Examining the Potential of Natural Disaster Monuments as Surrogate Indicators for Disaster Hazards in Japan

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It is vital to gain a deeper understanding of disaster-prevention measures to mitigate these effects. One aspect of this understanding is the construction of natural disaster monuments to educate future generations about lessons learned from past disasters. This study focuses on the location and distribution of these monuments and examines their potential as surrogate indicators of disaster hazards. To achieve this, we employ a two-pronged approach. The first approach is to analyze the relationship between the location of disaster monuments and disaster hazards. It involves plotting the locations of monuments from a database and investigating the relationship between the disaster covered by each monument and the current hazard using a hazard map portal site. The second approach is to assess the potential for disaster hazard mapping based on the disaster monuments. It involves creating Voronoi maps based on the location of disaster monuments and applying them to the entire national land area. It produced a disaster hazard map for Japan, including areas with no disaster monuments. The results provide aggregate information on the relationship between the location of disaster monuments and disaster hazards and the effectiveness of the Voronoi diagram-based disaster hazard maps. In many cases, the current hazard at the location of disaster monuments still exists, and 70% of the areas around tsunami-related monuments are still exposed to tsunami hazards. Additionally, this study suggests that Voronoi maps are promising for disasters and can accurately represent specific disaster hazard areas. Natural disaster monument Hazard map Japan GIS Voronoi diagram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Japan has diverse extreme topographic, geological, and climatic conditions. Approximately 70% of the country is covered by mountains and hills, and these rugged landscapes are responsible for the rapid flow of heavy rainfall from peaks to the sea, resulting in frequent flooding and landslides. Moreover, Japan is positioned in the seismically active Pacific Rim Volcanic Zone, which leads to a high frequency of earthquakes across the country's landmasses. Furthermore, the effects of global warming are expected to exacerbate the frequency and intensity of such disasters (MLIT 2020 ). In light of this, infrastructure development and the promotion of disaster prevention awareness among the general public must be prioritized. Governments and local authorities have implemented measures to enhance social infrastructure to minimize human suffering and economic loss resulting from disasters and protect the well-being of individuals and the continuity of economic and social activities. Despite these initiatives, the devastating earthquake that struck the Tohoku and Kanto regions in 2011, the torrential rains that caused widespread damage in western Japan in 2018, and the earthquake that occurred on the Noto Peninsula in 2024 resulted in significant loss of life. It is vital to enhance public understanding of disaster prevention measures and strengthen the social infrastructure to mitigate the effects of natural disasters. Cognitive factors, including knowledge and awareness, and environmental factors, such as social context and elapsed time, are believed to impact an individual's disaster prevention behavior (Daimon et al. 2023 ). One way to achieve this is by preserving natural disaster monuments, which are permanent stone markers or remnants left behind by our ancestors, to convey the lessons learned from past natural calamities to future generations. These monuments typically include information such as the date of occurrence, type, and extent of the disaster, as well as details regarding the nature and scale of the damage caused by previous natural disasters (Toshigawa et al. 2020 ). The Geospatial Information Authority of Japan (GSI) has incorporated natural disaster monuments as map symbols on its topographic maps from 2019 onwards (Fujii et al. 2021 ). This change is anticipated to increase the public's visibility and recognition of monuments. How can we effectively learn from previous disasters and prepare for future disasters? Is it possible to create a secure existence without them? Examining the connection between disaster monuments and current disaster risks in Japan could offer insights into answering these questions. This study focuses on the location and distribution of monuments with natural disaster monuments. It examines the possibility of using them as alternative indicators of disaster hazard by clarifying the relationship between the location and distribution of monuments with disaster monument and disaster hazard. Literature Review The susceptibility of social groups to disaster risk is often due to their limited ability to adapt to the environmental conditions they encounter (Cardona 2013 ). Ancestral knowledge, a cultural practice or approach derived from a deep understanding of a particular environment inhabited for generations, plays a vital role in disaster risk reduction (Baumwoll 2008 ). Cultural values such as language, stories, the construction process of disaster prevention measures, and natural barriers such as those created by mangroves are among how this knowledge is transmitted (Mikulecký et al. 2023 ). In Japan, stone monuments are essential in disaster lores, with many monuments erected along the country's coast to commemorate past tsunamis (Mingren 2018 ). These monuments not only serve as historical records and memorials but also contribute to the understanding of disasters through visits by local communities (Garnier et al. 2022, Suppasri et al. 2013 , Boret et al. 2018, Saito et al. 2020). Since the Great East Japan Earthquake in 2011, the usefulness of stone monuments has received considerable attention. For example, a monument erected after the 1933 Showa Sanriku Tsunami warned residents not to build houses below a certain point, and the village that followed this advice was not damaged by the 2011 tsunami (Fackler, 2011 , Iuchi et al. 2016). Additionally, stone monuments were found to play a role in land use and evacuation. A survey conducted