Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors
Keywords:
Outliers, Pneumonia, Regression, Robust Geographically and Temporally Weighted M-Estimator
Abstract
Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.
References
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Breusch, T. S., & Pagan, A. R. (1980). Lagrange Multiplier Test and to Model Applications Specification in Econometrics. The Review of Economic Studies, 47(1), 239–253. https://doi.org/10.2307/2297111
Conita, & Purwaningsih, T. (2018). Under-Five Mortality Rate Modeling using Geographically and Temporal Weighted Regression ( GTWR ) in Central Java. Ahmad Dahlan International Conference on Mathematics and Mathematics Education Universitas Ahmad Dahlan, February, 13–14. http://seminar.uad.ac.id/index.php/adintercomme/article/view/42
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis, Third Edition. John Wiley & Sons. https://doi.org/10.1002/9781118625590
Erda, G., Indahwati, & Djuraidah, A. (2019). Outlier Handling of Robust Geographically and Temporally Weighted Regression. Journal of Physics: Conference Series, 1175(1). https://doi.org/10.1088/1742-6596/1175/1/012041
Febrianti, L., Andriyana, Y., & Faidah, D. Y. (2023). Pemodelan dan Pemetaan Kejadian Pneumonia pada Balita di Kota Bandung menggunakan Metode Geographically and Temporally Weighted Regression ( GTWR ). E-Journal BIAStatistics \ Departemen Statistika FMIPA Universitas Padjajaran, 2, 277–295. http://biastatistics.statistics.unpad.ac.id/index.php/biastatistics/article/view/226/229
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons. https://doi.org/10.1353/geo.2003.0008
Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47(4), 431–452. https://doi.org/10.1111/gean.12071
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). Gwmodel: An R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models. GISRUK 2013, 3–5. https://doi.org/10.18637/jss.v063.i17
Gujarati, D. (2003). Basic Econometrics (S. Zain, Trans). Erlangga. https://doi.org/10.1126/science.1186874
Guo, Y., Tang, Q., Gong, D. Y., & Zhang, Z. (2017). Estimating Ground-level PM2.5 Concentrations in Beijing using a Satellite-based Geographically and Temporally Weighted Regression Model. Remote Sensing of Environment, 198, 140–149. https://doi.org/10.1016/j.rse.2017.06.001
Haryanto, S., Nur Aidi, M., & Djuraidah, A. (2019). Modelling of GRDP the Construction Sector in Java Island using Robust Geographically and Temporally Weighted Regression (RGTWR). International Journal of Scientific Research in Science, Engineering and Technology, 6(1), 165–174. https://doi.org/10.32628/ijsrset196141
Huang, B., Wu, B., & Barry, M. (2010). Geographically and Temporally Weighted Regression for Modeling Spatio-temporal Variation in House Prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469
Huber, P. J. (1964). Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics, 35(1), 73–101. https://doi.org/10.1214/aoms/1177703732
Islamiyah, N. I. (2020). Pemodelan Generalized Poisson Regression pada Faktor-Faktor yang mempengaruhi Kasus Pneumonia pada Balita di Provinsi Sulawesi Selatan Tahun 2018 [Universitas Islam Negeri Alauddin Makassar]. http://clik.dva.gov.au/rehabilitation-library/1-introduction-rehabilitation%0Ahttp://www.scirp.org/journal/doi.aspx?DOI=10.4236/as.2017.81005%0Ahttp://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/as.2012.34066%0Ahttp://dx.doi.org/10.1016/j.pbi.201
Kementerian Kesehatan Republik Indonesia. (2019). Profil Kesehatan Indonesia 2018. Kementerian Kesehatan Republik Indonesia. https://www.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html
Kementerian Kesehatan Republik Indonesia. (2020). Profil Kesehatan Indonesia 2019. Kementerian Kesehatan Republik Indonesia. https://www.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html
