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. 2021 Aug 19;11(1):16848.
doi: 10.1038/s41598-021-96124-x.

Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

Oluyemi A Okunlola  1 , Mohannad Alobid  2 , Olusanya E Olubusoye  3 , Kayode Ayinde  4 , Adewale F Lukman  1 , István Szűcs  5
Affiliations

Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

Oluyemi A Okunlola et al. Sci Rep. .

Abstract

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
An illustration of the contiguity-based neighbourhood.
Figure 2
Figure 2
Moran’s I scatter plot for annual precipitation.
Figure 3
Figure 3
LISA (top) and significant (bottom) maps showing the spatial distribution of precipitation.
Figure 4
Figure 4
Precipitation surface map (mm).
Figure 5
Figure 5
Variogram plot for precipitation.
Figure 6
Figure 6
Map of Nigeria Showing the Six Geopolitical Zones (top) and Monthly distribution of Precipitation of Nigeria (bottom).
See this image and copyright information in PMC

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