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geographic knowledge system applies expertise-based reason ing capabilities to environmental planning problems - land use and management, environmental protection and monitoring, resource utilization and assessment - to identify impacts of and alternatives to development, to describe environmental effects of various activities, and to explain complicated resource issues. The sophistication of the EGKS concept requires an equally sophisticated inference engine as well as data, information, and knowledge bases that are complete, accurate, current, and consistent. Where this is done, planning and decision- making in complex natural and institutional environments can benefit enormously. REFERENCES Blumberg, M. H. 1975, A Generalized Geographic Information System for Managing a Non-Redundant Demographic and Resource Data Base: Computers, Local Government and Productivity, Volume !_, O. M. Anochie, ed., p.181-193, URISA,Chicago. (Papers from the Thirteenth Annual Conference of the Urban and Regional Information Systems Association, August 24-28, 1975, SeattleT)IBM, Federal Systems Division. Dangermond, J. 1983, A Classification of Software Components Commonly Used in Geographic Information Systems: <strong>Proceedings</strong>, United States/Australia Workshop on Design and Implementation of Computer-Based Geographic Information Systems, D~. Peuquet and J. OCallaghan, eds., p. 70-91, IGU Commission on Geographical Data Sensing and Processing, Amherst (NY). Environmental Systems Research Institute. Hayes-Roth, F., Waterman, D. A., and Lenat, D. B. 1983, Building Expert Systems, Addison-Wesley Publishing Company, Inc.,ReadingCMA).pTeknowledge Series in Knowledge Engineering.) Teknowledge, Inc.; The Rand Corporation; Stanford University. Hewitt, C. April 1985, The Challenge of Open Systems: Byte, 10(4): 223-244. MIT Artificial Intelligence Laboratory. Infotech 1981, Machine Intelligence (collected papers), Pergamon Infotech Limited, Maidenhead, Berkshire, England. 407 pages. Waterman, D. A. 1981, Rule-Based Expert Systems: Machine Intelligence, Infotech, ed., p. 323-338, Pergamon Infotech Limited, Maidenhead, Berkshire, England. 407 pages. The Rand Corporation. 529
Are Cartographic Expert Systems Possible? Peter F. Fisher Department of Geography, Kent State University, Kent, Ohio 44242. William A. Mackaness School of Geography, Kingston Polytechnic, Penryhn Road, Kingston Upon Thames, KT1 2EE, UK. ABSTRACT One of the major current thrust areas for computer software development is artificial intelligence and particularly expert systems. Several attempts have been made to implement cartographic design expert systems. None of these, however, can either understand why particular decisions are reached, or explain the reasoning to the user. This self-knowledge is one of the principle properties of any expert system and so it is doubtful whether any of the systems reported to date deserve the epithet "expert". This omission is not the fault of the system developers, but is caused by a lack of any systematised and accepted methodology for cartographic assessment. The cartographic community is urged to address this problem expeditiously. INTRODUCTION There can be no doubt that artificial intelligence and its associated programming techniques have made a major and increasing contribution to the field of computer science in recent years. A brief perusal of the shelves of any academic bookshop stocking computer science textbooks will reveal any number of tomes, too many to list here, with titles varying on the theme of Artificial Intelligence and related areas. Equally there is an increasing literature on the application of these programming techniques, particularly expert systems, to many science subjects. In the area of Geography and Geology, a recent review by the authors and others revealed eighteen expert systems of which details are published, while Waterman (1986) identifies some sixteen systems and any number of other systems are in preparation. These could all be described as experimental, to a degree, but have proved most successful and are in day-to-day use in the oil and mineral extraction industries (e.g. PROSPECTOR Cambell et al. 1982, MUD Kahn and McDermott 1984) and in environmental management (FIRES Davis et al. 1986). 530
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Proceedings Eighth International Sy
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1987 by the American Society for Ph
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A NOTE ON THE FUTURE OF AUTO-CARTO
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Algorithms for spatial search or qu
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Research into electronic maps and a
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Sandhu, Jatinder 403 Shea, K. Stuar
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integration of the findings on tech
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departments of municipalities are p
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systems, similar cost reduction in
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exist here: it is rarely possible t
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REFERENCES Chris man, N. and Nieman
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THE USER PROFILE FOR DIGITAL CARTOG
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second is to reduce all lines to a
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OVERLAY PROCESSING IN SPATIAL INFOR
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digital terrain models, or magnetic
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(2-dimensional) or volumes (3-dimen
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whereas the lattice induced by spat
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5.2 Overlay Operation One of the mo
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permit the determination of the len
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more about the quality of the avail
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of the user interface and would res
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FUNDAMENTAL PRINCIPLES OF GEOGRAPHI
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of continuous space (the model of A
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look like is transmitted through th
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(Sullivan and others, 1985). On the
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PRESCRIPTIONS To carry out the prin
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AN ADAPTIVE METHODOLOGY FOR AUTOMAT
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They reorganize available space and
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Global Filtering Basics: This filte
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If the geometry is determined throu
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SYSTEMATIC SELECTION OF VERY IMPORT
