DOI: https://doi.org/10.14710/geoplanning.13.1.%25p

Bayesian Geoadditive Modeling of Poverty Distribution: A Spatial Analysis of Indonesia

Abdul Karim orcid scopus publons  -  Doctoral Programme of Mathematics and Natural Sciences, Airlangga University, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Surabaya – 60115, Indonesia
*Toha Saifudin orcid scopus  -  Department of Mathematics, Airlangga University, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Surabaya – 60115, Indonesia
Nur Chamidah orcid scopus  -  Department of Mathematics, Airlangga University, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Surabaya – 60115, Indonesia
Agus Riyadi orcid scopus  -  Islamic Community Development, Universitas Islam Negeri Walisongo, Jl. Prof. Dr. Hamka, Tambakaji, Kec. Ngaliyan, Kota Semarang, Jawa Tengah 50185, Indonesia

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Abstract

Zakat, as an instrument of financial redistribution in the Islamic economy, has significant potential for poverty alleviation; however, the spatial distribution patterns of its impact in Indonesia have not yet been extensively analysed using spatial modelling. In this paper, we measure the spatial pattern of the impact of zakat on poverty data in Indonesia and form a zakat cluster in Indonesia using the Bayesian geoadditive model approach. In this study, we collected data from the Indonesian Central Statistics Agency and the National Amil Zakat Agency (BAZNAS) in 2024, respectively, to map and model socio-economic and spatial determinants, namely the relationship between zakat and poverty in Indonesia. We found strong support for our approach to mapping and modeling flexibly to include spatial analysis. The results of the analysis indicate that zakat has a significant effect on poverty, with a posterior mean of 21,042.1 (SD = 28,142.4; 95% PCI: 12,665.3–30,185.0), where the entirely positive credible interval indicates a consistent spatial association between the distribution of zakat and the concentration of the poor. Furthermore, the structured spatial effects reveal regional heterogeneity with a significant East–West divide: the Papua region exhibits the highest positive spatial effect (up to +317.67), indicating a poverty burden far exceeding the model’s predictions, whilst Java and parts of Kalimantan show a negative effect (up to −317.67), reflecting the greater effectiveness of zakat distribution. The model identifies distinct regional cluster structures, demonstrating significant spatial heterogeneity across regions in Indonesia. The Bayesian geoadditive approach proved superior to conventional models in capturing these non-linear patterns and spatial inequalities. The resulting maps could serve as a new analytical method for policymakers to design more targeted zakat collection and distribution strategies in support of national poverty alleviation.

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Keywords: Spatial modeling; Bayesian modeling; bayesian geoadditive model; poverty; zakat

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  1. Abdi Ali, D., Mohamed, N. A., Ismail, A. I., Moahmed, J., & Sahabuddin, M. (2025). Modelling the determinants of rural household poverty: empirical evidence from Somalia. Cogent Food and Agriculture, 11(1). https://doi.org/10.1080/23311932.2024.2445139
  2. Alnashwan, O. S., Alrwis, K. N., Ghanem, A. M., Naji, A. M., & Alaagib, S. A. B. (2021). Studying the phenomenon of poverty in the countryside of the dahhar district in Yemen. Journal of Experimental Biology and Agricultural Sciences, 9(2), 147–151. https://doi.org/10.18006/2021.9(2).147.151
