IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v37y2010i1p147-165.html
   My bibliography  Save this article

New Tests of Spatial Segregation Based on Nearest Neighbour Contingency Tables

Author

Listed:
  • ELVAN CEYHAN

Abstract

. The spatial clustering of points from two or more classes (or species) has important implications in many fields and may cause segregation or association, which are two major types of spatial patterns between the classes. These patterns can be studied using a nearest neighbour contingency table (NNCT) which is constructed using the frequencies of nearest neighbour types. Three new multivariate clustering tests are proposed based on NNCTs using the appropriate sampling distribution of the cell counts in a NNCT. The null patterns considered are random labelling (RL) and complete spatial randomness (CSR) of points from two or more classes. The finite sample performance of these tests are compared with other tests in terms of empirical size and power. It is demonstrated that the newly proposed NNCT tests perform relatively well compared with their competitors and the tests are illustrated using two example data sets.

Suggested Citation

  • Elvan Ceyhan, 2010. "New Tests of Spatial Segregation Based on Nearest Neighbour Contingency Tables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 147-165, March.
  • Handle: RePEc:bla:scjsta:v:37:y:2010:i:1:p:147-165
    DOI: 10.1111/j.1467-9469.2009.00667.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9469.2009.00667.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9469.2009.00667.x?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
    ---><---

    References listed on IDEAS

    as
    1. Kulldorff, Martin, 2006. "Tests of Spatial Randomness Adjusted for an Inhomogeneity: A General Framework," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1289-1305, September.
    2. Elvan Ceyhan, 2009. "Class‐specific tests of spatial segregation based on nearest neighbor contingency tables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 149-182, May.
    3. M. N. M. Van Lieshout & A. J. Baddeley, 1999. "Indices of Dependence Between Types in Multivariate Point Patterns," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(4), pages 511-532, December.
    4. A. J. Baddeley & J. Møller & R. Waagepetersen, 2000. "Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 329-350, November.
    5. Ceyhan, Elvan, 2009. "Overall and pairwise segregation tests based on nearest neighbor contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2786-2808, June.
    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. Mousaei Sanjerehei, Mohammad, 2011. "Determination of an appropriate quadrat size and shape for detecting association between plant species," Ecological Modelling, Elsevier, vol. 222(10), pages 1790-1792.

    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. Elvan Ceyhan, 2009. "Class‐specific tests of spatial segregation based on nearest neighbor contingency tables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 149-182, May.
    2. Ceyhan, Elvan, 2009. "Overall and pairwise segregation tests based on nearest neighbor contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2786-2808, June.
    3. Carlos Comas & Jorge Mateu & Aila Särkkä, 2010. "A third‐order point process characteristic for multi‐type point processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(1), pages 19-44, February.
    4. Barry Kronenfeld & Timothy Leslie, 2015. "Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure," Journal of Geographical Systems, Springer, vol. 17(1), pages 1-28, January.
    5. Isabelle M. Nilsson & Oleg A. Smirnov, 2017. "Clustering vs. relative location: Measuring spatial interaction between retail outlets," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 721-741, November.
    6. Giuseppe Espa & Giuseppe Arbia & Diego Giuliani, 2013. "Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan," Journal of Geographical Systems, Springer, vol. 15(1), pages 31-50, January.
    7. Edith Gabriel & Peter J. Diggle, 2009. "Second‐order analysis of inhomogeneous spatio‐temporal point process data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 43-51, February.
    8. Arbia, Giuseppe & Espa, Giuseppe & Giuliani, Diego & Dickson, Maria Michela, 2014. "Spatio-temporal clustering in the pharmaceutical and medical device manufacturing industry: A geographical micro-level analysis," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 298-304.
    9. Kateřina Koňasová & Jiří Dvořák, 2021. "Stochastic Reconstruction for Inhomogeneous Point Patterns," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 527-547, June.
    10. Redenbach, Claudia & Särkkä, Aila, 2013. "Parameter estimation for growth interaction processes using spatio-temporal information," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 672-683.
    11. Saltré, F. & Chuine, I. & Brewer, S. & Gaucherel, C., 2009. "A phenomenological model without dispersal kernel to model species migration," Ecological Modelling, Elsevier, vol. 220(24), pages 3546-3554.
    12. Mousaei Sanjerehei, Mohammad, 2011. "Determination of an appropriate quadrat size and shape for detecting association between plant species," Ecological Modelling, Elsevier, vol. 222(10), pages 1790-1792.
    13. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    14. Giuseppe Arbia & Patrizia Cella & Giuseppe Espa & Diego Giuliani, 2015. "A micro spatial analysis of firm demography: the case of food stores in the area of Trento (Italy)," Empirical Economics, Springer, vol. 48(3), pages 923-937, May.
    15. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    16. Ondřej Šedivý & Antti Penttinen, 2014. "Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 225-249, August.
    17. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    18. Jakub Staněk & Ondřej Šedivý & Viktor Beneš, 2014. "On Random Marked Sets with a Smaller Integer Dimension," Methodology and Computing in Applied Probability, Springer, vol. 16(2), pages 397-410, June.
    19. Amanda S. Hering & Sean Bair, 2014. "Characterizing spatial and chronological target selection of serial offenders," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 123-140, January.
    20. repec:jss:jstsof:12:i06 is not listed on IDEAS
    21. Tilman M. Davies & Martin L. Hazelton, 2013. "Assessing minimum contrast parameter estimation for spatial and spatiotemporal log‐Gaussian Cox processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 355-389, November.

    More about this item

    Statistics

    Access and download statistics

    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:bla:scjsta:v:37:y:2010:i:1:p:147-165. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.