Two-stage spatio-temporal clustering of tuberculosis incidence in West Java
DOI:
https://doi.org/10.58524/jgsa.v2i1.78Keywords:
Bivariate LISA , Join Count Statistics , Spatio-Temporal Analytics , Time Series Clustering , TuberculosisAbstract
Understanding the complex spatio-temporal dynamics of tuberculosis (TB) is critical for effective control in high-burden regions. This study introduces a two-stage analytical framework applied to annual TB data (2016–2024) in West Java. The first stage identifies temporal trends using hierarchical agglomerative clustering (HAC), while the second evaluates spatial structures using join count statistics and bivariate local Moran’s index (LISA). Results reveal four distinct typologies differing in shape rather than magnitude, capturing a divergence between pandemic-sensitive volatile regions and rigid stable regions. While global spatial autocorrelation indicates randomness ( ), bivariate LISA uncovers significant micro-spatial structures, including “High–High” hotspots in Tasikmalaya ( ) and Cianjur ( ). These findings highlight a duality, global independence alongside local interdependence, advocating for spatially explicit interventions that prioritize resources based on temporal resilience rather than uniform policies.
Downloads
References
Alqahtani, A., Ali, M., Xie, X., & Jones, M. W. (2021). Deep time-series clustering: a review. Electronics, 10(23), 3001. https://doi.org/10.3390/electronics10233001
Anselin, L. (1988). Spatial Econometrics: Methods and Models (Vol. 4). Springer Netherlands. https://doi.org/10.1007/978-94-015-7799-1
Anselin, L. (1995). Local indicators of spatial association—lisa. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Arisanti, R., Pontoh, R. S., Winarni, S., Putri, N. A., & Maurin, S. (2025). Identification and modelling tuberculosis incidence risk factors in west java with negative binomial mixed model random forest. CAUCHY: Jurnal Matematika Murni Dan Aplikasi, 10(2), 507–518. https://doi.org/10.18860/cauchy.v10i2.29750
Benevento, A., Durante, F., & Pappadà, R. (2024). Tail-dependence clustering of time series with spatial constraints. Environmental and Ecological Statistics, 31(3), 801–817. https://doi.org/10.1007/s10651-024-00626-6
Cliff, A. D., & Ord, J. K. (1981). Spatial processes: Models & Applications. Pion.
Javed, A., Lee, B. S., & Rizzo, D. M. (2020). A benchmark study on time series clustering. Machine Learning with Applications, 1.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data. Wiley. https://doi.org/10.1002/9780470316801
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441
Kwon, H., & Koylu, C. (2023). Revealing associations between spatial time series trends of covid-19 incidence and human mobility: an analysis of bidirectionality and spatiotemporal heterogeneity. International Journal of Health Geographics, 22(1), 33. https://doi.org/10.1186/s12942-023-00357-0
Lan, Y., & Delmelle, E. (2023). Space-time cluster detection techniques for infectious diseases: a systematic review. Spatial and Spatio-Temporal Epidemiology, 44, 100563. https://doi.org/10.1016/j.sste.2022.100563
Lestari, T., Kamaludin, Lowbridge, C., Kenangalem, E., Poespoprodjo, J. R., Graham, S. M., & Ralph, A. P. (2022). Impacts of tuberculosis services strengthening and the covid-19 pandemic on case detection and treatment outcomes in Mimika district, Papua, Indonesia: 2014–2021. PLOS Global Public Health, 2(9), e0001114. https://doi.org/10.1371/journal.pgph.0001114
McQuaid, C. F., Vassall, A., Cohen, T., Fiekert, K., COVID/TB Modelling Working Group *, & White, R. G. (2021). The impact of covid-19 on TB: a review of the data. The International Journal of Tuberculosis and Lung Disease, 25(6), 436–446. https://doi.org/10.5588/ijtld.21.0148
Naim, S., Hastono, S. P., Rahayu, S., & Wangi, M. P. (2022). A spatial analysis of dengue hemorrhagic fever (dhf), hygiene, and latrines in Depok City in 2020. Jurnal Kesehatan Lingkungan, 14(2), 122–129. https://doi.org/10.20473/jkl.v14i2.2022.122-129
Palacios Gutiérrez, A., Valencia Delfa, J. L., & Villeta López, M. (2023). Time series clustering using trend, seasonal and autoregressive components to identify maximum temperature patterns in the Iberian Peninsula. Environmental and Ecological Statistics, 30(3), 421–442. https://doi.org/10.1007/s10651-023-00572-9
Paparrizos, J., & Bogireddy, S. P. T. R. (2025). Time-series clustering: a comprehensive study of data mining, machine learning, and deep learning methods. Proceedings of the VLDB Endowment, 18(11), 4380–4395. https://doi.org/10.14778/3749646.3749700
Peng, H., Li, W., Jin, C., Yang, H., & Guan, J. (2023). MuSTC: a multi-stage spatio–temporal clustering method for uncovering the regionality of global SST. Atmosphere, 14(9), 1358. https://doi.org/10.3390/atmos14091358
Purnama, T. B., Kamigaki, T., Minsarnawati, M., & Oshitani, H. (2022). Spatial distribution of tuberculosis in the western region of Java island, Indonesia. Journal of Health Science and Medical Research, 41(1), 1–12. https://doi.org/10.31584/jhsmr.2022889
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Siljander, M., Uusitalo, R., Pellikka, P., Isosomppi, S., & Vapalahti, O. (2022). Spatiotemporal clustering patterns and sociodemographic determinants of covid-19 (SARS-CoV-2) infections in Helsinki, Finland. Spatial and Spatio-Temporal Epidemiology, 41, 100493. https://doi.org/10.1016/j.sste.2022.100493
Surendra, H., Elyazar, I. R. F., Puspaningrum, E., Darmawan, D., Pakasi, T. T., Lukitosari, E., Sulistyo, S., Deviernur, S. M., Fuady, A., Thwaites, G., van Crevel, R., Shankar, A. H., Baird, J. K., & Hamers, R. L. (2023). Impact of the covid-19 pandemic on tuberculosis control in Indonesia: a nationwide longitudinal analysis of programme data. The Lancet Global Health, 11(9), e1412–e1421. https://doi.org/10.1016/S2214-109X(23)00312-1
Wahono, T. (2024). Implementasi kebijakan district public private mix (dppm) dalam penanganan tuberculosis di kota Tasikmalaya pada masa pandemi covid-19. Inovasi, 21(1), 69–78. https://doi.org/10.33626/inovasi.v21i1.695
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
Warren Liao, T. (2005). Clustering of time series data — a survey. Pattern Recognition, 38(11), 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025
West Java Provincial Health Office. (2024). Number of Tuberculosis Cases by Regency/City in West Java. Open Data Jabar.
Wulandini, F., Hermawati, E., & Wulandari, M. (2025). Map of tuberculosis incidence in West Java province in 2023. Indonesian Journal of Global Health Research, 7(3), 519–528.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sarah Fadhlia, Eko Primadi Hendri, Ayu Septiani (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