  3. Amalia, E., & Sari, L. K. (2019). ANALISIS SPASIAL UNTUK MENGIDENTIFIKASI TINGKAT PENGANGGURAN TERBUKA BERDASARKAN KABUPATEN/KOTA DI PULAU JAWA TAHUN 2017. Indonesian Journal of Statistics and Its Applications, 3(3). https://doi.org/10.29244/ijsa.v3i3.240
  4. Andriono, A., & Hidayatulloh, A. (2020). Determinan Kepatuhan Wajib Pajak Muslim untuk Membayarkan Pajak. Kompartemen: Jurnal Ilmiah Akuntansi, 18(2). https://doi.org/10.30595/kompartemen.v18i2.7524
  5. Aqbar, K., & Iskandar, A. (2019). Kontekstualisasi Ekonomi Zakat dalam Mengentaskan Kemiskinan: Studi Kebijakan Zakat Umar Bin Khattab dan Perzakatan di Indonesia. Laa Maisyir, 6(2)
  6. Asmawi, A., Sjoraida, D. F., & Anwar, R. K. (2017). MASALAH DAN DINAMIKA IMPLEMENTASI KEBIJAKAN PUBLIK TENTANG TRANSPORTASI (Kajian Di Kota Tasikmalaya, Jawa Barat). CosmoGov, 3(2). https://doi.org/10.24198/cosmogov.v3i2.14729
  7. Ayuniyyah, Q., Pramanik, A. H., Md Saad, N., & Ariffin, M. I. (2022). The impact of zakat in poverty alleviation and income inequality reduction from the perspective of gender in West Java, Indonesia. International Journal of Islamic and Middle Eastern Finance and Management, 15(5). https://doi.org/10.1108/IMEFM-08-2020-0403
  8. Bilqis, N., & Zaki, I. (2020). DAMPAK PENGELOLAAN ZIS LAZNAS AL-AZHAR BAGI PEMUDA PADA PROGRAM RUMAH GEMILANG INDONESIA SURABAYA. Jurnal Ekonomi Syariah Teori Dan Terapan, 7(4). https://doi.org/10.20473/vol7iss20204pp659-667
  9. Bogaevskaya, O. V. (2019). National food assistance system in the USA. World Economy and International Relations, 63(1), 75–82. https://doi.org/10.20542/0131-2227-2019-63-1-75-82
  10. BPS. (2024). Statistics Indonesia 2024. Badan Pusat Statistik Republik Indonesia
  11. Brezger, A., Kneib, T., & Lang, S. (2005). BayesX: Analyzing Bayesian structured additive regression models. Journal of Statistical Software, 14. https://doi.org/10.18637/jss.v014.i11
  12. Chai, J., Hao, Y., Wu, H., & Yang, Y. (2021). Do constraints created by economic growth targets benefit sustainable development? Evidence from China. Business Strategy and the Environment, 30(8). https://doi.org/10.1002/bse.2864
  13. Chen, Y. (2016). Spatial autocorrelation approaches to testing residuals from least squares regression. PLoS ONE, 11(1). https://doi.org/10.1371/journal.pone.0146865
  14. Congdon, P. (2021). Bayesian Modeling of Spatial Data. In Handbook of Regional Science: Second and Extended Edition: With 238 Figures and 78 Tables. https://doi.org/10.1007/978-3-662-60723-7_79
  15. Czyżewski, B., Trojanek, R., Dzikuć, M., & Czyżewski, A. (2020). Cost-effectiveness of the common agricultural policy and environmental policy in country districts: Spatial spillovers of pollution, bio-uniformity and green schemes in Poland. Science of the Total Environment, 726. https://doi.org/10.1016/j.scitotenv.2020.138254
  16. Digdowiseiso, K., Hermansah, & Erlina. (2020). Fiscal decentralization and poverty in Indonesia: Evidence from panel regression and spatial econometrics. Cogent Economics & Finance, 8(1), 1–16
  17. Dumelle, M., Higham, M., & Ver Hoef, J. M. (2023). spmodel: Spatial statistical modeling and prediction in R. PLoS ONE, 18(3 March). https://doi.org/10.1371/journal.pone.0282524
  18. Fahrmeir, L., & Lang, S. (2001). Bayesian inference for generalized additive mixed models based on Markov random field priors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 50(2), 201–220
  19. Geyer, S., Papaioannou, I., & Straub, D. (2021). Bayesian analysis of hierarchical random fields for material modeling. Probabilistic Engineering Mechanics, 66. https://doi.org/10.1016/j.probengmech.2021.103167
  20. Goodchild, M. F. (2009). First Law of Geography. In International Encyclopedia of Human Geography. https://doi.org/10.1016/B978-008044910-4.00438-7
