Geographic Weighted Regression Models for Contraceptive Use in West Sumatera
DOI:
https://doi.org/10.37134/ejsmt.vol12.sp.6.2025Keywords:
Contraceptive Use, Geographic Weighted Regression, Spatial HeterogeneityAbstract
Spatial heterogeneity in family planning is a critical issue that reflects significant variations in contraceptive use and related indicators across regions, highlighting the need for targeted interventions to enhance family planning services. The Geographically Weighted Regression (GWR) model, a spatial analytical approach, incorporates geographic weights at each observation site to estimate localized regression parameters. This study aims to identify the GWR model and determine the factors influencing the Contraceptive Prevalence Rate (CPR) in 19 districts and cities in West Sumatera Province in 2023. The Adaptive Kernel Bisquare Function is employed to assign spatial weighting, while Cross-Validation (CV) is used to optimize the bandwidth, ensuring robust and localized model estimation. Using secondary data from the West Sumatera Central Bureau of Statistics, the study reveals substantial spatial variability in the factors influencing CPR. Key determinants include the number of family planning clinics, the number of village family planning service posts, the proportion of poor households, and the proportion of women of reproductive age using contraceptives. The GWR model achieves a high coefficient of determination (R²) of 86.98%, indicating strong model performance in explaining CPR variation. These findings underscore the methodological importance of Adaptive Kernel Bisquare weighting and cross-validation in capturing spatial heterogeneity and provide actionable insights for localized family planning strategies.
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[1] Kumar, S., Biswas, S., Mandal, D., Roy, H. N., Chakraborty, S., Kabir, S. N., Banerjee, S., & Mondal, N. B. (2007). Chenopodium album seed extract: a potent sperm-immobilizing agent both in vitro and in vivo. Contraception, 75(1), 71–78. https://doi.org/10.1016/j.contraception.2006.07.015.
[2] Rahmi Dian Agustino. (2023, May). Strengthening Data to Reduce Maternal Deaths in Indonesia. United Nations Population Fund (UNFPA). https://indonesia.unfpa.org/en/news/strengthening-data-reduce-maternal-deaths-indonesia
[3] Sangar, S. (2018). Bringing a women’s perspective to family planning. Current Science, 115(4), 633–637. https://doi.org/10.18520/cs/v115/i4/633-637
[4] Djatmika, G. H., & Hendriyana, A. (2020). Penerapan Sistem Informasi Manajemen Keluarga Berencana Pada Badan Kependudukan Keluarga Berencana Daerah. Ilmu Ekonomi Manajemen Dan Akuntansi, 1(1), 82–93. https://doi.org/10.37012/ileka.v1i1.400
[5] Petruney, T., Wilson, L. C., Stanback, J., & Cates, W. (2014). Family planning and the post-2015 development agenda. Bulletin of the World Health Organization, 92(8), 359–360. https://doi.org/10.2471/BLT.14.142893
[6] Jaiswal, J., Naik, S., Rangari, R., & Sinha, A. (2021). Awareness and acceptance of various contraceptive methods among postpartum women in a tertiary care center. International Journal of Reproduction, Contraception, Obstetrics and Gynecology, 10(4), 1352. https://doi.org/10.18203/2320-1770.ijrcog20210996
[7] Kirana, K., & Idris, H. (2022). Determinants of Modern Contraceptive Use Among Married Women in Indonesia Urban. Jurnal Ilmu Kesehatan Masyarakat, 13(1), 85–96. https://doi.org/10.26553/jikm.2022.13.1.85-96.
[8] Idris, H. (2019). Factors Affecting the Use of Contraceptive in Indonesia: Analysis from the National Socioeconomic Survey (Susenas). Jurnal Kesehatan Masyarakat, 15(1), 117–123. https://doi.org/10.15294/kemas.v15i1.14098
[9] Widiawaty, M. A., Lam, K. C., Dede, M., & Asnawi, N. H. (2022). Spatial differentiation and determinants of COVID-19 in Indonesia. BMC Public Health, 22(1), 1–16. https://doi.org/10.1186/s12889-022-13316-4
[10] O’Sullivan, D. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (review). Geographical Analysis, 35(3), 272–275. https://doi.org/10.1353/geo.2003.0008
[11] Arifin, S., & Herdiani, E. T. (2023). Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression. Jurnal Matematika, Statistika Dan Komputasi, 19(2), 342–350. https://doi.org/10.20956/j.v19i2.23481
[12] Fortheringham, A. (2002). Geographically Weighted Regression : the Analysis of Spatially Varying Relationships. West Sussex : John Willey & Sons, May, 554–556.
