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All Journal Communications in Humanities and Social Sciences PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Paramitasari, Nurina
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Religion Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Granular Multidimensional Poverty Index Using Grid-Based Spatial Modeling: A Case Study of East Java, Indonesia Januardi, Ryan Willmanda; Paramitasari, Nurina
Communications in Humanities and Social Sciences Vol. 5 No. 2 (2025): CHSS
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia (KIPMI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/chss.5.2.2025.106

Abstract

Capturing multidimensional poverty through conventional poverty statistics is challenging in view of their limited spatial resolution and focus on monetary indicators. In Indonesia, poverty measurement remains largely expenditure-based, potentially obscuring localized deprivations in education, health, and living standards. The objective of this present study is to address this limitation by developing a granular spatial mapping framework for the Multidimensional Poverty Index (MPI) in East Java Province. Employing the Alkire–Foster approach and Susenas 2023 data, the provincial MPI is estimated at 0.0479, and MPI values are spatially predicted at a 3 × 3 km grid resolution by integrating geospatial indicators of infrastructure accessibility, education and healthcare facilities, nighttime light intensity, and population density. The spatial models demonstrate strong predictive performance (R2 ≈ 0.97; AUC ≈ 0.98), revealing pronounced fine-scale variation in multidimensional poverty and identifying deprivation clusters that are not observable in administrative-level statistics. Areas characterized by geographic isolation and limited-service accessibility consistently exhibit elevated predicted MPI values. The findings of this study highlight the significance of high-resolution multidimensional poverty mapping in facilitating the development of more spatially targeted and evidence-based poverty reduction policies at the local level.
Improving The Accuracy of Area Sampling Frame Estimators for Agricultural Surveys Using Unequal Clustered Segment Sampling: The Case of Indonesia Zikra, Hazanul; Buana, Widyo Pura; Bimarta, Yocco; Paramitasari, Nurina
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.477

Abstract

Accurate rice production data are vital for maintaining national food security and formulating effective agricultural policies. In Indonesia, the Area Sampling Frame (KSA) method has been widely implemented to estimate rice harvest areas using segments of 300 meters×300 meters represented by nine observation points. However, this approach faces limitations, particularly the risk of undercoverage bias when estimating areas across different rice growth stages, especially if the observation points fall outside the target rice-growing regions  as population area. To address this issue, the present study introduces the Unequal Clustered Segment Sampling method as an alternative to the traditional KSA approach. The Unequal Clustered Segment Sampling method improves estimation accuracy by refining the sampling frame and excluding non-target segments, spatial points located outside actual rice-growing regions. Through a design-based estimation framework, the proposed method accounts for unequal cluster sizes, allowing a more representative depiction of field conditions. The empirical results demonstrate that the Unequal Clustered Segment Sampling method significantly reduces bias and enhances the precision of rice area estimates compared to the conventional KSA. These findings suggest that incorporating unequal clustered segment sampling designs into KSA-based surveys can yield more reliable and representative estimates, particularly in heterogeneous or fragmented agricultural landscapes.
Co-Authors Bimarta, Yocco Buana, Widyo Pura Januardi, Ryan Willmanda Zikra, Hazanul