Computer Architectures For Spatially Distributed Data

Computer Architectures for Spatially Distributed Data

These are the proceedings of a NATO Advanced Study Institute (ASI) held in Cetraro, Italy during 6-17 June 1983. The title of the ASI was Computer Arehiteetures for SpatiaZZy vistributed Vata, and it brouqht together some 60 participants from Europe and America. Presented ~ere are 21 of the lectures that were delivered. The articles cover a wide spectrum of topics related to computer architecture s specially oriented toward the fast processing of spatial data, and represent an excellent review of the state-of-the-art of this topic. For more than 20 years now researchers in pattern recognition, image processing, meteorology, remote sensing, and computer engineering have been looking toward new forms of computer architectures to speed the processing of data from two- and three-dimensional processes. The work can be said to have commenced with the landmark article by Steve Unger in 1958, and it received a strong forward push with the development of the ILIAC III and IV computers at the University of Illinois during the 1960's. One clear obstacle faced by the computer designers in those days was the limitation of the state-of-the-art of hardware, when the only switching devices available to them were discrete transistors. As aresult parallel processing was generally considered to be imprae tieal, and relatively little progress was made.

Computer Architectures for Spatially Distributed Data (Volume 18).

Mapping and Spatial Modelling for Navigation

The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.