Indexed By
Article Tools
Email this article (Login required)
Email the author (Login required)
About The Authors

N W Surya Wardhani
Brawijaya University

Senior Lecturer

H Pramoedyo
Brawijaya University Malang


Y N Dianati
Brawijaya University Malang


Author Guidelines

SJR Rank

SCImago Journal & Country Rank

Sinta Rank

Sinta Rank

Visitor Statistic

Food security and vulnerability modeling of East Java Province based on Geographically Weighted Ordinal Logistic Regression Semiparametric (GWOLRS) model

N W Surya Wardhani, H Pramoedyo, Y N Dianati
  J. Degrade. Min. Land Manage. , pp. 231-234  
Viewed : 1354 times


Modeling of food security based on the characteristics of the area will be affected by the geographical location which means that geographical location will affect the region’s potential. Therefore, we need a method of statistical modeling that takes into account the geographical location or the location factor observations. In this case, the research variables could be global means that the location affects the response variables significantly; when some of the predictor variables are global and the other variables are local, then Geographically Weighted Ordinal Logistic Regression Semiparametric (GWOLRS) could be used to analyze the data. The data used is the resilience and food insecurity data in 2011 in East Java Province. The result shows that three predictor variables that influenced by the location are the percentage of poor (%), rice production per district (tons) and life expectancy (%). Those three predictor variables are local because they have significant influence in some districts/cities but had no significant effect in other districts/cities, while other two variables that are clean water and good quality road length (km) are assumed global because it is not a significant factor for the whole districts/towns in East Java.


East Java; food security; GWOLRS

Full Text:



Ariningsih, E. dan Rachman, H.P.S. 2008. Strategi peningkatan ketahanan pangan rumah tangga rawan pangan. Analisis Kebijakan Pertanian 6 (3) : 239-255.

Bogale, A. and Shimelis. A. 2009. Household level determinants of food insecurity in rural areas of Dire Dawa, Eastern Ethiopia. African Journal of Food Agriculture Nutrition and Development 9 : 1914-1926.

Chasco, C., I. Garcia, I. and Vicens, J. 2007. Modelling Spatial Variation ini Household Disposible Income with Geographically Weighted Regression. Munich Personal RePEc Arkhive (MPRA) Working Papper No.1682

Fotheringham, A.S., Brunsdon, C. and Charlton, M. 2002. Geographically Weighted Regression, Jhon Wiley & Sons, Chichester, UK

Hosmer, D.W. and Lemeshow, S. 2000. Applied Logistic Regression, John Willey and Sons. New York.

Leung, Y., Mei, C.C.I. and Zhang W.X. 2000. Statistical test for spatial non-stationarity based on the GWR model. Environment and Planning A 32 9-32.

Nakaya, T., Fotheringham, A.S., Brunsdon, C. and Charlton, M. 2005. Geographically weighted poisson regression for disease association mapping. Statistics in Medicine 24 : 2695 -2717.


  • There are currently no refbacks.

Copyright (c) 2014 Journal of Degraded and Mining Lands Management

License URL:

Indexed By