## 1 Introduction
In one study [68], global seismic data from 1962 to 2011 were analyzed using techniques such as mutual information and cluster analysis. The resulting visual maps offered an intuitive understanding of complex relationships within earthquake data that are often difficult to discern on traditional geographic maps [68]. In Indonesia, hexagonal tessellation was applied for geovisualizing earthquake data [70]. This method enhanced the clarity of epicenter density visualization, making it easier to identify patterns in the spatial distribution of earthquakes and their proximity to tectonic faults. Another study analyzed global wildfire trends using the EM-DAT database, revealing that climate change--marked by rising temperatures and prolonged droughts--has a direct correlation with the increasing frequency and severity of wildfires [71]. The study provided vital insights into how climate change is exacerbating these disasters, underscoring the pressing need for decisive global climate action. On the other side, one study revealed a notable rise in residential fires and fire emergency situations in Serbia between 2012 and 2022 [72]. According to the results, there were 38,279 residential fires during this period, resulting in 665 fatalities, 1747 injuries, and 2134 rescues. The annual figures for fires and deaths are as follows: 2012 (946 fires/7 deaths), 2013 (836/6), 2014 (887/8), 2015 (827/5), 2016 (872/10), 2017 (899/18), 2018 (842/14), 2019 (796/10), 2020 (842/23), and 2021 (828/21) [72]. In contrast, one study utilized data from the EM-DAT database to analyze the economic impacts of different types of natural disasters [71]. The findings revealed that economic losses vary depending on the type of disaster, with earthquakes and floods causing the most significant damage [71]. While one study uses data from the EM-DAT database to analyze economic impacts, the study in [73] compares disaster damage records from two major databases, EM-DAT and DesInventar, for 70 countries from 1995 to 2013. Focusing on droughts, floods, earthquakes, and storms, it uses descriptive statistics to evaluate differences in recorded events, matched events, and large-scale events between the two databases, highlighting significant discrepancies in damage estimates. The comparison reveals that DesInventar records more events than EM-DAT for all events. However,for hand-matched events, EM-DAT reports higher mean disaster damages and a wider statistical range compared to DesInventar [73]. There is strong evidence pointing to a sharp rise in economic damages from extreme natural events, especially in temperate regions, driven by their increasing severity [56]. Patterns of droughts and floods reveal alternating positive and negative correlations over time, with a notable lag effect where floods tend to follow droughts by about 4-5 years [74]. The number of people affected by natural disasters has surged, leading to more fatalities, injuries, and property damage [74]. People's information-seeking behavior during disasters shifts across different phases, underscoring the need for varied communication strategies before, during, and after such events [34]. To analyze the temporal and spatial distribution of disasters, researchers are increasingly turning to advanced methods like time series analysis, spatial statistics, and big data techniques [75; 76]. In addition to all these studies, it has been found that some authors examine the quality of these databases. The researchers [PERSON] et al. [77] analyzed the EM-DAT database, which records natural disasters from 1990 to 2020, and uncovered notable gaps, particularly in the data related to economic losses. They determined that the year, income classification, and type of disaster were significant predictors of these data gaps. To address these issues, they emphasized the importance of employing advanced statistical methods to minimize bias and enhance the reliability of the data [77].