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CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

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Abstract

Analysis of mobile big data enables smart cities from aspects of traffic pattern, human mobility, air quality, and so on. Co-occurrence pattern in human mobility has been proposed in recent years and sparked high attentions of academia and industry. Co-occurrence pattern has shown enormous values in aspects of urban planning, business, and social applications, such as shopping mall promotion strategy making, and contagious disease spreading. What’s more, human mobility has strong relation with regional functions, because each urban region owns a major function to offer specialized services for city’s operations and such location-based services attract massive passenger flow, which is exactly the essence of urban human mobility pattern. Therefore, in this paper, we put forward a co-occurrence pattern mining scheme (CoPFun) based on regional function discovery utilizing various mobile data. First, we do traffic modeling to map trajectory data into population groups, which include temporal partition and map segmentation. Then we employ a frequent pattern mining algorithm to mine co-occurrence event data. Meanwhile, we exploit TF-IDF method to process POI data and LDA algorithm to process trajectory data to discover urban regional functions. We apply CoPFun to real mobile data to extract co-occurrence event data and compare it with OD data to analyze urban co-occurrence pattern from a perspective of regional functions. The experiment results verify the effectiveness of CoPFun.

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References

  1. Akbari, M., Samadzadegan, F., Weibel, R.: A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. J. Geogr. Syst. 17 (3), 249–274 (2015)

    Article  Google Scholar 

  2. Assem, H., Xu, L., Buda, T.S., O’Sullivan, D.: Spatio-temporal clustering approach for detecting functional regions in cities. In: 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI), pp. 370–377. San Jose, USA (2016)

  3. Aydin, B., Kempton, D., Akkineni, V., Gopavaram, S.R., Pillai, K.G., Angryk, R.: Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns. In: 2014 IEEE international conference on big data (Big Data), pp. 1–10. Washington, DC, USA (2014)

  4. Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: A survey. Geoinformatica 19(3), 525–565 (2015)

    Article  Google Scholar 

  5. Barua, S., Sander, J.: Sscp: Mining statistically significant co-location patterns. In: International symposium on spatial and temporal databases, pp. 2–20. Berlin, Germany (2011)

  6. Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A.: Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Trans. Knowl. Data Eng. 20(10), 1322–1335 (2008)

    Article  Google Scholar 

  7. Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Yoo, J.S.: Mixed-drove spatio-temporal co-occurence pattern mining: A summary of results. In: 6th international conference on data mining, 2006. ICDM’06, pp. 119–128. Hong Kong, China (2006)

  8. Chen, D.: Research on traffic flow prediction in the big data environment based on the improved rbf neural network. IEEE Trans. Ind. Inf. 13(4), 2000–2008 (2017)

    Article  Google Scholar 

  9. Faust, K., Sathirapongsasuti, J.F., Izard, J., Segata, N., Gevers, D., Raes, J., Huttenhower, C.: Microbial co-occurrence relationships in the human microbiome. PLoS computational biology 8(7), e1002,606 (2012)

    Article  Google Scholar 

  10. Ferreira, N., Poco, J., Vo, H.T., Freire, J., Silva, C.T.: Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips. IEEE Trans. Vis. Comput. Graph. 19 (12), 2149–2158 (2013)

    Article  Google Scholar 

  11. Gao, S., Janowicz, K., Couclelis, H.: Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans. GIS 21(3), 446–467 (2017)

    Article  Google Scholar 

  12. Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier, New York (2011)

    MATH  Google Scholar 

  13. Hong, L., Zheng, Y., Yung, D., Shang, J., Zou, L.: Detecting urban black holes based on human mobility data. In: Sigspatial International Conference on Advances in Geographic Information Systems, p. 35 (2015)

  14. Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)

    Article  Google Scholar 

  15. Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  16. Kong, X., Song, X., Xia, F., Guo, H., Wang, J., Tolba, A.: Lotad: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web. https://doi.org/10.1007/s11280-017-0487-4 (2017)

  17. Kong, X., Xia, F., Ning, Z., Rahim, A., Cai, Y., Gao, Z., Ma, J.: Mobility dataset generation for vehicular social networks based on floating car data. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2017.2788441 (2018)

  18. Kong, X., Xia, F., Wang, J., Rahim, A., Das, S.K.: Time-location-relationship combined service recommendation based on taxi trajectory data. IEEE Trans. Ind. Inf. 13(3), 1202–1212 (2017)

    Article  Google Scholar 

  19. Li, F., Li, Z., Sharif, K., Liu, Y., Wang, Y.: Multi-layer-based opportunistic data collection in mobile crowdsourcing networks. World Wide Web. https://doi.org/10.1007/s11280-017-0482-9 (2017)

  20. Liu, Y., Liu, C., Yuan, N.J., Duan, L., Fu, Y., Xiong, H., Xu, S., Wu, J.: Exploiting heterogeneous human mobility patterns for intelligent bus routing. In: 2014 IEEE International Conference on Data Mining, pp. 360–369 (2014)

