Geographic Modeling on The Infant Mortality Rate In West Java

Nusar Hajarisman, Yayat Karyana

Abstract


In geographic modeling, global models such as ordinary linear regression (OLR) model theoretically it provides quite reliable local information if there is not any spatial diversity by region. In other words, OLR model cannot describe the relations between variables in heterogeneous difference of each region. This study will consider a model that will be used to estimate or predict the infant mortality rate in the several regencies / cities in West Java Province. Because the response variable observed in this study is count data which is assumed Poisson distributed, geographically weighted Poisson regression model (GWPR) is used. A better model is used to analyze the data of infant deaths in each regency / city in West Java based on the AIC value, GWPR model has the smallest value (compared to Poisson regression model), in which there is an interesting and important difference from each regency/city about the factors that significantly influence the Infant Mortality rate in each region.


Keywords


geographically weighted Poisson regression, Poisson regression, Infant Mortality Rate.

References


Brunsdon C., A.S. Fotheringham, M.E. Charlton (1996) Geographical Analysis 28(4):281–298.

Brunsdon C., A.S. Fotheringham, M.E. Charlton (1999) Journal of Regional Science 39(3): 497–524.

Datta GS, Lahiri P, dan Lu KL. 1999. Hierarchical Bayes Estimation of Unemployment Rates for the States of the U.S. Journal of the American Statistical Association, 94, 1074-1082.

Fotheringham A.S., C. Brunsdon and M.E. Charlton (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. New York: Wiley.

Hajarisman, N. (2012b). Pemodelan Area Kecil untuk Data Cacahan dalam Pendugaan Mortalitas melalui Pendekatan Bayes Berhirarki: Kasus Pendugaan Mortalitas Level Kecamatan Jawa Barat. Laporan Penelitian tidak dipublikasikan. Bandung: LPPM Universitas Islam Bandung.

Jetz W., C. Rahbek, J.W. Lichstein (2005) Global Ecology and Biogeography 14 (2005) 97–98.

Leung Y., C.L. Mei, W.X. Zhang (2000) Environment and Planning A 32 9–32.

Malec D, Sedransk J, Moriarity CL, dan LeClere FB. 1997. Small Area Inference for Binary Variables in The National Health Interview Survey. Journal of the American Statistical Association, 92, 815-826.

Mennis J., The Cartographic Journal 43(2):171–179.

Pavlyuk D (2009) Transport and Telecommunication 10(2):26–32.

Propastin P.P, M. Kappas, R. Muratova (2006) Proceedings of XXIII FIG Congress TS 83: 1–16.

Shariff N.M., S. Gairola, A. Tahib (2010) Proceeding of 2010 International Congress on Environmental Modelling and Software.




DOI: https://doi.org/10.29313/mimbar.v32i1.1720

Refbacks

  • There are currently no refbacks.




MIMBAR : Jurnal Sosial dan Pembangunan is licensed under  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License