Urban Land Use Function Recognition Method Using Sequential Mobile Phone Data

PENG Zhenghong; SUN Zhihao; CHENG Qing; JIAO Hongzan; CHEN Wei

doi:10.13203/j.whugis20170329

Volume 43 Issue 9

Sep. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(9): 1399-1407, 1437. > DOI: 10.13203/j.whugis20170329

PENG Zhenghong, SUN Zhihao, CHENG Qing, JIAO Hongzan, CHEN Wei. Urban Land Use Function Recognition Method Using Sequential Mobile Phone Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(9): 1399-1407, 1437. DOI: 10.13203/j.whugis20170329

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Urban Land Use Function Recognition Method Using Sequential Mobile Phone Data

1.
School of Urban Design, Wuhan University, Wuhan 430072, China
2.
Wuhan Planning and Design Institute, Wuhan 430014, China

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The National Natural Science Foundation of China 41401400

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Author Bio:
PENG Zhenghong, professor, specializes in engineering graphics, computer graphics and digital city. E-mail: laopeng129@vip.sina.com
Corresponding author:
CHENG Qing, PhD, lecturer. E-mail: qingcheng@whu.edu.cn
Received Date: January 29, 2018
Published Date: September 04, 2018

Graphical Abstract

Abstract

Abstract

The spatial and temporal characteristics of human activities are closely related to the function of urban land use, so the social-economic function of the urban parcel can be inferred by the spatial aggregation and dispersion of human activities. Cell phone is the most popular communication terminal equipment and the distribution of cell phone users is able to reflect the distribution of population accurately. Local basic service (LBS), which is acquired from residents' cell mobile data, is constantly emerging and make it possible to achieve spatial and temporal coverage and meticulous monitoring of urban people's activities. Therefore, the mobility data of cell phone users have the potential to infer the land use function of the urban parcels. In this paper, the call detail records(CDRs), will be adopted to cluster the urban land use patterns. Firstly, the clustering characteristics of call aggregation for local scale are extracted, then a spectral clustering recognition method for urban land use is proposed. Taking Wuhan as an experimental area, the average accuracy of the method for urban land use identification is 54.6%, and the results show that this method has advantages in urban land use identification.
- call detail records,
- spectral clustering,
- temporal features,
- urban land use,
- function recognition

