A linear tessellation model to identify spatial pattern in urban street networks. Issue 8 (3rd August 2017)

• Record Type: Journal Article
• Authors: He, Yakun; Ai, Tinghua; Yu, Wenhao; Zhang, Xiang
• Is Part Of: International journal of geographical information science. Volume 31:Issue 8(2017)
• Page Start: 1541
• Page End: 1561
• Publication Date: 2017-08-03
• Subjects: Network space -- spatial tessellation -- network neighborhood -- street pattern -- pattern recognition; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1298768
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Street patterns reflect the distribution characteristics of a street network and affect the urban structure and human behavior. The recognition of street patterns has been a topic of interest for decades. In this study, a linear tessellation model is proposed to identify the spatial patterns in street networks. The street segments are broken into consecutive linear units with equal length. We define five focal operations using neighborhood analysis to extract the geometric and topological characteristics of each linear unit for the purpose of grid-pattern recognition. These are then classified by Support Vector Machine, and the result is optimized based on Gestalt principles. The experimental results demonstrate that our method is effective for mining grid patterns in a street network.