A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.


<b>Citation:</b> Li, J. A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R. Appl. Sci. 2019, 9, 2048. https://doi.org/10.3390/app9102048

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2018-02-26
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2025-02-10T01:03:45
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Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/129511

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MDPI

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Li, J.

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Applied Sciences

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Volume 9 Issue 10

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Commonwealth of Australia (Geoscience Australia)

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Nichol, S.

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theme.ANZRC Fields of Research.rdf

  • EARTH SCIENCES

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  • spatial predictive models

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  • predictive accuracy

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  • model assessment

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  • variable selection

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  • feature selection

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  • model validation

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  • spatial predictions

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  • reproducible research

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Australian Government Security ClassificationSystem

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2018-11-01T00:00:00
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urn:uuid/2f71f19d-b047-47c1-8fbb-3f04c490414a

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English
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Commonwealth of Australia (Geoscience Australia)

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Brooke, B.

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Nichol, S.

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2018-02-26T05:59:41
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2018-02-26T06:01:45

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ISO 19115-1:2014

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feature selection model assessment model validation predictive accuracy reproducible research spatial predictions spatial predictive models variable selection
theme.ANZRC Fields of Research.rdf
EARTH SCIENCES

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