Yuria Sari

Kajian Spasial Temporal Kemiskinan Di Provinsi Jawa Tengah

Introduction

Kajian spasial temporal kemiskinan di provinsi jawa tengah. Kajian spasial temporal kemiskinan di Jawa Tengah (2012, 2017, 2022) mengungkap pola mengelompok dan enam hotspot. Analisis Moran Index & LISA mengidentifikasi daerah miskin.

Abstract

Various approaches are taken to examine how poverty grows and develops, accompanied by the search for solutions and appropriate strategies to overcome it. Poverty in a spatial perspective can provide a picture of how the condition of the region where people live can be a cause and effect of the existence of poverty. This research was conducted in Central Java Province with the unit of analysis at the district/city level at three points, namely 2012, 2017, and 2022. The analysis used is the Moran Index and Local Indicator of Spatial Autocorrelation (LISA). The results found that poverty in Central Java Province based on the Moran Index analysis, the value of I in 2012, 2017, and 2022 was 0.258, 0.303, and 0.229, respectively. The Index value at the three points of the year is positive, so it is concluded that the relationship forms a clustered pattern even though the strength of the relationship is quite weak. Then the results at three points of the year show similarities, namely that six districts are poverty hotspots in Central Java based on data on the percentage of poor people. These six regencies include Cilacap, Banyumas, Purbalingga, Banjarnegara, Kebumen, and Purworejo. This means that poverty in all of these areas is high, and so is the surrounding areas.

Review

This paper, "Kajian Spasial Temporal Kemiskinan Di Provinsi Jawa Tengah," presents a highly relevant and timely analysis of poverty dynamics. The study's focus on understanding the spatial and temporal dimensions of poverty in Central Java Province is crucial for developing effective and geographically targeted interventions. By utilizing well-established spatial analysis techniques, specifically the Moran Index and Local Indicator of Spatial Autocorrelation (LISA), and examining data across three distinct time points (2012, 2017, and 2022) at the district/city level, the research offers a valuable longitudinal perspective on a critical development issue. A significant strength of this research lies in its clear identification of persistent poverty patterns. The findings consistently show a clustered spatial distribution of poverty in Central Java, even if the clustering strength is noted as weak. More importantly, the study successfully pinpointed six specific districts—Cilacap, Banyumas, Purbalingga, Banjarnegara, Kebumen, and Purworejo—as consistent poverty hotspots across the entire study period. This empirical identification of stable, high-poverty regions is a key contribution, providing essential geographical intelligence for policymakers to concentrate resources and design interventions with greater precision and impact. While the abstract provides a compelling summary of the findings, a complete manuscript would greatly benefit from deeper analytical exploration. Future work or the full paper should endeavor to investigate the underlying causal factors contributing to the observed spatial clusters and, particularly, the persistence of the identified hotspots. Exploring the socio-economic, environmental, policy, or historical contexts that perpetuate high poverty in these six districts would add significant explanatory power. Furthermore, an expanded discussion on the policy implications directly derived from these spatial-temporal patterns, offering actionable strategies specifically tailored to the unique challenges of the hotspot regions, would substantially enhance the practical relevance and overall impact of this otherwise robust and important research.

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