more instructive to examine contingency tables obtained from a compar-

A

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z z Q w m w E CL

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z- W” W”

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237

238 J. Shepherd

TABLE 1. Confusion Matrix for Classification Using Equal Prior Probabilities

True Class

lmaee Ul U2 U3 Ar Pa Wo BS

Class

U-85.0 Av. Act. = 90.9%

u2 11.9 100.0 0.0 0.0 0.0 0.0 0.0 Ov. Act. = 93.2%

u3 0.0 0.0 89.4 0.0 0.0 0.0 0.0

Ar 0.0 0.0 0.0 97.2 0.0 0.0 0.0

Pa 0.0 0.0 0.0 0.0 66.3 0.0 0.0

wo 0.0 0.0 0.0 0.0 0.0 loo.0 0.0

BS 0.0 0.0 0.0 0.0 0.0 0.0 98.7

Null 3.1 0.0 10.6 2.8 33.7 0.0 1.3

A Priori (8) U1 U2 U3 Ar Pa Wo BS

Urban 14.286 14.286 14.286 14.286 14.286 14.286 14.286

Non-Urban 14.286 14.286 14.286 14.286 14.286 14.286 14.286

Class Test Producer’ Confidence Consumer Confidence Errors Pixels Accuracy Interval Accuracy Interval Om. / Comm.

(%) (%) (8) (46) (8) (8)

Ul 675 85.0 82.1 - 87.5 100.0 99.3 - 100.0 15.0 0.0

u2 675 100.0 99.4 - 100.0 89.4 87.0 - 91.4 0.0 11.9

u3 160 89.4 83.6 - 93.3 100.0 97.4 - loo.0 10.6 0.0

Ar 425 97.2 95.1 - 98.4 100.0 99.1 - 100.0 2.8 0.0

Pa 240 66.3 60.1 - 71.9 100.0 97.6 - 100.0 33.7 0.0

wo 879 100.0 99.6 - 100.0 100.0 99.6 - 100.0 0.0 0.0

BS 79 98.7 93.2 - 99.8 100.0 95.3 - 100.0 1.3 0.0

Key :-

Ul = High density residential, U2 = Low density residential, U3 = Commercial/Industrial.

Ar = Arable, Pa = Pasture, Wo = Woodland, BS = Bare soil.

ison of the original digital map data and the classified imagery for the whole study area. The results for such a comparison-achieved by overlaying the map and classified image data, and carrying out a logical union of the two is shown in Tables 3 and 4. These show, with much greater accuracy, that the majority of unclassified pixels fall within green belt land. This is important since it may be a requirement that the proportions of different land cover/land use types within this planning unit are known more accurately than, say, for residential areas. In summary terms, the tables also show that, for the conventional classification (Table 3) some 69% of the area common to both image and map data is assigned to its correct class. Of the remainder, only 9% represents areas of disagreement between map and image, while 22% is unclassified in the image. The segmented image (Table 4) shows an improvement of 4% in the agreement between map and image.

Advances and Appiications in Geographic information Systems in the UK 239

TABLE 2. Confusion Matrix for Classification Uslna Uneaual Prior Probabilltles

u2

u3

Ar

Pa Wo BS Null

True Class Ul U2 U3 Ar Pa Wo BS

90.2 0.0 1.3 0.0 0.0 0.0 0 . 0 Av. Act. = 93.3%

5.6 100.0 0.0 0.0 0.0 0.0 0.0 Ov. Act. = 95.2%

0.0 0.0 98.1 0.0 0.0 0.0 0.0

0.0 0.0 0.0 100.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 66.3 0.0 0.0

0.0 0.0 0.0 0.0 0.0 100.0 0.0

0.0 d.0 0.0 0.0 0.0 0.0 98.7

4.1 0.0 0.6 0.0 33.7 0.0 1.3

A Priori (7%) Ul U2 U3 AI Pa Wo BS

Urban 35.00 25.00 16.00 6.00 6.00 6.00 6.00

Non-Urban 7.00 7.00 7.00 20.00 30.00 15.00 14.00

Class Test Produce? Confidence Consumer Confidence Errors Pixels Accuracy Interval Accuracy Interval Cm. I comm.

