By Geoffrey H. Dutton
While spatial info is digitized to be used in geographic info platforms and different software program, information regarding its unique scale, answer and accuracy is often misplaced. consequently, utilizing such info at assorted scales and mixing it with information from different assets might be tough. Mapping vector information at smaller than the unique scale calls for its generalization, that's often dealt with by means of post-processing in ways in which are just weakly supported by way of databases. The types and techniques defined during this ebook conquer many such difficulties through delivering a multi-resolution info illustration that enables retrieval of map facts at a hierarchy of scales, in addition to documenting the accuracy of each spatial coordinate.
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Additional info for A Hierarchical Coordinate System for Geoprocessing and Cartography (Lecture Notes in Earth Sciences)
Consequently, spatial data were often not reusable by other applications, even for different projects in the same organization. This situation has much improved over the last decade, but still today, even with abundant spatial metadata resources to document datasets and software that can reconcile coordinate systems, many lapses in data quality go undetected until far too late in the cycle of data reuse, if ever. 1 A Typology of Spatial Data Models Each GIS has its own data modeling philosophy that is likely to be only in partial agreement with those of others.
Most of the research and solutions in this area are concerned with a single subproblem, that of line simplification, for which a large number of algorithms have been developed. Other work has focused on characterizing point features, aggregating areal features (including administrative units, land use polygons and groups of buildings), text placement, reclassifying thematic values and collapsing feature hierarchies. Besides simplification, other manual techniques (usually called generalization operators) that have been identified and automated include selection, smoothing, displacement, aggregation, amalgamation, collapse, exaggeration, enhancement and typification (McMaster and Shea 1992; Brassel and Weibel 1988).
Chapter three descends into greater detail to describe salient geometric, topological and numerical properties of the model. Details concerning notation and coordinate conversion are presented (pseudocode for the latter is provided in appendix A). Also dealt with is spatial indexing; a derived hierarchical construct for this purpose is introduced and discussed in this context, with some implementation details provided in chapter 4. Use of our model to index global point data is summarized in appendix E.
A Hierarchical Coordinate System for Geoprocessing and Cartography (Lecture Notes in Earth Sciences) by Geoffrey H. Dutton