after the Great East Japan Earthquake showed that recognizing stone monuments influenced evacuation behavior following tsunami warnings (Sato et al. 2017 ). The location of lore monuments can contribute to disaster prevention education and provide a deeper understanding of past disasters in that area. A study in Hiroshima Prefecture, Japan, found that approximately 70% of flood-related monuments were in prominent locations within the disaster zone and along the river that caused the disaster (Oyama et al. 2017 ). The study also revealed that the current assumed flood inundation depths exceeded those indicated by monuments (Okatani 2022 ). Information from the monuments provides a quantitative indication of the disaster hazard at the site. This study aims to evaluate the connection between the erection status of disaster-related monuments and current disaster hazards. It is crucial to learn from past disasters and to prepare for future disasters by relying on traditional monuments. Past natural disaster experiences will influence future disaster preparedness and whether a similar level of catastrophe is anticipated. However, Japan has experienced more severe and frequent disasters, and there are many areas with latent significant disaster risks, including those that have not experienced disasters before. Furthermore, since Japan experienced rapid suburban development between 1960 and 2000, it is vital to rethink possible disaster risks with systematic simulation aside from past disaster experiences. This study aimed to answer how much we can learn from past disasters and prepare for the future. Methodology Data The foundation of this study consists of data regarding the sites of natural disaster monuments and national disaster hazard information. The data on natural disaster monuments is derived from the Geospatial Information Authority of Japan (GSI) database (GSI 2024 ). GSI regularly updates this database, and the number of monuments included is growing continuously. This study utilizes the database as of November 30, 2023. The database comprises each monument's name, year of construction, location, disaster name, type of disaster, details of tradition, and location information, such as latitude and longitude. Figure 1 illustrates the distribution of disasters commemorated by each type of monument. Disaster lore in Japan varies from floods, landslides, earthquakes, tsunamis, and storm surges (Matsuo et al. 2010 ), and this is also true for disaster monuments. Floods (n = 848) were the most prevalent of the four types of disaster, followed by tsunamis (n = 512). The distribution of each monument type reveals that flood monuments are frequently situated at the heart of large cities, such as Tokyo and Osaka. In contrast, tsunami monuments are often erected in rural regions, such as Tohoku, the Kii Peninsula, and the coastal areas of Shikoku Island. The number of storm surge monuments was relatively small (n = 127), primarily located in the Tokai region. Regarding landslide disasters (n = 512), most monuments commemorating these events are erected in areas with many slopes, such as Chubu, Hyogo, and Hiroshima. Figure 2 provides information on the construction dates of the monuments categorized by the type of disaster. Notably, a significant portion of these monuments, amounting to more than half, was constructed prior to 1969, which was over half a century ago. Among the most recently erected monuments commemorating tsunamis built during the 2010s are the most numerous, comprising 20% of the total. Most of these tsunami-related monuments were dedicated to the Great East Japan Earthquake and primarily located in the Tohoku region. The Ministry of Land, Infrastructure, Transport, and Tourism has established a hazard map portal site as the primary source of nationwide disaster hazard information (MLIT 2024 ). Although the National Land Information download site, accessible as GIS data (National Land Numerical Information 2024), is generally user-friendly for analyzing location data, it has some limitations. Specifically, the data format varies among prefectures, and some need to disclose their data, making it unsuitable for conducting nationwide disaster hazard analysis. This study obtains data in a uniform national format from the Hazard Map portal site. The website can display the hazard level for a specific location based on its coordinates. However, the maintenance status assumed risk and scale legend of the disaster vary across different municipalities. It is important to note that while the data is available in a uniform format throughout Japan, the assumed hazard and its scope differ from municipality to municipality. Therefore, the data obtained from the hazard map portal site were organized as shown in Table 1 and Table 2 and analyzed in this study. Table 1 Conversion process Table 2 Conversion process (continue) Method This study followed a two-pronged approach to the analysis, as shown in Fig. 3 . The first analysis is titled "Analysis on the Relationship between Monument Location and Disaster Hazard." The first step in this analysis involved classifying the 2,049 disaster monuments extracted from the database into four categories based on the types of disasters noted: flood, tsunami, storm surge, and sediment. Note that disaster monuments like floods and sediments may cover multiple disasters. In such cases, they are treated as separate disaster monuments. The second step involved plotting the disaster monuments' location and then matching the disaster hazard to the monument's location using a hazard map portal site. The third step was to examine the relationship between the disaster monuments' targeted disasters and the current hazard. For example, the current degree of flood hazard at the location of flood-related disaster monuments was examined. The second analysis assesses the possibility of using a Voronoi map consisting of the locations of disaster monuments as a disaster hazard map. Unlike the first analysis, which concentrated on specific areas where disaster monuments existed, this analysis covered the entire land area of Japan, including regions where no disaster monuments were present. First, mesh data with a 1 km resolution were obtained from the National Land Numerical Information website (National Land Information