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Submitted
Apr 1, 2026
Accepted
Jun 25, 2026
Published
Jun 29, 2026
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Abstract

Reliable statistics on crime victims are essential for monitoring public safety and supporting evidence-based policies. In line with Sustainable Development Goal (SDG) 16, which aims to promote peaceful and inclusive societies by reducing all forms of violence, district- and municipality-level estimates of crime victims are particularly important for regional development planning in the Papua region. However, direct survey estimates at this level often suffer from low precision due to limited sample sizes. Therefore, this study aimed to estimate the percentage of crime victims at the district/municipality level in Papua Island in 2024 using the Hierarchical Bayes Small Area Estimation (HB-SAE) approach based on the area-level Fay–Herriot model. The model incorporated three auxiliary variables derived from the 2024 Village Potential Statistics (PODES) published by Statistics Indonesia (BPS): the proportion of villages with base transceiver stations (BTS), the proportion of villages with markets, and the proportion of villages reporting theft incidents. The results showed that the HB-SAE approach produced significantly more precise estimates than direct estimation. The mean Relative Standard Error (RSE) decreased from 47.35% under direct estimation to 37.02% under the HB-SAE approach, representing a 21.82% improvement in estimation precision, and the Page test confirmed that the reduction in RSE was statistically significant (p = 0.01). Furthermore, the HB-SAE approach successfully generated estimates for all districts/municipalities, including four non-sampled areas. These findings indicate that the HB-SAE approach provides reliable small-area estimates to support crime-related policy formulation and regional development planning in Papua Island.

Keywords

crime victims SDGs small area estimation hierarchical bayes

Article Details

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How to Cite
Hakim, M. T., & Rahman, F. F. (2026). ESTIMATION OF THE PERCENTAGE OF CRIME VICTIMS IN PAPUA REGION IN 2024 USING HIERARCHICAL BAYES SMALL AREA ESTIMATION. Parameter: Journal of Statistics, 6(1), 22-31. https://doi.org/10.22487/27765660.2026.v6.i1.18044

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