Literature DB >> 14565696

Geographic analysis of forest health indicators using spatial scan statistics.

John W Coulston1, Kurt H Riitters.   

Abstract

Geographically explicit analysis tools are needed to assess forest health indicators that are measured over large regions. Spatial scan statistics can be used to detect spatial or spatiotemporal clusters of forests representing hotspots of extreme indicator values. This paper demonstrates the approach through analyses of forest fragmentation indicators in the southeastern United States and insect and pathogen indicators in the Pacific Northwest United States. The scan statistic detected four spatial clusters of fragmented forest including a hotspot in the Piedmont and Coastal Plain region. Three recurring clusters of insect and pathogen occurrence were found in the Pacific Northwest. Spatial scan statistics are a powerful new tool that can be used to identify potential forest health problems.

Mesh:

Year:  2003        PMID: 14565696     DOI: 10.1007/s00267-002-0023-9

Source DB:  PubMed          Journal:  Environ Manage        ISSN: 0364-152X            Impact factor:   3.266


  3 in total

1.  Childhood leukaemia in Sweden: using GIS and a spatial scan statistic for cluster detection.

Authors:  U Hjalmars; M Kulldorff; G Gustafsson; N Nagarwalla
Journal:  Stat Med       Date:  1996 Apr 15-May 15       Impact factor: 2.373

2.  Breast cancer clusters in the northeast United States: a geographic analysis.

Authors:  M Kulldorff; E J Feuer; B A Miller; L S Freedman
Journal:  Am J Epidemiol       Date:  1997-07-15       Impact factor: 4.897

3.  Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.

Authors:  M Kulldorff; W F Athas; E J Feurer; B A Miller; C R Key
Journal:  Am J Public Health       Date:  1998-09       Impact factor: 9.308

  3 in total
  6 in total

1.  Hot spots of perforated forest in the eastern United States.

Authors:  Kurt H Riitters; John W Coulston
Journal:  Environ Manage       Date:  2005-04       Impact factor: 3.266

2.  Spatial clustering and local risk of leprosy in São Paulo, Brazil.

Authors:  Antônio Carlos Vieira Ramos; Mellina Yamamura; Luiz Henrique Arroyo; Marcela Paschoal Popolin; Francisco Chiaravalloti Neto; Pedro Fredemir Palha; Severina Alice da Costa Uchoa; Flávia Meneguetti Pieri; Ione Carvalho Pinto; Regina Célia Fiorati; Ana Angélica Rêgo de Queiroz; Aylana de Souza Belchior; Danielle Talita Dos Santos; Maria Concebida da Cunha Garcia; Juliane de Almeida Crispim; Luana Seles Alves; Thaís Zamboni Berra; Ricardo Alexandre Arcêncio
Journal:  PLoS Negl Trop Dis       Date:  2017-02-27

3.  Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Authors:  Yue Ma; Fei Yin; Tao Zhang; Xiaohua Andrew Zhou; Xiaosong Li
Journal:  PLoS One       Date:  2016-01-28       Impact factor: 3.240

4.  Vector Transmission Alone Fails to Explain the Potato Yellow Vein Virus Epidemic among Potato Crops in Colombia.

Authors:  Diego F Cuadros; Anngie Hernandez; Maria F Torres; Diana M Torres; Adam J Branscum; Diego F Rincon
Journal:  Front Plant Sci       Date:  2017-09-25       Impact factor: 5.753

5.  A log-Weibull spatial scan statistic for time to event data.

Authors:  Iram Usman; Rhonda J Rosychuk
Journal:  Int J Health Geogr       Date:  2018-06-13       Impact factor: 3.918

6.  Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic.

Authors:  Wei Wang; Tao Zhang; Fei Yin; Xiong Xiao; Shiqi Chen; Xingyu Zhang; Xiaosong Li; Yue Ma
Journal:  Sci Rep       Date:  2020-03-17       Impact factor: 4.379

  6 in total

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