skip to main content
research-article

A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

Published: 15 April 2020 Publication History

Abstract

Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of more sophisticated models to characterize human mobility in urban areas. In this work, our main goal is to extract spatio-temporal features that characterize user mobility and, based on the similarity of these features, identify user communities. To this end, we propose a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images. These features are then fed to a clustering algorithm that identifies the underlying community structures. We use a diverse urban mobility dataset to validate the proposed framework. Our results show that the proposed framework is able to significantly increase the similarity between intra-community nodes (by up to 107%) as well as dissimilarity between inter-community nodes (up to 54%) when compared against no pre-processing of the datasets, i.e without pre-processing the datasets through any feature fusion method. Moreover, it was also able to reach up to 100% improvement when compared against community identification using Principal Component Analysis (PCA). Our results also show that the proposed approach yields significant increase in contact time amongst users belonging to the same community, by up to 80% when compared to the average contact time when not considering community structures, and by up to 150% when compared to the baseline. To the best of our knowledge, our proposal is the first to consider deep convolutional autoencoding to perform automatic extraction of non-linear spatio-temporal mobility features characterizing individual users from raw mobility datasets with the goal of identifying user communities.

References

[1]
Charu C. Aggarwal. 2018. Neural Networks and Deep Learning. Springer International Publishing, Cham.
[2]
Majeed Alajeely, Robin Doss, and Asma’a Ahmad. 2017. Routing protocols in opportunistic networks: A survey. IETE Tech. Rev. 35, 4 (2017), 1--19.
[3]
Vito Albino, Umberto Berardi, and Rosa Dangelico. 2015. Smart cities: Definitions, dimensions, performance, and initiatives. J. Urb. Technol. 22, 1 (2015), 3--21.
[4]
Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, and Daniel Cremers. 2018. Clustering with deep learning: Taxonomy and new methods. CoRR abs/1801.07648 (2018).
[5]
Pierre Baldi and Kurt Hornik. 1989. Neural networks and principal component analysis: Learning from examples without local minima. Neural Netw. 2, 1 (1989), 53--58.
[6]
Favyen Bastani, Yan Huang, Xing Xie, and Jason W. Powell. 2011. A greener transportation mode: Flexible routes discovery from GPS trajectory data. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS’11). ACM, New York, NY, 405--408.
[7]
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent. 2012. Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538 1 (2012).
[8]
C. M. Bishop and N. M. Nasrabadi. 2007. Pattern recognition and machine learning. J. Electron. Imag. 16, 4 (2007).
[9]
Geoff Boeing. 2017. The structure and dynamics of cities: Urban data analysis and theoretical modeling, by Marc Barthelemy. J. Amer. Plann. Assoc. 83, 4 (2017), 418--418.
[10]
Francesco Calabrese, Laura Ferrari, and Vincent D. Blondel. 2014. Urban sensing using mobile phone network data: A survey of research. ACM Comput. Surv. 47, 2 (Nov. 2014), 25:1--25:20.
[11]
Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 1293--1304.
[12]
David Charte, Francisco Charte, Salvador García, María J. del Jesus, and Francisco Herrera. 2018. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Inf. Fusion 44 (2018), 78--96.
[13]
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8, 1 (2018), 6085.
[14]
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder--decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724--1734.
[15]
Jun Jin Choong, Xin Liu, and Tsuyoshi Murata. 2018. Learning community structure with variational autoencoder. In Proceedings of the IEEE International Conference on Data Mining (ICDM’18). IEEE, 69--78.
[16]
M. Chuah and A. Coman. 2009. Identifying connectors and communities: Understanding their impacts on the performance of a DTN publish/subscribe system. In Proceedings of the International Conference on Computational Science and Engineering, Vol. 4. 1093--1098.
[17]
Sina Dabiri and Kevin Heaslip. 2018. Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C: Emerg. Technol. 86 (2018), 360-371.
[18]
Abhijit Dasgupta and Adrian E. Raftery. 1995. Detecting features in spatial point processes with clutter via model-based clustering. J. Amer. Stat. Assoc. 93 (1995), 294--302.
[19]