  21. Hadiyati, N. (2019). INTEGRASI PAJAK DAN ZAKAT SEBAGAI SUMBER PEMBIAYAAN PEMBANGUNAN NASIONAL. Legality : Jurnal Ilmiah Hukum, 26(2). https://doi.org/10.22219/jihl.v26i2.7777
  22. Hamrullah, H., Saudi, N. D. S., Djam’an, F., & Hamzah, S. (2026). Spatial spillovers in regional economic inequality: Evidence from macro indicators—Remote sensing in Eastern Indonesia. Economies, 14(4), 109. https://doi.org/10.3390/economies14040109
  23. Harahap, A. H. (2021). Pengaruh zakat terhadap pertumbuhan ekonomi dengan pengentasan kemiskinan sebagai variabel moderating di Provinsi Sumatera Utara
  24. Heri Mis Cicih Lembaga Demografi, L., Ekonomi Bisnis, F., Nurjono Agung Nugroho Pusat Penelitian dan Pengembangan Kependudukan, D., Jl Permata No, B., Perdana Kusuma, H., & Timur, J. (2021). KONDISI LANJUT USIA DI INDONESIA ERA BONUS DEMOGRAFI. Sosio Informa, 7(2). https://doi.org/10.33007/INF.V7I2.2681
  25. Herianingrum, S., Fauzi, Q., Supriani, I., Widiastuti, T., Sukmana, R., Effendie, E., & Shofawati, A. (2023). Zakat as an instrument of poverty reduction in Indonesia. Journal of Islamic Accounting and Business Research, 14(3), 456–475. https://doi.org/10.1108/jiabr-11-2021-0307
  26. Herlita, J. (2017). Sinergi Pelayanan Zakat dan Pajak Dalam Upaya Mengurangi Kemiskinan. Alhadharah: Jurnal Ilmu Dakwah, 16(32). https://doi.org/10.18592/alhadharah.v16i32.1927
  27. Hunjra, A. I., Arunachalam, M., & Hanif, M. (2024). The Role of Islamic Social Finance in Poverty Eradication. In Islamic Finance in the Modern Era: Digitalization, FinTech and Social Finance. https://doi.org/10.4324/9781003366751-3
  28. Ichdayati, L. I., & Puspitasari, R. A. (2021). INTENSITAS PETANI MUZAKI MEMBAYAR ZAKAT PADI (Studi Kasus Kabupaten Indramayu). Sharia Agribusiness Journal
  29. IMD. (2025). World Competitiveness Ranking. https://www.imd.org/centers/wcc/world-competitiveness-center/rankings/world-competitiveness-ranking/rankings/wcr-rankings/
  30. Jamaluddin, H., Tinggi, S., Islam, A., & Watampone, N. (2021). REAKTUALISASI FIKIH ZAKAT MENUJU ADMINISTRASI IDEAL. AL-IQTISHAD, 1(1), 72–92
  31. Kalimah, S. (2020). Pandangan Ulama Empat Mazhab dalam Memutuskan Upah Amil Zakat Guna Meningkatkan Optimalisasi Keprofesionalan Amil Zakat. Salimiya: Jurnal Studi Ilmu Keagamaan Islam, 1(1)
  32. Kandala, N. B. (2006). Bayesian geo-additive modelling of childhood morbidity in Malawi. Applied Stochastic Models in Business and Industry, 22(2). https://doi.org/10.1002/asmb.624
  33. Kang, H. Bin, Jung, Y. J., & Park, J. (2024). Fast Bayesian Functional Regression for Non-Gaussian Spatial Data. Bayesian Analysis, 19(2). https://doi.org/10.1214/22-BA1354
  34. Karim, A., Faturohman, A., Suhartono, S., Prastyo, D. D., & Manfaat, B. (2017). Regression Models for Spatial Data: An Example from Gross Domestic Regional Bruto in Province Central Java. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 18(2), 213. https://doi.org/10.23917/jep.v18i2.4660
  35. Karim, A., Mudhofi, M., & Arwani, W. (2020). ANALISIS SPASIAL POTENSI ZAKAT DAN KEMISKINAN DI INDONESIA. Ijtimaiyya: Jurnal Pengembangan Masyarakat Islam, 13(2), 117–130. https://doi.org/10.24042/IJPMI.V13I2.6853
  36. Karim, A., Mursinto, D., & Abdillah, K. (2022). Spatial spillover impact of sectoral government expenditure on poverty alleviation in South Kalimantan Province. Journal of Indonesia Sustainable Development Planning, 3(3), 361–378. https://doi.org/10.46456/jisdep.v3i3.361
  37. Karim, A., Saifudin, T., Chamidah, N., & Riyadi, A. (2025). Machine Learning with Spatial Context: Comparing Bayesian Geoadditive Regression Tree, Random Forest, and Gradient Boosting Machines for Gross Domestic Regional Product in Indonesia. 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML), 1–5. https://doi.org/10.1109/SIML65326.2025.11080715
