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.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: Publisher’s description: This text is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part shows cases of more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book’s own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, environmental sciences, ecology, public health and disease control, economics, public administration and political sciences. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found:
    1st 08/2008; Springer., ISBN: 978-0-387-78170-9
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.
    PLoS ONE 12/2014; 9(12):e114702. · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We discuss the identification of pediatric cancer clusters in Florida between 2000 and 2010 using a penalized generalized linear model. More specifically, we introduce a Poisson model for the observed number of cases on each of Florida's ZIP Code Tabulation Areas (ZCTA) and regularize the associated disease rate estimates using a generalized Lasso penalty. Our analysis suggests the presence of a number of pediatric cancer clusters during the period over study, with the largest ones being located around the cities of Jacksonville, Miami, Cape Coral/Fort Meyers and Palm Beach.
    Statistics and public policy (Philadelphia, Pa.). 12/2014; 1(1):86-96.