New developments in the spatial scan statistic

Article English OPEN
Read, S. ; Bath, P.A. ; Willett, P. ; Maheswaran, R. (2013)
  • Publisher: SAGE Publications

The quantity and variety of spatial data have increased over recent years, and the variety and sophistication of tools for analysing this type of data have also increased. One such tool is the spatial scan statistic, which is freely available (www.satscan.org) and has been the subject of much scholarly research since its introduction in 1995 owing to its numerous applications in epidemiology, criminology and other fields. This paper provides readers with a non-technical introduction to the spatial scan statistic, together with an overview of associated research, which focuses particularly on work conducted at the University of Sheffield’s Information School, in collaboration with the School of Health and Related Research. This work falls into three main areas. First, we provide an examination of the probability of obtaining false alerts when using the statistic, and ways in which this can be managed. Second, we describe the development of a definitive way of measuring the spatial accuracy of the statistic. Third, and potentially the most important in terms of impact, we discuss a means of substantially increasing the detection capability of the statistic by placing a realistic constraint on the strength of any cluster that is likely to be present in the data. The paper also provides a discussion of potential future research directions.
  • References (43)
    43 references, page 1 of 5

    [1] Maheswaran R, Craglia M (eds). GIS in Public Health Practice. Boca Raton: CRC Press 2004.

    [2] Read S, Bath, PA, Willett, P and Maheswaran R. Measuring the spatial accuracy of the spatial scan statistic. Spatial and Spatio-temporal Epidemiology 2011; 2(2): 68-79.

    [3] Read S, Bath PA, Willett P and Maheswaran R. A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic. [submitted for publication]

    [4] Read S, Bath PA, Willett P and Maheswaran R. A spatial accuracy assessment of a beta-Bernoulli spatial scan statistic. [submitted for publication]

    [5] Duczmal L and Buckeridge DL. A workflow spatial scan statistic. Statistics in Medicine 2006; 25(5): 743-754.

    [6] Haining, RP. Spatial data analysis: theory and practice. Cambridge: Cambridge University Press, 2003.

    [7] DeLuca PF and Kanaroglou PS. Effects of alternative point pattern geocoding procedures on first and second order statistical measures. Journal of Spatial Science 2008; 53(1): 131-141.

    [8] Olson KL, Grannis SJ and Mandl KD. Privacy protection versus cluster detection in spatial epidemiology. American Journal of Public Health 2006; 96(11): 2002-2008.

    [9] Mandl KD, Overhage JM, Wagner MM, Lober WB, Sebastiani P, Mostashari F, Pavlin JA, Gesteland PH, Treadwell T, Koski E, Hutwagner L, Buckeridge DL, Aller RD and Grannis S. Implementing syndromic surveillance: a practical guide informed by the early experience. Journal of the American Medical Informatics Association 2004; 11(2): 141-150.

    [10] Tobler WR. A computer movie simulating urban growth in the Detroit region. Economic Geography (supplement: Proceedings. International Geographical Union. Commission on quantitative methods) 1970; 46: 234-240.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    16
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    White Rose Research Online - IRUS-UK 0 16
Share - Bookmark