  38. Karim, A., Suharno, Y., & Arwani, W. (2019). Pemberdayaan Sekolah Islam untuk Meningkatkan Kompetensi Profesionalisme Guru di Kabupaten Semarang. Jurnal Ilmu Dakwah, 39(1). https://doi.org/10.21580/jid.v39.1.4421
  39. Karim, A., Suhartono, & Prastyo, D. D. (2020). Spatial spillover effect of transportation infrastructure on regional growth. Economy of Region, 16(3), 911–920. https://doi.org/10.17059/ekon.reg.2020-3-18
  40. Karim, A., Utami, T. W., Fathurohman, A., Setyowati, C. I., Suhartono, & Prastyo, D. D. (2020). Assessing the indicators of economic growth in Indonesia using spatial principal components analysis. Journal of Physics: Conference Series, 1446(1). https://doi.org/10.1088/1742-6596/1446/1/012051
  41. Kim, Y. R., Williams, A. M., Park, S., & Chen, J. L. (2021). Spatial spillovers of agglomeration economies and productivity in the tourism industry: The case of the UK. Tourism Management, 82. https://doi.org/10.1016/j.tourman.2020.104201
  42. Krapu, C., Stewart, R., & Rose, A. (2023). A Review of Bayesian Networks for Spatial Data. ACM Transactions on Spatial Algorithms and Systems, 9(1). https://doi.org/10.1145/3516523
  43. Lacombe, D. J., & McIntyre, S. G. (2016). Local and global spatial effects in hierarchical models. Applied Economics Letters, 23(16), 1168–1172. https://doi.org/10.1080/13504851.2016.1142645
  44. Lee, S. A., Economou, T., & Lowe, R. (2022). A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping. Journal of the Royal Society Interface, 19(194). https://doi.org/10.1098/rsif.2022.0440
  45. Ling, Y., & Le Gallo, J. (2023). Bayesian spatial panel models: a flexible Kronecker error component approach. Letters in Spatial and Resource Sciences, 16(1). https://doi.org/10.1007/s12076-023-00362-8
  46. Louzada, F., Do Nascimento, D. C., & Egbon, O. A. (2021). Spatial statistical models: An overview under the bayesian approach. Axioms, 10(4). https://doi.org/10.3390/axioms10040307
  47. Lu, B., & Dong, G. (2022). GWmodelS: A High-Performance Computing Framework for Geographically Weighted Models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13614 LNCS. https://doi.org/10.1007/978-3-031-24521-3_11
  48. Maulidina, T. P., & Oktora, S. I. (2020). ANALISIS SPASIAL KETERTINGGALAN DAERAH DI INDONESIA TAHUN 2018 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION. Indonesian Journal of Statistics and Its Applications, 4(3). https://doi.org/10.29244/ijsa.v4i3.690
  49. Mawardi, I., Widiastuti, T., Al Mustofa, M. U., & Hakimi, F. (2023). Analyzing the impact of productive zakat on the welfare of zakat recipients. Journal of Islamic Accounting and Business Research, 14(1), 118–140. https://doi.org/10.1108/JIABR-05-2021-0145
  50. Moraga, P., Dean, C., Inoue, J., Morawiecki, P., Noureen, S. R., & Wang, F. (2021). Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique. Spatial and Spatio-Temporal Epidemiology, 39. https://doi.org/10.1016/j.sste.2021.100440
  51. Murshed, S. M., & Tadjoeddin, M. Z. (2009). Revisiting the greed and grievance explanations for violent internal conflict. Journal of International Development: The Journal of the Development Studies Association, 21(1), 87–111
  52. Nurzaman, M. S., Khanifa, F., & Kurniaeny, K. (2024). Achieving sustainable impact of zakah in community development programs. Islamic Economic Studies, 26(2), 145–167
  53. Omre, H., & Rimstad, K. (2021). Bayesian spatial inversion and conjugate selection gaussian prior models. SIAM-ASA Journal on Uncertainty Quantification, 9(2). https://doi.org/10.1137/19M1302995
  54. Pratama, N. W., & Purba, I. (2021). STRATEGI BADAN AMIL ZAKAT NASIONAL KABUPATEN TUBAN DALAM PENGELOLAAN ZAKAT SEBAGAI UPAYA MENURUNKAN ANGKA KEMISKINAN UNTUK …. Kajian Moral Dan …
