<![CDATA[Since 1968, socio-economic and infrastructure development focused on the accessible region such as urban and sub-urban area. Rural, rim-land, coastal area, highland, and isolated area were still ignored in national development priorities by the central and local government. This paper discusses the spatial analysis of disparities in Banyumas Regency, Central Java, based on indicators of socio-economic and infrastructure. We had collected data by doing a survey in 27 sub-districts in Banyumas Regency. Disparities in socio-economic development and infrastructure were measured using 14 variables, i.e. unemployment rate, consumer price index, wage index, poverty index, quality of life, quality of health, quality of education, criminal rate, quality of roadway, public transportation accessibility, quantity of traditional market, quantity of bridge, and the quantity of public school building. Multivariate statistical analyses based on the principal component analysis (PCA) and hierarchical cluster analyses (HCA) were used to analyze the disparities. The analysis on 14 socio-economic parameter displays that unemployment rate, poverty index, quality of health, and quality of education are the main contributor for socio-economic and infrastructure disparity. Ajibarang, Gumelar, Cilongok, and Rawalo had still a classic problem in economic development due to being the pockets of unemployment and poverty in Banyumas Regency. Based on social indicators, 6 sub-districts including Gumelar, Karanglewas, Lumbir, Pekuncen, Somagede, and Tambak should receive more attention by local government due to a high percentage of poor people to access medical and health facilities. In addition, the ratio of people who are able to access higher education in Jatilawang, Purwojati and Wangon was still less than 10%. There were no significance disparities in infrastructure indicator because the infrastructure had been developed equally in all sub-districts in Banyumas Regency. Keywords : patial disparities, socio-economic development, infrastructure, multivariate analyses]]>