Division 2024), and point data for the center of gravity of each mesh were generated. This dataset comprises 178,347 meshes with a population greater than zero in 2015. Subsequently, the latitude and longitude coordinates of the point data for the center of gravity of each mesh were obtained using GIS functionality. Access to the hazard map portal site was then used to extract the disaster hazard corresponding to the center of gravity point. In other words, 178,347 accesses to the portal site were required for this extraction. Maps were created for each disaster hazard, resulting in the current national disaster hazard map. In parallel, Voronoi maps were generated for each disaster based on the location information of the disaster lore monuments. A Voronoi map divides multiple points positioned arbitrarily at a specific distance into regions based on the proximity of other points at the same distance to the points. This map is commonly used in facility layout planning. In this study, a Voronoi map was created using GIS. Finally, the Voronoi map was compared with the disaster hazard map to determine whether the distribution of disaster lore monuments could serve as a risk map to replace the disaster hazard map. Results and Discussion Analysis on the Relationship between Location of Monuments and Disaster Hazard Tables 3 through 6 show the aggregate results for the current disaster hazard for each heritage monument. These tables indicate that over half of monument types are still at risk. Notably, 70% of the locations around tsunami-related monuments continue to face tsunami hazards. It may be because of the challenges of implementing hardware measures to mitigate hazards. For example, the percentage of flood-related monuments built before the 1990s, which are expected to be inundated by more than 2 m (14 ~ 19%), is lower than those built after the 2000s (22 ~ 50%). However, this trend was not observed for tsunami-related monuments. It reveals that, although flood control projects can help reduce the damage caused by floods, constructing tsunami breakwaters to prevent tsunamis remains difficult. To examine the differences in disaster hazards according to location, we analyzed the urbanization status of disaster monuments. The data for urban conditions were systematically arranged by cross-referencing the geographic coordinates of disaster monument sites with vector data for urbanized zones, as defined by Japan's City Planning Law (2018). The information was obtained from the National Land Information Download Service (National Land Information Division 2024). The results indicate that a more significant proportion of flood and storm surge monuments are situated in areas currently at risk of disaster when they are located in urbanized areas. It suggests that urbanization exacerbates disaster hazards. The sediment-related monuments' years of construction and location do not differ. It suggests that the unique characteristics of Japanese topography make it challenging to mitigate hazards effectively through landslide countermeasures. Table 3 Disaster hazard at the location of flood disaster monuments Table 4 Disaster hazard at the location of tsunami disaster monuments Table 5 Disaster hazard at the location of storm-surge disaster monuments Table 6 Disaster hazard at the location of sediment disaster monuments Potential Use of Voronoi Diagrams as Disaster Hazard Maps Based on the Location of Disaster Monuments The Voronoi diagrams in Fig. 4 depict the spatial distribution of the monuments for each disaster. These diagrams reveal that smaller areas exist in regions with higher monument concentrations. Nevertheless, peninsulas and islands exhibit smaller areas, regardless of their traditional monuments, a characteristic inherent to Voronoi diagrams. When evaluating these areas, it is essential to determine whether there are associated disaster lores. Figure 5 illustrates the current disaster hazard, measured in units of a 1 km mesh, and categorizes the hazard levels based on the severity of the hazard. The legends in the figures correspond to Tables 1 and 2 . There are three levels for flood, tsunami, and storm surge, and meshes without colors are outside the area or have no data. There are two colors for sediment: high-hazard areas are purple, and low-hazard areas are light blue. The distribution of flood hazards is widespread across Japan, with particularly high-hazard areas in the Tokyo and Tokai metropolitan areas. As expected, tsunami hazards were limited to the coastal regions. The concentration of hazards is noteworthy in the Kanto and Tohoku regions and in the cities of Nagoya, Osaka, Tokushima, and Kochi, which were affected by the Great East Japan Earthquake. Storm surge hazards are focused on several key locations, including Tokyo, Nagoya, Osaka, Saga, and Kumamoto. On the other hand, landslide hazards are less prevalent and not present in Hokkaido or the Tokyo metropolitan area. Figure 6 illustrates the correlation between the size of the Voronoi diagram area and the disaster hazard. The vertical axis of the figure represents the percentage of hazard to the number of 1 km meshes corresponding to each area within the Voronoi diagram. The hazard count was 1 for minor hazards, 2 for moderate hazards, and 3 for significant hazards for floods, tsunamis, and storm surges. The number of sediment disasters is 1 for minor and 2 for significant hazards. The data presented herein pertain only to areas that are not exceedingly small, possess at least one 1 km mesh, and do not have a disaster hazard of zero, which is often the case in island regions. Figure 7 shows an example of this calculation for floods, tsunamis, and storm surges. A Voronoi diagram comprises four 1 km mesh areas: one mesh had more than 2m of inundation, one had more than 1m and less than 2m of inundation, one had less than 1m of inundation, and one had outside hazards. Because the diagram consists of four meshes and the total disaster hazard is six (equivalent to 3 + 2 + 1), the hazard ratio for the Voronoi diagram was calculated as 1.5 (=(3 + 2 + 1)/4). However, in the case of sediment hazards, the maximum value of the ratio of disaster hazard is 2, given the existence of two distinct hazard levels. Note that the sample sizes for each hazard category differed