“Second-Order Analysis of Spatial Clustering for Inhomogenous Populations,”

Department of Mathematics, Lancaster University, England.
Biometrics (Impact Factor: 1.57). 10/1991; 47(3):1155-63. DOI: 10.2307/2532668
Source: PubMed


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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    • "The D (Diggle and Chetwynd 1991) function compares the K function of points of interest (cases) to that of other points (controls). Its null hypothesis is random labeling. "
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    ABSTRACT: The dbmss package for R provides an easy-to-use toolbox to characterize the spatial structure of point patterns. Our contribution presents the state of the art of distance-based methods employed in economic geography and which are also used in ecology. Topographic functions such as Ripley's K, absolute functions such as Duranton and Overman's K d and relative functions such as Marcon and Puech's M are implemented. Their confidence envelopes (including global ones) and tests against counterfactuals are included in the package.
    Journal of statistical software 10/2015; 67(3):1-15. DOI:10.18637/jss.v067.c03 · 3.80 Impact Factor
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    • "using the splancs library ( Spatial autocorrelation was investigated using the D-function, which illustrates excess spatial clustering of cases versus non-cases [21]. The simulated p-value for determining spatial autocorrelation in the D-function was assessed over 100 km of spatial separation. "
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    ABSTRACT: The spread of PRRSV among pig herds has been investigated experimentally, but few observational studies have investigated this subject. Because PRRSV is endemic and live modified vaccines are used in Ontario, the spatial and temporal distributions of 6 PRRSV genotypes were investigated in the province during the period from 2004-2007. The purpose was to find evidence of spread of PRRSV genotypes and determine if spread could be attributed to supplier or ownership connections between herds. Sequence information from PRRSV ORF5 and related source-herd demographic information were obtained from diagnostic submissions to the Animal Health Laboratory, University of Guelph. A spatial cluster that could not be attributed to supplier or ownership connections among herds in the cluster was detected for RFLP type 1-3-4. Because of genetic dissimilarity among members of the cluster, it was considered to be a result of past spread of the RFLP type. A spatio-temporal cluster detected for RFLP type 1-18-4 was attributed to a shared gilt supplier among the herds in the cluster. Significant spatio-temporal patterns detected for RFLP type 2-5-2, which is considered to be a vaccine-type virus were most likely due to grouping of herds in an ownership that used the corresponding vaccine. Clustering within herd-ownership was a risk factor for presence of five of the six genotypes investigated in the present study. Although the literature indicates that PRRSV can spread via aerosol between pig herds, the present study found no strong evidence of this occurring in Ontario. The evidence pointed toward transmission of PRRSV occurring in this population by common sources of animals or similarity of herd ownership, which is a proxy measure for other connections between herds. It is also apparent that the recognition and testing of these connections between herds is a necessary part of interpreting spatio-temporal patterns of PRRSV genotypes.
    BMC Veterinary Research 04/2014; 10(1):83. DOI:10.1186/1746-6148-10-83 · 1.78 Impact Factor
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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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    ABSTRACT: Standard lots, which are used to assess values of individual lots in Korea, have been criticized for their improper distribution. However, there has been very little evaluation for the spatial distribution of standard lots, and an evaluation method has never been developed. In order to overcome this situation, we attempt to assess the appropriateness of the spatial distribution of standard lots using the L-index and Monte Carlo simulation. The L-index is a well-known indicator of the complete spatial randomness (CSR) of points in spatial statistics. If the L-index of standard lots is similar to that of individual lots, the former is considered to be randomly distributed according to the latter. By analyzing L-indices of two study areas, Gangnam and Seongdong, we find a statistically significant difference in Gangnam area and a relatively small difference in Seongdong area. We confirm that the spatial distribution of standard lots is not CSR and that the L-index is useful as an evaluation method. These results suggest that the standard lot selection and management guidelines need to be modified to apply the spatial distribution of individual lots to the standard lot selection process.
    Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography 12/2013; 31(6_2). DOI:10.7848/ksgpc.2013.31.6-2.601
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