Article

Second-Order Analysis of Spatial Clustering for Inhomogeneous Populations

Department of Mathematics, Lancaster University, England.
Biometrics (Impact Factor: 1.52). 10/1991; 47(3):1155-63. DOI: 10.2307/2532668
Source: PubMed

ABSTRACT Motivated by recent interest in the possible spatial clustering of rare diseases, the paper develops an approach to the assessment of spatial clustering based on the second-moment properties of a labelled point process. The concept of no spatial clustering is identified with the hypothesis that in a realisation of a stationary spatial point process consisting of events of two qualitatively different types, the type 1 events are a random sample from the superposition of type 1 and type 2 events. A diagnostic plot for estimating the nature and physical scale of clustering effects is proposed. The availability of Monte Carlo tests of significance is noted. An application to published data on the spatial distribution of childhood leukaemia and lymphoma in North Humberside is described.

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    • "This can be assessed by the L-index, which was invented by Ripley (1976) as a method for globally analyzing the distribution patterns of point entities using distances between a specific point and all other points. Diggle and Chetwynd (1991) and Gatrell et al. (1996) developed the L-index method by verifying the relationship between the outbreak of disease and environmentally harmful facilities. These previous studies suggest that the L-index can be used as the indicator of the complete spatial randomness (CSR) of points. "
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