Nearest neighbor analysis in one dimension

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Since its initial presentation by Clark and Evans, nearest neighbor analysis for spatial randomness has gained considerable popularity in fields as diverse as geography, ecology, archaeology, cell biology, forestry, meteorology, and epidemiology. Epidemiologists are often interested in determining whether disease cases are clustered, dispersed, or randomly distributed, since different patterns of disease incidence over time or space can provide dues to the etiology of the disease. An environmental hazard or a transmissable agent can produce a cluster of disease events, i.e. a set of events occurring unusually dose to each other in time, space, or both time and space. In ... continued below

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66 p.

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Weingart, M. & Selvin, S. February 1, 1995.

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Description

Since its initial presentation by Clark and Evans, nearest neighbor analysis for spatial randomness has gained considerable popularity in fields as diverse as geography, ecology, archaeology, cell biology, forestry, meteorology, and epidemiology. Epidemiologists are often interested in determining whether disease cases are clustered, dispersed, or randomly distributed, since different patterns of disease incidence over time or space can provide dues to the etiology of the disease. An environmental hazard or a transmissable agent can produce a cluster of disease events, i.e. a set of events occurring unusually dose to each other in time, space, or both time and space. In spite of its wide applicability, few attempts have been made to adapt the nearest neighbor method to the analysis of points distributed along a line. This report outlines the theoretical derivation of the moments of the mean nearest neighbor distance in the one dimension case and the correction of its expected value in order to overcome the boundary problem. It presents the derivation of the moments of order statistics, for specific sample sizes and for the general case. These results are then used for the derivation of the moments of nearest neighbor distances, and for the derivation of the moments of the mean nearest neighbor distance. Then the boundary problem and an examination of five alternative ways to compensate for it in the calculation of the expected value of the mean nearest neighbor distance are discussed. Finally, the results from a large scale computer simulation which compares the various correction methods are presented.

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66 p.

Notes

OSTI as DE95009378

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  • Other Information: PBD: Feb 1995

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  • Other: DE95009378
  • Report No.: LBL--36888
  • Grant Number: AC03-76SF00098
  • DOI: 10.2172/33152 | External Link
  • Office of Scientific & Technical Information Report Number: 33152
  • Archival Resource Key: ark:/67531/metadc678400

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • February 1, 1995

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • April 5, 2016, 10:22 a.m.

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Weingart, M. & Selvin, S. Nearest neighbor analysis in one dimension, report, February 1, 1995; California. (digital.library.unt.edu/ark:/67531/metadc678400/: accessed December 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.