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ISPRS Int. J. Geo-Inf. 2017, 6(7), 205; doi:10.3390/ijgi6070205

A Visual Analysis Approach for Inferring Personal Job and Housing Locations Based on Public Bicycle Data

1,2,* , 1,2
,
1,2
and
1,2
1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2
Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Academic Editors: Maria Antonia Brovelli, Hussein M. Abdulmuttalib, Peter Mooney, Chris Pettit and Wolfgang Kainz
Received: 2 May 2017 / Revised: 22 June 2017 / Accepted: 29 June 2017 / Published: 7 July 2017
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
View Full-Text   |   Download PDF [2681 KB, uploaded 7 July 2017]   |  

Abstract

Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on human mobility data. However, because the travel patterns of residents are variable, simple rule-based methods are unable to generalize highly changing and complex travel modes. In this paper, we propose a visual analysis approach to assist the analyzer in inferring personal job and housing locations interactively based on public bicycle data. All users are first clustered to find potential commuting users. Then, several visual views are designed to find the key candidate stations for a specific user, and the visited temporal pattern of stations and the user’s hire behavior are analyzed, which helps with the inference of station semantic meanings. Finally, a number of users’ job and housing locations are detected by the analyzer and visualized. Our approach can manage the complex and diverse cycling habits of users. The effectiveness of the approach is shown through case studies based on a real-world public bicycle dataset. View Full-Text
Keywords: visual analysis; job-housing places; public bicycle data; urban mobility; spatio-temporal visualization visual analysis; job-housing places; public bicycle data; urban mobility; spatio-temporal visualization
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Shi, X.; Yu, Z.; Fang, Q.; Zhou, Q. A Visual Analysis Approach for Inferring Personal Job and Housing Locations Based on Public Bicycle Data. ISPRS Int. J. Geo-Inf. 2017, 6, 205.

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