GIS and Networks: Business Anomalies and Topological Errors, Linear Elements Case ()
Omar Bachir Alami,
Hatim Lechgar,
Mohamed El Imame Malaainine,
Fatima Bardellile
Ecole Hassania des Travaux Publics, Casablanca, Maroc.
Faculté des Sciences Ain Chock, Université Hassan II, Casablanca, Maroc.
DOI: 10.4236/jgis.2014.62013
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Abstract
The quality control of geographic data, especially from a topological and semantic perspective, is a must for its good management and use. However, while updating spatial data, some sorts of anomalies are affecting it, due to negligence or non-respect of business and topological rules. Hence the necessity of a solution that enables detecting theses anomalies. Nowadays, Geographic Information Systems (GIS) have become essential for decision-making in any project that manages spatial data. GIS functionalities and tools give the possibility of defining the topology of vector data. Nevertheless, the topology alone does not respond to the needs in matter of defining specific rules for every facility network. This means, we could find topological errors in the spatial database, but taking into account business rules, they are correct and vice versa. The main objective of this article is firstly to define business rules for the linear elements of a network. Secondly to premeditate the algorithms that detect the violation of the defined rules in order to have a good quality control of geographic data.
Share and Cite:
Alami, O. , Lechgar, H. , Malaainine, M. and Bardellile, F. (2014) GIS and Networks: Business Anomalies and Topological Errors, Linear Elements Case.
Journal of Geographic Information System,
6, 115-122. doi:
10.4236/jgis.2014.62013.
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1]
|
ISO (1994) International Organization for Standardization, Norme Internationale—Management de la qualite et assurance qualite—Vocabulaire. ISO 8402:1994 (E/F/R), 2eme edition.
|
[2]
|
Bonin, O. (2002) Modele d’erreurs dans une base de donnees geographiques et grandes deviations pour des sommes ponderees; application à l’estimation d’erreurs sur un temps de parcours. These de doctorat, Universite de Paris VI, 147.
|
[3]
|
Papadopoulos, A.N. and Manolopoulos, Y. (2005) Nearest Neighbor Search: A Database Perspective. Series in Computer Science. Springer, 170.
|