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Improvements The first improvement
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RESULTS ANALYSIS There are two test
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Figure 4. A TIN generated from VIP
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only or the most important one. Unt
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terrain surface, may have in corres
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Advantages of determining the Media
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egards points, only the endpoints o
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contours some measures of size and
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MEASURING THE DIMENSION OF SURFACES
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dimension of an entity, is constant
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terrain. In order to avoid a sampli
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APPLICATIONS TO DIGITAL ELEVATION M
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1.20 1.16 • D I M 1.12 E N S 1.06
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Stability of Map Topology and Robus
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The 0-cells in the usual topologica
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Notice in Figure 1 that the interme
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Algorithm for computing robustness
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We may sum the forces by a straight
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and computational algorithms, which
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The geopositioning model presented
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It should be noted that only the fr
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While Voronoi polygons have often b
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additional vector rotation about th
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inconsistency. The problem stems fr
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Chain Intersection Determining chai
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node(6,[h(l8),t(l9),t(24),h(21)]).
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Sliver Removal. We remove a sliver
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Little, J.J. and Peucker, T.K. 1979
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measured control points. In this pa
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TEST RESULTS For testing the above
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A SPATIAL DECISION SUPPORT SYSTEM F
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data by time period, by category, a
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BASIC version of the PLACE suite re
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procedural language. The classifier
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FIGURE 1: SOFTWARE COMPONENTS FOR S
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Realistic Flow Analysis Using a Sim
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TIN FORMAT VS. MATRIX FORMAT The pr
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One of the major challenges involve
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Because TINFLOW is a PC-based GIS,
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Art; I DitH ot twin 1.1610 Strew He
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RATIONALE Gridded surface data sets
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5. For each polygon, for each cell
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A mapping function was used to topo
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CONCLUSIONS This depression-finding
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Methods For spatial analusis The tw
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1. Sampling to determine the sample
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A simcle multiscale model . Instead
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Complex multiscale models. The one-
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Mandelbrot, B.B., 198E. The Fractal
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A number of authors have proved the
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Once having the Fourier-transf arm
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From the above listed aspects (2) i
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A considerably large number of labo
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Oliver,M.A., R.Webster (1936) Semi-
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model can be used as the basis for
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ts phase space by analogy to phase
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Figure 3. Classified 64 by 64 raste
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in the delimitation of domains in p
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Mark, D.M. and Aronson, P.B. 1984,
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To make decisions about this world,
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Limitations inherent to the modeliz
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operations delimiting rights to the
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Uncertainty absorption is very diff
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Minsky, M. L. 1965, Matter, Minds,
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data, covering extremely large area
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Figure 1. a) Test data set with, tr
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Figure 2. Four pyramids - line segm
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convenient to consider grids to be
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REFERENCES Davis, J.C., 1973, Stati
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the data element, in this case an a
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EXPERIENCE WITH R-TREES IN A CIS LA
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REFERENCES Guttman, A. 1984, R-Tree
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World Shoreline Vectors Shoreline v
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Table 4. Applications of gridded el
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classes desired for a given applica
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Table 6. The impact of block size o
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Jones, Christopher B. and Abraham,
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DISADVANTAGES OF A SINGLE COVERAGE
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faster and less complex than genera
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NOS detailed source data 20 meter r
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si '- /W^ ?f ? ( " ( I?