from those in the previous analyses, as the count was based on the number of Voronoi maps and not the number of disaster monuments. The relationship between the area and the hazard ratio depicted in Fig. 6 demonstrates that the disaster hazard ratio tends to decrease as the area increases for all disasters. The regression model in Tables 7 and 8 illustrates this relationship. In each model, the negative and statistically significant relationship between the size of the area and the hazard ratio indicates that the Voronoi diagram based on the disaster tradition monument partially accurately represents the current disaster hazard. In other words, the areas with small areas in the Voronoi diagram are where the disaster monuments are concentrated, which means that the current disaster hazard is high. Figure 8 shows the results expanding the regions where the number of samples exceeds a certain level, and the accuracy is relatively high. Floods are on the upper left of the figure, and it shows that the Voronoi area is small in the coastal areas of the Hokuriku region, where the current flood hazard is severe. The relationship between the two is solid around the Shogawa River in Takaoka City, Toyama Prefecture (the circle in the figure). The tsunami is on the Tohoku region's Pacific side in the figure's upper right. The tsunami hazard is high on the Pacific side of the Tohoku region in the upper right of the figure, which was greatly affected by the Great East Japan Earthquake, and the accumulation of monuments and high tsunami hazards are seen along the Sanriku coast. In the case of storm surges, the relationship between the two is particularly evident in the Tokai region. The area circled by the circle was severely damaged by Typhoon Isewan in 1959, and the storm surge hazard remains. In the case of sediment, the Kinki region has a deep relationship. The hazards are widely distributed inland. The areas with tiny Voronoi areas and serious landslide hazards are the Rokko, Kizu River, and Kiiyama. Table 7 Result of linear regression model Table 8 Result of linear regression model (continue) Discussion Analysis of the relationship between the monument's location and the disaster hazard revealed that the monument's location does not necessarily correspond with the current hazard map. Specifically, 50% of the monument locations for flood and sediment hazards were outside the hazard area. Recognizing that being designated a hazardous area does not necessarily guarantee safety is essential. Although hazard maps raise residents' awareness of disaster prevention, they have certain limitations (Saiga et al. 2011 ). For example, in the 2019 typhoon that hit eastern Japan, victims were found in areas not designated as landslide hazards. Even accurate information is not trusted if it goes against people's desire for safety, and even inaccurate information is believed if it is embedded in the local body of knowledge (Isoda et al. 2019 ). The reassurance that residents would trust the measures to protect them, even though they were aware of the warnings, is misplaced (Van Luik et al. 2013 ). Assessing hazards through lessons learned from past disasters, such as disaster monuments, aside from surveys and simulation s , is crucial. The relationship between the concentration of disaster monuments and the severity of disaster hazards can be observed from a macro perspective, as indicated by the analysis of Voronoi maps and actual disaster hazards based on disaster legacy monuments. Preparing for future disasters using traditional monuments has the potential to reduce the damage caused by disasters with a high probability of occurrence. Monuments in the Tohoku region, many of which were erected after the Great East Japan Earthquake of 2011, are still fresh in our minds. However, in areas likely to be affected by the Nankai Trough Earthquake (MLIT 2020 ), which has a 70–80% probability of occurring within the next 30 years with a magnitude of 8–9 on the Richter scale, there are differences between areas with and without traditional monuments. For instance, disaster monuments are concentrated in Tokushima, Kochi, and Nagasaki prefectures but rarely in Aichi Prefecture. In such areas, disaster preparedness that relies on more than just the traditions of past disasters is essential. Conclusion Visualization techniques in disseminating disaster information have been demonstrated to enhance citizens’ awareness of disasters (Andrienko et al. 2014 ). Furthermore, advancements in Geographic Information Systems have facilitated the comprehension of intricate disaster problems (Cariolet et al. 2019 ). A project has begun to map past disaster cases (Suzuki 2019 ). In 2019, the Geospatial Information Authority of Japan (GSI) began including disaster lore monuments on topographic maps (Fujii et al. 2021 ). This development enables citizens to access the most recent disaster analysis findings and historical records of disasters through a single platform. This study endeavored to quantify ancient disaster monuments' potential, which commemorate past catastrophes' lessons, to serve as contemporary disaster hazard maps. Utilizing the disaster-related monument database made available by the Geospatial Information Authority of Japan (GSI), we analyzed to elucidate the present disaster hazard at the location where each monument was erected while also examining the relationship between the concentration of monuments and the current distribution of disaster hazards. The findings of this analysis offer valuable insights into how to prepare for future disaster hazards effectively. Depending on the presence or absence of traditional monuments in areas with similar hazards, different approaches can be employed in disaster education. If a monument exists but there is no current hazard due to preventive measures, it may be beneficial to convey the limitations of hazard maps. However, it is essential to acknowledge the limitations of the present study. The analysis is based solely on the monument's location, without considering the distance between the monument and the disaster site. This aspect should be addressed in greater detail for a more accurate analysis. Declarations Funding JSPS KAKENHI Grant Number 22K04580 supported this work. Compliance with ethical standards Conflict of interest The author declares that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. References Andrienko G, Fabrikant SI, Griffin AL, Dykes J, Schiewe J (2014) Geoviz: Interactive maps that help people think. Int J Geogr Inf Sci 28(10):2009–2012. https://doi.org/10.1080/13658816.2014.937719 Baumwoll J (2008) The value of indigenous knowledge for disaster risk reduction: A unique assessment tool for reducing community vulnerability to natural disasters. Webster University Boret SP, Shibayama A (2018) The roles of monuments for the dead during the aftermath of the Great East Japan Earthquake. 