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Alcohol mortality: a comparison of spatial clustering methods

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  • Hanson, Craig E.
  • Wieczorek, William F.

Abstract

The identification of spatial clusters of alcohol mortality can be a key tool in identifying locations that are suffering from alcohol-related problems or are at risk of experiencing those types of problems. This study compares two methods for identifying statistically significant spatial clusters of county-level alcohol mortality rates in New York. One method utilizes a local indicator of spatial association to determine which groups of neighboring counties have rates that are significantly related to each other. The other method is a spatial scan technique that calculates a maximum likelihood ratio of cases relative to the underlying population to identify the group of counties that rejects the null hypothesis of "no clustering". The results show that because each technique bases its cluster detection on its own criteria, different counties are selected by each method. However, the overlap of the selections indicates that the two analytic methods illustrate different elements of the same clusters. Consequently, these spatial analytic techniques are seen as complimentary and are best used in tandem rather than individually. These findings suggest that multiple methods are a preferred approach to identifying clusters of alcohol-related mortality at the county level.

Suggested Citation

  • Hanson, Craig E. & Wieczorek, William F., 2002. "Alcohol mortality: a comparison of spatial clustering methods," Social Science & Medicine, Elsevier, vol. 55(5), pages 791-802, September.
    • Handle: RePEc:eee:socmed:v:55:y:2002:i:5:p:791-802
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    Cited by:

    1. Dorothée Brécard & Rémy Le Boennec & Frédéric Salladarré, 2018. "Accessibility, local pollution and housing prices. Evidence from Nantes Métropole, France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 500-501-5, pages 97-115.
    2. Rashidi, Parinaz & Wang, Tiejun & Skidmore, Andrew & Vrieling, Anton & Darvishzadeh, Roshanak & Toxopeus, Bert & Ngene, Shadrack & Omondi, Patrick, 2015. "Spatial and spatiotemporal clustering methods for detecting elephant poaching hotspots," Ecological Modelling, Elsevier, vol. 297(C), pages 180-186.
    3. Johnson, Lallen T. & Taylor, Ralph B. & Groff, Elizabeth R., 2015. "Metropolitan local crime clusters: Structural concentration effects and the systemic model," Journal of Criminal Justice, Elsevier, vol. 43(3), pages 186-194.
    4. Siriwan Hassarangsee & Nitin Kumar Tripathi & Marc Souris, 2015. "Spatial Pattern Detection of Tuberculosis: A Case Study of Si Sa Ket Province, Thailand," IJERPH, MDPI, vol. 12(12), pages 1-14, December.

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