Deriving multi‐scale GEODATA from TOPO‐250K road network Data

Sharon Kazemi · Samsung Lim

9 citations0/0 files storedJournal of Spatial Science

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Geographic Information Systems Studies Historical Geography and Cartography 3D Modeling in Geospatial Applications

Abstract

This paper presents a generalization methodology to derive multi‐scale GEODATA through an evaluation of ESRI ArcGIS™ software that was used as a testbed based on the principles of generalization. It focuses on integration and utilization of generalization operators in order to generalize a road network database and produce small scale maps at 1:500 000 and 1:1 000 000 from GEODATA TOPO‐250K Series 2 data. The derived maps are satisfactory when compared with the existing small‐scale road maps such as the Global Map at scale of 1:1000 000. It is suggested that a comprehensive evaluation of generalization systems and their performance is essential to marry the cartographic knowledge from experts and bring this into a generalization framework. Therefore, there is an opportunity to evaluate other generalization systems to derive a multi‐scale database from a master database in future investigations to enhance the generalization methodology.

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References

1966The Principles of Selection 1998Line Generalization Based on Analysis of Shape Characteristics 1995GIS and generalization : methodology and practice 1989The Integration Of Simplification And Smoothing Algorithms In Line Generalization 1999Scale, Sinuosity, and Point Selection in Digital Line Generalization 1989Geographic Logic In Line Generalization 1995Simplification and Generalization of Large Scale Data for Roads: A Comparison of Two Filtering Algorithms 2004Geographic and Cartographic Contexts in Generalization

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2005Integration of cartographic knowledge with generalization algorithms 2021A Machine-Learning Approach to Generalisation of GIS Data

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