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

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.

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.

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