The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Spatial priority search: an access technique for scaleless maps
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Reactive data structures for geographic information systems
Reactive data structures for geographic information systems
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R-File: An Efficient Access Structure for Proximity Queries
Proceedings of the Sixth International Conference on Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Spatial Indexing with a Scale Dimension
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Efficient Access Technique Using Levelized Data in Web-Based GIS
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
An Access Method for Integrating Multi-scale Geometric Data
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
A multi-level data structure for vector maps
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Extensions of GAP-tree and its implementation based on a non-topological data model
International Journal of Geographical Information Science
A quantitative scale-setting approach for building multi-scale spatial databases
Computers & Geosciences
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An important requirement in Geographic Information Systems (GISs) is the ability to display numerous geometric objects swiftly onto the display window. As the screen size is fixed, the scale of a map displayed changes as the user zooms in and out of the map. Spatial indexes like R-tree variants that are particularly efficient for range queries are not adequate to generate large maps that may be displayed at different scales. We propose a generalization for the family of R-trees, called the Multi-scale R-tree, that allows efficient retrieval of geometric objects at different levels of detail. The remedy offered here consists of two generalization techniques is cartography: selection and simplification. Selection means some objects that are relatively unimportant to the user at the current scale will not be retrieved. Simplification means that, given a scale, the display of objects is shown with sufficient but not with unnecessary detail. These two together reduce the time required to generate a map on a screen. A major obstacle to the effectiveness of a Multi-scale R-tree is the proper decomposition of geometric objects required by the simplification technique. To investigate the problem, a Multi-scale Hilbert R-tree is designed and implemented. Extensive experiments are then performed on real-life data and a general and simple design heuristic is found to solve the decomposition problem. We show that, with the proposed design heuristic, the Multi-scale R-tree is a desirable spatial index for both querying and display purposes.