title: Prediction of the spatial distribution of ecological data using kriging and binary regression
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanova, Olga A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2016-11-24T19:17:02Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2016-11-24T19:17:02Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2016-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Bure V. M., Mitrofanova O. A. Prediction of the spatial distribution of ecological data using kriging and binary regression. Vestnik of Saint Petersburg University. Series 10. Applied mathematics. Computer science. Control processes, 2016, issue 3, pp. 97–105.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu10.2016.309
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/5694
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: There are many ecological problems associated with the prediction of the spatial distribution
  of ecological parameters. The paper deals with one of these tasks. Suppose we have a set of
  ecological data measured by contact way (for example, plant leaf color intensity by N-tester),
  as well as an air photo of the object (for example, field). It is necessary to estimate the spatial
  distribution of ecological parameters. This paper proposes an approach to the solution of such
  problems with the joint use of kriging and binary regression. At first the uniform field areas
  (clusters) in the photo are determined using classification method. It is assumed that each
  selected area has a set of ecological data. Next, we will consider each zone separately. It is
  necessary to assess the level of the indicator in the given area. First variograms analysis is
  performed leading to the construction of the variogram model. Next construct a set of ecological
  parameter estimates is built using the method of ordinary kriging. Then, we set a threshold
  value of the ecological parameter for the zone under study. We introduced a variable that takes
  the value 1, if the parameter exceeds a threshold, and 0 otherwise. Thus we get a basis for
  logistic regression, where factors include a set of estimates predicted by kriging. In addition,
  these factors may include the color characteristics from air photos. As a result, we can calculate
  for each point the probability, if it will be close to 1, there is reason to believe that at this point
  the parameter value is greater than the threshold, and if the probability is close to 0, there
  is reason to assume that the parameter value is below the threshold. Furthermore, this paper
  provides an example of the approach for simulated data using R. Refs 8. Figs 4. Table 1.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of Saint Petersburg University. Series 10. Applied Mathematics. Computer Science. Control Processes;Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: ecological data
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ordinary kriging
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: logistic regression
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: R
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Prediction of the spatial distribution of ecological data using kriging and binary regression
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 09-Bure.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/5694/1/09-Bure.pdf
resources.format: Adobe PDF
resources.size: 405,43 kB