Advanced Search
MyIDEAS: Login

Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models

Contents:

Author Info

  • Pierre Goovaerts

    ()

  • Geoffrey M. Jacquez

    ()

Registered author(s):

    Abstract

    This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Moran’s I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Moran’s I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Moran’s I was applied under the null hypothesis of constant risk. Copyright Springer-Verlag Berlin Heidelberg 2005

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://hdl.handle.net/10.1007/s10109-005-0154-7
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Springer in its journal Journal of Geographical Systems.

    Volume (Year): 7 (2005)
    Issue (Month): 1 (October)
    Pages: 137-159

    as in new window
    Handle: RePEc:kap:jgeosy:v:7:y:2005:i:1:p:137-159

    Contact details of provider:
    Web page: http://www.springerlink.com/link.asp?id=103079

    Related research

    Keywords:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

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

    Cited by:
    1. Qiang Cai & Gerard Rushton & Budhendra Bhaduri, 2012. "Validation tests of an improved kernel density estimation method for identifying disease clusters," Journal of Geographical Systems, Springer, vol. 14(3), pages 243-264, July.
    2. Nica, M., 2010. "Small Business Clusters in Oklahoma: MAR or Jacobs Effects?," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(2).

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:7:y:2005:i:1:p:137-159. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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