FAKTOR-FAKTOR DETERMINAN HUMAN DEVELOPMENT INDEX KABUPATEN/KOTA DI JAWA: PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION

• Main Authors: , Raden Witcaksono Setyo Putro, , Prof. Dr. R. Rijanta, M.Sc
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/97849/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=53808
• Institution: Universitas Gadjah Mada

The Human Development Index (HDI) is a composite index that measures the average achievements of a region in three basic dimensions of human development: a long and healthy life, knowledge, and a decent standard of living. In last decades, progress of human development in Indonesia has been very impressive. However, there are also clear disparities of human development between regions, including between regencies and cities in Java. Java is the most populated island in the world, with only 6% of the total area of Indonesia, but inhabited by 58% of Indonesian population, and contribute 58% of Indonesia�s economy. Firstly, this paper examined human development patterns in Java. We performed a spatial autocorrelation measures to assess the level of spatial dependency. Secondly, we developed a model using Geographically Weighted Regression (GWR) to estimate the strength of relationship between HDI and factors associated. GWR is an extended development of regression method that incorporates spatial structure. The data used were the 2008 Village Census (Podes) from the BPS at regency/city-level, which were aggregated from villagelevel. The district�s budget data was compiled from Indonesian Ministry of Finance. For spatial data, we use administration map from National Survey and Mapping Coordination Agency (Bakosurtanal) and digital elevation map from USGS. Examining spatial structure of the HDI, the results show prominent disparities on HDI between regions of Java. Strong patterns of spatial association were found proving the presence of clusters on the distribution of HDI, corresponding to Tobler�s First Law of Geography. Using GWR, we found that educational infrastructure ratio, farm workers, and distance to the nearest school, were negatively associated with the HDI. Meanwhile, health infrastructure ratio, and percent of family with electricity were positively associated with the HDI. The results of the GWR model were compared to the global model. GWR can help better understand which predictors are associated at specific locations. The GWR maps produced were not only confirmed, but also demonstrated the spatial varying association. We found that the GWR performs better to model HDI determinants than the OLS model, shown with goodness-of-fit statistics. GWR�s errors were also free from spatial autocorrelation