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Adaptive generation of variable-scale network maps for small displays based on line density distribution

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Abstract

This paper presents an integrated strategy for adaptive generation of variable-scale network maps for different small displays. It is based on the line density distribution and comprised of three steps, i.e. (a) to estimate the line density by a grid-based method, (b) to adaptively generate variable-scale maps based on density distribution for given display sizes and (c) to improve the map readability by map generalization. The proposed strategy has been tested by using two real-life network datasets, with a statistical analysis and a perceptual evaluation. Experimental results show that this strategy is able to better exploit the limited map space so as to significantly improve map clarity and readability and at the same time to preserve map recognition ability compared to its original shape.

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Acknowledgments

This project is supported by the National 863 Program (2013AA12A202), the Hong Kong PolyU (1.34.37.87T5 and G-U686) and the National Natural Science Foundation of China (Grant No. 41201475) and the Program for New Century Excellent Talents in University (NCET-12-0942). The road network map of the city of Marion in USA and the city of Lower Hutt in New Zealand are obtained from TIGER data of US Census Bureau (http://www.census.gov/geo/www/tiger/) and the Land Information of New Zealand respectively. The critical comments by the anonymous reviewers are also very much appreciated, which help improve the quality of this paper.

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan, People’s Republic of China

    Zhilin Li & Peng Ti

  2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Zhilin Li & Peng Ti

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  1. Zhilin Li
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  2. Peng Ti
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Correspondence to Peng Ti.

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Li, Z., Ti, P. Adaptive generation of variable-scale network maps for small displays based on line density distribution. Geoinformatica 19, 277–295 (2015). https://doi.org/10.1007/s10707-014-0212-8

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  • DOI: https://doi.org/10.1007/s10707-014-0212-8

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