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Sakamoto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxgYGZoYPBxgYDJgRIoS1MM4gSQsQMDPzgLQQ7TDm/sOPjW3O2MibszOwSTDU2DEwzyZgDeOMNOPknBtphjubQVqOJTMwzjlASAuD8eGcD4cTDA7zf5NgYDsAFEkgoKX/+OfDFmAtIFv+EaOlIcc4meEGVAtjGzFaZuQUG/acSTPccJiB2SKxL5mHoF8M+49vlvhxzEbe4PwBxhsfvtnJGRIKMUMUeaCTeAxn4NfBII8pIkFAyygYBaNgFIw4AAA3e0BozYl3XgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1765-7661","institution":"Kochi Daigaku","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Sakamoto","suffix":""}],"badges":[],"createdAt":"2024-02-04 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2","display":"","copyAsset":false,"role":"figure","size":4574,"visible":true,"origin":"","legend":"\u003cp\u003eYear of erection of disaster monuments\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/98d083f5d4486099d9a534ee.png"},{"id":51512763,"identity":"7fed6efa-3e0c-451a-a4db-5b2b7f093b8f","added_by":"auto","created_at":"2024-02-22 21:19:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77241,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis procedure\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/a3a0b65355704ec14a7b5c4b.png"},{"id":51512766,"identity":"c8bd1601-7fb9-48c6-97db-352c3acbfbdf","added_by":"auto","created_at":"2024-02-22 21:19:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321729,"visible":true,"origin":"","legend":"\u003cp\u003eVoronoi diagram of disaster monuments\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/bb7fcd7faf66d680286ec79f.png"},{"id":51512768,"identity":"82d93307-8477-48e9-9774-4340b445fcc1","added_by":"auto","created_at":"2024-02-22 21:19:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197157,"visible":true,"origin":"","legend":"\u003cp\u003eDisaster hazard per 1 km mesh\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/e943d9d6db175693180b15ab.png"},{"id":51512770,"identity":"7734ccd3-f2b9-4617-8137-28936be346a3","added_by":"auto","created_at":"2024-02-22 21:19:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25912,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Voronoi diagram area and disaster hazard\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/de5af0e05e6e5336008b6638.png"},{"id":51513435,"identity":"9a04f5da-3925-48aa-b4a6-5531ed745944","added_by":"auto","created_at":"2024-02-22 21:27:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7251,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the calculation\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/b030b3437442b7c887bd1064.png"},{"id":51512769,"identity":"1e031248-2055-447e-9b21-c276718cce16","added_by":"auto","created_at":"2024-02-22 21:19:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":459457,"visible":true,"origin":"","legend":"\u003cp\u003eProvinces with a strong relationship between the Voronoi and disaster hazard\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/f0457d72cd69769b36986a5a.png"},{"id":59438848,"identity":"9373bec7-8025-48c4-9bd0-323766a2ebec","added_by":"auto","created_at":"2024-07-01 20:12:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1722716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929158/v1/01b5c2ea-9f81-4589-8617-65234d6bb530.pdf"}],"financialInterests":"","formattedTitle":"Examining the Potential of Natural Disaster Monuments as Surrogate Indicators for Disaster Hazards in Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eJapan has diverse extreme topographic, geological, and climatic conditions. Approximately 70% of the country is covered by mountains and hills, and these rugged landscapes are responsible for the rapid flow of heavy rainfall from peaks to the sea, resulting in frequent flooding and landslides. Moreover, Japan is positioned in the seismically active Pacific Rim Volcanic Zone, which leads to a high frequency of earthquakes across the country's landmasses. Furthermore, the effects of global warming are expected to exacerbate the frequency and intensity of such disasters (MLIT \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In light of this, infrastructure development and the promotion of disaster prevention awareness among the general public must be prioritized.\u003c/p\u003e \u003cp\u003eGovernments and local authorities have implemented measures to enhance social infrastructure to minimize human suffering and economic loss resulting from disasters and protect the well-being of individuals and the continuity of economic and social activities. Despite these initiatives, the devastating earthquake that struck the Tohoku and Kanto regions in 2011, the torrential rains that caused widespread damage in western Japan in 2018, and the earthquake that occurred on the Noto Peninsula in 2024 resulted in significant loss of life.\u003c/p\u003e \u003cp\u003eIt is vital to enhance public understanding of disaster prevention measures and strengthen the social infrastructure to mitigate the effects of natural disasters. Cognitive factors, including knowledge and awareness, and environmental factors, such as social context and elapsed time, are believed to impact an individual's disaster prevention behavior (Daimon et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One way to achieve this is by preserving natural disaster monuments, which are permanent stone markers or remnants left behind by our ancestors, to convey the lessons learned from past natural calamities to future generations. These monuments typically include information such as the date of occurrence, type, and extent of the disaster, as well as details regarding the nature and scale of the damage caused by previous natural disasters (Toshigawa et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Geospatial Information Authority of Japan (GSI) has incorporated natural disaster monuments as map symbols on its topographic maps from 2019 onwards (Fujii et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This change is anticipated to increase the public's visibility and recognition of monuments.