Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non- Vaccinated Measles Populations in Nigeria

Abstract

Linearized Models on measles vaccination related centroids in literature cannot provide pertinent data for local government measles managers. Spatial analysis is a cost cutting epidemiological tool for large scale immunization programs. A multivariate regression model was constructed to determine anthropogenic related covariates. In addition, we quantitated the clustering tendencies in the auto- correlated dataset using orthogonal eigenvectors and also illustrated problem hot spots for effective vaccine coverage. Data was retrieved from Demographic Health survey 2013 for Nigeria (N=28,337). Poverty, illiteracy level, and no vitamin A supplements were strong determinants of measles non-vaccination at a statistically significant level of (P<0.0001). The first order autocorrelation statistics (DW=0.1647, P<0.0001), (DW=0.2406, P<0.0001); and second order correlation (Moran’s I=0.456, Z score=1208), (Moran’s I=0.442, Z score=608) demonstrated a positive spatial autocorrelation for rural and urban geo-locations respectively. Land cover land use (LCLU) maps from Google earth and Diva-GIS were uploaded into ArcMap to visually represent the hot spot areas. Significant Mapped data showed that children not vaccinated against measles are clustered in the rural areas of Muslim dominated northern parts of Nigeria.