Journal
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Volume 31, Issue 8, Pages 1541-1561Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2017.1298768
Keywords
Network space; spatial tessellation; network neighborhood; street pattern; pattern recognition
Categories
Funding
- National Natural Science Foundation of China [41531180]
- National High Technology Research and Development Program of China (863 Program) [2015AA1239012]
- Fundamental Research Funds for the Central Universities [2015205020202]
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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.
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