Work

Road density analysis based on skeleton partitioning for road generalization

Year: 2009

Type: article

Abstract: This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurements of road density based on grid method, fractal geometry and mesh density are reviewed, and a new method for computing road density based on skeleton partitioning is proposed. Experiments illustrate that road density based on skeleton partitioning may reveal the overall road distribution. The proposed measurement is further tested against road maps at 1:10k scale and their generalized version at 1:50k scale. By comparing the deletion percentage within different density interval, a road density threshold can be found, which indicate the need for further operations during generalization. Proposed road density may be used to examine the quality of road generalization, to explore the variation of road network through temporal and spatial changes, and it also has future usage in urban planning, transportation and estates evaluation practice.

Source: Geo-spatial Information Science

DOI: 10.1007/s11806-009-0012-8

Authors: Xingjian Liu, Tinghua Ai, Yaolin Liu

Institution: Texas State University

Language: English

Topic: Geographic Information Systems Studies

Subfield: Geography, Planning and Development

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,993