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Rahma Fitriani
Department Of Mathematics, University Of Brawijaya, Malang
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Journal : Science and Technology Indonesia

 1 
Block Bootstrap for Spatiotemporal Data in Generalized Space Time Autoregressive (GSTAR) Sumarminingsih, Eni; Fitriani, Rahma; Darmanto; Maulana, Eka Dani; Aulia, Natasha; Ruszardi, Luzar Dwain
Science and Technology Indonesia Vol. 11 No. 2 (2026): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.2.701-731

Abstract

Generalized Space-Time Autoregressive is a model that can be used for data with spatial and temporal dependence. The GSTAR model is widely used in various phenomena such as rainfall, temperature, inflation, and others. GSTAR assumes normality of errors and non-autocorrelation. If the assumption of normality of errors is not met, then inference on parameters cannot be made. One solution to this problem is to use bootstrapping. However, bootstrapping for spatiotemporal data in the GSTAR model has not been developed. Therefore, this study aims to develop a bootstrapping method for spatiotemporal data in the GSTAR model. This development is done by adapting bootstrapping methods for time series data, namely, the non-overlapping block bootstrap (NBB) and the moving block bootstrap (MBB). This research continued with a series of simulations to evaluate the performance of the block bootstrap method as the number of observations, block length, and number of bootstrap replications were varied. Furthermore, the method’s effectiveness was tested using rainfall data from Malang Regency. Simulation results show that both resampling schemes satisfy the asymptotic condition, where the bias decreases monotonically with increasing sample size (T) and block length. MBB consistently produces lower bias than NBB due to its more intensive use of overlapping data, which effectively reduces boundary effects. Although inference on autoregressive parameters can be accurate, inference on spatial autoregressive parameters yields less satisfactory results, indicating the limitations of time blocks in capturing complex spatial dependencies. Increasing the number of replications above B=100 does not significantly improve the precision of the variance estimate, indicating computational efficiency at that threshold. The t-test results confirm that there is no statistically significant difference in performance between NBB and MBB. Nevertheless, MBB is more recommended for practical applications due to its higher information density and better estimation stability.
Spatial Data Science for Regional Pattern Analysis: Dynamic Time Warping-Based Clustering of East Java’s Economic Indicators Fitriani, Rahma; Sumarminingsih, Eni; Diartho, Herman Cahyo
Science and Technology Indonesia Vol. 11 No. 2 (2026): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.2.569-578

Abstract

Motivated by the need to better capture dynamic regional disparities, this study examines spatial and temporal development patterns in East Java, Indonesia, using spatial panel data from 2020 to 2023. A data-driven framework is proposed that integrates Principal Component Analysis (PCA) for dimensionality reduction, Dynamic TimeWarping (DTW) for temporal similarity measurement, and spatially constrained clustering using the SKATER algorithm. PCA compresses multiple socio-economic indicators, GDP growth, GDP level, Human Development Index (HDI), and population density, into a unified development profile, enabling comparison of regional trajectories over time. DTW captures non-linear temporal alignment, while SKATER preserves spatial coherence in cluster formation. The resulting clusters are used to construct an endogenous spatial weight matrix that reflects functional regional relationships rather than purely geographic adjacency. Validation using Moran’s I indicates stronger spatial autocorrelation compared to conventional contiguity-based weights, suggesting improved representation of spatial interaction. Four clusters reveal distinct development patterns and uneven regional trajectories. By integrating dimensionality reduction with temporal alignment and spatial clustering, the proposed approach extends dynamic spatial weighting toward a functional interpretation of regional dependence and offers a transferable framework for spatial data science and regional policy analysis.
Co-Authors Abdila, Naufal Shela Agung Murti Nugroho Agus Dwi Sulistyono, Agus Dwi Akhmad Mansyur, Akhmad Al Jauhar, Hafizh Syihabuddin Alfi Fadliana Ani Budi Astuti Antonius Totok Priyadi Atiek Iriani, Atiek Atiek Iriany Aulia, Natasha Azizah, Amelia Nur Darmanto Darmanto Darmanto Dianiati, Aldila Nur Eddi Basuki Kurniawan Encep Supriatna Eni Sumarminingsih Fernandes, Adji Achmad Rinaldo Firdausi, Rizka Firsa, Pocut Zahran Nada Gusganda Suria Manda Handoyo, Samingun Hapsari, Ulfalina Henny Pramoedyo Herman Cahyo Diartho Hermanto, Tutut Istiqomah, Nur Jaka Pratama Musashi Jannah, Friendtika Miftaqul Jannah, Friendtika Miftaqul Korniasari, Leli Dwi Kusdarwati, Heni La Onu, La Ola Lestari, Kartika Ayu Liduina Asih Primandari, Liduina Asih Loekito Adi Soehono Luthfatul Amaliana, Luthfatul Maharani, Kasih Mahendra, Di Aidil Maulana, Eka Dani Menufandu, Dahlia Gladiola Rurina Mitakda, Maria Bernadetha Ni Wayan Surya Wardhani Nofriadi Nofriadi Nugroho, Salma Fitri Nur Aisyah Nurachmad Sujudwijono Pasca, Paunfia Meiditha pramoedyo, henny Pratama, Muhamad Liswansyah Pribadi, Teddy Ramadhan, Apry Zakaria Ramifidisoa, Lucius Risfandi Ruszardi, Luzar Dwain Ry, Mohd Dzaky Sari , Imelda Sarini Yusuf Septya Hadiningrum Sesilia Seli Sholihah, Suma Suci Solimun, Solimun Suci Astutik Sumarminingsih, Eni Sundyni, Reza Chyta Syukrilla, Wara Alfa Tampubolon, Risma Hartati Tri Pratiwi, Elly Andita Umami, Asri Rizza Vierkury Metyopandi Vivit Senja, Dinda Rinai Waego Hadi Nugroho Widya Reza Wigbertus Ngabu Wilandari , Angestika Yusrina Nur Dianati Zakaria Zamelina, Armando Jacquis Federal