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

• Published in: Scientific reports Vol. 11; no. 1; p. 16848
• Main Authors: Okunlola, Oluyemi A., Alobid, Mohannad, Olubusoye, Olusanya E., Ayinde, Kayode, Lukman, Adewale F., Szűcs, István
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19-08-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 704/106; 704/844; Climate change; Developing countries; Geostatistics; Humanities and Social Sciences; Humidity; Hydrologic data; LDCs; multidisciplinary; Neural networks; Precipitation; Radiation; Regression analysis; Science; Science (multidisciplinary); Spatial distribution; Statistical analysis; Variables; Nigeria
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-96124-x

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.