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REFERENCES Aronson P. and Morehouse
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with the purpose of optimizing geom
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purposes, the value 1/e may be thou
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V describing points/ / y/ B / /•*
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clarity, the number of describing p
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ACKNOWLEDGMENTS The author wishes t
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INTRODUCTION In any application of
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7T 0 — 7T X Hay River Figure 1: A
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0 — 7T Alluvial fan contour Figur
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Peucker, 1973) can be used here; th
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Mark, D. M., 1985, Fundamental spat
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•©—e—©—e- Figure 1.1: Spl
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SLAU- 9/1000 Figure 1.5: Results of
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t-0 I.I 7.4 /.6 1.8 2.0 Figure 2.1:
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THE TIGER STRUCTURE Christine Kinne
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to another tail record. There are f
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Since records are fixed length, a n
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areal data is stored, but additiona
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KEY TO SUBFILE ABBREVIATIONS (CONTI
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TOPOLOGICAL ENTITIES Corbett's topo
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TOPOLOGICAL RULES The preceding def
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File and the new linear feature ref
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Encoding and Referencing (TIGER) Sy
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of the boundary are found and corre
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CONCLUSION The GTUB routines have b
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SELECTION OF EQUIPMENT The least ef
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operations. The site and staff for
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RESULTS Table one shows the numeric
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would no longer have to determine a
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THE DATA BASE ADVANTAGE Most of the
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SUMMARY AND CONCLUSIONS The major d
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the complexity o-f I/O processing.
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is on one and only one topological
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•features represented in the data
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intersection is -found -for this -f
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An easy implementation o-f spatial
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organisations most precious asset -
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no reason why we should not model t
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- A VERTEX identifies a unique spat
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to the organisation and indexing of
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the retrieval of data from the disp
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original goals, through design, and
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Contents and Queries Each geographi
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number of tables required. On the o
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generic, db-internal read/write/del
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we are guaranteed that each occurre
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The db bottlenecks we found were, f
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Bibliography [1] Date, C.J. 1983 -
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model is an object-oriented extensi
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SPECIALIZATIONS FOR SPATIAL DATA Th
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for each x in S, for each y in S if
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entity IMAGE PIXELS(IMAGE) - set of
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Orenstem, J.A. 1984, "A Class of Da
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understand the data model on which
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Internally, KGIS is implemented on
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AREA > 10; Spatial relationships be
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Once established, a GEOVIEW remains
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___________, 1985, THE USE OF SPATI
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• the cross sections, profiles an
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• Rigid and homogeneous ores can
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Borehole data \ Geologist's ^^ know
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Figure 3 : A Portion of the Ore Bod
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REFERENCES Baumgart, E.G. (1975) -
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This paper is organized into five s
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yields the resulting table: Poly* 1
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tightly bound to the map; there is
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A partial example of the relational
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REFERENCES Chrisman N. and Niemann,
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GEOGRAPHIC DATABASE TOPOLOGY SPATIA
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of a coordinate in n-space, usually
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Airport refines Aviation; — This
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THE dbmap SYSTEM Donald F. Cooke Ge
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procedurally, like Pascal, FORTRAN
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een lassoed (and line-segments in o
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Figure 4 Result of Lasso Operation
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The goal of a DSS is to help decisi
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chosen structure must provide a mea
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First, a general schema diagram is
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Hopkins, L.D., and Armstrong, M.P.
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******* DATABASE IDENTIFICATION AND
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Because of these limitations, furth
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cannot share the data. A minicomput
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Historically the whole orientation
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To reach the stated goal of this pa
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mapping techniques conceptualized a
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The future skill level, and trainin
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€6€ COMPUTER SCIENTIST ELECTRIC
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U* '^ OUTPUT \ / REVIEWIM. CORRECTI
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We may conceptually divide a VNA in
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most interest for transmitting navi
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necessary) in such a display. This
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ENHANCEMENT AND TESTING OF A MICROC
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make maximum use of the limited, lo
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I Text Display Window (Toggle On/Of
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as well, using the PLINK86 Plus ove
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AN INTEGRATED PC BASED CIS FOR INST
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RAW DATA INPUT DATA BASE MAPh ANALY
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Figure 3. Example of output from SH
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Figure 8. CONTOUR of elevation on v
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Figure 11. COLOR map on printer of
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IDRISI : A COLLECTIVE GEOGRAPHIC AN
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felt to be negligible compared to t
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Although the IDRISI system was prim
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data file, and a second backwards t
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TABLE 1 : IDRISI PROGRAM MODULES IN
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CLASSLESS CHOROPLETH MAPPING WITH M
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polygon. The process of selecting s
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There are a number of places where
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COMPUTER-ASSISTED TERRAIN ANALYSIS
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2. Input. A digitizing tablet to al
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terrain analysis data. Items 3. and
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Database Structure Currently, DMA-p
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RESULTS OF THE DANE COUNTY LAND REC
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E. G.
