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A Composite Grid Clustering Algorithm Based on Density and Balance Degree

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Spatial Data and Intelligence (SpatialDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13887))

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Abstract

With the rapid growth of bike-sharing comes the challenge of unregulated bike-sharing parking in cities, which can lead to an unbalanced distribution of bikes and negatively impact the user experience and the operating costs of bike-sharing companies. To address these challenges, bike-sharing companies can create temporary parking stations or electronic fencing and implement bicycle rebalancing strategies across districts. However, these strategies require real-time data analysis and should take into account other factors, such as the inflow and outflow of bikes in each zone. To solve this problem, we proposes a composite clustering algorithm based on density and inflow-outflow balance to divide the city into a grid and extract hotspots as suitable areas for bicycle docking stations. Comparative experiments on common clustering algorithms for shared bicycles demonstrate the reasonableness and high precision of our method.

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References

  1. Wang, H., Chen, X.J., Wang, Y., et al.: Local maximum density approach for small-scale clustering of urban taxi stops. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42(2/W13) (2019)

    Google Scholar 

  2. MacQueen, J.: Classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability. Los Angeles LA USA: University of California, pp. 281–297 (1967)

    Google Scholar 

  3. Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96(34), 226–231 (1996)

    Google Scholar 

  4. Ankerst, M., Breunig, M.M., Kriegel, H.P., et al.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  5. Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14

    Chapter  Google Scholar 

  6. Dockhorn, A., Braune, C., Kruse, R.: An alternating optimization approach based on hierarchical adaptations of DBSCAN. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 749–755. IEEE (2015)

    Google Scholar 

  7. Wang, W., Yang, J., Muntz, R.: STING: a statistical information grid approach to spatial data mining. VLDB 97, 186–195 (1997)

    Google Scholar 

  8. Sheikholeslami, G., Chatterjee, S., Zhang, A.: WaveCluster: a multi-resolution clustering approach for very large spatial databases. VLDB 98, 428–439 (1998)

    Google Scholar 

  9. Agrawal, R., Gehrke, J., Gunopulos, D., et al.: Automatic subspace clustering of high dimensional data for data mining applications. Proceed. ACM SIGMOD Int. Conf. Manag. Data 1998, 94–105 (1998)

    Article  Google Scholar 

  10. Zhaohua, C.: Improvement and application of cluster analysis algorithm CLIQUE. Central South University, Changsha (2009)

    Google Scholar 

  11. Wu, X., Zurita-Milla, R., Kraak, M.J., et al.: Clustering-based approaches to the exploration of spatio-temporal data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42, 1387–1391 (2017)

    Article  Google Scholar 

  12. Changzheng, X., Fei, W., Lili, W.: Density grid-based data stream clustering algorithm with parameter automatization. J. Front. Comput. Sci. Technol. 5(10), 953 (2011)

    Google Scholar 

  13. Oleinikova, S.A., Kravets, O.J., Silnov, D.S.: Analytical estimates for the expectation of the beta distribution on the basis of the known values of the variance and mode. International Information Institute (Tokyo). Inf. 19(2), 343 (2016)

    Google Scholar 

  14. Cai, Z., Ji, M., Mi, Q., et al.: Dynamic grid-based spatial density visualization and rail transit station prediction. ISPRS Int. J. Geo Inf. 10(12), 804 (2021)

    Article  Google Scholar 

  15. Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquit. Comput. 7, 275–286 (2003)

    Article  Google Scholar 

  16. Yaohui, L., Zhengming, M., Fang, Y.: Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy. Knowl.-Based Syst. 133, 208–220 (2017)

    Article  Google Scholar 

  17. Cai, Z., Wang, J., Li, T., et al.: A novel trajectory based prediction method for urban subway design. ISPRS Int. J. Geo-Inf. 11(2), 126 (2022)

    Google Scholar 

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Correspondence to Zhi Cai .

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Guo, L., Li, D., Cai, Z. (2023). A Composite Grid Clustering Algorithm Based on Density and Balance Degree. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-32910-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32909-8

  • Online ISBN: 978-3-031-32910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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