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3844--3852.
[20]
Manlio De Domenico, Antonio Lima, and Mirco Musolesi. 2013. Interdependence and predictability of human mobility and social interactions. Perv. Mob. Comput. 9, 6 (2013), 798-807.
[21]
Richard Dosselmann and Xue Dong Yang. 2011. A comprehensive assessment of the structural similarity index. Sig. Image Video Proc. 5, 1 (2011), 81--91.
[22]
Nathan Eagle and Alex Sandy Pentland. 2006. Reality mining: Sensing complex social systems. Person. Ubiq. Comput. 10, 4 (2006), 255--268.
[23]
Yuki Endo, Hiroyuki Toda, Kyosuke Nishida, and Akihisa Kawanobe. 2016. Deep feature extraction from trajectories for a transportation mode estimation. In Advances in Knowledge Discovery and Data Mining, James Bailey, Latifur Khan, Takashi Washio, Gill Dobbie, Joshua Zhexue Huang, and Ruili Wang (Eds.). Springer International Publishing, Cham, 54--66.
[24]
D. L. Ferreira, B. A. A. Nunes, and K. Obraczka. 2018. Scale-free properties of human mobility and applications to intelligent transportation systems. IEEE Trans. Intell. Transport. Syst. 19, 11 (2018), 3736--3748.
[25]
Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779.
[26]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
[27]
Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. 2007. Multi-dimensional recurrent neural networks. In Artificial Neural Networks—ICANN 2007, Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, and Danilo Mandic (Eds.). Springer Berlin, 549--558.
[28]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.
[29]
Andrea Hess, Karin Anna Hummel, Wilfried N. Gansterer, and Günter Haring. 2015. Data-driven human mobility modeling: A survey and engineering guidance for mobile networking. Comput. Surv. 48, 3 (Dec. 2015), 38:1--38:39.
[30]
S. Hong, K. Lee, and I. Rhee. 2010. STEP: A spatio-temporal mobility model for humans walks. In Proceedings of the 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS’10). 630--635.
[31]
T. Florian Jaeger. 2008. Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. J. Mem. Lang. 59, 4 (2008), 434--446. Special Issue: Emerging Data Analysis.
[32]
Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2017. Variational deep embedding: An unsupervised and generative approach to clustering. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). 1965--1972.
[33]
Faina Khoroshevsky and Boaz Lerner. 2016. Human mobility-pattern discovery and next-place prediction from GPS data. In Proceedings of the IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-computer Interaction. Springer, 24--35.
[34]
David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. 2009. CRAWDAD Data Set Dartmouth/campus (v. 2009-09-09). Retrieved from http://crawdad.cs.dartmouth.edu/dartmouth/campus.
[35]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the International Conference on Advances in Neural Information Processing Systems 25. 1097--1105.
[36]
Richard B. Langley. 1998. The UTM grid system. GPS World 9, 2 (1998), 46--50.
[37]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.
[38]
Feng Li and Jie Wu. 2009. LocalCom: A community-based epidemic forwarding scheme in disruption-tolerant networks. In Proceedings of the 6th IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’09). 1--9.
[39]
N. Liu, M. Liu, J. Cao, G. Chen, and W. Lou. 2010. When transportation meets communication: V2P over VANETs. In Proceedings of the IEEE 30th International Conference on Distributed Computing Systems. 567--576.
[40]
Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E. Alsaadi. 2016. A survey of deep neural network architectures and their applications. Neurocomputing 234 (12 2016).
[41]
Zhidan Liu, Zhenjiang Li, Kaishun Wu, and Mo Li. 2018. Urban traffic prediction from mobility data using deep learning. IEEE Netw. 32, 4 (2018), 40--46.
[42]
Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3431--3440.
[43]
Rémi Louf and Marc Barthelemy. 2014. How congestion shapes cities: From mobility patterns to scaling. Sci. Rep. 4, 5561 (2014).
[44]
Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, and Yunpeng Wang. 2017. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17, 4 (2017), 818.
[45]
Abhinav Mehrotra and Mirco Musolesi. 2018. Using autoencoders to automatically extract mobility features for predicting depressive states. Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 3 (2018), 127.
[46]
Abhinav Mehrotra and Mirco Musolesi. 2018. Using autoencoders to automatically extract mobility features for predicting depressive states. Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 3, Article 127 (Sept. 2018), 20 pages.
[47]
