464
Views
14
CrossRef citations to date
0
Altmetric
Refereed Paper

Use of Artificial Neural Networks for Selective Omission in Updating Road Networks

&
Pages 38-51 | Received 04 Jul 2012, Accepted 26 Feb 2013, Published online: 05 Dec 2013

REFERENCES

  • Allouche M. K. and Moulin B.. (2005). ‘Amalgamation in cartographic generalization using Kohonen’s feature nets’, International Journal of Geographical Information Science, 19, pp. 899–914.
  • Anders K. H., Sester M. and Bobrich J.. (2007). ‘Incremental Update in an MRDB’, in 23th International Cartography Association Conference, Moscow, Aug 4–10.
  • Bação F., Lobo V. and Painho M.. (2008). ‘Applications of different self-organizing map variants to geographical information science problems’, in Self-Organizing Maps: Applications in Geographic Information Science, ed. by Agarwal P. and Skupin A., Chapter 2, John Wiley & Sons, New York.
  • Balboa J. L. G. and López F. J. A.. (2008). ‘Generalization-oriented road line classification by means of an artificial neural network’, Geoinformatica, 12, pp. 289–312.
  • Basheer I. A. and Hajmeer M. M.. (2000). ‘Artificial neural networks: fundamentals, computing, design, and application’, Journal of Microbiological Methods, 43, pp. 3–31.
  • Crowther P. S. and Cox R. J.. (2005). ‘A Method for Optimal Division of Data Sets for Use in Neural Networks’, Lecture Notes in Computer Science, 3684, pp. 1–67.
  • Chaudhry O. and Mackaness W.. (2005). Rural and urban road network generalization deriving 1∶250000 from OS MasterMap, http://www.era.lib.ed.ac.uk/bitstream/1842/1137/1/ochaudry001.pdf (accessed 31 January 2009).
  • Chen J., Hu Y. G., Li Z. L., Zhao R. L. and Meng L. Q.. (2009). ‘Selective omission of road features based on mesh density for automatic map generalization’, International Journal of Geographical Information Science, 23, pp. 1013–1032.
  • Crucitti P., Latora V. and Porta S.. (2006). ‘Centrality measures in spatial networks of urban roads’, Physical Review E, 73, pp. 0361251–5.
  • Dai H. and Macbeth C.. (1997). ‘Effects of learning parameters on learning procedure and performance of a BPNN’, Neural Network, 10, pp. 1505–1521.
  • Edwardes A. and Mackaness W. A.. (2000). ‘Intelligent Generalisation of Urban Road Network’, in Geographical Information Systems Research UK 2004 Conference (GISRUK 2000), pp. 81–85, York, Apr 5–7.
  • Fischer M. M. and Leung Y.. (2001). Geocomputational Modelling: Techniques and Applications, Springer, Berlin/Heidelberg.
  • Freeman L. C.. (1979). ‘Centrality in social networks: conceptual clarification’, Social Networks, 1, pp. 215–239.
  • Harrie L. and Hellström A. K.. (1999). ‘A prototype system for propagating updates between cartographic data sets’, The Cartographic Journal, 36, pp. 133–140.
  • Haunert J. H.. (2005). ‘Link Based Conflation of Geographic Datasets’, in 8th ICA Workshop on Generalisation and Multiple Representation, A Coruña, Jul 7–8.
  • Hearty ÁP and Gibney M. J.. (2008). ‘Analysis of meal patterns with the use of supervised data mining techniques – artificial neural networks and decision trees’, The American Journal of Clinical Nutrition, 88, pp. 1632–1642.
  • Ito F. and Murata A.. (2009). ‘Artificial neural network model estimating land use change in the southwestern part of Nagareyama City, Chiba Prefecture’, in New Frontiers in Urban Analysis: In Honor of Atsuyuki Okabe, pp. 65–79, CRC Press (Taylor & Francis Group), Boca Raton, FL.
  • Jiang B. and Claramunt C.. (2004). ‘A structural approach to the model generalization of urban street network’, GeoInformatica, 8, pp. 157–173.
  • Jiang B. and Harrie L.. (2004). ‘Selection of roads from a network using self-organizing maps’, Transactions in GIS, 8, pp. 335–350.
  • Karlik B. and Olgac A. V.. (2011). ‘Performance analysis of various activation functions in generalized MLP architectures of neural networks’, International Journal of Artificial Intelligence and Expert Systems, 1, pp. 75–122.
  • Kilpeläinen T. and Sarjakoski T.. (1995). ‘Incremental generalization for multiple representations of geographic objects’, in GIS and Generalization, ed. by Müller J C, Lagrange J P and Weibel R., pp. 209–218, Taylor & Francis, London.
