Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The hB-tree: a multiattribute indexing method with good guaranteed performance
ACM Transactions on Database Systems (TODS)
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
On two-dimensional indexability and optimal range search indexing
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The P-range tree: a new data structure for range searching in secondary memory
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
The gliding box method for multifractal modeling
Computers & Geosciences - Fractals and Multifractals
Multidimensional binary search trees used for associative searching
Communications of the ACM
Spatial cluster test based on triplets of districts
Computers & Geosciences
Delaunay implementation to improve kriging computing efficiency
Computers & Geosciences
Introduction to Algorithms
QUADRO: a program to estimate principal curvatures of folds
Computers & Geosciences
A Robust Multi-Attribute Search Structure
Proceedings of the Fifth International Conference on Data Engineering
Spatial Data Structures: Concepts and Design Choices
Algorithmic Foundations of Geographic Information Systems, this book originated from the CISM Advanced School on the Algorithmic Foundations of Geographic Information Systems
Optimal dynamic interval management in external memory
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
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Digital terrain models (DTMs) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregularly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly spaced data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.