Among various issues on regional development planning in Indonesia, the vast coverage area and its regional diversity become one of main challenges. Regionalization is one of spatial techniques that can be used to facilitate regional development planning process. This study aims to analyze regionalization of Indonesia with a focus on Java Island by highlighting the main contemporary issues including poverty, COVID-19 pandemic, and gender. This paper draws the conditions of poverty, the cases of the COVID-19 pandemic and the needs of gender-based development in 119 regencies/cities on the island of Java using spatial distribution analysis. The results of the analysis show that the distribution of the percentage of the poor and the poverty line in Java forms a clustered pattern. This also happened in the case of COVID-19 where the high concentration of cases was around DKI Jakarta and DI Yogyakarta. As for the gender aspect, there is a tendency for a scattered pattern for the value of the gender development index, although there are some areas with high values (H-H quadrant) and low values (L-L quadrant). In general, the use of regional analysis may facilitate the identification of spatial phenomena, their distribution and the patterns formed, therefore it can be used for determining suitable regional policies.Keywords: Java Island, Regionalization, Poverty, COVID-19 Pandemic, Gender

The economic disparity between the North and South Coast of Java is due to uneven infrastructure development. The impact of Southern Cross Road (JJLS) on the development of South Coast tourism (Pansela) Bantul Regency is expected to be able to increase Regional Original Income (PAD). The purpose of this study is 1)Comparing tourist visits number, tourism service entrepreneurs number, and PAD before and after JJLS, 2)Analyzing the effect of tourist visits and tourism service entrepreneurs on PAD, and 3)Calculating the contribution of Pansela’s PAD to Bantul Tourism’s PAD and Bantul’s PAD. This study uses quantitative methods and secondary data from related agencies. The analytical technique uses comparison two correlated samples test (Paired Sample T-Test and Wilcoxon Match Pairs Test), panel data regression Random Effect Model, and quantitative descriptive. The results showed that PAD experienced a significant difference but not in tourist visits number and tourism service entrepreneurs number before and after JJLS. Furthermore, Pansela’s PAD is significantly affected by tourist visits number but not by tourism service entrepreneurs number. Pansela’s PAD is able to make a dominant contribution to Bantul Tourism’s PAD, which is 3-7% of its total contribution to Bantul’s PAD 4-12%.