E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long. 2018. A survey of clustering with deep learning: From the perspective of network architecture. IEEE ACCESS 6 (2018), 39501--39514.
[48]
Rahul Nair, Elise Miller-Hooks, Robert C. Hampshire, and Ana Busic. 2013. Large-scale vehicle sharing systems: Analysis of Vélib’. Int. J. Sustain. Transport. 7, 1 (2013), 85--106.
[49]
Michal Piorkowski, Natasa Sarafijanovic-Djukic, and Matthias Grossglauser. 2009. CRAWDAD Data Set Epfl/mobility (v. 2009-02-24). Retrieved from http://crawdad.cs.dartmouth.edu/epfl/mobility.
[50]
Miguel Rocha, Paulo Cortez, and José Neves. 2007. Evolution of neural networks for classification and regression. Neurocomputing 70, 16 (2007), 2809--2816.
[51]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Cham, 234--241.
[52]
Jorge M. Santos and Mark Embrechts. 2009. On the use of the Adjusted Rand Index as a metric for evaluating supervised classification. In Proceedings of the 19th International Conference on Artificial Neural Networks: Part II (ICANN ’09). Springer-Verlag, 175--184.
[53]
Katarzyna Siła-Nowicka, Jan Vandrol, Taylor Oshan, Jed A. Long, Urška Demšar, and A. Stewart Fotheringham. 2016. Analysis of human mobility patterns from GPS trajectories and contextual information. Int. J. Geogr. Inf. Sci. 30, 5 (2016), 881--906.
[54]
Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.
[55]
Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu. 2014. Learning deep representations for graph clustering. In Proceedings of the 28th AAAI Conference on Artificial Intelligence.
[56]
Eran Toch, Boaz Lerner, Eyal Ben-Zion, and Irad Ben-Gal. 2019. Analyzing large-scale human mobility data: A survey of machine learning methods and applications. Knowl. Inf. Syst. 58, 3 (2019), 501--523.
[57]
Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 1 (Jan. 2014), 3221--3245.
[58]
Hao Wang, GaoJun Liu, Jianyong Duan, and Lei Zhang. 2017. Detecting transportation modes using deep neural network. IEICE Trans. Inf. Syst. 100, 5 (2017), 1132--1135.
[59]
Kunfeng Wang, Chao Gou, Nanning Zheng, James M. Rehg, and Fei-Yue Wang. 2017. Parallel vision for perception and understanding of complex scenes: Methods, framework, and perspectives. Artif. Intell. Rev. 48, 3 (2017), 299--329.
[60]
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Proc. 13, 4 (Apr. 2004), 600--612.
[61]
Sebastian J. Wetzel. 2017. Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders. Phys. Rev. E 96 (Aug. 2017), 022140. Issue 2.
[62]
R. Wu, G. Luo, J. Shao, L. Tian, and C. Peng. 2018. Location prediction on trajectory data: A review. Big Data Mining Anal. 1, 2 (June 2018), 108--127.
[63]
W. Li, Y. Zhao, J. Xia, K. M. Curtin. 2015. A new model for a carpool matching service. PLoS One 10, 6 (2015), 46--50.
[64]
Dongkuan Xu and Yingjie Tian. 2015. A comprehensive survey of clustering algorithms. Ann. Data Sci. 2, 2 (1 June 2015), 165--193.
[65]
Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, and Weixiong Zhang. 2016. Modularity based community detection with deep learning. In International Joint Conference on Artificial Intelligence (IJCAI’16), Vol. 16. 2252--2258.
[66]
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. IEEE Computat. Intell. Mag. 13, 3 (2018), 55--75.
[67]
Shiqi Yu, Sen Jia, and Chunyan Xu. 2017. Convolutional neural networks for hyperspectral image classification. Neurocomputing 219 (2017), 88--98.
[68]
Peiyan Yuan, Lilin Fan, Ping Liu, and Shaojie Tang. 2016. Recent progress in routing protocols of mobile opportunistic networks: A clear taxonomy, analysis and evaluation. J. Netw. Comput. Applic. 62 (2016), 163--170.
[69]
Yu Zheng. 2015. Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. 6, 3 (2015), 29.
[70]
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 38.
[71]
Yu Zheng, Xing Xie, and Wei-Ying Ma. 2010. GeoLife: A collaborative social networking service among user, location, and trajectory. IEEE Data. Eng. Bull. 33, 2 (June 2010). Retrieved from http://research.microsoft.com/apps/pubs/default.aspx?id=131038.
[72]
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web. ACM, 791--800.
[73]
Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2018. Trajectory-user linking via variational AutoEncoder. In International Joint Conference on Artificial Intelligence (IJCAI’18). 3212--3218.
[74]
W. Zhu, W. Peng, C. Hung, P. Lei, and L. Chen. 2014. Exploring sequential probability tree for movement-based community discovery. IEEE Trans. Knowl. Data Eng. 26, 11 (Nov. 2014), 2717--2730.
[75]
A. Ziat, E. Delasalles, L. Denoyer, and P. Gallinari. 2017. Spatio-temporal neural networks for space-time series forecasting and relations discovery. In Proceedings of the IEEE International Conference on Data Mining (ICDM’17). 705--714.

