Several Multivariate Adaptive Regression Spline (MARS) approaches are available to model categorical and numerical (especially continuous) data. Currently, there are other numerical data types—discrete or count data—that call for specific consideration in modeling. Additionally, spatially correlated count data is frequently observed. This has been seen in the case of health data, for example, the number of newborn fatalities, tuberculosis patients, hospital visitors, etc. However, currently no structurally consistent nonparametric regression and MARS model for count data incorporating spatial lag autocorrelation. The SAR-MAGPRS estimator (Spatial Autoregressive - Multivariate Adaptive Generalized Poisson Regression Spline) is developed to fill this gap. Although it can be applied to different count distributions, the estimator was developed in this study under the assumption of a Generalized Poisson distribution. This paper provides an information-theoretic framework for incorporating knowledge of the spatial structure and non-parametric regression models, especially MARS for the count data types. Moreover, the proposed method can assist in modeling the number of diseases while health policies are being developed. The framework presents an application of the Penalized Least Square (PLS) method to estimate the SAR – MAGPRS model.

Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a bibliometric analysis in a spatial and Clustering SAR Kriging model on climate change. It presents a Systematic Literature Review (SLR) with the development of the Clustering SAR Kriging model, incorporating articles from the Google Scholar, ScienceDirect, Dimensions AI, and Scopus databases from 2011-2021. Furthermore, two stages of analysis have been conducted, first, bibliometric analysis was performed for mapping and thematic evolution using VOSviewer software and R-biblioshiny. This analysis generated 185 papers after conducting a duplication check and developed a network of research on evolutionary subject matters at this stage. Second, research subjects were analyzed using the Clustering SAR Kriging model. More screening criteria were followed, and 18 articles were obtained for the SLR analysis. Furthermore, the development of the Clustering SAR Kriging model was observed for the prediction and description of climate change. The results are predicted to benefit applicable businesses to predict climate phenomena in unobserved places.