Abstract
Most of the existing researches were divided jobs-housing zones based on temporal activity variation, which were lack of mining spatio-temporal interaction characteristics. With the trend of big data and artificial intelligence, mobile phone data is provided an emerging source for urban research. This paper is proposed traffic semantic concept to extract commuters’ origins and destinations. According to extracted data, four characteristic indexes (including the volumes of user, aggregation, dissipation and new increment) are analyzed traffic semantic attribute. Combining with the geographic information of base stations and traffic semantic, an unsupervised k-means clustering algorithm based on weighted Mahalanobis distance function is used to divide 200 jobs-housing zones in Shenzhen. Moreover, the commuting index is calculated to measure tendency of jobs-housing zones. Compared with the actual land use data, the results are verified reliability of method. All these findings can be helpful to analyze travel behaviors and make urban planning.
This conference paper is retracted on request of co-author Luxi Dong. After publication, Luxi Dong informed the publisher that he submitted the manuscript and signed the copyright transfer form on behalf of all co-authors without informing them and provided a false email address of the corresponding author to the publisher. The other co-authors confirmed that they were not aware of publication of this conference paper. All authors agree to this retraction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Change history
28 August 2021
This conference paper is retracted on request of co-author Luxi Dong. After publication, Luxi Dong informed the publisher that he submitted the manuscript and signed the copyright transfer form on behalf of all co-authors without informing them and provided a false email address of the corresponding author to the publisher. The other co-authors confirmed that they were not aware of publication of this conference paper. All authors agree to this retraction.
References
Jie, G.: The characteristics and enlightenment of the land classification system in the UK. Urban Plan. Int. 27(6), 16–21 (2012)
Yao, C., Jie, G., Min, Z.: Land use classification system and multiple-objective land management: lessons from international experience. Urban Plan. Int. 27(6), 3–9 (2012)
Yang, Y., Tian, L., Yeh, A.G.O., Li, Q.Q.: Zooming into individuals to understand the collective: a review of trajectory-based travel behaviour studies. Travel Behav. Soc. 1(2), 69–78 (2014)
Chaberko, T., Kretowicz, P.: Geographical input to local public transport planning in Poland. Bull. Geogr. Socio Econ. Ser. 22(22), 35–46 (2013)
Demissie, M.G., et al.: Inferring origin-destination flows using mobile phone data: a case study of Senegal. In: 13th International Conference on Electrical Engineering/Electronics. IEEE (2016)
Oshin, T.O., Poslad, S., Zhang, Z.: Energy-efficient real-time human mobility state classification using smartphones. IEEE Trans. Comput. 64(6), 1680–1693 (2015)
Jiang, S., Ferreira, J., Gonzalez, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3(2), 208–219 (2017)
Soto, V., Frías-Martínez, E.: Automated land use identification using cell-phone records. In: ACM International Workshop on MobiArch (2011)
Frias-Martinez, V., et al.: Characterizing urban landscapes using geolocated tweets. In: Privacy, Security, Risk & Trust, pp. 239–248. IEEE (2013)
Pei, T., et al.: A new insight into land use classification based on aggregated mobile phone data. Int. J. Geogr. Inf. Sci. 28(9), 1988–2007 (2014)
Zhang, T., Sun, L., Yao, L., Rong, J.: Impact analysis of land use on traffic congestion using real-time traffic and POI. J. Adv. Transp. 1, 1–8 (2017)
Wu, L., et al.: Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS ONE 9(5), e97010 (2014)
Yan, L., et al.: Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai. Transportation 46, 777–792 (2018)
Jia, Y., et al.: Urban land use mapping by combining remote sensing imagery and mobile phone positioning data. Remote Sens. 10(3), 1–21 (2018)
Okabe, A., Boots, B., Sugihara, K.: Spatial tessellations: concepts and applications of Voronoi diagrams. Coll. Math. J. 43(1), 1–14 (2000)
Acknowledgment
This article is partially supported by the Beijing Natural Science Foundation (No. 8172018) and National Key Research and Development Program of China (No. 2018YFB1601003) and the China Postdoctoral Science Foundation (No. 2017M620673) and the Beijing Municipal Natural Science Foundation (No. 8184070). The authors gratefully thank anonymous referees for their useful comments and editors for their work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, X., Dong, L., Jia, M., Tan, J. (2020). RETRACTED CHAPTER: Urban Jobs-Housing Zone Division Based on Mobile Phone Data. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_43
Download citation
DOI: https://doi.org/10.1007/978-981-15-2777-7_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2776-0
Online ISBN: 978-981-15-2777-7
eBook Packages: Computer ScienceComputer Science (R0)