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R-tree node has a maximum of 12 entries. A typical data set size for a CIS geounit is 30,000 to 50,000 topological entities and 8,000 to 12,000 cartographic entities. Performance With the data set size and R-tree organization described in the above paragraph, virtual windowing and entity pick functions can be performed with response times adequate to support interactive operations. The R-tree search time is well described by the following relation: TR = A m log n + B where T = retrieval time, wall clock m = the number of retrieved entities n = the total number of entities in the R-tree index A = a constant B = b constant -4 In our application, A is about 2.5 x 10 seconds and B is about 4 seconds. Note that the search time is linear in number of retrieved entities but logarithmic in the size of the database. The slow search time growth with tree size is a main advantage of tree structures. Future Possibilities Most of the nodes in an R-tree are at the leaf level, but most of the node traversals during search, intersection, and deletion take place at levels in the tree above leaf level. An obvious R-tree usage speed improvement could be obtained by holding all R-tree nodes above leaf level in random access memory. The number of node entities above leaf level is approximately equal to n/m (m-1) where n is the total number of database entries and m is the average node fill rate. For n = 50,000 and m = 10, the number of nodes above leaf level in 555, and these can be held in 140 k bytes of memory. Preliminary work reveals that a performance improvement of between 6 and 10 to 1 is possible with this mechanism. R-trees are in no way restricted to two-dimensional spatial indexing. They generalize readily to a space of an arbitrary number of dimensions. An obvious extension is to include the elevation of features in a 3-D minimum bounding rectangular solid (MBRS) and use the MBRS's to build an R-tree index. Such an index might be useful for the use of a CIS for the production of special products such as air navigation hazard guides. Less obvious is the possibility of treating attributes, properly hierarchically coded, as a "dimension" of an abstract space, and including the attribute dimension in the R- tree index. Such an implementation would permit rapid simultaneous spatial and cartographic layer retrievals from the database. For example, a query to obtain and display all hydrologic features in a horizontal zone could be quickly processed. 199
REFERENCES Guttman, A. 1984, R-Trees: A Dynamic Index Structure for Spatial Searching: Proc. of ACM SIGMOD Conference on Management of Data, Boston, June 1984. Roussopoulos, N. and D. Leifker 1985, Direct Spatial Search on Pictorial Databases Using Packed R-trees: ACM Transactions on Database Systems, Vol.5, 1985, pp. 17-31. 200
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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
Page 159 and 160: 1. Sampling to determine the sample
Page 161 and 162: A simcle multiscale model . Instead
Page 163 and 164: Complex multiscale models. The one-
Page 165 and 166: Mandelbrot, B.B., 198E. The Fractal
Page 167 and 168: A number of authors have proved the
Page 169 and 170: Once having the Fourier-transf arm
Page 171 and 172: From the above listed aspects (2) i
Page 173 and 174: A considerably large number of labo
Page 175 and 176: Oliver,M.A., R.Webster (1936) Semi-
Page 177 and 178: model can be used as the basis for
Page 179 and 180: ts phase space by analogy to phase
Page 181 and 182: Figure 3. Classified 64 by 64 raste
Page 183 and 184: in the delimitation of domains in p
Page 185 and 186: Mark, D.M. and Aronson, P.B. 1984,
Page 187 and 188: To make decisions about this world,
Page 189 and 190: Limitations inherent to the modeliz
Page 191 and 192: operations delimiting rights to the
Page 193 and 194: Uncertainty absorption is very diff
Page 195 and 196: Minsky, M. L. 1965, Matter, Minds,
Page 197 and 198: data, covering extremely large area
Page 199 and 200: Figure 1. a) Test data set with, tr
Page 201 and 202: Figure 2. Four pyramids - line segm
Page 203 and 204: convenient to consider grids to be
Page 205 and 206: REFERENCES Davis, J.C., 1973, Stati
Page 207 and 208: the data element, in this case an a
Page 209: EXPERIENCE WITH R-TREES IN A CIS LA
Page 213 and 214: World Shoreline Vectors Shoreline v
Page 215 and 216: Table 4. Applications of gridded el
Page 217 and 218: classes desired for a given applica
Page 219 and 220: Table 6. The impact of block size o
Page 221 and 222: Jones, Christopher B. and Abraham,
Page 223 and 224: DISADVANTAGES OF A SINGLE COVERAGE
Page 225 and 226: faster and less complex than genera
Page 227 and 228: NOS detailed source data 20 meter r
Page 229 and 230: si '- /W^ ?f ? ( " ( I?