\u003c/p\u003e \u003cp\u003eHow can we effectively learn from previous disasters and prepare for future disasters? Is it possible to create a secure existence without them? Examining the connection between disaster monuments and current disaster risks in Japan could offer insights into answering these questions. This study focuses on the location and distribution of monuments with natural disaster monuments. It examines the possibility of using them as alternative indicators of disaster hazard by clarifying the relationship between the location and distribution of monuments with disaster monument and disaster hazard.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe susceptibility of social groups to disaster risk is often due to their limited ability to adapt to the environmental conditions they encounter (Cardona \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Ancestral knowledge, a cultural practice or approach derived from a deep understanding of a particular environment inhabited for generations, plays a vital role in disaster risk reduction (Baumwoll \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Cultural values such as language, stories, the construction process of disaster prevention measures, and natural barriers such as those created by mangroves are among how this knowledge is transmitted (Mikuleck\u0026yacute; et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Japan, stone monuments are essential in disaster lores, with many monuments erected along the country's coast to commemorate past tsunamis (Mingren \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These monuments not only serve as historical records and memorials but also contribute to the understanding of disasters through visits by local communities (Garnier et al. 2022, Suppasri et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Boret et al. 2018, Saito et al. 2020). Since the Great East Japan Earthquake in 2011, the usefulness of stone monuments has received considerable attention. For example, a monument erected after the 1933 Showa Sanriku Tsunami warned residents not to build houses below a certain point, and the village that followed this advice was not damaged by the 2011 tsunami (Fackler, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Iuchi et al. 2016). Additionally, stone monuments were found to play a role in land use and evacuation. A survey conducted after the Great East Japan Earthquake showed that recognizing stone monuments influenced evacuation behavior following tsunami warnings (Sato et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe location of lore monuments can contribute to disaster prevention education and provide a deeper understanding of past disasters in that area. A study in Hiroshima Prefecture, Japan, found that approximately 70% of flood-related monuments were in prominent locations within the disaster zone and along the river that caused the disaster (Oyama et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The study also revealed that the current assumed flood inundation depths exceeded those indicated by monuments (Okatani \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Information from the monuments provides a quantitative indication of the disaster hazard at the site.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the connection between the erection status of disaster-related monuments and current disaster hazards. It is crucial to learn from past disasters and to prepare for future disasters by relying on traditional monuments. Past natural disaster experiences will influence future disaster preparedness and whether a similar level of catastrophe is anticipated. However, Japan has experienced more severe and frequent disasters, and there are many areas with latent significant disaster risks, including those that have not experienced disasters before. Furthermore, since Japan experienced rapid suburban development between 1960 and 2000, it is vital to rethink possible disaster risks with systematic simulation aside from past disaster experiences. This study aimed to answer how much we can learn from past disasters and prepare for the future.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData\u003c/h2\u003e\n \u003cp\u003eThe foundation of this study consists of data regarding the sites of natural disaster monuments and national disaster hazard information. The data on natural disaster monuments is derived from the Geospatial Information Authority of Japan (GSI) database (GSI \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). GSI regularly updates this database, and the number of monuments included is growing continuously. This study utilizes the database as of November 30, 2023. The database comprises each monument\u0026apos;s name, year of construction, location, disaster name, type of disaster, details of tradition, and location information, such as latitude and longitude.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the distribution of disasters commemorated by each type of monument. Disaster lore in Japan varies from floods, landslides, earthquakes, tsunamis, and storm surges (Matsuo et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), and this is also true for disaster monuments. Floods (n\u0026thinsp;=\u0026thinsp;848) were the most prevalent of the four types of disaster, followed by tsunamis (n\u0026thinsp;=\u0026thinsp;512). The distribution of each monument type reveals that flood monuments are frequently situated at the heart of large cities, such as Tokyo and Osaka. In contrast, tsunami monuments are often erected in rural regions, such as Tohoku, the Kii Peninsula, and the coastal areas of Shikoku Island. The number of storm surge monuments was relatively small (n\u0026thinsp;=\u0026thinsp;127), primarily located in the Tokai region. Regarding landslide disasters (n\u0026thinsp;=\u0026thinsp;512), most monuments commemorating these events are erected in areas with many slopes, such as Chubu, Hyogo, and Hiroshima.