Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models (Case Study: Crime in South Sulawesi)

Indi Nur Ridwan; Sudarmin; Zakiyah  Mar'ah

doi:10.35580/variansiunm503

Indi Nur Ridwan Universitas Negeri Makassar
Sudarmin Universitas Negeri Makassar
Zakiyah Mar'ah Universitas Negeri Makassar

DOI: https://doi.org/10.35580/variansiunm503

Keywords: Criminality, Geographically Weighted Regression (GWR), Mixed Geographically Weighted Regression (MGWR).

Abstract

The Geographically Weighted Regression (GWR) model operates by taking into account how the relationships between different factors change across geographic space. Meanwhile, the Mixed Geographically Weighted Regression (MGWR) model permits certain variables to exhibit spatially varying (local) effects, while other variables are assumed to have constant effects across all locations. Both models are relevant to be applied in crime studies influenced by variations in regional conditions. The objective of this study is to evaluate the GWR and MGWR approaches in selecting the best model to explain factors associated with crime cases in South Sulawesi. The data used include the number of crime cases in South Sulawesi in 2024 along with factors presumed to influence them. The investigation's outcomes suggest the GWR model demonstrates higher appropriateness compared to the MGWR model, evidenced by its reduced Akaike Information Criterion (AIC) score and a 98.44% coefficient of determination . Based on the best-fitting model, population density and the number of poor residents were identified as the main factors influencing criminality in South Sulawesi in 2024.

References

Bakri, N. A., Annas, S., & Aidid, M. K. (2024). Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 6(1), 11–20. https://doi.org/10.35580/variansiunm194
BPS Provinsi Sulawesi Selatan. (2025). Provinsi Sulawesi Selatan dalam Angka 2025 (Vol. 12). Badan Pusat Statistik Provinsi Selatan Selatan.
Caraka, R. E., & Yasin, H. (2017). Geographically Weighted Regression (GWR): Sebuah Pendekatan Regresi Geografis (H. Caraka, Rezzy Eko & Yasin, Ed.; pertama). Mobius.
Daulay, S. H., & Simamora, E. (2023). Pemodelan Faktor-Faktor Penyebab Kemiskinan di Provinsi Sumatera Utara Menggunakan Metode Geographically Weighted Regression (GWR). Jurnal Riset Rumpun Matematika Dan Ilmu Pengetahuan Alam (JURRIMIPA), 2(1), 47–60.
Febrianti, E., Susetyo, B., & Silvianti, P. (2023). Pemodelan Tingkat Kriminalitas di Indonesia Menggunakan Analisis Geographically Weighted Panel Regression. Journal of Statictics, 12(1), 91–109.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression the analysis of spatially varying relationships.
Hakim, A. R., Yasin, H., & Suparti. (2014). Pemodelan Persentase Penduduk Miskin di Kabupaten dan Kota Di Jawa Tengah dengan Pendekatan Mixed Geographically Weighted Regression. Jurnal Gaussian, 3(4), 575–584. http://www.ejournal-s1.undip.ac.id/index.php/gaussian/article/view/8068%5Cnhttp://www.ejournal-s1.undip.ac.id/index.php/gaussian/article/download/8068/7848
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
Lutfiani, N., Sugiman, & Scolastika, M. (2019). Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-square. UNNES Journal of Mathematics, 8(1), 82–91. http://journal.unnes.ac.id/sju/index.php/ujmUJM8
Mar’ah, Z., Djuraidah, A., & Hamim Wigena, A. (2017). Semi-parametric Geographically Weighted Regression Modelling using Linear Model of Coregionalization. 178–186.
Marizal, M., & Mulyani, S. (2024). Analisis Jumlah Kriminal Di Indonesia dengan Geographically Weighted Regression. 10–19.
Maulani, A., Herrhyanto, N., & Suherman, M. (2016). Aplikasi Model Geographically Weighted Regression (GWR) Untuk Menentukan Faktor-Faktor Yang Anak Balita di Jawa Barat. 4(1), 46–63.
Monica, S., Susanta, A., & Yensy, N. A. (2020). Pengaruh Pendekatan Realistic Mathematics Education Terhadap Kemampuan Pemecahan Masalah Siswa. Jurnal Penelitian Pembelajaran Matematika Sekolah, 4(2), 220–228.
Nidyashofa, N., & Darsyah, M. Y. (2020). Pemilihan Model Regresi Spasial Pada Tingkat Pengangguran Terbuka di Provinsi Jawa Tengah. Jurnal Statistika, 8(1), 88–96.
Nurhuda, I., & Jaya, I. G. N. M. (2018). Pemodelan Kriminal di Jawa Timur dengan Metode Gographically Weighted Regression (GWR). Jurnal Matematika “MANTIK,” 4(2), 150–158.
Pratiwi, N., Suyitno, S., & Siringoringo, M. (2020). Penerapan Model Geographically Weighted Logistic Regression Pada Data Status Kesejahteraan Masyarakat di Kalimantan Tahun 2017. Jurnal Eksponensial, 11(1), 83–92. https://doi.org/10.30872/eksponensial.v11i1.648
Purhadi, & Yasin, H. (2012). Mixed geographically weighted regression model (case study: The percentage of poor households in Mojokerto 2008. European Journal of Scientific Research, 69(2), 188–196.
Simamora, P. A., & Ratnasari, V. (2014). Pemodelan Persentase Kriminalitas Dan Faktor- Faktor Yang Mempengaruhi Di Jawa Timur Dengan Pendekatan Geographically Weighted Regression (GWR). Jurnal Sains Dan Seni ITS, 3(1), 18–23.
Utami, P. F., Rusgiyono, A., & Ispriyanti, D. (2021). Pemodelan Semiparametric Geographically Weighted Regression Pada Kasus Pneumonia Balita Provinsi Jawa Tengah. Jurnal Gaussian, 10(2), 250–258. https://doi.org/10.14710/j.gauss.v10i2.30945
Widayaka, P. G., Mustafid, & Rahmawati, R. (2016). Pendekatan Mixed Geographically Weighted Regression Untuk Pemodelan Pertumbuhan Ekonomi Menurut Kabupaten/Kota di Jawa Tengah. Jurnal Gausian, 5(4), 727–736.