Raster-mode area generalization for land use and land cover maps

Syracuse, New York, and a Research Geographer in the Office of Geographic and Cartographic Research, National Mapping Division, U.S. Geological Survey, Reston, Virginia, 22092. He thanks Robin Fegeas for assistance with USGS computing, plotting, and data communications systems, and with the Geographic Information Retrieval and Analysis System (GIRAS). MS submitted August 1983 CARTOGRAPHICA Vol 20 No 4 1983 pp 65-91 Nhomakorabea66
MARK STEPHEN MONMONIER
plays. These advances, in both interactive and hardcopy display units, include color CRT displays and plasma panels; fast, efficient electrostatic dot-matrix printers and ink-jet plotters; and laser-beam drum scanner/plotters, which generate final color-separation negatives for offset lithography.4 Map generalization algorithms must adapt to both an increasing demand for the cartographic products of remote sensing and the likely replacement of the pen plotter by raster-mode display technology. This paper demonstrates that the computer-assisted generalization of maps of land cover and other area features can be straightforward and operationally efficient when the data are accorded the grid-cell organization of raster-oriented display hardware. After presenting land cover generalization as a problem of manipulating area features rather than line features, this paper demonstrates several strategies for feature selection, smoothing, and shifting. Although developed for reducing the 1:100,000- and 1: 250,000-scale data of the U.S. Geological Survey (USGS) Land Use and Land Cover program to the National Atlas Regional Reference scale of 1:2,000,000, the principles discussed here are applicable to other polygon data; for example, census tracts, counties, drainage basins, and soil mapping units.
Use of the digital computer in cartographic generalization both necessitates and encourages a variety of generalization strategies. Among the principal considerations are the type of feature (point, line, or area), feature density, degree of scale reduction, and type of display device. All four factors interact in the selection of an appropriate data structure and algorithm to address the three basic facets of generalization - feature selection, feature smoothing and simplification, and feature shifting. Most early algorithms for computer-assisted generalization treated linear features of very low density, typically just a single, isolated feature intended for display on a pen plotter after a modest scale reduction. Conditions were so tame that feature selection and feature displacement could safely be ignored.1 In contrast, an operational algorithm must be able to deal with the less than ideal, but more challenging circumstances of locally dense features and a scale reduction to less than 25 percent of the original scale. Such requirements demand more than the selective reduction of a list of point coordinates. Particularly promising among attempts to develop a holistic algorithm for line generalization have been the combined vector-raster approach of Fred Christ at Frankfurt am Main and the raster-mode linear-feature shifting algorithm of Wilson.2 Yet both efforts still call for further development, and it is unlikely that an operational algorithm could ever approach in simplicity the naively straightforward point-elimination techniques of the early 1970s. Events of the last decade have altered drastically the future demands for, and limitations upon, computer-assisted map generalization. For the first time, remote sensing provided rapid, frequent and geographically extensive land cover imagery. Impressed by the products of these missions, earth scientists are eager for still more detailed and more temporally extensive coverage. Therefore the map data generated by remote sensing continue to swell with continued improvements in resolution and corresponding increases in the number of pixels per scene.3 Only somewhat related to advances in remote sensing technology have been advances in computer-driven display hardware, principally in high-resolution raster disMARK STEPHEN MONMONIER is a Professor in the Department of Geography, Syracuse University,
RASTER-MODE AREA GENERALIZATION FOR LAND USE AND LAND COVER MAPS
MARK STEPHEN MONMONIER
Syracuse University
ABSTRACT Raster mode is more appropriate than vector mode for the computer-assisted generalization of land use and land cover data. A raster-mode approach promotes the partitioning of thin or insignificant polygons and the growing or linking of others. If the scale is reduced substantially, partitioning and region growing are needed to guarantee a graphically stable image, which will resist misregistration during reproduction. The test examples presented here demonstrate the efficacy of a raster-mode approach in reducing 1 : 250,000-scale land use and land cover data in the U.S. Geological Survey's Geographic Information Retrieval and Analysis System (GIRAS) to 1 :2,000,000, the scale of the National Atlas series of regional reference maps. A variety of category weights and priority rankings and several nonmutually exclusive options for smoothing and data aggregation confront the map author, who must provide detailed specifications. With these techniques generalized land use and land cover maps can be biased to emphasize a category or a combination of categories.