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Research Article |

Mixed Autoregressive Model for Spatial Data: A Bayesian Application to Poverty Mapping

Poverty can be defined as the lack of income considered necessary to purchase goods and services in order to maintain a marginal living standard. Its eradication is a global problem especially in developing countries. The objective of this study was to determine the socio economic and environmental indicators as well as to produce a predictive map of poverty in Ghana using the Ghana Living Standard Survey (GLSS7) data. To achieve these objectives, a Spatial Mixed Autoregressive (MAR) model was used. Global and Local Moran’s I statistics were computed to test for spatial dependence in the data. Prediction of the risk of poverty was made via a Bayesian ordinary Kriging technique. Results of the study indicated that household size, total annual household expenditure, marital status (divorce), location (rural), educational level of household heads (JHS), deplorable roads and ecological Zone (Savanna) were statistically significant. Moreover, the predictive map showed a high positive spatial dependence of poverty across Upper East, Upper West and Northern Regions, with the extremely poor dominating in these areas. The varied characteristics of households that determine poverty levels should be incorporated into policy decisions to ensure that the country's rural and urban areas develop at the same pace.

Spatial Mixed Autoregressive Model, Poverty Mapping, Spatial Dependence, Spatial Error Model, Spatial Lag Model, Global and Local Moran's I

APA Style

Alexander Kwaku Boateng, Richard Puurbalanta, Gideon Mensah Engmann, Ernest Zamanah, Angela Osei-Mainoo. (2023). Mixed Autoregressive Model for Spatial Data: A Bayesian Application to Poverty Mapping. International Journal of Statistical Distributions and Applications, 9(3), 81-89. https://doi.org/10.11648/j.ijsd.20230903.12

ACS Style

Alexander Kwaku Boateng; Richard Puurbalanta; Gideon Mensah Engmann; Ernest Zamanah; Angela Osei-Mainoo. Mixed Autoregressive Model for Spatial Data: A Bayesian Application to Poverty Mapping. Int. J. Stat. Distrib. Appl. 2023, 9(3), 81-89. doi: 10.11648/j.ijsd.20230903.12

AMA Style

Alexander Kwaku Boateng, Richard Puurbalanta, Gideon Mensah Engmann, Ernest Zamanah, Angela Osei-Mainoo. Mixed Autoregressive Model for Spatial Data: A Bayesian Application to Poverty Mapping. Int J Stat Distrib Appl. 2023;9(3):81-89. doi: 10.11648/j.ijsd.20230903.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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