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MAD-STEC: a method for multiple automatic detection of space-time emerging clusters

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Abstract

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.

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Acknowledgments

The authors would like to thank the kind suggestions and insights provided by Renato Martins Assunção that helped to improve the manuscript. Also, the third author would like to thank FAPEMIG-Brazil and CNPq-Brazil for partial financial support. The authors would also like to thank the referees for the interesting comments that improved the paper.

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Bráulio M. Veloso

  2. Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Thais R. Correa & Marcos O. Prates

  3. Department of Automation and Control Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Gabriel F. Oliveira

  4. Cadence Design Systems, San Jose, USA

    Andréa I. Tavares

Authors
  1. Bráulio M. Veloso
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  2. Thais R. Correa
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  3. Marcos O. Prates
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  4. Gabriel F. Oliveira
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  5. Andréa I. Tavares
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Correspondence to Thais R. Correa.

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Veloso, B.M., Correa, T.R., Prates, M.O. et al. MAD-STEC: a method for multiple automatic detection of space-time emerging clusters. Stat Comput 27, 1099–1110 (2017). https://doi.org/10.1007/s11222-016-9673-y

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  • DOI: https://doi.org/10.1007/s11222-016-9673-y

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