alexa Differentiating Non-Homoscedasticity and Geospatially E
ISSN: 2469-4134

Journal of Remote Sensing & GIS
Open Access

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Research Article

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

Samuel Alao, Komi Mati and Benjamin Jacob*

Department of Global Health, College of Public Health, University of South Florida, Tampa, FL, USA

*Corresponding Author:
Benjamin Jacob
Department of Global Health
College of Public Health, University of South Florida
Tampa, FL, USA
Tel: 8139742011
E-mail: [email protected]

Received date: November 04, 2016; Accepted date: December 08, 2016; Published date: December 10, 2016

Citation: Alao S, Mati K, Jacob B (2016) 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. J Remote Sensing & GIS 5:185. doi: 10.4182/2469-4134.1000185

Copyright: © 2016 Alao S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

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.

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