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section maps of tax parcels maintai
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Public Land Survey System (PLSS) co
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Sheets, Proc. ACSM, 1:153-161. Chri
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von Meyer, N.R. 1984b. Westport Ine
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While meeting the above criteria pr
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oundary line. An example of the sam
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Four groups of approximately thirty
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LEGEND MULTIPLE BOUNDARIES STATE, C
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TABLE 2 shows that more respondents
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What features are considered when d
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position is insignificant. Thus, th
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ASSESSING COMMUNITY VULNERABILITY T
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Yale University was selected for st
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distribution of hazardous materials
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»++ 0000000001000OCCCOOOOOOCOOOOCO
Page 490 and 491: 44* 444 444 ICO 9 CO 9CO to CO ECO
Page 492 and 493: IMPROVEMENT OF GBF/DIME FILE COORDI
Page 494 and 495: Establishment of Control Points In
Page 496 and 497: transformation parameters for a par
Page 498 and 499: of using a fixed set of transformat
Page 500 and 501: vertex coordinates into exact corre
Page 502 and 503: Friedman, J., Baskett, F. and Shust
Page 504 and 505: and small freehold land. It does no
Page 506 and 507: The next step in the planning proce
Page 508 and 509: TSO on UNB's IBM 3090 mainframe com
Page 510 and 511: 3. Kettela, E.G. 1975. Aerial spray
Page 512 and 513: The integration of developing techn
Page 514 and 515: and photographic processes are used
Page 516 and 517: Figure 4 EXAMPLE OF PUBLISHED FIRM
Page 518 and 519: EXAMPLE OF COMPUTER-GENERATED FIRM
Page 520 and 521: BIBLIOGRAPHY Federal Emergency Mana
Page 522 and 523: more detailed introduction to exper
Page 524 and 525: MAPEX is a rule-based system for au
Page 526 and 527: discrimination nets, Click et al (1
Page 528 and 529: example, FES (Goldberg et al, 1984.
Page 530 and 531: Pereira, L.M., P. Sabatier, and E.
Page 532 and 533: e flexible enough to address a wide
Page 534 and 535: intermediate hypotheses are "posted
Page 536 and 537: All cited rules become part of the
Page 538 and 539: The Geographic Information System L
Page 542 and 543: In view of this it is not suprising
Page 544 and 545: placement, the main area of cartogr
Page 546 and 547: EXPERT SYSTEM INTERFACE TO A GEOGRA
Page 548 and 549: ASPENEX automates the analysis of s
Page 550 and 551: Rule-Base Creation SYSTEM OPERATION
Page 552 and 553: the rulebase created by the aspen e
Page 554 and 555: AUTOMOBILE NAVIGATION IN THE PAST E
Page 556 and 557: oadside equipment to provide equipp
Page 558 and 559: algorithm and provides step-by-step
Page 560 and 561: DRIVER INPUTS • DESTINA1ION • R
Page 562 and 563: Vehicular Technology Conference, 35
Page 564 and 565: Map retrieval is also crucial to na
Page 566 and 567: elative coordinate accuracy is very
Page 568 and 569: destination does not require knowin
Page 570 and 571: PRESENTATION The extremes of styles
Page 572 and 573: REFERENCES Ashkenazi, V. 1986, Coor
Page 574 and 575: decision-making, learning, and natu
Page 576 and 577: that these travellers felt that wri
Page 578 and 579: e weak, if present at all. Since ma
Page 580 and 581: Figure 2: An example of a distorted
Page 582 and 583: directions were produced in real ti
Page 584 and 585: Figure 1. Concept of an Automatic V
Page 586 and 587: AVL COMPONENTS AND THEIR FUNCTIONS
Page 588 and 589: OUTPUT FACILITIES V7MIC1Z «IflT KA
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need extensive customizing. This cu
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PRACTICAL EXPERIENCE WITH AVL 2000
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ACKNOWLEDGEMENT This research has b
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separate AVL system is needed (spec
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Sources Two plausible sources of a
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o t=z ATTRIBUTE RELATIONSHIP ENTITY
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(a) optimal route between the pair
Page 604 and 605:
REFERENCES Cooke, D.F., Vehicle Nav
Page 606 and 607:
THE BBC DOMESDAY SYSTEM: A NATION-W
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which are not commonplace: it permi
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Geochenrstrj Climate VNater fiature
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(ii) measure area and distance in m
Page 614 and 615:
Rhind D.W. and Mounsey H.M. (1986).