  55. Pratama, Y. C. (2015). PERAN ZAKAT DALAM PENANGGULANGAN KEMISKINAN (Studi Kasus : Program Zakat Produktif Pada Badan Amil Zakat Nasional). Tauhidinomics: Journal of Islamic Banking and Economics, 1(1)
  56. Qomari, N. (2017). Zakat: Solusi Pengentasan Kemiskinan. Iqtishodia Jurnal Ekonomi Syariah, 2(2)
  57. Razen, A., & Lang, S. (2020). Random scaling factors in Bayesian distributional regression models with an application to real estate data. Statistical Modelling, 20(4), 347–368. https://doi.org/10.1177/1471082X18823099
  58. Roman, Z. J., & Brandt, H. (2023). A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data. Multivariate Behavioral Research, 58(1), 90–114. https://doi.org/10.1080/00273171.2021.1957663
  59. Rural Poverty and the Nonfarm Sector in Rural Asia. (2024). In Reducing Rural Poverty in Asia: Challenges and Opportunities for Microenterprises and Public Employment Schemes. https://doi.org/10.4324/9781003578345-2
  60. Samatar, E. H. (2025). Social Safety Nets and Their Effectiveness in Poverty Alleviation: Evidence from Banadir Region, Somalia. Journal of Environmental and Earth Sciences, 7(3), 77–87. https://doi.org/10.30564/jees.v7i3.8063
  61. Satpathy, A., Ayyalasomayajula, S., & Srivastava, A. (2023). Predictive Risk Assessment for Homelessness. 29th Annual Americas Conference on Information Systems, AMCIS 2023
  62. Setiyowati, A. (2017). Analisis Peranan Pengelolaan Dana ZISWAF Oleh Civil Society Dalam Pemberdayaan Ekonomi Umat (Studi Kasus Lazismu Surabaya). Jurnal Ekonomi Dan Perbankan Syariah, 2(1)
  63. Shaifudin, A. (2020). PENDIDIKAN ISLAM UNTUK KEUTUHAN NEGARA KESATUAN REPUBLIK INDONESIA (NKRI). El-Wasathiya: Jurnal Studi Agama, 8(1)
  64. Sofianto, A. (2019). INTEGRASI TARGET DAN INDIKATOR SUSTAINABLE DEVELOPMENT GOALS (SDGs) KE DALAM PERENCANAAN PEMBANGUNAN DAERAH DI JAWA TENGAH. Jurnal Litbang Provinsi Jawa Tengah, 17(1). https://doi.org/10.36762/jurnaljateng.v17i1.784
  65. Sulaiman, A. J., Ahmad, S. N. H. J. N. N., Bidin, Z., Abd Aziz, S., & Ibrahim, I. (2025). Empowering Lives: Unveiling the Impact of LZNK Business Capital Assistance through the MAQASID SHARIAH Lens in Alignment with the Sustainable Development Goals (SDG). Journal of Information Systems Engineering and Management, 10(5), 162–171. https://doi.org/10.52783/jisem.v10i5s.600
  66. Suryahadi, A., Mclaughlin, D., & Suryadarma, D. (2019). Double burden of poverty reduction in Indonesia. Journal of Development Studies, 55(6), 1189–1207
  67. Syari’ah, F., Surakarta, I., & Efendi, M. (2017). PENGELOLAAN ZAKAT PRODUKTIF BERWAWASAN KEWIRAUSAHAAN SOSIAL DALAM PENGENTASAN KEMISKINAN DI INDONESIA. Al-Ahkam Jurnal Ilmu Syari’ah Dan Hukum, 2(1). https://doi.org/10.22515/AL-AHKAM.V2I1.679
  68. Tadjoeddin, M. Z., Yumna, A., Gultom, S. E., Rakhmadi, M. F., Hidayat, M. F., & Suryahadi, A. (2019). Inequality and stability in democratic and decentralized Indonesia. In SMERU Working Paper (Vol. 33)
  69. Thalgi, M. J. (2024). Altruism and Social Cohesion: An Islamic Perspective of Charitable Acts for Sustainable Development. Journal of Islamic Thought and Civilization, 14(2), 42–62. https://doi.org/10.32350/jitc.142.04
  70. Wang, G. (2022). Laplace approximation for conditional autoregressive models for spatial data of diseases. MethodsX, 9. https://doi.org/10.1016/j.mex.2022.101872
  71. Zhao, Y., & Xu, D. (2023). A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models. Mathematics, 11(4). https://doi.org/10.3390/math11040987
  72. Zhou, Y., & Liu, Y. (2022). The geography of poverty: Review and research prospects. Journal of Rural Studies, 93. https://doi.org/10.1016/j.jrurstud.2019.01.008

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