  • Kohonen T.. (2001). Self-Organizing Maps, 3rd ed., Springer, Berlin.
  • Kreveld M. and Peschier J.. (1998). ‘On the Automated Generalization of Road Network Maps’, in 3rd International Conference on GeoComputation, http://www.geocomputation.org/1998/21/gc_21.htm (accessed 31 January 2010).
  • Leitner M. and Buttenfield B. P.. (1995). ‘Acquisition of procedural cartographic knowledge by reverse engineering’, Cartography and Geographic Information Systems, 22, pp. 232–241.
  • Li Z. L.. (2006). Algorithmic Foundation of Multi-scale Spatial Representation, CRC Press (Taylor & Francis Group), Bacon Raton, FL.
  • Li Z. L. and Choi Y. H.. (2002). ‘Topographic map generalization: association of road elimination with thematic attributes’, The Cartographic Journal, 39, pp. 153–166.
  • Li Z. L. and Zhou Q.. (2012). ‘Integration of linear- and areal-hierarchies for continuous multi-scale representation of road networks’, International Journal of Geographical Information Science, 26, pp. 855–880.
  • Mackaness W. A. and Beard M. K.. (1993). ‘Use of graph theory to support map generalization’, Cartography and Geographic Information Systems, 20, pp. 210–211.
  • Mackaness W.. (1995). ‘Analysis of urban road networks to support cartographic generalization’, Cartography and Geographic Information Systems, 22, pp. 306–316.
  • Mackaness W. and Mackechine G.. (1999). ‘Automating the detection and simplification of junctions in road networks’, GeoInformatica, 3, pp. 185–200.
  • Masters T.. (1993). Practical Neural Network Recipes in C++, Academic Press, New York.
  • Peschier J.. (1997). Computer aided generalization of road network maps. MSc thesis, Department of Computer Science, Utrecht University, Utrecht, The Netherlands.
  • Porta S., Crucitti P. and Latora V.. (2006). ‘The network analysis of urban roads: a dual approach’, Physica A: Statistical Mechanics and Its Applications, 369, pp. 853–866.
  • Rakotomalala R.. (2005). ‘TANAGRA: A Free Software for Research and Academic Purposes’, in EGC 2005, RNTI-E-3, Vol. 2, pp. 697–702, Amsterdam, Feb 14–16 (in French).
  • Refaeilzadeh P., Tang L. and Liu H.. (2009). ‘Cross-validation’, in Encyclopedia of Database Systems, pp. 532–538, Springer, Berlin.
  • Sester M.. (2005). ‘Optimization approaches for generalization and data abstraction’, International Journal of Geographical Information Science, 19, pp. 871–897.
  • Sester M., Anders K. H. and Walter V.. (1998). ‘Linking objects of different spatial data sets by integration and aggregation’, GeoInformatica, 2, pp. 335–358.
  • Thomson R. and Richardson D.. (1995). ‘A Graph Theory Approach to Road Network Generalisation’, in 17th International Cartographic Conference, pp. 1871–1880, Barcelona, Sep 3–9.
  • Thomson R. and Richardson D.. (1999). ‘The “Good Continuation” Principle of Perceptual Organization Applied to the Generalization of Road Networks’, in 19th International Cartographic Conference, pp. 1215–1223, Ottawa, Ont., Aug 14–21.
  • Thomson R. and Brooks R.. (2007). ‘Generalisation of geographical networks’, in Generalization of Geographic Information: Cartographic Modeling and Applications, ed. by Ruas A., Mackaness W. A. and Sarjakoski L. T., pp. 255–267, Elsevier, Oxford.
  • Touya G.. (2007). ‘A Road Network Selection Process Based on Data Enrichment and Structure Detection’, in 10th ICA Workshop on Generalisation and Multiple Representation, pp. 595–614, Moscow, Aug 2–3.
  • Töpfer F. and Pillewizer W.. (1966). ‘The principle of selection’, The Cartographic Journal, 3, pp. 10–16.
  • Webos P. J.. (1994). The Roots of Backpropagation, John Wiley & Sons, New York.
  • Werschlein T. and Weibel R.. (1994). ‘Use of Neural Networks in Line Generalization’, in EGIS ’94, pp. 76–85, Paris, Mar 30–Apr 1.
  • Yu Z.. (1993). The effects of scale change on map structure. Doctoral thesis, Department of Geography, Clark University, Worcester, MA.
  • Zhang Q.. (2004). ‘Road Network Generalization Based on Connection Analysis’, in 11th International Symposium on Spatial Data Handling, pp. 343–353, Leicester, Aug 23–25.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.