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[1]	Tao P, Sobolevsky S, Ratti C, et al. A New Insight into Land Use Classification Based on Aggregated Mobile Phone Data[J]. International Journal of Geographical Information Science, 2014, 28(9):1988-2007 doi: 10.1080/13658816.2014.913794
[2]	Foody G M. Fully Fuzzy Supervised Classification of Land Cover from Remotely Sensed Imagery with an Artificial Neural Network[J]. Neural Computing & Applications, 1997, 5(4):238-247 doi: 10.1007/BF01424229
[3]	Zhong Y, Zhu Q, Zhang L. Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(11):6207-6222 http://ieeexplore.ieee.org/document/7119589/
[4]	Zhong Y, Zhao B, Zhang L. Multiagent Object-Based Classifier for High Spatial Resolution Imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 52(2):841-857 http://ieeexplore.ieee.org/document/6494281/
[5]	Liu Y, Liu X, Gao S, et al. Social Sensing:A New Approach to Understanding Our Socioeconomic Environments[J].Annals of the Association of Ame-rican Geographers, 2015, 105(3):512-530 doi: 10.1080/00045608.2015.1018773
[6]	龙瀛, 张宇, 崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报, 2012, 67(10):1339-1352 doi: 10.11821/xb201210005 Long Ying, Zhang Yu, Cui Chengyin. Identifying Commuting Pattern of Beijing Using Bus Smart Card Data[J]. Acta Geographica Sinica, 2012, 67(10):1339-1352 doi: 10.11821/xb201210005
[7]	Liu Y, Wang F, Xiao Y, et al. Urban Land Uses and Traffic Source-Sink Areas:Evidence from GPS-Enabled Taxi Data in Shanghai[J]. Landscape & Urban Planning, 2012, 106(1):73-87 http://www.sciencedirect.com/science/article/pii/S0169204612000631
[8]	Yao Y, Li X, Liu X, et al. Sensing Spatial Distribution of Urban Land Use by Integrating Points-of-Interest and Google Word2Vec Model[J]. International Journal of Geographical Information Systems, 2016, 31(4):825-848 doi: 10.1080/13658816.2016.1244608
[9]	Steenbruggen J, Tranos E, Nijkamp P. Data from Mobile Phone Operators:A Tool for Smarter Cities?[J]. Telecommunications Policy, 2015, 39(3):335-346 http://www.sciencedirect.com/science/article/pii/S0308596114000603
[10]	徐仲之, 曲迎春, 孙黎, 等.基于手机数据的城市人口分布感知[J].电子科技大学学报, 2017, 46(1):126-132 doi: 10.3969/j.issn.1001-0548.2017.01.018 Xu Zhongzhi, Qu Yingchun, Sun Li, et al. Urban Population Sensing via Mobile Phone Data[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(1):126-132 doi: 10.3969/j.issn.1001-0548.2017.01.018
[11]	钮心毅, 丁亮, 宋小冬, 等.基于手机数据识别上海中心城的城市空间结构[J].城市规划学刊, 2014(6):61-67 doi: 10.3969/j.issn.1000-3363.2014.06.009 Niu Xinyi, Ding Liang, Song Xiaodong, et al.Understanding Urban Spatial Structure of Shanghai Central City Based on Mobile Phone Data[J]. Urban Planning Forum, 2014(6):61-67 doi: 10.3969/j.issn.1000-3363.2014.06.009
[12]	Tu W, Cao J, Yue Y, et al. Coupling Mobile Phone and Social Media Data:A New Approach to Understanding Urban Functions and Diurnal Patterns[J]. International Journal of Geographical Information Science, 2017, 31(12):2331-2358 doi: 10.1080/13658816.2017.1356464
[13]	Toole J L, Ulm M, Bauer D. Inferring Land Use from Mobile Phone Activity[C]. The ACM SIGKDD International Workshop on Urban Computing, Beijing, China, 2012
[14]	González M C, Hidalgo C A, Barabási A L. Understanding Individual Human Mobility Patterns[J]. Nature, 2008, 453(7196):779-782 doi: 10.1038/nature06958
[15]	Kuusik A, Ahas R, Tiru M. Analysing Repeat Visitation on Country Level with Passive Mobile Positioning Method:An Estonian Case Study[J]. Discussions on Estonian Economic Policy, 2009, 17:140-155 http://www.researchgate.net/publication/307685047_Analysing_Repeat_Visitation_on_Country_Level_with_Passive_Mobile_Positioning_Method_an_Estonian_Case_Study
[16]	Song C, Qu Z, Blumm N, et al. Limits of Predictability in Human Mobility[J]. Science, 2010, 327(5968):1018-1021 doi: 10.1126/science.1177170
[17]	Traag V A, Browet A, Calabrese F, et al. Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference[C]. The Third International Conference on Privacy, Security, Risk and Trust, Boston, USA, 2011
[18]	Luxburg U. A Tutorial on Spectral Clustering[J]. Statistics & Computing, 2007, 17(4):395-416 doi: 10.1007-s11222-007-9033-z/
[19]	Shi J, Malik J. Normalized Cuts and Image Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000, 22(8):888-905 http://d.old.wanfangdata.com.cn/Periodical/jsjyyyj200702054
[20]	Caliński T, Harabasz J. A Dendrite Method for Cluster Analysis[J]. Communications in Statistics, 1974, 3(1):1-27 doi: 10.1080-03610927408827101/
[21]	Calabrese F, Ferrari L, Blondel V D. Urban Sen-sing Using Mobile Phone Network Data:A Survey of Research[J]. ACM Computing Surveys (CSUR), 2015, 47(2):1-25 https://www.sciencedirect.com/science/article/pii/S1566253517303421
[22]	Louail T, Lenormand M, Cantu Ros O G, et al. From Mobile Phone Data to the Spatial Structure of Cities[J]. Scientific Reports, 2014, 4:5276-5290 http://pubmedcentralcanada.ca/pmcc/articles/PMC4055889/

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