Cited By

View all
  • (2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
  • (2024)Federated Learning assisted framework to periodically identify user communities in urban spaceAd Hoc Networks10.1016/j.adhoc.2024.103589163(103589)Online publication date: Oct-2024
  • (2023)Analysing Fairness of Privacy-Utility Mobility ModelsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610676(359-365)Online publication date: 8-Oct-2023
  • Show More Cited By

Index Terms

  1. A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Transactions on Spatial Algorithms and Systems
        ACM Transactions on Spatial Algorithms and Systems  Volume 6, Issue 3
        Special Issue on Deep Learning for Spatial Algorithms and Systems
        September 2020
        171 pages
        ISSN:2374-0353
        EISSN:2374-0361
        DOI:10.1145/3394669
        Issue’s Table of Contents
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 15 April 2020
        Accepted: 01 January 2020
        Revised: 01 September 2019
        Received: 01 May 2019
        Published in TSAS Volume 6, Issue 3

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Deep learning
        2. T-sne
        3. autoencoder
        4. clustering algorithm
        5. convolutional neural networks
        6. geographical preference
        7. human mobility
        8. mobility feature extraction
        9. principal component analysis
        10. user community

        Qualifiers

        • Research-article
        • Research
        • Refereed

        Funding Sources

        • Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001
        • US National Science Foundation

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)54
        • Downloads (Last 6 weeks)6
        Reflects downloads up to 07 Jan 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
        • (2024)Federated Learning assisted framework to periodically identify user communities in urban spaceAd Hoc Networks10.1016/j.adhoc.2024.103589163(103589)Online publication date: Oct-2024
        • (2023)Analysing Fairness of Privacy-Utility Mobility ModelsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610676(359-365)Online publication date: 8-Oct-2023
        • (2023)Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban AnalyticsACM Transactions on Spatial Algorithms and Systems10.1145/35717419:2(1-25)Online publication date: 12-Apr-2023
        • (2023)Classifying habitat characteristics of wetlands using a self-organizing mapEcological Informatics10.1016/j.ecoinf.2023.10204875(102048)Online publication date: Jul-2023
        • (2022)Spatio-Temporal Data Clustering using Deep Learning: A Review2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS51927.2022.9787701(1-10)Online publication date: 25-May-2022
        • (2022)An Auditing Framework for Analyzing Fairness of Spatial-Temporal Federated Learning Applications2022 IEEE World AI IoT Congress (AIIoT)10.1109/AIIoT54504.2022.9817283(699-707)Online publication date: 6-Jun-2022
        • (2022)Machine Learning for Urban ComputingMachine Learning and the City10.1002/9781119815075.ch20(249-262)Online publication date: 27-May-2022
        • (2021)Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion—A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area (GBA)Remote Sensing10.3390/rs1309180113:9(1801)Online publication date: 5-May-2021
        • (2021)Community-Structured Decentralized Learning for Resilient EIProceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning10.1145/3477114.3488764(13-15)Online publication date: 25-Oct-2021
        • Show More Cited By

        View Options

        Login options

        Full Access

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media