A new local stochastic method for predicting data with spatial heterogeneity
Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict sp...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Eduem (Editora da Universidade Estadual de Maringá)
2020-11-01
|
| Series: | Acta Scientiarum: Agronomy |
| Subjects: | |
| Online Access: | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947 |
| _version_ | 1818920952290869248 |
|---|---|
| author | Anderson Rodrigo da Silva Ana Paula Alencastro Silva Lauro Joaquim Tiago Neto |
| author_facet | Anderson Rodrigo da Silva Ana Paula Alencastro Silva Lauro Joaquim Tiago Neto |
| author_sort | Anderson Rodrigo da Silva |
| collection | DOAJ |
| description | Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity. |
| first_indexed | 2024-12-20T01:29:55Z |
| format | Article |
| id | doaj.art-f61324bdb3884739b314c4dc4b8f4172 |
| institution | Directory Open Access Journal |
| issn | 1679-9275 1807-8621 |
| language | English |
| last_indexed | 2024-12-20T01:29:55Z |
| publishDate | 2020-11-01 |
| publisher | Eduem (Editora da Universidade Estadual de Maringá) |
| record_format | Article |
| series | Acta Scientiarum: Agronomy |
| spelling | doaj.art-f61324bdb3884739b314c4dc4b8f41722022-12-21T19:58:08ZengEduem (Editora da Universidade Estadual de Maringá)Acta Scientiarum: Agronomy1679-92751807-86212020-11-0143e49947e4994710.4025/actasciagron.v43i1.4994749947A new local stochastic method for predicting data with spatial heterogeneityAnderson Rodrigo da Silva0Ana Paula Alencastro Silva1Lauro Joaquim Tiago Neto2Instituto Federal GoianoInstituto Federal GoianoInstituto Federal Goiano Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity.https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947moving window kriging; spatial prediction; soil nematodes. |
| spellingShingle | Anderson Rodrigo da Silva Ana Paula Alencastro Silva Lauro Joaquim Tiago Neto A new local stochastic method for predicting data with spatial heterogeneity Acta Scientiarum: Agronomy moving window kriging; spatial prediction; soil nematodes. |
| title | A new local stochastic method for predicting data with spatial heterogeneity |
| title_full | A new local stochastic method for predicting data with spatial heterogeneity |
| title_fullStr | A new local stochastic method for predicting data with spatial heterogeneity |
| title_full_unstemmed | A new local stochastic method for predicting data with spatial heterogeneity |
| title_short | A new local stochastic method for predicting data with spatial heterogeneity |
| title_sort | new local stochastic method for predicting data with spatial heterogeneity |
| topic | moving window kriging; spatial prediction; soil nematodes. |
| url | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947 |
| work_keys_str_mv | AT andersonrodrigodasilva anewlocalstochasticmethodforpredictingdatawithspatialheterogeneity AT anapaulaalencastrosilva anewlocalstochasticmethodforpredictingdatawithspatialheterogeneity AT laurojoaquimtiagoneto anewlocalstochasticmethodforpredictingdatawithspatialheterogeneity AT andersonrodrigodasilva newlocalstochasticmethodforpredictingdatawithspatialheterogeneity AT anapaulaalencastrosilva newlocalstochasticmethodforpredictingdatawithspatialheterogeneity AT laurojoaquimtiagoneto newlocalstochasticmethodforpredictingdatawithspatialheterogeneity |