Page 231 and 232: REFERENCES Aronson P. and Morehouse
Page 233 and 234: with the purpose of optimizing geom
Page 235 and 236: purposes, the value 1/e may be thou
Page 237 and 238: V describing points/ / y/ B / /•*
Page 239 and 240: clarity, the number of describing p
Page 241 and 242: ACKNOWLEDGMENTS The author wishes t
Page 243 and 244: INTRODUCTION In any application of
Page 245 and 246: 7T 0 — 7T X Hay River Figure 1: A
Page 247 and 248: 0 — 7T Alluvial fan contour Figur
Page 249 and 250: Peucker, 1973) can be used here; th
Page 251 and 252: Mark, D. M., 1985, Fundamental spat
Page 253 and 254: •©—e—©—e- Figure 1.1: Spl
Page 255 and 256: SLAU- 9/1000 Figure 1.5: Results of
Page 257 and 258: 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
Page 392 and 393:
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
Page 458 and 459:
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
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44* 444 444 ICO 9 CO 9CO to CO ECO
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IMPROVEMENT OF GBF/DIME FILE COORDI
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Establishment of Control Points In
Page 496 and 497:
transformation parameters for a par
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of using a fixed set of transformat
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vertex coordinates into exact corre
Page 502 and 503:
Friedman, J., Baskett, F. and Shust
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and small freehold land. It does no
Page 506 and 507:
The next step in the planning proce
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TSO on UNB's IBM 3090 mainframe com
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3. Kettela, E.G. 1975. Aerial spray
Page 512 and 513:
The integration of developing techn
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and photographic processes are used
Page 516 and 517:
Figure 4 EXAMPLE OF PUBLISHED FIRM
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EXAMPLE OF COMPUTER-GENERATED FIRM
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BIBLIOGRAPHY Federal Emergency Mana
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more detailed introduction to exper
Page 524 and 525:
MAPEX is a rule-based system for au
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discrimination nets, Click et al (1
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example, FES (Goldberg et al, 1984.
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Pereira, L.M., P. Sabatier, and E.
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e flexible enough to address a wide
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intermediate hypotheses are "posted
Page 536 and 537:
All cited rules become part of the
Page 538 and 539:
The Geographic Information System L
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geographic knowledge system applies
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In view of this it is not suprising
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placement, the main area of cartogr
Page 546 and 547:
EXPERT SYSTEM INTERFACE TO A GEOGRA
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ASPENEX automates the analysis of s
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Rule-Base Creation SYSTEM OPERATION
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the rulebase created by the aspen e
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AUTOMOBILE NAVIGATION IN THE PAST E
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oadside equipment to provide equipp
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algorithm and provides step-by-step
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DRIVER INPUTS • DESTINA1ION • R
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Vehicular Technology Conference, 35
Page 564 and 565:
Map retrieval is also crucial to na
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elative coordinate accuracy is very
Page 568 and 569:
destination does not require knowin
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PRESENTATION The extremes of styles
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REFERENCES Ashkenazi, V. 1986, Coor
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decision-making, learning, and natu
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that these travellers felt that wri
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e weak, if present at all. Since ma
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Figure 2: An example of a distorted
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directions were produced in real ti
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Figure 1. Concept of an Automatic V
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AVL COMPONENTS AND THEIR FUNCTIONS
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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
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REFERENCES Cooke, D.F., Vehicle Nav
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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
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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
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Table 5. Fuzzy Membership Values fo
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Table 7. Results of Zimraermann's M
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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
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PAT GRP NUM 1 2 3 4 5 6 7 8 9 10 11
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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
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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
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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
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next box. The entry (m = s) is ther
Page 710 and 711:
Figure 3.-Portion of test data set
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PRACTICAL EXPERIENCE WITH A MAP LAB
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£ 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
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AUTOMATIC RECOGNITION AND RESOLUTIO
Page 722 and 723:
FUNCTION REQUIRED l)ls a point on a
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distance by finding the square root
Page 726 and 727:
Proportional Radial Enlargement As
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distribution). If the thresholds ar
Page 730 and 731:
CALCULATING BISECTOR SKELETONS USIN
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Thiessen centroid than to any other
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there are exactlv n boundary skelet
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AREA MATCHING IN RASTER MODE UPDATI
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THE AREA MATCHING PROCESS This proc
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defaults are made of very short seg
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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
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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
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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
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2. An Overview of MAPS The MAPS spa
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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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