(%) (%I (%) (46) (96) (%I

Ul 675 90.2

u2 675 100.0

u3 f60 98.1

Ar 425 100.0

Pa 240 66.3 wo 879 100.0 BS 79 98.7

87.7 - 92.2 99.7 98.8 - 99.9 9.8 1.3 99.4 - 100.0 94.7 92.8 - 96.1 0.0 5.6 94.6 - 99.4 100.0 97.6 - 100.0 1.9 0.0

99.1 - 100.0 100.0 99.1 - loo.0 0.0 0.0

60.1 - 71.9 100.0 97.6 - 100.0 33.7 0.0 99.6 - 100.0 100.0 99.6 - 100.0 0.0 0.0 93.2 - 99.8 100.0 95.3 - 100.0 1.3 0.0

Key :-

Ul = High density residential, U2 = Low density residential, U3 = Commercial/htdustrial,

Ar = Arable, Pa = Pasture, Wo = Woodland, BS = Bare soil.

The research shows that improvements in the classification accuracy of RS images can be obtained by incorporating ancillary map data. However, these still appear to be small in rela- tion to the requirements of the planner. This research is therefore being carried forward by SERRL in two main ways: the incorporation of an “expert” assignment of prior probabilities and the use of OS 1:5CKKXI digital map data incorporated into RS images. This work is sup- ported by a grant obtained under the recent ESRClNERC Geographic Data Having research initiative.

CONCLUSION

These three illustrations of SERRL work in the field of settlement and infrastructure databases are indicative of the strategy SERRL staff have adopted for the critical first two

240 J. Shepherd

TABLE 3. Contingency Table for the Union of the Dlgital Map Data and the Classified Remotely Sensed Imagery (Using Equal Prior Probabilities for Each Class in Both Segments). Values = % of

Total Area

MAP

Urban

G.BeIt

u.0.s

SSSI

Total

IMAGE

U1 U2 U3 Ar Pa Wo BS Un Total

21.30 25.88 0.68 0.08 0.13 0.22 0.00 3.24 51.55

1.79 3.85 0.01 8.83 4.09 6.56 0.20 16.20 41.53

1.21 I .49 0.00 0.16 0.20 0.22 0.00 2.04 5.32

0.02 0.11 0.00 0.13 0.01 1.07 0.00 0.26 1.60 24.32 31.33 0.69 9.20 4.43 8.07 0.20 21.76 100.00

TABLE 4. Contingency Table for the Union of the Digital Map Data and the Classified Remotely Sensed imagery (Using Unequal Prior Probabilities for Each Class According to the Map-Based

Segmentation). Values = % of Total Area

MAP

urban

G.Belt

u.0.s

SSSI

Totat

IMAGE

Ul U2 U3 Ar Pa Wo BS Un Total

24.44 24.73 0.66 0.M 0.13 0.17 0.00 I.35 51.55

1.37 2.23 0.00 9.39 5.24 6.50 0.19 16.61 41.53

1.07 1.07 0.00 0.17 0.24 0.22 0.00 2.55 5.32

0.02 0.04 0.00 0.14 0.01 1.06 0.00 0.33 1.60

26.90 28.07 0.66 9.77 5.62 7.95 0.19 20.84 lW.W

Key :- Image : Ul = High density residential, U2 = Low density residential. U3 = Industrial/CmnmerciaI, Ar = Arable, Pa = Pasture. Wo = Woodland. BS = Bare soil. Un = Unclassified. Map : G.Bek = Green Belt. U.0.S = Urban Open Space, SSSI = Site of Special Scientifc Interest.

years of the life of the Laboratory. It recognizes the need to develop practical demonstrations of the potential of GIS in this field whilst at the same time conducting research on some funda- mental issues.The strategy consists of a number of elements: to establish a settlement and infrastructure database large enough to test the functionality of GIS in a range of applications and to present significant issues of database management and development, to work closely with governmental and commercial clients for GIS products both in order to expand the database itself and to identify and resolve the problems involved in practical GIS applications, and, finally, to evaluate data sources in relation to database needs and objectives and to estab- lish the criteria for the improvement of data quality.

In the next two years we see two broad, essentially nontechnical requirements that need to be developed alongside the technical work, both of which can be met within the context of a set- tlement and infrastructure database. These are, to explore the substantive relationship between settlement and infrastructure through improvements to the database itself and to monitor the

Advances and Applications in Geographic Information Systems in the UK 241

role of settlement and infrastructure databases in the planning decision-making process. Both of these objectives will require continuing close contact with GIS users outside academia.

Acknowledgements: The author would like to thank the following people who have been closely associated with the work described in the case studies: Laurie Becker, Nick Green, Neville Mackay, Paul Brignall, Graham Sadler, Simon Lewis, and Mike Bamsley. The support of ESRC under the RRL programme is also gratefully acknowledged.

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