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language expression. One of the mot
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This process begins from a position
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E(l(k)) be the expectation of I ( k
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There are several approaches to con
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In this paper we will discuss preml
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Table 2. Length of Sessions and The
Page 628 and 629:
Table 5. Fuzzy Membership Values fo
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Table 7. Results of Zimraermann's M
Page 632 and 633:
Chamberlain, D.D. and R.F. Boyce. 1
Page 634 and 635:
ACCESSING LARGE SPATIAL DATA BASES
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WORKSTATION #2 1 - IBM-AT; 640K, 20
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RECORD 1 —— coordinates 1-256 f
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Future developments include the pol
Page 642 and 643:
Once features of two coverages have
Page 644 and 645:
Figure 3a Partial street network co
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Figure 3e Coverages aligned with 50
Page 648 and 649:
Solving the Feature Matching Proble
Page 650 and 651:
References Chen, Z. and A. Guevara,
Page 652 and 653:
determined that a conversion of dra
Page 654 and 655:
PAT GRP NUM 1 2 3 4 5 6 7 8 9 10 11
Page 656 and 657:
various raster and vector operation
Page 658 and 659:
Figure 4a - Portion of scan-digitiz
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the set of points. This will introd
Page 662 and 663:
which represents the terrain by a t
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leaf for 4 and 5). Chen and Tobler
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fixed polynomial order for each run
Page 668 and 669:
RESULTS As noted above, the program
Page 670 and 671:
REFERENCES Chen, Z.-T., and Tobler,
Page 672 and 673:
Shortcomings in Existing Raster-to-
Page 674 and 675:
Template design parameters are base
Page 676 and 677:
RESULTS REPORT type RESULTS REPORT
Page 678 and 679:
Significance of the AFT Technology
Page 680 and 681:
REFERENCES Antell, R.E. (1983), "Th
Page 682 and 683:
Grid systems do not permit vertical
Page 684 and 685:
SCHEMA structures an urban model ar
Page 686 and 687:
The second data structure regards t
Page 688 and 689:
The resulting polygon "hole" is the
Page 690 and 691:
REFERENCES Brassel, Kurt E., and Do
Page 692 and 693:
The link between these two bodies o
Page 694 and 695:
The same technique of Fourier analy
Page 696 and 697:
The similarities between this and t
Page 698 and 699:
In many respects, a terrain model c
Page 700 and 701:
AN ALGORITHM FOR LOCATING , CANDIDA
Page 702 and 703:
The left and right boundaries of a
Page 704 and 705:
limits. The overall problem of gene
Page 706 and 707:
To find the cluster limits, the sim
Page 708 and 709:
next box. The entry (m = s) is ther
Page 710 and 711:
Figure 3.-Portion of test data set
Page 712 and 713:
PRACTICAL EXPERIENCE WITH A MAP LAB
Page 714 and 715:
£ X lfk = 1 k=1,2,...,K. (1) i If
Page 716 and 717:
Deleted Labels The optimization alg
Page 718 and 719:
Figure 2. Point Symbol Labels With
Page 720 and 721:
AUTOMATIC RECOGNITION AND RESOLUTIO
Page 722 and 723:
FUNCTION REQUIRED l)ls a point on a
Page 724 and 725:
distance by finding the square root
Page 726 and 727:
Proportional Radial Enlargement As
Page 728 and 729:
distribution). If the thresholds ar
Page 730 and 731:
CALCULATING BISECTOR SKELETONS USIN
Page 732 and 733:
Thiessen centroid than to any other
Page 734 and 735:
there are exactlv n boundary skelet
Page 736 and 737:
AREA MATCHING IN RASTER MODE UPDATI
Page 738 and 739:
THE AREA MATCHING PROCESS This proc
Page 740 and 741:
defaults are made of very short seg
Page 742 and 743:
POLYGONIZATION AND TOPOLOGICAL EDIT
Page 744 and 745:
If no label exists between the chai
Page 746 and 747:
these internal (non-boundary) chain
Page 748 and 749:
However, a cartographic database ne
Page 750 and 751:
"WYSIWYG" MAP DIGITIZING: REAL TIME
Page 752 and 753:
with tolerance circles such that it
Page 754 and 755:
CONCLUSION For many years a major i
Page 756 and 757:
implementation methodology and the
Page 758 and 759:
SPLIT SCREEN o o oo SIDE BY SIDE o
Page 760 and 761:
determined applicability aided by s
Page 762 and 763:
the 384 LPI resolution patch used t
Page 764 and 765:
TESTING A PROTOTYPE SPATIAL DATA EX
Page 766 and 767:
2. An Overview of MAPS The MAPS spa
Page 768 and 769:
containment tree can be used to eff
Page 770 and 771:
ROLE: BUILDING UNKNOWN MEDICAL CENT
Page 772 and 773:
and the segment direction within ea
Page 774:
seg : nodes: points south west: 20.
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