Laksana, S. E. A. (2018). Pemodelan Kebakaran Hutan di Provinsi Riau dengan Metode Geographically-Temporally Weighted Regression [Institut Teknologi Sepuluh Nopember]. https://repository.its.ac.id/56605/1/06211440000034-Undergraduate_Theses.pdf
Liu, J., Zhao, Y., Yang, Y., Xu, S., Zhang, F., Zhang, X., Shi, L., & Qiu, A. (2017). A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-temporal Variations from Global and Local Perspectives. Entropy, 19(2). https://doi.org/10.3390/e19020053
Ma, X., Zhang, J., Ding, C., & Wang, Y. (2018). A Geographically and Temporally Weighted Regression Model to Explore the Spatiotemporal Influence of Built Environment on Transit Ridership. Computers, Environment and Urban Systems, 70(December 2017), 113–124. https://doi.org/10.1016/j.compenvurbsys.2018.03.001
Nadya, M., Rahayu, W., & Santi, V. M. (2017). Analisis Geographically Weighted Regression (GWR) pada Kasus Pneumonia Balita di Provinsi Jawa Barat. Jurnal Statistika Dan Aplikasinya, 1(1), 23–32. https://doi.org/10.21009/jsa.01103
Olive, D. J. (2008). Applied Robust Regression. Southern Illinois University. http://parker.ad.siu.edu/Olive/run.pdf
Putra, Z. (2019). Model Regresi Terboboti Geografis Temporal Kekar untuk Tingkat Kriminalitas di Provinsi Jawa Tengah dan Provinsi Jawa Timur [Institut Pertanian Bogor (IPB)]. https://repository.ipb.ac.id/handle/123456789/101695
Renika, I., & Amin, C. (2021). Identifikasi Faktor Risiko Ekstrinsik Pneumonia pada Balita di Pulau Jawa Tahun 2018 Menggunakan Geographically Weighted Regression [Universitas Muhammadiyah Surakarta]. http://eprints.ums.ac.id/id/eprint/91411%0Ahttp://eprints.ums.ac.id/91411/1/Naskah Publikasi %28Irene Renika%29 OK FIX.pdf
Ristiani, D. A. (2021). Analisis Faktor-Faktor yang mempengaruhi Jumlah Balita yang Terkena Penyakit Pneumonia di Provinsi Jawa Barat dengan Regresi Terboboti Geografis. [Universitas Islam Negeri Syarif Hidayatullah Jakarta]. https://lib.ui.ac.id/file?file=digital/20289601-S-Dian Rahayu Pamungkas.pdf
Siswanto, Raupong, & Anisa. (2017). Estimasi Regresi Robust M Pada Faktorial Rancangan. Jurnal Matematika, Statistika Dan Komputasi, 13(2), 171–181. https://doi.org/10.20956/jmsk.v13i2.3505
UNICEF. (2019). Lembaga Kesehatan dan Anak Memeringatkan Satu Anak Meninggal akibat Pneumonia setiap 39 Detik. https://www.unicef.org/indonesia/id/press-releases/lembaga-kesehatan-dan-anak-memeringatkan-satu-anak-meninggal-akibat-pneumonia-setiap
Wu, C., Ren, F., Hu, W., & Du, Q. (2019). Multiscale Geographically and Temporally Weighted Regression: Exploring the Spatiotemporal Determinants of Housing Prices. International Journal of Geographical Information Science, 33(3), 489–511. https://doi.org/10.1080/13658816.2018.1545158
Wu, C., Ye, X., Ren, F., & Du, Q. (2018). Check-in Behaviour and Spatio-temporal Vibrancy: An Exploratory Analysis in Shenzhen, China. Cities, 77(January), 104–116. https://doi.org/10.1016/j.cities.2018.01.017
Yin, J., & Du, Z. (2016). Exploring Multi-scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach. ISPRS International Journal of Geo-Information, 5(10), 1–19. https://doi.org/10.3390/ijgi5100187
Zhang, H., & Mei, C. (2011). Local Least Absolute Deviation Estimation of Spatially Varying Coefficient Models: Robust Geographically Weighted Regression Approaches. International Journal of Geographical Information Science, 25(9), 1467–1489. https://doi.org/10.1080/13658816.2010.528420
Published
2023-05-16
How to Cite
[1]
M. Musdalifah, S. Siswanto, and N. Ilyas, “Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors”, Jurnal Varian, vol. 6, no. 2, pp. 105- 118, May 2023.
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