Analisis Spasial Persebaran Jumlah Kasus Malaria di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Spatial of Autocorrelation
Abstract
Spatial analysis is an analysis that considers the location and distance of an object in the research data. Moran’s index is one of the spatial methods used to analyze spatial autocorrelation globally. Furthermore, there is the Local Indicator of Spatial Autocorrelation (LISA) method which is used to analyze spatial autocorrelation locally. This study aims to determine whether there is spatial autocorrelation and determine the distribution pattern formed in the data on the average number of malaria cases in East Kalimantan based on the regency/city during 2018-2022. The results showed that based on the Moran index globally, there was no spatial autocorrelation in the average number of malaria cases in East Kalimantan in 2018-2022. The type of spatial pattern in the distribution of malaria cases in East Kalimantan is a clustering pattern indicated by the clustering of malaria cases in each district/city in East Kalimantan. Furthermore, the results of spatial autocorrelation using LISA show that locally there is spatial autocorrelation in several districts/cities in East Kalimantan, namely Paser, Kutai Timur, Kutai Barat and Penajam Paser Utara.
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References
Basuki, B., S. (2022). Hingga Juni 2022 Kasus Malaria di Kaltim Capai 901 Kasus, Total 5 Pasien Dilaporkan Meninggal Dunia. https://diksi.co/hingga-juni-2022-kasus-malaria-di-kaltim-capai-901-kasus-total-5-pasien-dilaporkan-meninggal-dunia?page=2. Diakses pada 15 Februari 2023.
Hartoyo, G. M., Nugroho, Y., Bhirowo, A., & Khalil, B. (2010). Modul Pelatihan Sistem Informasi Geografis (SIG) Tingkat Dasar. Bogor: Tropenbos International Indonesia Programme.
Hayati, N.M., Purnamasari, I. & Wahyuningsih, S. (2019). Analysis of Spatial Autocorrelation of Diarrhe Events in East Kalimantan in 2015-2017 Using a Local Indicator of Spatial Autocorrelation. Journal of Physics : Conference Series. 1277 012045. doi:10.1088/1742-6596/1277/1/012045.
Hikmaulida, N.T., Hayati, N.M., & Wahyuningsih, S. (2020). Analisis Spasial Persebaran dan Pemetaan Kerawanan Daerah Titik Panas di Kalimantan Timur Menggunakan Spatial Pattern Analysis dan Flexibly Shaped Spatial Scan Statistic. Progressive Physics Journal. Vol. 1, No.1:20-28.
Kemenkes RI Direktorat Jenderal Pelayanan Kesehatan. Malaria. (2022). Jakarta: Kementerian Kesehatan RI;2022.
Larasati, M. N., Subiyanto S., & Sukmono, A. (2017). Analisis Penggunaan dan Pemanfaatan Tanah (P2T) Menggunakan Sistem Informasi Geografis Kecamatan Banyumanik Tahun 2016. Jurnal Geodesi Undip. Vol. 6, No. 4:89-97.
Lee, J. & Wong, D.W.S. (2001). Statistical Analysis with ArcviewGIS. United Stated of America: John Wiley & Sons, Inc.
Ludwig, J.A, and J.F. Reynolds, (1988), Statistical Ecology, John Wiley & Sons, Inc. Canada.
Qona’ah, N. & Sutikno. (2016). Analisis Pola Persebaran ISPA (Infeksi Slauran Pernafasan Akut) sebagai Dampak Industri Migas di Kabupaten Bojonegoro Menggunakan Spatial Pattern Analysis dan Flexibly Shaped Spatial Scan Statistic. Jurnal Sains dan Seni ITS. Vol. 5, No. 2:462-467.
Sugiarti, S. (2018). Karakteristik Tempat Perindukan Nyamuk Anopheles sp. yang Potensial Sebagai Vektor Malaria di Wilayah Kerja Puskesmas Hanura Kabupaten Pesawaran. Jurnal Kesehatan Universitas Lampung.
Sunarsih, E., Zulkarnain, M., Hanum, L., Flora, R. & Damiri, N. (2021). Spatial Pattern Analysis of Malaria Cases in Muara Enim Regency Using Moran Index and Local Indicator Spatial Autocorrelation. Open Access Macedonian Journal of Medical Science. Vol. 1, No. 3:695-701.
Xu, Y. & Eugene K. (2015). “An Introduction to Spatial Analysis in Social Science Research”. The Quantilative Methods of Psichology Journal. Vol. 11 No. 1:22-31. DOI: 10.20982/tqmp.11.1.p022.
