Mapping of Spatial Distribution and Spatial Autocorrelation Patterns of Poverty in All Regencies/Cities in Indonesia

Abstract

Abstract Poverty alleviation programs in Indonesia are the same and uniform in all regions. Of course this ignores the characteristics and causes of poverty that vary in each region. The uniformity of poverty alleviation programs affects the slow pace of decline in the poor population. Spatial influence on poverty can be identified by spatial autocorrelation; there is a relationship of poverty in one region with other regions that are closed together. This study was aimed to analyzing poverty spatial distribution in all regencies/cities in Indonesia; analyzing the spatial distribution patterns of poverty in all regencies/cities in Indonesia; and knowing local spatial autocorrelation of poverty in all regencies/cities in Indonesia. The research methods used are Moran Index analysis, Moran’s scatterplot analysis, and Local Indicators of Spatial Autocorrelation (LISA) analysis. The analysis results show that the highest average of poor population percentage was in Papua and the lowest one was in Kalimantan. The results of analysis of Moran Index showed that the spatial distribution pattern of poverty in regencies/cities in Indonesia was clustered, it was called by poverty pocket. Pockets of poverty that occured do not correspond to government administrative boundaries, therefore poverty alleviation needs an integrative approach. In addition, this study also results that not all regencies/cities have significant spatial autocorrelation. This means that not all poverty conditions in a regencies/cities have a relationship with other regencies/cities. The fact that there are heterogeneity of poverty characteristics like this shows that poverty alleviation programs must vary in each regency/city. Keywords: City, LISA, Moran, Povety, Regency, Spatial

Keywords

• city, lisa, moran, povety, regency, spatial