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides information on the construction dates of the monuments categorized by the type of disaster. Notably, a significant portion of these monuments, amounting to more than half, was constructed prior to 1969, which was over half a century ago. Among the most recently erected monuments commemorating tsunamis built during the 2010s are the most numerous, comprising 20% of the total. Most of these tsunami-related monuments were dedicated to the Great East Japan Earthquake and primarily located in the Tohoku region.\u003c/p\u003e\n \u003cp\u003eThe Ministry of Land, Infrastructure, Transport, and Tourism has established a hazard map portal site as the primary source of nationwide disaster hazard information (MLIT \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although the National Land Information download site, accessible as GIS data (National Land Numerical Information 2024), is generally user-friendly for analyzing location data, it has some limitations. Specifically, the data format varies among prefectures, and some need to disclose their data, making it unsuitable for conducting nationwide disaster hazard analysis. This study obtains data in a uniform national format from the Hazard Map portal site. The website can display the hazard level for a specific location based on its coordinates. However, the maintenance status assumed risk and scale legend of the disaster vary across different municipalities. It is important to note that while the data is available in a uniform format throughout Japan, the assumed hazard and its scope differ from municipality to municipality. Therefore, the data obtained from the hazard map portal site were organized as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and analyzed in this study.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Conversion process\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Conversion process (continue)\u003c/p\u003e\n \u003cp\u003e\u003cimg 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npymvXWlrbR1xa24LP9uC60r26vj8Fck02A6iuEeNIQJdF/mvo6swwIOcFQDddI6fE3lIzM9Rd7XWaspOJvMWhfcSJJ18SBvKZmjKqGSihfqbF3nWGcwQImBAtlOiBp8dH427Yqxzm5CCL1Ua52ErKwz0chDgLyva5nVFFzPBJlS1gXXmsmGeJC30PqoeQevIetEPg/Q0brJ337Vknds2j5dJ0xLZUYyPYWsS61U4mwyZ10wtZaIUtY6c6EgY13HWGeyo2/WmSRkNcM00chDAFmXWqmcbr43mrMuOPNtK6FS1jpzRyfIWFfZuXVqTNpW2Xmf+qI6a6xj6/qHsC6qy1EI/plxPTNvHU4PqU3aOjnDtEhbR+eldVass4opVmJkrVN3ayJp1XLFnmGaJoRtHZ1UnWO1VTEMCOuijfiUfMfTembeOppl0o1cyjo5w7RIW0fn5Rg3vSPa0oRSOMbW9RthXVDWz8bp0V3WOlpIoUUUsO4CeSVUSs1NibR1dLloKk7oWGcyQLyiRKosEHLBiawrxjq7CWH3QqpZRbtiGBDWBdPbSjt6dJe1jhZSbtOSyULr0vfolbAufNDSc0tB2joaC4V1YuyL7kOXWGdZl8pHZnqKtA5HMplJjBZF9WQmKNToyUEtLFRKa2r8w4UTO4UZroP7vcK8dBaKqc9hgmrtYp1fVLHOIvsYOhzDDo0TpUY8HYSL9Rd1G4Gxzm4imG3dwYcK2As0UEnI5VZtrFCZsKKRhwBpXVDcjqvwf1WTNxt4hxdtJG+OTC630LrV8cLlk9/S+EcLJ61zs81PjoCdNPTBCAj3e5M31VCZ+RwmqHYivNbEjGOx4lJ8eAzqfj72IdWBaf8MlXY+MtNTlHWZ7xyU4Dbu3lQ9Sf58I0nmC4XPkyS+AacoiLk+b6UwF+5BzT21wqm/aqAKXcQ6g0Y61lmOW9Dtl/Ab5+FQ5iOLWopFFpHI4JJqovh9I1m/KHoRScjx1xdHwLXkoxetCtnYaZR1me8cFOE27n75+yT57zeayUcvFT7FCGgY/vB5XfLko0aycWmllaCGk3fhsvtqhTP3nYPzW0lcXWx8c6ks113g3OtUt/3NpSrV/XrU5CPL6GOmV2jrGIfQ1jFewta5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fkOfTGAYn+GEZKYLeKzzG7bORdg6v2HrXISt8xu2zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdg6v2HrXISt85uDW9fziGRGw9b5DVlXwkQdiiXong4RyVkuQNfzVmYruCo3PmI6w9b5jRzrXo0x5WNfRHJH511ZqheCGcvnsHbjaBBesTYDY9rA1vmNtC6/h/OeqH3Ud6NMe2CqndaBsIYVw7Md7CHB1vnNQawTocldWCfTKEtWlM/neA1btwdsnd8cwDoZRdKFdTLuPNey1KAemE6wdX6Tt85EHqtYZB2PTNygryyV0aXXMay4ZCcezycVrJP3iKVY2GXF2wHhjJ2oXJpPLtyIk/iNqXtwIQ+BBFvnNznrTOSxikXW8cgSGfMT1pL5ozDw1UKdgow7qGNbGd6jc7ZS1i1Bo/iCPKBL1l4MZlq4wHKhRfmrDFvnNznrTOSxikW24pEJbR3OGzGpR6cgo4ngl0jvMS0zY10QwnCph044eRaqKDWE7/ckbJ3fZK2TBfRExSJb8chEzjpz1J11OMKZQY2sE+GPbJ2ErfObrHV25LGKRTbxyETeOp2C3Mk6PGdTaqiUHrauHWyd3+StE6IQKhZZxyMTOetMCnLGOtmrfIBgEW2ydbvB1vlNxrqlz5R+AhmLrH8iWeum8JK21sngZfkAAYlW6EypYfRi6/KwdX6Tti6sXSFvCBWLbMUjE1nrLuBxW+vg/g1XTcQnVIioTkX5yI9g6/KwdX4jrXtVWBfVqybyWMUiW/HIhAhNNtaVTApy1jqRtFwnxUox9AO/AFqlPofJ1uVh6/yGrBPfOSCqJvJYxSLreGQJLvzPFyjduHYFXwEr8VgGGIv2BXzALh7OkbhhBXOYdaAyKIyRvfUL+Co6489FA2yd38ixjnEKts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhc5uHVn7jZOj8gy4ypsnYuQdcVt3NGifkzWdcVCM4m7sW7yi9q4LDKD5+DWcUJy/5Bj3Svbqy/RcfdED7oY6yZvNpNVtu7wIOsGmJCM2SIA70m0K9K64vbqETrunrAb6xa+W2+xdYeIHOsGlZDM1nXFQaybvj4Kr11ZB4132LpDRFpn73fZJWI/zN3JJSQHS6bIdOQA1lWad/Aatm4YOIh1T5GQzNZ1Rd668GYrif8AR1R4D4a1EG7OqEBca+IU4twIWDcz10wenx4p3m0kyeMToOCZuWYV67As0dYVbzbfub6dxG9N3W3hVVTJ9JkDWPdUCclsXTfkrIs27hwJzjfvjEYbtSNBZXEUamrjVJBM78ixrnVrAga+1fHKynhQ3Kofg4ME106wTjQFlHWVHTLz9R1s8a87T46zdoMgb11/E5Lpvu6epSsnJLchZ93tzWPw32G5cbq8A8NbEW7hplVBoq3DGWa0UT82u/icPAITz1HdcdEUMDPMhSacEy3C+42X2bpBkLOuzwnJcjVFO8wJye3IWlfcosLt1rnSVlwmM4qqIMlZh5XdWifdY+sGRM66PickT8KvKawlVooIpYtwQnKKrHXTdNcGnBuBG7iNN9ENXRDkrZv87VdwC8jWOUjWOlnob0IyOKYHO2EdZ/qkyFt3R08lz3zbSuhIF4j8DLN569QYj3VOkrVuMAnJ5ZY4gbB1eTLW3f6sIfUjYHIJLtkFIGtdeQu84hmmm+StMz70LyGZrdudtHXh8tWV1mvCB1o/uQ0u6QJV5627tPNJ96spbN1gyVjX74RkSZVME7B1eaR1rwjroo13T27FcA83efNk6dtjuLYyHhRVQYIeFS6PautOb9RPhNea8VujbJ1zpK3re0KyINq0PhHG1uUh68R3Doj3RifvtpLkfnlk8tNWEv8RXNQFCRiW3HppFpddVq/KxZe4utj45lKV6v6MwZxStekdPIsHVHpyCV9Xr9Lr89SC6SvSukElJJfxk9BTa2vWAwK2Lo8c6xhPIesGmZC8jkszpjNOSG4HW+c3cqxjnIKt8xu2zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdg6v2HrXISt8xu2zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdg6v2HrXISt8xu2zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdi6QRDeiJMkvveqPBwgbJ2LsHUDoESZja+YBI/B0QPrDicqeepLs7NRaa6FOx51h32hq7B1/Ufu0hrWDsu6qJ5Y28xiAEh229hdgObdWJeJSn7azNZF+K1KntlWQvuMdcViyw5Z6Ezhy0PcN4mt6z9q2zvMdhOJjINDjnWFuhYNFKwbO7rgQFHJT2tdahe/cHEfY13UzfZ/hblWssrW+Yy1sbJMZBwc0rpoZUVtFVu9rrez7A6xMebu5KKSnzo9MrV35n6s62rTzcX1dbbOb8IabeEKqETGoDAPE7J7NOTgbdOFeG1KpivOULqi9TaXmY5hZR33m5xaA2XkJVFhjfrDrZmxEUVBZtDW0b7pQPj5VLfWyXG5C+vyUcmHYV35Bu4n3uVWt9Mtts5v0I41SquSu5nTnLNQj8E/vG36gOTBCNQZ2t/1gjVVk5mOeGtWC8FfOCUvqWIvhTpOF00UZBZtXUG6U/ocLunKOjUud2FdPir5EKybbd1h6xhDWMGMYfBOWKeHBnyvgkm4wgn6gYNiS3P7fa4zHcWm6bTbubyErqBZnYmCzKKti+T0b6ksN0w3w60aOtMjph6Xod/9RyXTfZ2dlIxRyRdzUcmlL7HHF7EIXc5il3AQXodae1EErQvhViy+iqZQ6d0R/VNwHfeXTqDdSnx+voW/wXR+Zq41i3X0iwQZ60pzrYs3Gvin+5Ku7S9s3WDAbczhbS+sk29SoYmSRbxjpXvGH53pKByV1pkLUSa5XGPe8wZjnZBNF8xwq4bO7Igpx2X4PQeISparKWbEax+VXIWDUgMPcKESDmZbtbHoETQsPEpbF20ujcHVMJxRdsns0oj6KdsEZTXWJR9F1M+s6tzqWzQF0tZhC/il52mN5e1W/QVZ3yfYukFRgLu7knh7pt6k7a17BeaS9M7