Comparison of Geographically Weighted Artificial Neural Network and Geographically Weighted Generalized Poisson Regression on Crime Cases in East Java Indonesia

Dewi Sinta Nur Fajarini, Yuliani Setia Dewi, Mohamat Fatekurohman

© 2024 Yuliani Setia Dewi, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International. (CC BY-NC 4.0).

Citation Information: SAR Journal. Volume 7, Issue 1, Pages 19-23, ISSN 2619-9955,, March 2024.

Received: 12 February 2024.
Revised:   14 March 2024.
Accepted: 20 March 2024.
Published: 27 March 2024.


Crime is behavior that violates laws and social norms. One area of Indonesia with a relatively high number of crimes is East Java. Data on the number of crimes in East Java in 2020 showed overdispersion and multicollinearity. This study aims to model the number of crimes reported in East Java by considering the spatial effects in the data. The methods used to analyze the data are GPR, GWANN and GWGPR. Moreover, we also determine the results of comparing the three methods using R2 and RMSE. The study results show that GWANN provides better results to model the number of reported crimes compared to GWGPR and GPR. The results show that the GWANN model results in eight groups using the three highest value of the variable importance.

Keywords – crime, GWANN, GWGPR, variable importance.


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