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

Oluyemi A Okunlola,István Szűcs,Mohannad Alobid,Adewale F Lukman,Kayode Ayinde,Olusanya E Olubusoye

doi:10.1038/s41598-021-96124-x

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.

Highlights

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis
The quest for a new framework that accounts for dependence structure in the data to fill the vacuum in the classical regression led to spatial statistics
The results agreed with previous studies on the superiority of spatial models over ordinary least squared regression (OLS)

Summary

Introduction

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. This study is motivated to discuss a simplified approach that accounts for spatial dependence in the regression model, illustrates spatial regression analysis, and applies the technique to investigate a linear relationship between precipitation and its likely predictors, namely northing, easting, and e levation[3,4]. Statistics cannot be over-emphasized and mathematical statistics is a viable tool with wide application in climatology r esearch[6] They reported that climatology, to a large degree, is studying the statistics of climate and have been described using several adjectives depending upon whether they define relationships in time (serial correlation, lagged correlation), space (spatial correlation, tele-connection), or between different climate variables (cross-correlation)[6]. The spatial autocorrelation inherent in the data can be addressed by spatial statistics and other related a pproaches[7]

Methods

Findings

Discussion

Conclusion