[13] Yuan-f, T. (2014). Spatial-temporal Pattern Analysis and Spatial Disparity Research on Provincial Transition of Mechanisms in Stabilizing Low Fertility in China. Population Journal, Southwestern University of Finance and Economics.
[14] Kamata, K., Iwasawa, M., & Tanaka, K. (2010). Spatial Variations in Fertility ： Geographically Weighted Regression Analyses for Town-and-Village-level TFR in Japan. The Annual Meeting of Population Association of America April 15 – 17, 2010, Dallas, Texas.
[15] Chekol, Y. M. (2023). Geographic weighted regression analysis of hot spots of modern contraceptive utilization and its associated factors in Ethiopia. PLoS ONE, 18(11). https://doi.org/10.1371/journal.pone.0288710
[16] Ekong, A. H., & Olayiwola, O. M. (2020). A geographically weighted regression approach to examine the dynamics of fertility differentials across Africa. Statistical Journal of the IAOS, 36(S1), S87–S102. https://doi.org/10.3233/SJI-200717
[17] Diastina, A. R. N., Handajani, S. S., & Slamet, I. (2019). Analisis Model Geographically Weighted Regression (GWR) pada Kasus Jumlah Peserta KB Aktif di Provinsi Jawa Tengah. Prosiding Seminar Nasional Geotik, 364–373.
[18] Simatupang, P. D. K., & Satrianto, A. (2020). Faktor-Faktor yang Mempengaruhi Tenaga Kerja Wanita Menikah Menggunakan Alat Kontrasepsi di Sumatera Barat. Jurnal Kajian Ekonomi Dan Pembangunan, 2(1), 9. https://doi.org/10.24036/jkep.v2i1.8794
[19] Pujihastuty, R. (2017). Profile of Contraceptive Use: Disparity between Rural and Urban Areas. Jurnal Kependudukan Indonesia, 12(2), 105–118.
[20] Rati Sumanti, Henri Prianto Sinurat, & Ervina Yunita. (2022). Strategi Peningkatan Partisipasi Keluarga Berencana di Kabupaten Kepulauan Mentawai. Jurnal Administrasi Publik, 18(2), 283–300. https://doi.org/10.52316/jap.v18i2.122
[21] Kankanala, S., & Zinde-Walsh, V. (2024). Kernel-weighted specification testing under general distributions. Bernoulli, 30(3), 1921–1944. https://doi.org/10.3150/23-BEJ1658
[22] Leung, Y., Mei, C. L., & Zhang, W. X. (2000). Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32(1), 9–32. https://doi.org/10.1068/a3162
[23] Okezie, C. A., Ogbe, A. O., & Okezie, C. R. (2010). Socio-economic determinants of contraceptive use among rural women in Ikwuano Local Government Area of Abia State, Nigeria. International NGO Journal, 5(4), 74–77. http://www.academicjournals.org/INGOJ
[24] Mahmud, A., Ekoriano, M., Titisari, A. S., Wijayanti, U. T., Sitorus, M. A., & Rahmadhony, A. (2021). Determinants Of Modern Contraceptives Use In Indonesia : A Spatial Analysis. Sys Rev Pharm, 12(3), 769–777. http://www.sysrevpharm.org/fulltext/196-1616361202.pdf?1616361978
[25] Palamuleni, M. E. (2013). Socio-economic and demographic factors affecting contraceptive use in Malawi. African Journal of Reproductive Health, 17(3), 91–104.
[26] Yulia Anas. (2024, November). Masyarakat Miskin Ekstrem di Kabupaten Kepulauan Mentawai. Langgam. https://langgam.id/masyarakat-miskin-ekstrem-di-kabupaten-kepulauan-mentawai.
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