Area-patch generalisation: a competitive approach

Müller, Jean-Claude; Wang, Zeshen

doi:10.1179/000870492787859763

Cited by 25 publications

(9 citation statements)

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“…Muller and Wang () stipulate that generalization algorithms should not ‘create’ additional features. This presents problems for Stream/River features where compactness and total area do not provide suitable measures because such measurements assume that Stream/River features are uniform in character.…”

Section: Discussionmentioning

confidence: 99%

“…Buffering, to expand or contract features, has been applied by others in generalization algorithms. Muller and Wang () applied buffers as a function of a polygon feature's area and compactness to either emphasize larger features (‘rich get richer’) or deemphasize smaller features (‘poor get poorer’). The Muller and Wang () algorithm allows overlapping features to agglomerate.…”

Section: Introductionmentioning

confidence: 99%

“…Muller and Wang () applied buffers as a function of a polygon feature's area and compactness to either emphasize larger features (‘rich get richer’) or deemphasize smaller features (‘poor get poorer’). The Muller and Wang () algorithm allows overlapping features to agglomerate. Mackaness, Perikleous, and Chaudhry () developed an algorithm to generalize forest patches using five buffering steps in sequence to aggregate nearby polygons, contract or expand features (incorporating Muller and Wang's () algorithm), and then to simplify polygon features.…”

Section: Introductionmentioning

confidence: 99%

“…The Muller and Wang () algorithm allows overlapping features to agglomerate. Mackaness, Perikleous, and Chaudhry () developed an algorithm to generalize forest patches using five buffering steps in sequence to aggregate nearby polygons, contract or expand features (incorporating Muller and Wang's () algorithm), and then to simplify polygon features. Anderson‐Tarver, Leyk, and Buttenfield () applied fuzzy logic to aggregate swamp polygons to preserve proportionate textures in a reduced scale version.…”

Section: Introductionmentioning

confidence: 99%

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Partial polygon pruning of hydrographic features in automated generalization

Stum

Buttenfield

Stanislawski

2017

Transactions in GIS

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This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal featuressuch as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US. K E Y W O R D S Automated generalization, feature collapse, hydrography, multiscale, pattern recognition, partial polygon pruning 1 | I NTR OD U CTI ON Generalization research has been active in several countries for over 50 years (see for example seminal papers by Pannakoek,

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Partial polygon pruning of hydrographic features in automated generalization

Stum

Buttenfield

Stanislawski

2017

Transactions in GIS

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“…Existing approaches dedicated to the generalisation of categorical data concern either very specific kinds of data (e.g. Jaakkola 1998, Mueller andWang 1992) or the isolated implementation of single geometric algorithms (e.g. Bader andWeibel 1997, Galanda 2001).…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Framework for Polygonal Subdivision Generalisation

Galanda

Weibel

2002

Advances in Spatial Data Handling

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Comprehensive techniques for the generalisation of polygonal subdivisions are still lacking in research as well as in commercial GIS, but required for the derivation of arbitrary scales from a master database. This paper outlines a framework for the automated generalisation of polygon mosaics (polygonal subdivisions) based on a multi agent system by extending previous research carried out by the AGENT consortium. After listing generic properties of agents spatial levels of polygon generalisation agents are identified, namely map, group, polygon and line. Each of them is linked to a specific agent type. Both the process of polygon generalisation based on a multi agent system as well as the evolution of an agent during the generalisation process is discussed theoretically. Then, a worked example clarifies and illustrates the concepts and methods presented before. Finally, foreseeable challenges of the framework's implementation based on preliminarily experiments are pointed out.

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Introduction

Yan¹

2019

Description Approaches and Automated Generalization Algorithms for Groups of Map Objects

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Area-patch generalisation: a competitive approach

Cited by 25 publications

References 0 publications

Partial polygon pruning of hydrographic features in automated generalization

Partial polygon pruning of hydrographic features in automated generalization

An Agent-Based Framework for Polygonal Subdivision Generalisation

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