Autocorrelation testing on residual spatial logistic regression model with Euclidean distance matrix approach
Abstract
The existence of spatial effects should not be ignored because it will reduce the goodness of the model. One type of regression analysis that is quite widely used is logistic regression analysis. Spatial logistic regression modelling incorporates spatial effects into the logistic regression model with the expectation that the residuals generated from the model are independent or there is no autocorrelation. The purpose of this study was to obtain the results of spatial autocorrelation testing using a spatial logistic regression model with a Euclidean matrix approach. The results of the study were applied to natural disaster mitigation data in Kupang Regency, Nusa Tenggara Timur Province in 2020, where the distribution of areas in Kupang Regency by village/urban village has spatial autocorrelation. Spatial autocorrelation testing was carried out with Moran's I test to determine the presence of spatial autocorrelation. In this study, a standardized Euclidean distance matrix approach was used to accommodate this spatial effect. The results of the autocorrelation test of the binary spatial logistic model with the Euclidean distance matrix approach were able to accommodate the spatial effect.
Copyright © 2022 Devi Yanti, Toni Toharudin, Yusep Suparman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
D. Yanti, T. Toharudin, Y. Suparman, Autocorrelation testing on residual spatial logistic regression model with Euclidean distance matrix approach, J. Math. Comput. Sci. 12 (2022), 122. https://doi.org/10.28919/jmcs/7264
BibTeX
@article{Yanti2022,
author = {Yanti, Devi and Toharudin, Toni and Suparman, Yusep},
title = {Autocorrelation testing on residual spatial logistic regression model with Euclidean distance matrix approach},
journal = {J. Math. Comput. Sci.},
volume = {12},
year = {2022},
pages = {122},
doi = {10.28919/jmcs/7264}
}