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Spatio-temporal hotspots and application on a disease analysis case via GIS

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Abstract

Hotspot analysis is a spatial analysis that uses cluster techniques for determining areas with elevated concentrations of localized events. We use the consolidated Extended Fuzzy C-Means algorithm to determine the hotspot areas on the map as circles, moreover the advantages of this technique are the linear computational complexity, the robustness to noise and outliers, the automatic determination of the optimal number C of clusters (in the classical FCM algorithm C is chosen a priori). Furthermore it prevents the problem of shifting the clusters with low density area of data points in areas with higher density of such points. We apply this method to study the spatio-temporal variations of the hotspot areas by testing this process on a specific disease problem, precisely we have clusterized 5,000 point-events correspondent to cases of brain cancer detected in the state of New Mexico from 1973 to 1991. We also show that the same results are obtained by using the Extended Gustafson–Kessel algorithm which gives elliptical clusters. We have implemented both algorithms in a Geographic Information System environment. Thus we establish the areas which seem not interested from the incidence of the disease and those areas in which the phenomenon appears to be temporarily attenuated either increased or constant or quite disappeared.

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References

  • Avogadri R, Valentini G (2009) Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artif Intell Med 45:173–183

    Article  Google Scholar 

  • Bezdek JC (1982) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Google Scholar 

  • Chainey SP, Rei S, Stuart N (2002) When is a hotspot a hotspot? A procedure for creating statistically robust hotspot geographic maps of crime. In: Kidner D, Higgs G, White S (eds) Innovations in GIS 9: socioeconomic applications of geographic information science. Taylor and Francis, London, pp 21–36

    Google Scholar 

  • Chertov O, Aleksandrova M (2013) Soft computing: state of the art theory. STUDFUZZ 291. In: Yager RR (ed) Fuzzy clustering with prototype extraction for census data analysis. Springer, Berlin, pp 289–313

    Google Scholar 

  • Di Martino F, Loia V, Sessa S (2007) Extended fuzzy c-means cluste ring algorithm for hotspot events in spatial analysis. Int J Hybrid Intell Syst 4:1–14

    Google Scholar 

  • Di Martino F, Sessa S (2009) Implementation of the extended fuzzy C-means algorithm in geographic information systems. J Uncertain Syst 3(4):298–306

    Google Scholar 

  • Di Martino F, Sessa S (2011) The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS. Expert Syst Appl 38:11829–11836

    Article  Google Scholar 

  • Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intelligence 11:773–781

    Article  Google Scholar 

  • Gustafson DE, Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of IEEE Conference on Decision and Control, San Diego, pp 761–766

  • Kaymak U, Babuska R, Setnes M, Verbruggen HB (1997) Methods for simplification of fuzzy models. In: Ruan D (ed) Intell Hybrid Syst. Kluwer Academic Publishers, Dordrecht, pp 91–108

    Chapter  Google Scholar 

  • Kaymak U, Setnes M (2002) Fuzzy clustering with volume prototype and adaptive cluster merging. IEEE Trans Fuzzy Syst 10:705–712

    Article  Google Scholar 

  • Kobayashi S, Fujioka T, Tanaka Y, Inoue M, Niho Y, Miyoshi A (2010) A geographical information system using the Google API for guidance to referral hospitals. J Med Syst 34:1157–1160

    Article  Google Scholar 

  • Krishnapuram R, Kim J (2002) Clustering algorithms based on volume criteria. IEEE Trans Fuzzy Syst 8:228–236

    Article  Google Scholar 

  • Loia V, Pedrycz W, Senatore S (2004) P-FCM: a proximity-based fuzzy clustering. Fuzzy Sets Syst 148:21–41

    Article  MATH  MathSciNet  Google Scholar 

  • Masulli F, Schenone A (1999) A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif Intell Med 16:129–147

    Article  Google Scholar 

  • Mullner RM, Chung K, Croke KG, Mensah EK (2004) Introduction: geographic information systems in public health and medicine. J Med Syst 28:215–221

    Article  Google Scholar 

  • Murray AT, McGuffog I, Western JS, Mullins P (2001) Exploratory spatial data analysis techniques for examining urban crime. British J Criminol 41:309–329

    Article  Google Scholar 

  • Polat K (2012) Application of attribute weighting method based on clustering centers to discrimination of linearly non-separable medical datasets. J Med Syst 36:2657–2673

    Article  Google Scholar 

  • Wei CK, Su S, Yang MC (2012) Application of data mining on the development of a disease distribution map of screened community residents of Taipei County in Taiwan. J MedSyst 36:2021–2027

    Google Scholar 

  • Windischberger C, Barth M, Lamm C, Gur RC, Moser E (2003) Fuzzy cluster analysis of high-field functional MRI data. Artif Intell Med 29:203–223

    Article  Google Scholar 

  • Zhang DQ, Chen SC (2004) A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif Intell Med 32:37–50

    Article  Google Scholar 

Download references

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The authors declare that they have no conflict of interests.

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Correspondence to Salvatore Sessa.

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Communicated by G. Acampora.

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Di Martino, F., Sessa, S., Barillari, U.E.S. et al. Spatio-temporal hotspots and application on a disease analysis case via GIS. Soft Comput 18, 2377–2384 (2014). https://doi.org/10.1007/s00500-013-1211-7

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  • DOI: https://doi.org/10.1007/s00500-013-1211-7

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