Abstract
The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographical context. Geographical databases need to be enriched in terms of shape description structures (geometrical knowledge), knowledge of appropriate order of operations and of appropriate algorithms (procedural knowledge). Stored knowledge should take account of semantic and morphological characteristics, and of cartographic constraints.
This paper proposes and discusses three experiments on knowledge acquisition using unsupervised and supervised learning techniques. In order to exploit geometrical shape knowledge, classifications were computed according to a set of morphological measures using unsupervised learning. Choice of appropriate operations was determined by the results of a test with IGN cartographers considering line characteristics. These results were given to a supervised learning algorithm, along with corresponding computed measures in order to discover rules. The approach and the resulting rules are presented and discussed. Tests have also been conducted on the tuning of parameter values, applying a Gaussian smoothing tolerance value to a set of lines using the supervised learning algorithm. The values obtained by means of the learning algorithm have been compared with interactive choices of an expert. Results are promising with a prediction rate higher than 80% having been obtained.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M. Armstrong. “Knowledge classification and organization.” In Buttenfield and McMaster, editors, Map Generalization, pages 86-102. Longman Scientific & Technical London, 1991.
N.M. Bigolin and C. Marsala. “Fuzzy spatial SQL for fuzzy knowledge discovery in databases, to appear in.” Proc. 2nd European Symposium on Principles of Data Mining and Knowledge Discovery, September 1998.
K. Brassel and R. Weibel. A review and conceptual framework of automated map generalization. International Journal of Geographical Information System, 2(3):229-244, 1988.
B. Buttenfield. “A rule for describing line feature geometry.” In B. Buttenfield and R. McMaster, editors, Map Generalization, chapter 3, pages 150-171. Harlow Essex England: Longman scientific, 1991.
H. Chamussy, J. Charre, P. Dumolard, M.G. Durand, and M. Le Berre. “Initiation aux pratiques statistiques en géographie.” In Collection Géographie, pages 161-170. Ed. Masson, 1994.
G. H. Dutton. “Working Through the Scales: A Hierarchical Coordinate System for Digital Map Data.” Ph.D. Thesis, Department of Geography, University of Zurich, 1997.
M. Ester, H.-P. Kriegel, and J. Sander. “Spatial data mining: A database approach.” Advances in Spatial Databases, 5th International Symposium, Berlin, Germany, 47-66, July 1997. Lecture Notes in Computer Science n.1262, Springer.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. “From data mining to knowledge discovery in databases.” AI Magazine, 3(17):37-54, 1996.
J.-G. Ganascia. “CHARADE: A rule learning system,” in Proc. 10th International Joint Conference in Artificial Intelligence, 345-347, 1987.
J. Han, K. Koperski, and N. Stefanovic. “Geominer: A system prototype for spatial data mining,” in Proc. 1997 ACM-SIGMOD International Conference on Management of Data, 553-556, May 1997.
J.A. Hartigan and Wong M.A. “A k-means clustering algorithm,” Statistical Algorithms, 101-108, 1979.
M. Holsheimer and A. Siebes, “Data mining: the search for knowledge in databases,” Technical report 78p, CS-R9406, Centrum voor Wiskunde en Informatica, January 1994.
E. Imhof. “Positioning names on maps,” The American Cartographer, 2(2):128-144, 1975.
A.K. Jain and R.C. Dube. Algorithms for Clustering Data. Prentice Hall, 1988.
L. Kaufman and P.J. Rousseeuw. Finding groups in data: An introduction to cluster analysis. John Wiley and Sons, 1990.
K. Koperski and J. Han. “Discovery of spatial association rules in geographic information databases.” Advances in Spatial Databases, 4th International Symposium, 47-66, August 1995. Lecture Notes in Computer Science n.951, Springer.
F. Lecordix, C. Plazanet, and J.P. Lagrange. “A platform for research in generalization: Application to caricature,” GeoInformatica, 1 (2):161-182, August 1997.
F. Lecordix, C. Plazanet, J.P. Lagrange, F. Chirié, T. Banel, and Y. Cras, “Placement automatique des écritures d'une carte avec une qualité cartographique,” in Proc. EGIS'94, Paris, 1:22-32, 1994.
M. Leitner. “Prototype rules for automated map generalization.” Master's Thesis, University of New York, Buffalo, 1993.
D.B. Lenat. “The ubiquity of discovery,” Artificial Intelligence, 9:257-285, 1977.
R. B. McMaster and K.S. Shea. Generalization in digital cartography. Association of American Geographers, 1992.
C. Plazanet. “Enrichissement des bases de données géographiques: analyse de la géometrie des objets linéaires pour la généralisation cartographique,” Ph.D. Thesis, University of Marne La Vallée, Paris, France, October 1996.
C. Plazanet. “Geometry modelling for linear feature generalisation,” In Geographic Information Research: Bridging the Atlantic., chapter 17, pages 264-279. Taylor and Francis, ESF/NSF summer Institute Portland Maine USA, 1996.
C. Plazanet, J-G. Affholder, and E. Fritsch, “The importance of geometric modelling in linear feature generalization,” Cartography and Geographic Information Systems. Special Issue on Map Generalization, 22(4):291-305, 1996.
J.R. Quinlan, “Induction of decision trees,” Machine Learning, 1:81-106, 1986.
P.L. Rosin, “Multiscale representation and matching of curves using codons,” Computer Vision, Graphics and image processing journal, 55(4):286-310, 1993.
A. Ruas and C. Plazanet, “Strategies for automated generalization,” in Proc. 7th International Symposium on Spatial Data Handling, 1:6.1-6.18, 1996.
S.A. Saga, “Structural knowledge to support the generalization of a coast line,” in Proc. 10th International Cartographic Conference, 2:1491-1495, 1995.
H.A. Simon, “Why should machines learn?” In Machine Learning, An Artificial Intelligence Approach, volume 1, 28-3. Springer-Verlag, 1983.
G. Weger, “Cours de cartographie,” Ecole Nationale des Sciences Géographiques, 1994.
R. Weibel, S. Keller, and T. Reichenbacher, “Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computacional intelligence,” in Proc. COSIT, pages 139-156, 1995. Lecture Notes in Computer Science n.988.
J.-D. Zucker, V. Corruble, J. Thomas, and G. Ramalho, “Dice: a discovery environment integrating inductive bias,” In AAAI-94, Workshop on Knowledge Discovery in Databases, Seattle, USA., 275-286, 1994.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Plazanet, C., Bigolin, N.M. & Ruas, A. Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization. GeoInformatica 2, 315–333 (1998). https://doi.org/10.1023/A:1009753320636
Issue Date:
DOI: https://doi.org/10.1023/A:1009753320636