<![CDATA[This study aims to analyze the spatial patterns of the number of disaster events in Indonesia with the spatial data exploration approach (ESDA) and the Local Indicators of Spatial Association (LISA) to identify the existence of spatial autocorrelation and disaster prone area clusters. This research is an exploratory quantitative research using Exploratory Spatial Data Analysis (ESDA). The data used in this study are secondary data obtained from Indonesian Disaster Information Data (Dibi) on Flood Disaster Statistics in Indonesia in 2024. The variable used in this study was the number of flood events in Indonesia in 2024. Spatial analysis in this study reached the explore stage conducted by the Esda method. The ESDA method is an exploration data analysis (EDA) to detect the spatial properties of the data where for each variable value there is location data. This location data refers to the point or area referred to by variables. This esda method is also a visual and numerical method used for hypothesis testing and identifying spatial relationships and patterns through the use of spatial weight matrices. The results of the study show that the results of spatial data exploration analysis of flood disasters in Indonesia in 2024, found that the distribution of events is not random, but rather forms certain spatial patterns. This is supported by a significant global autocorrelation test results, both through the Morans I and Gearys C index. Morans I value is 0.182 (p-value = 0.020) and Gearys C of 0.798 (P-value = 0.033) shows the presence of positive spatial spatial autocor tall.]]>
