Eksplorasi Spasial Tingkat Kemiskinan di Pulau Sulawesi Tahun 2025
DOI:
https://doi.org/10.31537/estimator.v3i2.2874Keywords:
LISA, Moran's Index, Poverty, Spatial AutocorrelationAbstract
Poverty in Indonesia exhibits significant spatial variation between regions, including on the island of Sulawesi, which possesses diverse geographic and economic characteristics. This study aims to analyze the inequality in poverty rates between regencies/cities in Sulawesi in 2025 and to identify the patterns of their spatial clustering. Using secondary data from Statistics Indonesia (BPS) for 2025 covering 81 regencies/cities, this research applies a spatial exploration approach through global spatial autocorrelation analysis (Moran's Index and Geary's Index) and local analysis (LISA). The results indicate high inequality between regency areas (average 10.48%) and city areas (average 5.31%). A significant positive global spatial autocorrelation was detected (Moran's Index = 0.25 and Geary's Index = 0.75), indicating that poverty tends to cluster geographically. LISA analysis identified High-High clusters in the central and southeastern regions (particularly Gorontalo Province, Central Sulawesi, and Southeast Sulawesi) and Low-Low clusters in urban areas and the northern part of the island. These findings underscore the importance of spatially-based and integrated poverty alleviation policies for priority cluster regions.
References
A. Z. Karnaen, M. M. Samosir, N. Khairunnisa, T. Pujiarto, dan M. Nasrudin, “Analisis perbedaan profil sosial ekonomi kabupaten dan kota di Indonesia berdasarkan angka harapan hidup, kemiskinan, dan usia melahirkan pertama,” Data Science Indonesia, vol. 5, no. 1, hal. 75–83, 2025.
BPS, Data dan Informasi Kemiskinan Kabupaten/Kota. Jakarta: Badan Pusat Statistik, 2025.
M. S. Bilqisthi, S. R. Ilahi, A. Afrianto, F. Saputra, R. M. Al-Faris, dan Y. Gulo, “Klasterisasi berdasarkan data penduduk miskin di Asia dan wilayah Indonesia menggunakan metode k-means,” PESHUM: Jurnal Pendidikan, Sosial, dan Humaniora, vol. 4, no. 6, hal. 8418–8427, Okt. 2025.
A. O. Sihombing, “Analisis spasial kemiskinan di Sumatera Utara,” Journal of Analytical Research, Statistics and Computation, vol. 1, no. 1, hal. 64–77, Sep. 2022.
A. Artino dan R. Zakiah, “Pola spasial global kemiskinan desa di Kabupaten Lombok Utara,” Jurnal of Development Economic and Digitalization, vol. 3, no. 2, hal. 57–67, 2024.
K. P. Utama dan L. K. Sari, “Analisis spasial indeks kedalaman kemiskinan tiga provinsi di Pulau Jawa tahun 2021,” Seminar Nasional Official Statistics, 2023, hal. 353–362.
A. D. Pratama, I. W. Suparta, dan U. Ciptawaty, “Spatial autoregressive model and spatial patterns of poverty in Lampung Province,” Eko-Regional, vol. 16, no. 1, hal. 14–28, Mar. 2021.
A. Naldi, H. Siregar, dan S. Mulatsih, “Analisis pola spasial dan sebaran penduduk miskin di Kabupaten Mandailing Natal,” Tata Loka, vol. 25, no. 2, hal. 121–132, Mei 2023.
Y. Sari, “Kajian spasial temporal kemiskinan di Provinsi Jawa Tengah,” Journal of Demography, Etnography, and Social Transformation, vol. 4, no. 1, hal. 52–63, 2024.
Baharuddin, “Autokorelasi spasial prevalensi ketidakcukupan konsumsi pangan di Pulau Kalimantan tahun 2024,” Lontara Journal of Mathematics, Statistics and Application, vol. 2, no. 2, hal. 140–149, Nov. 2025.
W. Lestari, A. S. Brata, A. Anhar, dan S. Rahmati, “Analisis autokorelasi spasial global dan lokal pada data kemiskinan Provinsi Bali,” Jambura Journal of Mathematics, vol. 5, no. 1, hal. 218–229, Feb. 2023.
Baharuddin, I. Yahya, dan M. Ihwal, “Otokorelasi spasial pada prevalensi balita stunting, wasting, underweight, dan overweight di Pulau Sulawesi tahun 2022,” Journal of Mathematics, Computations and Statistics, vol. 7, no. 2, hal. 472–482, Okt. 2024.
S. N. Hasibuan dan M. H. Hasibuan, “Pemetaan efek spasial kemiskinan seluruh kabupaten di Indonesia,” Jurnal Reksabumi, vol. 1, no. 1, hal. 17–31, 2022.
