Analysis of the Spatial Distribution Pattern of Poverty Percentage in Central Java in 2024 Using the Spatial Autocorrelation Approach

Miftahus Sholihin, Gustriza Erda, Putri Dina Sari, Agung Satrio Wicaksono, Atia Sonda, Muhammad Fabian Reinhard Delano, Syukron Faiz

Abstract


Poverty remains a critical socio-economic issue in Central Java, Indonesia, exhibiting significant regional disparities. This study aims to analyze the spatial distribution pattern of poverty rates in Central Java in 2024 using a spatial autocorrelation approach with an inverse distance weight matrix. Secondary data from the Central Bureau of Statistics (BPS) of Central Java is utilized, covering poverty percentages across regencies and cities. The analysis method involves Moran’s I to assess global spatial autocorrelation and Local Indicators of Spatial Association (LISA) to identify local spatial clusters. The findings indicate a positive Moran’s I value, suggesting a significant spatial dependence in poverty distribution. Several high-poverty clusters are identified in specific regions, confirming spatial concentration patterns. The study highlights that regional proximity influences poverty rates, where areas with high poverty tend to be surrounded by regions with similar conditions. These results provide empirical evidence for policymakers to design targeted poverty alleviation programs based on spatial characteristics. The study concludes that understanding spatial autocorrelation in poverty distribution is crucial for formulating effective regional development policies and reducing socio-economic disparities.

Keywords


Spatial Autocorrelation; Moran’s I; Local Indicators of Spatial Association; Inverse Distance Weight; Regional Disparities

Full Text:

PDF

References


Y. Purwanti, “Clustering analysis of multidimensional poverty in central java province, indonesia,” 2023, doi: 10.56655/jid.v2i2.132.

F. Dwi Ratna Sari and S. Partiwi Ediwijojo, “Clustering Analysis Using K-Medoids on Poverty Level Problems in Central Java by District/City,” KnE Soc. Sci., vol. 2023, pp. 78–87, 2023, doi: 10.18502/kss.v8i9.13321.

Y. Chen, “Spatial autocorrelation equation based on Moran’s index,” Dent. Sci. reports, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-45947-x.

Q. Luo, K. Hu, W. W. Liu, and H. Wu, “Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021,” ISPRS Int. J. geo-information, vol. 11, no. 5, p. 309, 2022, doi: 10.3390/ijgi11050309.

L. Anselin and S. Rey, Perspectives on Spatial Data Analysis. New york: Springer, 2010.

L. Apriani, Azwardi, and Imelda, “Analisis Autokorelasi Spasial Kemiskinan Antardaerah di Sumatera,” Universitas Sriwijaya, 2022. [Online]. Available: https://repository.unsri.ac.id/79195/

M. F. D. Almismary, D. R. Panuju, and G. S. Indraprahasta, “Dimensi Spasial Determinan Kemiskinan Kabupaten/Kota di Provinsi Aceh,” J. Pembang. Wil. dan Kota, vol. 20, no. 4, pp. 489–509, 2024, doi: 10.14710/pwk.v20i4.59542.

[BPS] Badan Pusat Statistik Provinsi Jawa Tengah, Indikator Utama Sosial, Politik dan Keamanan Provinsi Jawa Tengah 2024, vol. 16. 2024.

L. Anselin, Spatial Autocorrelation. University of Illinois, 1999.

N. Wagesho, N. K. Goel, and M. K. Jain, “emporal and spatial variability of annual and seasonal rainfall over Ethiopia,” Hydrol. Sci. J., vol. 58, no. 2, pp. 354–373, 2013, doi: 10.1080/02626667.2012.754543.

A. O. Sihombing, “Analisis spasial Kemiskinan Di Sumatera Utara,” J. Anal. Res. Stat. Comput., vol. 1, no. 1, pp. 64–77, 2022, [Online]. Available: https://www.jarsic.org/main/article/view/6/3

W. Lestari, A. S. Brata, A. Anhar, and S. Rahmawati, “Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali,” Jambura J. Math., vol. 5, no. 1, pp. 218–229, 2023, doi: 10.34312/jjom.v5i1.18681.

N. Yuriantari, M. Hayati, and S. Wahyuningsih, “Analisis Autokorelasi Spasialtitik Panas Di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Of Spatial Autocorrelation (LISA),” J. Eksponensial, vol. 8, no. 1, pp. 63–70, 2017.

L. W. Adha and M. U. Basuki, “Analisis Spasial pada Kemiskinan di Kabupaten/Kota Provinsi Jawa Tengah Tahun 2011-2017,” Diponegoro Jorunal Econ., vol. 9, no. 2, pp. 135–143, 2020, Accessed: Feb. 15, 2025. [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/jme

L. Mason, B. Hicks, and J. S. Almeida, “Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation,” 2024, doi: 10.48550/arxiv.2408.02418.

G. Betti, “Can a neighbour region influence poverty? A fuzzy and longitudinal approach,” 2018.

Y. Sari, “Kajian Spasial Temporal Kemiskinan Di Provinsi Jawa Tengah,” J. Demogr. Etnography Soc. Transform., vol. 4, pp. 52–63, 2024.




DOI: http://dx.doi.org/10.62870/tjs.v1i1.31320

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Miftahus Sholihin

This work is licensed under Creative Commons Attribution 4.0 International

BRI303

BRI303

BRI303

BRI303

BRI303

BRI303

BRI303

BRI303

BRI303