Analisis Risiko Bencana pada kasus Curah Hujan Ekstrem Provinsi Sulawesi Barat dengan Metode Moran's I & Local Indicator Of Spatial Association - Reski Wahyu Yanti -; Meryta Febrilian Fatimah; Rahmah Abu Bakar (Program Studi Matematika Universitas Sulawesi Barat, 2023)
High rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by various disasters, it is important to look at the pattern of spatial distribution of areas that have a high risk of being affected by extreme rains. Information about distribution patterns of rainfall can assist policy makers in making decisions regarding extreme rain events in West Sulawesi which have the potential to cause various losses. Therefore, spatial analysis is carried out to detect connections between regions so that rainfall distribution patterns can be known. Based on this, this study aims to analyze the characteristics of the spatial distribution of rainfall in each sub-district of extreme rainfall on an annual scale in West Sulawesi. The results showed that there was a significant spatial autocorrelation. This showed that there was a correlation between the rainfall values between districts in West Sulawesi based on the spatial autocorrelation analysis with the Moran index test. Furthermore, the results of the Local Indicator Spatial Autocorrelation (LISA) analysis of rainfall data in each sub-district show that there are 10 sub-districts in 2019, 7 sub-districts in 2020, 13 sub-districts in 2021, and 7 sub-districts in 2022 which are identified as having spatial autocorrelation with a level significance 0.05. From all the results of the analysis carried out, namely the analysis of the moran index and LISA, it was obtained that the areas that have the greatest risk of high rainfall are the Kalukku, Tabang and Pana sub-districts. The research results obtained can be useful as information and evaluation material that is useful for preparing natural hazard mitigation programs and assisting the government in establishing policies based on information on areas that have a high risk of being affected by extreme rainfall.