Segmentation of Urban Areas Using Road Networks

Nicholas Jing Yuan and Yu Zheng

Abstract

Region-based analysis is fundamental and crucial in many geospatial-related applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here, each segmented region is bounded by the high-level road segments, covering some neighborhoods and low-level streets. Typically, road segments are classified into different levels (e.g., highways and expressways are usually high-level roads), providing us with a more natural and semantic segmentation of urban spaces than the grid-based partition method. We show that through simple morphological operators, an urban road network can be efficiently segmented into regions. In addition, we present a case study in trajectory mining to demonstrate the usability of the proposed segmentation method.

Please cite the following papers when using this segmentation tool:

[1] Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. Urban Computing with Taxicabs, ACM Ubicomp, 16 September 2011.

[2] Nicholas Jing Yuan, Yu Zheng and Xing Xie, Segmentation of Urban Areas Using Road Networks, MSR-TR-2012-65, 2012.

Details

Publication typeTechReport
NumberMSR-TR-2012-65
PublisherMicrosoft Technical Report

Previous versions

Jing Yuan, Yu Zheng, and Xing Xie. Discovering Regions of Different Functions in a City Using Human Mobility and POIs, ACM, 12 August 2012.

Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. Urban Computing with Taxicabs, ACM, 16 September 2011.

Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xing Xie. Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams, Association for Computing Machinery, Inc., 24 August 2011.

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