Cluster Computing

, Volume 22, Supplement 6, pp 14669–14677 | Cite as

Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS

  • Jin Zhu
  • Jiansong Li
  • Zilong JiangEmail author


Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%.


Spatiotemporal big data Spatiotemporal model Causal correlation Dimension reduction Unsteady Granger 


  1. 1.
    Bai, Z., Hui, Y., Jiang, D., Lv, Z., Wong, W.K., Zheng, S.: A new test of multivariate nonlinear causality. PLoS ONE 13(1), e0185155 (2018)CrossRefGoogle Scholar
  2. 2.
    Yin, C., Shi, Y., Wang, H., Wu, J.: Disaggregation of an urban population with M_IDW interpolation and building information. J. Urban Plan. Dev. 141(1), 4001–4012 (2014)Google Scholar
  3. 3.
    Hu, D., Huang, B., Tu, L., Chen, S.: Understanding social characteristic from spatial proximity in mobile social network. Int. J. Comput. Commun. Control 10(4), 539–550 (2015)CrossRefGoogle Scholar
  4. 4.
    Shi, Y., Deng, M., Yang, X., Liu, Q., Zhao, L., Lu, C.-T.: A framework for discovering evolving domain related spatio-temporal patterns in Twitter. ISPRS Int. J. Geo-Inf. 5(10), 193 (2016)CrossRefGoogle Scholar
  5. 5.
    Domènech, A., Gutiérrez, A.: A GIS-based evaluation of the effectiveness and spatial coverage of public transport networks in tourist destinations. ISPRS Int. J. Geo-Inf. 6(3), 83 (2017)CrossRefGoogle Scholar
  6. 6.
    Gu, Z., Zhang, Y., Chen, Y., Chang, X.: Analysis of attraction features of tourism destinations in a mega-city based on check-in data mining—a case study of ShenZhen, China. ISPRS Int. J. Geo-Inf. 5(11), 210 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhao, J., Guo, W., Huang, W., Huang, L., Zhang, D., Yang, H., Yuan, L.: Characterizing spatiotemporal dynamics of land cover with multi-temporal remotely sensed imagery in Beijing during 1978–2010. Arab. J. Geosci. 7(10), 3945–3959 (2014)CrossRefGoogle Scholar
  8. 8.
    Gao, Y., Kim, D.-S.: Process modeling for urban growth simulation with cohort component method, cellular automata model and GIS/RS: case study on surrounding area of Seoul, Korea. J. Urban Plan. Dev. 142(2), 5001–5007 (2015)Google Scholar
  9. 9.
    Huang, W., Li, S., Xu, S.: A three-step spatial-temporal-semantic clustering method for human activity pattern analysis. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLI-B2, 549–552 (2016). CrossRefGoogle Scholar
  10. 10.
    Korucu, M.K., Karademir, A.: Siting a municipal solid waste disposal facility, part II: the effects of external criteria on the final decision. J. Air Waste Manag. Assoc. 64(2), 131–140 (2014)CrossRefGoogle Scholar
  11. 11.
    Timpner, J., Schürmann, D., Wolf, L.: Trustworthy parking communities: helping your neighbor to find a space. IEEE Trans. Depend. Secure Comput. 13(1), 120–132 (2016)CrossRefGoogle Scholar
  12. 12.
    Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Effective and efficient trajectory outlier detection based on time-dependent popular route. World Wide Web 20(1), 111–134 (2017)CrossRefGoogle Scholar
  13. 13.
    Huang, Q.: Mining online footprints to predict user’s next location. Int. J. Geogr. Inf. Sci. 31(3), 523–541 (2017)CrossRefGoogle Scholar
  14. 14.
    Restrepo, A.C., Baker, P., Clements, A.C.: National spatial and temporal patterns of notified dengue cases, Colombia 2007–2010. Trop. Med. Int. Health 19(7), 863–871 (2014)CrossRefGoogle Scholar
  15. 15.
    Jozi, S.A., Ebadzadeh, F.: Application of multi-criteria decision-making in land evaluation of agricultural land use. J. Indian Soc. Remote Sens. 42(2), 363–371 (2014)CrossRefGoogle Scholar
  16. 16.
    Birou, M.M.: New rates of convergence for the iterates of some positive linear operators. Mediterr. J. Math. 14(3), 129 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lloyd-Jones, L.R., Nguyen, H.D., Mclachlan, G.J.: A globally convergent algorithm for lasso-penalized mixture of linear regression models. Comput. Stat. Data Anal. 119, 19–38 (2017)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhang, J., Zheng, J., Gao, Q.: Numerical solution of the Degasperis–Procesi equation by the cubic B-spline quasi-interpolation method. Appl. Math. Comput. 324, 218–227 (2018)MathSciNetzbMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

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