IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v89y2015icp134-146.html
   My bibliography  Save this article

An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters

Author

Listed:
  • Zhou, Ruoyu
  • Shu, Lianjie
  • Su, Yan

Abstract

The clustering methodologies based on minimum spanning tree (MST) have been widely discussed due to their simplicity and efficiency in signaling irregular clusters. However, most of the MST-based clustering methods estimate the most likely cluster based on the maximum likelihood ratio from the resulting subtrees after the removal of edges of the MST. They can only estimate one cluster even if there are multiple clusters actually present over the study region. To overcome this limitation, we propose an adaptive MST (AMST) method to detect irregularly-shaped clusters. The basic idea is to first determine the best number of partition over the study region using a validity index and then to determine the significance of the candidate clusters. The comparison results with both the static and dynamic MST methods favor the proposed method.

Suggested Citation

  • Zhou, Ruoyu & Shu, Lianjie & Su, Yan, 2015. "An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 134-146.
    • Handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:134-146
    DOI: 10.1016/j.csda.2015.03.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315000766
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2015.03.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daniel B. Neill, 2012. "Fast subset scan for spatial pattern detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 337-360, March.
    2. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    3. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    4. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
    5. Maravalle, Maurizio & Simeone, Bruno & Naldini, Rosella, 1997. "Clustering on trees," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 217-234, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ali Abolhassani & Marcos O. Prates & Safieh Mahmoodi, 2023. "Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 141-162, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Lima, Max Sousa & Duczmal, Luiz Henrique, 2014. "Adaptive likelihood ratio approaches for the detection of space–time disease clusters," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 352-370.
    2. Kunihiko Takahashi & Hideyasu Shimadzu, 2018. "Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    3. Wan, You & Pei, Tao & Zhou, Chenghu & Jiang, Yong & Qu, Chenxu & Qiao, Youlin, 2012. "ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 283-296.
    4. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    5. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    6. Andrea J. Cook & Diane R. Gold & Yi Li, 2007. "Spatial Cluster Detection for Censored Outcome Data," Biometrics, The International Biometric Society, vol. 63(2), pages 540-549, June.
    7. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    8. Sevvandi Kandanaarachchi & Rob J Hyndman & Kate Smith-Miles, 2020. "Early classification of spatio-temporal events using partial information," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
    9. Costa, Marcelo Azevedo & Assunção, Renato Martins & Kulldorff, Martin, 2012. "Constrained spanning tree algorithms for irregularly-shaped spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1771-1783.
    10. Toshiro Tango & Kunihiko Takahashi & Kazuaki Kohriyama, 2011. "A Space–Time Scan Statistic for Detecting Emerging Outbreaks," Biometrics, The International Biometric Society, vol. 67(1), pages 106-115, March.
    11. Loecher, Markus & Ropkins, Karl, 2015. "RgoogleMaps and loa: Unleashing R Graphics Power on Map Tiles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i04).
    12. Lianjie Shu & Wei Jiang & Kwok‐Leung Tsui, 2012. "A standardized scan statistic for detecting spatial clusters with estimated parameters," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 397-410, September.
    13. Demattei[diaeresis], Christophe & Molinari, Nicolas & Daures, Jean-Pierre, 2007. "Arbitrarily shaped multiple spatial cluster detection for case event data," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3931-3945, May.
    14. Fitzpatrick, Dylan & Ni, Yun & Neill, Daniel B., 2021. "Support vector subset scan for spatial pattern detection," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    15. Alexandre Xavier Ywata Carvalho & Pedro Henrique Melo Albuquerque & Gilberto Rezende de Almeida Junior & Rafael Dantas Guimarães & Camilo Rey Laureto, 2009. "Clusterização Hierárquica Espacial com Atributos Binários," Discussion Papers 1428, Instituto de Pesquisa Econômica Aplicada - IPEA.
    16. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    17. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    18. Sami Ullah & Hanita Daud & Sarat C. Dass & Hadi Fanaee-T & Husnul Kausarian & Alamgir, 2020. "Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019," IJERPH, MDPI, vol. 17(4), pages 1-10, February.
    19. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Pei‐Sheng Lin & Yi‐Hung Kung & Murray Clayton, 2016. "Spatial scan statistics for detection of multiple clusters with arbitrary shapes," Biometrics, The International Biometric Society, vol. 72(4), pages 1226-1234, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:134-146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.