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Fw: AI-GEOSTATS:SUM: Distribution of a plague

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  • Francisco Jesús Moral García
    Dear list members, here is a summary of the replies I got to my question. Any further idea will be very appreciated too. ... From: Francisco Jesús Moral
    Message 1 of 1 , Feb 17, 2003
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      Dear list members,

      here is a summary of the replies I got to my question. Any further idea will be very appreciated too.

      ----- Original Message -----
      From: Francisco Jesús Moral García
      To: ai-geostats@...
      Sent: Tuesday, February 11, 2003 11:57 AM
      Subject: AI-GEOSTATS: Distribution of a plague


      Dear list members,

      Now, I am studying the distribution of a plague (Helicoverpa armigera Hb.) in tomato parcels, using geostatistics techniques.

      I have got information related to the numbers of captures of the insect in each trap. All traps are regularly disposed in the considered parcel.

      So, I have, initially, discrete data, and I would like to get the continuous distribution or a map of the plague distribution in the parcels.

      I have a result, using ordinary punctual kriging and supposing that the variable is continuous, which it is not true.

      Can you tell me if it is a good way? if not, which is the correct way to get the distribution? Any reference about a similar work or any idea will be very appreciated.

      Thak you in advance

      Francisco Moral
      ----------------------------------------------------------------------------------

      1)

      Holla Francisco

      I would recommend and modelling strategy which takes into account the fact
      that you have a discrete variable, probably to be modelled with a Poisson
      distribution.
      One possible approach is to use a geostatistical model with a Poisson
      distribution. This would extend the usual geostatistical model in the
      same way that generalised linear models extends the usual linear model.

      There is a software implementation for Poisson and Binomial models in the
      geoRglm package for the R software:
      http://www.est.ufpr.br/geoRglm


      from Paulo Justiniano Ribeiro Jr
      -----------------------------------------------------------------------

      2)
      Hi,
      You can either use the methodology of Gotway and Stroup and krig the
      counts in a glm setting as Poisson random variables or use indicator
      kriging and estimate the attack risk. Another possibility is to krig
      insect density but that is a bit of a fudge.

      from Nicholas Lewin-Koh
      --------------------------------------------------------------------
      3)
      Why don't you try the kernel density smoothing method? It's appropriate for discrete data (e.g., an event location). It's been used for a variety of epidemiological analyses. My CrimeStat program has a single- and duel-kernel density routine with a number of different kernels (functions) that can be used as well as several options for bandwidth selection. The software and manual are freely downloadable at:

      http://www.icpsr.umich.edu/nacjd/crimestat.html


      Ned Levine

      ------------------------------------------------------------
      4)
      Here are some relevant references

      Lecoustre, R., Fargette, D., Fauquet, C., and de Reffye, P. 1989. Analysis and mapping of the spatial spread of African cassava mosaic virus using geostatistics and the kriging technique. Phytopathology 79:913-920.

      Liebhold, A. M., Rossi, R. E., and Kemp, W. P. 1993. Geostatistics and geographic information systems in applied insect ecology. Annual Review of Entomology 38:303-327.

      Barnes, J. M., Trinidad-Correa, R., Orum, T. V., Felix-Gastelum, R., and Nelson, M. R. 1999. Landscape ecology as a new framework for improved management of plant viruses and their insect vectors in agroecosystems. Ecosystem Health 5:26-35.

      Byrne, D. N., Rathman, R. J., Orum, T. V., and Palumbo, J. C. 1995. Localized migration and dispersal by the sweet potato whitefly, Bemisia tabaci, Oecologia 105:320-328.

      Nelson, M. R., Felix-Gastelum, R., Orum, T. V., Stowell, L. J., and Myers, D. E. 1994. Geographic information systems and geostatistics in the design and validation of regional plant virus management programs. Phytopathology 84:898-905.

      Nelson, M. R., Orum, T. V., Jaime-Garcia, R.,
      and Nadeem, A. 1999. Applications of geographic information systems and geostatistics
      in plant disease epidemiology and management. Plant Dis. 83:308-319.

      Orum, T. V., Bigelow, D. M., Nelson, M. R.,
      Howell, D. R., and Cotty, P. J. 1997. Spatial and
      temporal patterns of Aspergillus flavus strain
      composition and propagule density in Yuma
      County, Arizona, soils. Plant Dis. 81:911-916.




      a.. 1994, A. Stein,C.G. Kocks, J.C. Zadocks, H.D. Frinking, N.A. Russen and Myers, D.E., A geostatistical analysis of the spatial temporal development of downy mildew epidemics in cabbage. Phytopathology 84,1227-1239
      Donald E. Myers
      http://www.u.arizona.edu/~donaldm

      ----------------------------------------------------------------------------------------------
      5)
      Hi,
      The problem with density is the error propagation can be huge unless
      done really carefully. Density at a point is not very meaningful, so you
      should probably use block kriging for that and make sure you have robust
      density estimators from your trap data.

      for predicting risk look at the disease literature. I think Diggle has
      several papers on it. Also look at the bark beetle literature.
      Here are some references.

      131 Autologistic Model of Spatial Pattern of Phytophthora Epidemic in
      Bell Pepper: Effects of Soil Variables on Disease Presence
      Marcia L. Gumpertz, Jonathan M. Graham, and Jean B. Ristiano

      157 A Generalized Linear Model Approach to Spatial Data Analysis and
      Prediction
      C. A. Gotway and W. W. Stroup

      both in Journal of Agricultural, Biological, and Environmental
      Statistics (JABES) vol 2(2) 1997


      from Nicholas Lewin-Koh




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