  21. Long, Y., Shen, Z.: Discovering functional zones using bus smart card data and points of interest in Beijing, pp. 193–217 (2015)

  22. Machens, A., Gesualdo, F., Rizzo, C., Tozzi, A.E., Barrat, A., Cattuto, C.: An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infect. Dis. 13(1), 185 (2013)

    Article  Google Scholar 

  23. Ning, Z., Xia, F., Ullah, N., Kong, X., Hu, X.: Vehicular social networks: enabling smart mobility. IEEE Commun. Mag. 55(5), 16–55 (2017)

    Article  Google Scholar 

  24. Paik, J.H.: A novel tf-idf weighting scheme for effective ranking. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 343–352. New York, USA (2013)

  25. Palchykov, V., Mitrovic, M., Jo, H.H., Saramaki, J., Pan, R.K.: Inferring human mobility using communication patterns. Sci. Report. 4, 6174 (2014)

    Article  Google Scholar 

  26. Pillai, K.G., Angryk, R.A., Banda, J.M., Schuh, M.A., Wylie, T.: Spatio-temporal co-occurrence pattern mining in data sets with evolving regions. In: 2012 IEEE 12th international conference on data mining workshops (ICDMW), pp. 805–812. Brussels, Belgium (2012)

  27. Qi, X., Xiao, R., Li, C.G., Qiao, Y., Guo, J., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2199–2213 (2014)

    Article  Google Scholar 

  28. Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K.K., Xu, C., Tapia, E.M.: Mobileminer: Mining your frequent patterns on your phone. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, pp. 389–400. New York, USA (2014)

  29. Sun, M., North, C., Ramakrishnan, N.: A five-level design framework for bicluster visualizations. IEEE Trans. Vis. Comput. Graph. 20(12), 1713–1722 (2014)

    Article  Google Scholar 

  30. Wu, W., Xu, J., Zeng, H., Zheng, Y., Qu, H., Ni, B., Yuan, M., Ni, L.M.: Telcovis: Visual exploration of co-occurrence in urban human mobility based on telco data. IEEE Trans. Vis. Comput. Graph. 22(1), 935–944 (2016)

    Article  Google Scholar 

  31. Yang, Q., Gao, Z., Kong, X., Rahim, A., Wang, J., Xia, F.: Taxi operation optimization based on big traffic data. In: 2015 smart world congress, pp. 127–134. Beijing, China (2015)

  32. Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data 9(3), 1–41 (2015)

    Article  Google Scholar 

  33. Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28(10), 2566–2581 (2016)

    Article  Google Scholar 

  34. Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: ACM international on conference on information and knowledge management, pp. 1631–1640, New York, USA (2015)

  35. Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 186–194. New York, NY, USA (2012)

  36. Yuan, N.J., Zheng, Y., Xie, X.: Segmentation of urban areas using road networks. MSR-TR-2012–65, Tech. Rep. (2012)

  37. Yuan, N.J., Zheng, Y., Xie, X., Wang, Y., Zheng, K., Xiong, H.: Discovering urban functional zones using latent activity trajectories. IEEE Trans. Knowl. Data Eng. 27(3), 712–725 (2015)

    Article  Google Scholar 

  38. Zhong, C., Huang, X., Arisona, S.M., Schmitt, G.: Identifying spatial structure of urban functional centers using travel survey data: A case study of singapore. In: COMP@ SIGSPATIAL, pp. 28–33. New York, USA (2013)

  39. Zhong, C., Huang, X., Arisona, S.M., Schmitt, G., Batty, M.: Inferring building functions from a probabilistic model using public transportation data. Comput. Environ. Urban. Syst. 48, 124–137 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China under Grant no. 61572106, the Natural Science Foundation of Liaoning Province, China under Grant no. 201602154, Fundamental Research Funds for the Central Universities under Grant no. DUT18JC09, and China Scholarship Council under Grant no. 201706060067.

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Authors and Affiliations

  1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, 116620, China

    Xiangjie Kong, Menglin Li, Kaiqi Tian & Feng Xia

  2. School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia

    Jianxin Li

  3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Xiping Hu

Authors
  1. Xiangjie Kong
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  2. Menglin Li
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  3. Jianxin Li
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  4. Kaiqi Tian
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  5. Xiping Hu
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  6. Feng Xia
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Correspondence to Feng Xia.

Additional information

This article belongs to the Topical Collection: Special Issue on Geo-Social Computing

Guest Editors: Guandong Xu, Wen-Chih Peng, Hongzhi Yin, Zi (Helen) Huang

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Kong, X., Li, M., Li, J. et al. CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery. World Wide Web 22, 1029–1054 (2019). https://doi.org/10.1007/s11280-018-0578-x

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  • DOI: https://doi.org/10.1007/s11280-018-0578-x

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