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Identifying Clusters in Spatial Data Via Sequential Importance Sampling

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 795))

Abstract

Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.

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References

  1. Anderson, C., Lee, D., Dean, N.: Bayesian cluster detection via adjacency modelling. Spat. Spatio-Temporal Epidemiol. 16, 11–20 (2016)

    Article  Google Scholar 

  2. Beckage, B., Joseph, L., Belisle, P., Wolfson, D.B., Platt, W.J.: Bayesian change-point analyses in ecology. New Phytol. 174(2), 456–467 (2007)

    Article  Google Scholar 

  3. Braun, J.V., Muller, H.-G.: Statistical methods for DNA sequence segmentation. Stat. Sci. 13(2), 142–162 (1998)

    Article  Google Scholar 

  4. Chen, J., Gupta, A.K.: Parametric Statistical Change Point Analysis: With Applications to Genetics, Medicine, and Finance. Springer Science & Business Media (2011)

    Google Scholar 

  5. Chen, S.S., Gopalakrishnan, P.S.: Clustering via the bayesian information criterion with applications in speech recognition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998, vol. 2, pp. 645–648. IEEE (1998)

    Google Scholar 

  6. Cliff, A.D., Ord, J.K.: Spatial Processes: Models & Applications. Taylor & Francis (1981)

    Google Scholar 

  7. Cressie, N.: Statistics for Spatial Data. Wiley (2015)

    Google Scholar 

  8. Eckley, I.A., Fearnhead, P., Killick, R.: Analysis of changepoint models. In: Bayesian Time Series Models, pp. 205–224 (2011)

    Google Scholar 

  9. Fryzlewicz, P., et al.: Wild binary segmentation for multiple change-point detection. Ann. Stat. 42(6), 2243–2281 (2014)

    Article  MathSciNet  Google Scholar 

  10. Gangnon, R.E., Clayton, M.K.: Bayesian detection and modeling of spatial disease clustering. Biometrics 56(3), 922–935 (2000)

    Article  Google Scholar 

  11. Helterbrand, J.D., Cressie, N., Davidson, J.L.: A statistical approach to identifying closed object boundaries in images. Adv. Appl. Probab. 26(04), 831–854 (1994)

    Article  MathSciNet  Google Scholar 

  12. Killick, R., Eckley, I.A., Jonathan, P., Chester, U.: Efficient detection of multiple changepoints within an oceano-graphic time series. In: Proceedings of the 58th World Science Congress of ISI (2011)

    Google Scholar 

  13. López, I., Gámez, M., Garay, J., Standovár, T., Varga, Z.: Application of change-point problem to the detection of plant patches. Acta Biotheor. 58(1), 51–63 (2010)

    Article  Google Scholar 

  14. Manuguerra, M., Sofronov, G., Tani, M., Heller, G.: Monte Carlo methods in spatio-temporal regression modeling of migration in the EU. In: 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 128–134. IEEE (2013)

    Google Scholar 

  15. Olshen, A.B., Venkatraman, E., Lucito, R., Wigler, M.: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5(4), 557–572 (2004)

    Article  Google Scholar 

  16. Polushina, T., Sofronov, G.: Change-point detection in binary Markov DNA sequences by the cross-entropy method. In: 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 471–478. IEEE (2014)

    Google Scholar 

  17. Priyadarshana, W., Sofronov, G.: Multiple break-points detection in array CGH data via the cross-entropy method. IEEE/ACM Trans. Comput. Biol. Bioinf. (TCBB) 12(2), 487–498 (2015)

    Article  Google Scholar 

  18. Raveendran, N., Sofronov, G.: Binary segmentation methods for identifying boundaries of spatial domains. Computer Science and Information Systems (FedCSIS) 13, 95–102 (2017)

    Article  Google Scholar 

  19. Rubinstein, R.Y., Kroese, D.P.: Simulation and the Monte Carlo Method, vol. 10, 2nd edn. Wiley (2007)

    Google Scholar 

  20. Sofronov, G.: Change-point modelling in biological sequences via the bayesian adaptive independent sampler. Int. Proc. Comput. Sci. Inf. Technol. 5(2011), 122–126 (2011)

    Google Scholar 

  21. Sofronov, G.Y., Evans, G.E., Keith, J.M., Kroese, D.P.: Identifying change-points in biological sequences via sequential importance sampling. Environ. Model. & Assess. 14(5), 577–584 (2009)

    Article  Google Scholar 

  22. Sofronov, G.Y., Glotov, N.V., Zhukova, O.V.: Statistical analysis of spatial distribution in populations of microspecies of Alchemilla l. In: Proceedings of the 30th International Workshop on Statistical Modelling, vol. 2, pp. 259–262. Statistical Modelling Society, Linz, Austria (2015)

    Google Scholar 

  23. Tripathi, S., Govindaraju, R.S.: Change detection in rainfall and temperature patterns over India. In: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, pp. 133–141. ACM (2009)

    Google Scholar 

  24. Tung, A.K., Hou, J., Han, J.: Spatial clustering in the presence of obstacles. In: Proceedings of 17th International Conference on Data Engineering, 2001, pp. 359–367. IEEE (2001)

    Google Scholar 

  25. Upton, G.J., Fingleton, B.: Spatial Data Analysis by Example, vol. 1: Point Pattern and Quantitative Data. Wiley, Chichester, 1 (1985)

    Google Scholar 

  26. Wang, Y.: Change curve estimation via wavelets. J. Am. Stat. Assoc. 93(441), 163–172 (1998)

    Article  MathSciNet  Google Scholar 

  27. Yang, T.Y., Swartz, T.B.: Applications of binary segmentation to the estimation of quantal response curves and spatial intensity. Biometrical J. 47(4), 489–501 (2005)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Statistics, Macquarie University, Sydney, NSW, 2109, Australia

    Nishanthi Raveendran & Georgy Sofronov

Authors
  1. Nishanthi Raveendran
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  2. Georgy Sofronov
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Correspondence to Georgy Sofronov .

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Editors and Affiliations

  1. Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Raveendran, N., Sofronov, G. (2019). Identifying Clusters in Spatial Data Via Sequential Importance Sampling. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-99648-6_10

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