Road Density Partition and Its Application in Evaluation of Road Selection

TIAN Jing; XIONG Fuquan; CHENG Xueping; WANG Rui; FANG Huaqiang

doi:10.13203/j.whugis20130430

Volume 41 Issue 9

Sep. 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(9): 1225-1231. > DOI: 10.13203/j.whugis20130430

TIAN Jing, XIONG Fuquan, CHENG Xueping, WANG Rui, FANG Huaqiang. Road Density Partition and Its Application in Evaluation of Road Selection[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1225-1231. DOI: 10.13203/j.whugis20130430

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Road Density Partition and Its Application in Evaluation of Road Selection

1.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2.
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
3.
Key Laboratory of Digital Mapping and Land Information Application Engineering, Natural Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China

Funds:

National Science Foundation for Fostering Talents in Basic Research of the National Natural Science Foundation of China J1103409

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Author Bio:
TIAN Jing, PhD, specializes in automated map generalization and spatial data mining. E-mail: yutaka-2010@163.com
Received Date: August 13, 2014
Published Date: September 04, 2016

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Abstract

Abstract

Road density is a useful index widely applied in the analysis of ecological effects, road network planning, delimitation of urban areas and road network generalization. Extraction of dense and sparse areas of the road network is a key issue in the filed of automated map generalization. This paper proposes a method for road density partition. The method creates the Voronoi diagram of road intersections and endpoints, and then uses G_i^* to identify statistically significant spatial clusters of high values and low values for the area of a Voronoi cell. Finally, the method aggregates neighboring Voronoi cells from the statistically significant spatial clusters of high values and low values at a 95% confidence level. The road network of Hong Kong at the 14, 13 and 12 levels of Google Map are used as experimental data for evaluation of road network selection. Experimental results showed that the road density partitions using the proposed method generally reflected the density of the road network, while the road density contrasted well before and after road selection. Our method is superior to the grid density approach.
- road selection,
- road density,
- partition,
- Voronoi diagram,
- spatial autocorrelation

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