Search USGS⌕ Search

USGS · 70140568

Design of a spatial data structure using the relational normal forms

Abstract

In previous work, a relational data structure aimed at the exchange of spatial data between systems was developed. As this data structure was relational it was of first normal form, but compliance with the higher normal forms was not investigated. Recently, a new procedural method for composing fully normalized data structures from the basic data fields has been developed by H. C. Smith, as an alternative to the process of non-loss decomposition which is difficult to understand. Smith's method has been applied to data fields required to store points, lines and polygons in a chain-node spatial data model. When geographic domain, coverage layer and map are also considered, the procedure naturally leads to a catalogue model, needed for the exchange of spatial data. Although the method produces a fully normalized data structure, it is not as easy to identify which normal forms are responsible for the ultimate arrangement of the data fields into relations, but the benefits of these criteria for data base development also apply to spatial data structures and related ancillary data.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jan W. van Roessel. 1987. Design of a spatial data structure using the relational normal forms. https://doi.org/10.1080/02693798708927791

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Data integration through region-based nominal filtering

Resource models integrating disparate nominal or class grid-cell data can be implemented by using spatial filters. Most modelling procedures do not adequately handle noise created during the process of merging and integrating multiple grid-cell data sets. Data integration can be best accomplished in an environment where ready access to statistical and database management systems support the reclassification of noise grid-cells. These systems provide access to functionality and information which support the design of the spatial filter and the evaluation of the result of the spatial filter and the resource model.

International Journal of Geographical Information ↗

Simulating and mapping spatial complexity using multi-scale techniques

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields.

International Journal of Geographical Information ↗

Finite-element mesh generation from mappable features

A vector-based geographical information system (GIS) is used to generate a variably-sized triangular element finite-element mesh from mappable features. Important digitally-mapped features are automatically linked to nodes in the finite-element model, ensuring an efficient, virtually error-free alternative to the tedious process of mesh design and data-input preparation by other methods. The procedure permits the user to work interactively with graphically-displayed hydrologic information about the study area allowing different mesh sizes to be used as needed, based on hydrologic complexity. The mesh-generaiion programs are stand-alone macros within the GIS that set up the basic data defining a finite-element mesh for many different finite-element model programs.

International Journal of Geographical Information ↗