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ISPRS Int. J. Geo-Inf. 2018, 7(6), 203; https://doi.org/10.3390/ijgi7060203

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

1,2
,
2
,
2
and
2,3,*
1
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
2
College of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
3
Shenzhen Key Laboratory for Optimizing Design of Built Environment, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Received: 26 April 2018 / Revised: 21 May 2018 / Accepted: 27 May 2018 / Published: 29 May 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Abstract

Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. View Full-Text
Keywords: clustering method; spatial linkage; origin-destination trips; bike-sharing movement clustering method; spatial linkage; origin-destination trips; bike-sharing movement
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He, B.; Zhang, Y.; Chen, Y.; Gu, Z. A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data. ISPRS Int. J. Geo-Inf. 2018, 7, 203.

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