WmY4560TbKrzYUZAZjHWieRlzyKkLVE90qkdbrFJ/HNs6rMGr9hGVnE9KVgEjqZiRKkgjD8SZaLM+sYjH2RnmEqpJleUWjG+l6yPqp2xjrMMLsR80Uhxh36irFYuenWHCr8DW9Locn5K1fYKtGxj4Tu3aOvnOR2SmY946Ct/BOyrVXxss67B5+HlJdGEPt7K77IiZt84c7WkdkUpKFta1CfeRB9q66QYOSakmi63fUyVNNUuNeBor1U9Fzjqs3J910j22zgPwLY/Ae1K8PeX7W4TGyfd8Z+uwPRznraNZ2zxUqv7aYFmHV5U+D0UXtieyu+yI2ca6/UYl25mt7a0TPaasu9jCWWPGOrgrE0AbuINbfwOaqJ+SvHWF38nO2bpnj6hO72Oc1Im3p3jPQj3KohTqYJ3OdGwz1inXZH/tsKwDSatVGHs6W2cpAuSt23dU8p7WVVvQozjYyzpSSjD1KEnwUP0U5GeYrY9OiiO27tkjquO6IC3dy0RGWiIJ8fMc8I6oxfSp6E7WqUxHkdBYw4cK4pKwpqzD/mQUZBbbuqiOC5XCFTU8os/y12VHzJx1+49KtpOS21lXwnljV9bJGaYCZpeyA9Mqax3NVNm6Z5XwCkx0kvUKvCNFIiMIcg+mPjg5pFkiTOpoele/gK8yXVG+f02mYwnu7+5N4UcqiTK88xFa6tdRkFls60Bp9IhcsYdbaV12xMxZt++o5FRScjvrYCyEN3nGupMkR8a6N+gsUcKH4YvxSVpIoZUXQda6i3jM1jG9RUWwWisWOaR1hRUcVoUV4sG6NdyCSDTaZkZMOS4b67qPSp5ok5Tczjrw4MXwehK/AWVt3US1tYTL/CnrzsKoBq0KcyeD0qMJGiTVT9kGrKuNFSoj2jqwFzpvxXDrx9YxPSKcxyEwvKKHxXaQdaQnDnDXcb0TAY/0cCtHW2iXHjHluEyn9xmVPJZLSgYnoHEuKnmmIXukJ2aqy+txsn4RRnJpFJ2qv/olvN6bhl/1Gebkj+mfCjAs+ejFqvgN+ArEs0uNry+KOsrLl11O03njHR3D6IiVV+nV5PD3AbZuaKnSvBDe5GIi2B451jFOwdYNLdUEx7rCGq6QdIStcxG2bniZwUWar+1pXB62zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdg6v2HrXISt85uEYXznp/LN7gw81rkIj3V+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1ftMD6zirtef0wLozdxuncXOm/vCru42Xde/Fm81fj8rynvzqi8Yv+vfnGhLIOs5qzXAGc0fsPVcGDFkXbcA/0B31dr4Nf8n6MXmwNwvNJO7GuskvauOyuA8+ht61dZd3kqRr6/DKPa2b/NN2K/nbuwf4gw0JcqzjrFabcOVGFIS/z+wSNkDkWFf4XosGCtZP7GeMiB50MdZN3mwmqwd5c4f3rbEuXNjHWHf0/p5jXXiv9rNg8uvmX34mK7xDWsdZrTbhCu7C10XDfiGtix6stF4Tb9HK9c19jHRA2I11C9+tt57eOhjAurcuvNeFde/BX7/419a/dN3rkKGt46xWQ/gZtnDBuuntVdoCMPx0+vsurZu+Tu/VrqyD1juDsm7q2k/wR1fWnYPuwnvN/5v+FTxEW8dZrVlKDWp/GGjrXlpunMI/RPHTlza6s67S/ISudc26yz9+0q11ArDO/7GOs1rTWa3h+QZ1JjmUrNbowQuzzffwrXf77ARZN3mzkST3X8K/Ey5Svr39+FgI92bxH9SmuNea+Lc8B3/5B4035prJ49MjxbtwzWO8KTwz13wX66wbRGVd8WbznevbSfzW1N0WXkQng+IXeOnP4ehXN8WlUA4/3MZ//Yx14Z/gj/FfoC8qvA9//vC/7ogC8v/s4J8LNALr3ry5kzx+eaT4/zbgV4nOd96bg7p/NF3CDeD/pGWX4p92/s/rW62//Wbqi2byP7oT1nmMdUI2XaAskWc3q3URquKL+lpsAb90wFmtYF1EsqlCcQsmnJMbMdzs3Yb/R7nSTN6b2LhzJDi/c0e+u0EjNda1bk3AyLc6XlkZhwvh6gq888Gxij28SevwFMj4+k6y+nzwrztPjou/eWX5+aD4Q/34CF36fHB5Z/X56Ju1n49MfpMZ6ya+uzMevA7j2dHvsN3iTwJdEEz9XY11zY9fGLn8419+dnn5Z3DzVv8HOEiSv/wMRsPU8snU3/EQz/2Pfxz53/6OLf73vz/5X8xvHWKMdUIJzmpV4AbteqCkX4GtB5pzALKBXaDY9OIoWhcu0ywT3MMx5UFrFQa925tQZU0njXVYhVdVFp+TR3AJjILRev04NUX0DHOhCTdT4f0WzBXN9JEupSNx5ihcurBNhxnrbm/+g6id/hGGt+K154IpUchbhzPMo9/V/6ECRoojMZnEOtNncGtbDGy3dmiExBbQ+j9ZLYYXyzrOak1ZhwOcNaaRddK9QVo33Vw9En56agT9KW3R4zulEL4KA4MF1InIW2eO9rQOXlPWIRnrzv4AQ1jOuqtbWIv6nfwhfo1ux4p/lQVB3jr6j9HRuqm/y7UUsk6555916AtntVqUGlgrOSzrwuXG6dKnR8if6R3x0Pw2yiNEmqb7JWAX6yZ/+xVMRw9k3eRv/wMuta17ZwftyVr3Ed1OAr8e/XAn2XgL/5i6QLSxDjpvJn9rb93R/7kqp5ueWwe3VpzVKpoKou8dsA5uzd6rVkc7W6c/vSJoM8Ns3jo1drCxrrIDl6bHuk7WCaeQXz1sJXSkC0ibGeaPH58e6zDW0YNyge/WcVartC5app/2xYdnXbQRr9EPnGGu0lrlbaEQvsoZpiFn3VlsIY72a10RZ5PiaA/r5AxTApNLcMkutLHutR9gNBNHOess6by3DgY29IhcEdoIT6R1skqTs86brNboezQM7uvMQHl41sHIhsMZWhcuozVQxM9O2iJZ5Ky7hBUHs27qR7w0bd3LcDUepq37zTJeiOBCSvDx9i9GaCEFC1Sdt+6dH/9bp9WU8OMVs5rpsXWc1Zq2Dp/2pT+HeRjWFR68BK/TTfxU2ORG/cRIcSsuj4S3N/GBG4j0S/gBVW+OBJM36WE6tgaNCpfhHaqsOw0XhteaMdxi7dc6kOznYx+KS5V1xys7n4wHl3cyz+tO/oCtJv90uvTN8ZHiX2HkK6qCAB8ETP5nkFhZ94vv6v849uHO336DNqWsCz+WN6v0nMBb6zirFetMVmt4+Sso3p82/4UPI6u1iA+j68eC8MMjYB6Uk/h04S78nT7C311Rj8MnoSq5X1Z/VhAsufXSCJ1evSoXOeLqYuObS1V8M6/SX1OqJtZi4IAKTy5Rg6v0Sra8vpXE7y5u60up0xvbycY7y83kL1Io/M5B8uSXX8DJB2dHJ/+9mcT/Bt3rgiD8cCf5+AQ9m0v+8ge8BPgbdv5Oleqoc3piN/V3OknWUfnJ/4Gvf/kDtTOj4PAixzrGKeRYx3gKW+cibJ3fsHUuwtb5DVvnImyd37B1LsLW+Q1b5yJsnd+wdS7C1vkNW+cibJ3fsHUuwtb5DX4EgGG8hhOSmS7gsc5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb3pg3SASkuF3mL2JSnN6P7C9GYZA5Bxsnd+Qdf1PSAZroNmN9G6Y3WPnIdPmRF1b11UgMkaJAPY2gYcLW+c3cqzrc0JyWLtxNAivqA3D9k9q98tQ7OHXHV0EIrN1zGCR1vU5ITmsYeddNOxEyjq5c2Z3pK9sz2LVsV0W2Tq/0db1NyH5c2wxWOtEHjJbx7iHtq6vCckCldh6APZvndouna1jnENb19eEZCSc0ZcCpfnkAuYhz9h5yLIDzCneLQ+ZrAvnoA4zjKmA8Y6pQGSVh4zW7RmITPd1961fMNg85Dxsnd8Y64RsukAJPr1KSAZwZVRuuQ5QHnIBOsMlDJ2HbAKRcQ90KFfb5iGjdTIQ2cpBzgQiyywRsG7vQGS5mqKTWQech5yHrfMbYx14AQNXfxKSCcxFMNGsS2IgksORVMoEIpsskXweMlqnA5FFDjLcw+mCwFiHV2JHnQORKa75kT2cihSRFfHa7yyRPGyd31jW9TEhWVK1ngOSdco9WykjBsnSJg8ZrFOByAlm+YgcZF0Q5K0zR1nrCKjUg52wTrnH1jG9xbIOfelTQrKkFKtrO1mnA5G1dRfyGZEgG9zLCc6KHGSs1QWijXV7BCJPyysQto7pI5Z1MBT1KyFZEtX3sI460MMRXNDZOn0DZ3KQU4HIbWaYewQis3XMgLCt62NCMnaPPVBHRDvrrEDkPaxLByLrKGMr0zhnXedAZMksmSZg65g+YlsHDqBH5IrQBspaJFWlyVnXOSE5qtOV6gkf0s46KxBZW9cmDxmse6NGlwMqD9kUBDnrOgciC1JxzWwd00ekdX1OSI7qMHNNfw6znXXgwYvYAdydaeva5CHLRRQRiKxzkDOByDIPWXYu9O0QiDw992IQnHlkxzWzdUwfIev6npAcVr6Gor4SnYBjGByxgclDNoHIMrOYzmTykOlpmglELnwGvxRzkHVBIPKQRWM7EDluF4j8XZKsv6EvTeUh06V2QPkgYOv8Ro51jFP0wLozdxunaYrNuAdb5yJkXbQBo+ydUVl1G8bc+jF5sDcLzSTe07rJm9sw/lePyENmULB1LiLHusL3WjRQsH5iP2NX9GDPsS58UBsPJh+2VsdlBTMg2DoXkdZFD1Zarwl1Ktc39zHSAWE31r0LI2nxh+TcfnRmnh62zkW0ddPbqzT/Cz+d/r5L66av06S0K+tQ6fBB6301jWUGA1vnItq6l5Ybp9Cd4qcvbXRnXaX5CV3bhXUCsI7HugHD1rmItu6F2eZ7OBDdPjtB1k3ebCTJ/ZfQElykfHv78bHwZjOJ/6BWRK41EwA0AuvemGsmj0+PFO/CNY/xpvDMXPNdrEvdIEbr8csjQfFm853r20n81tTdFl4kTzJ9ga1zEWNdRLKpQnELJpyTGzHMDG+3kvhKM3lvYuPOkeD8zh26AJjeUWNd69YEjHyr45WVcbgQrq7sJMnqOPxILZ9M4yGeAhlf30lWnw/+defJcdaun7B1LmKsA7tAsenFUbQuXKZZJrgH2oBWqzDo3d6EKms6aazDKryqsvicPBKTyWi9fpyaChYaMNTho4Zzo0F4v/VrfBVVTL9g61zEsm66uXok/PTUCPpT2qLHd0ohfBUGBgv63ixvnTlqZ930jlhLIevgla0bAGydi1jWhcuN06VPj5A/0zviofltlEeINA1TQ2IX6yZ/+xVMR9tbF63L6SZbN0DYOhexrINbs/eq1dHO1ulPrwjazDCbt06NdRjr6EE5wdYNELbORWzroo14jX7gDHOV1ipvC4XwVc4wDTnrzmILcZSzzkjH1g0Sts5FbOtgZMPhDK0Ll9EaKKIrtkgWOesuYUV768KFFa0sWzdA2DoXkdYVHrwEr9NN/AjJ5Eb9xEhxKy6PhLc38YEbiPRL+AFVb44EkzfpYTq23lkdL1wGdZR1p+HC8FozfgvrUtaFC/RwL0mewDFbN0DYOhch64rbIET9WBB+eATMQzvi04W7rST+CKeZFfU4fBKqkvtlpQkIltx6aYROr16VWsXVxcY3l6q48rJK3yukaaleiXlynIpPLlGDq/T6vOiO6QdsnYvIsY7xFLbORdg6v2HrXISt85seWMcJyT2HrfMbsq7/CcnYJ5DeULN7+pyQDBS+VJshvd1IcJujQ4Wt8xs51vU5IflprTOb9yE9T0gOCnMttQXZYh3DfAYf45OCrfMbaV2fE5KDJRPmcyBS1uF+mLK0N+krO7C4vi6tm6aNMGcxH+gQYev8RlvX14Tkw7BuHwnJwLQIswtXaGfaUuNwBzu2zm+0df1NSB68dftJSAakdXLb9S4v6htsnd9o6/qbkEz3dfcsOZ1KSAakdaWGcFXsuE4cRloyW+c3xjohmy70NiFZrqboEc+thGRAWqfStIx1h5KWzNb5jbEOzICBq08JyZRBvGY/YpC5InI4kkodUkIy0NE6WR5sWjJb5zeWdX1PSIZKc3vXLtOHMGKQLANKSN7LOuUeW8c8PZZ16Et/E5LVFUh76w4tITlrXcP8OraO6TGWdXCz1eeE5D2tO7yEZL2aQj/CFev+ja1jeoxtXf8SkiVVNUwC7aw7xIRkaV20SR1E31vds3VMj7GtAwfQI3JFaANlLZKq0uSs65yQLKDOFO2sO8SEZGldID6VIj6hImHrmB4jretzQnJ5HrqZWsORVNHOOvDgMBKSEWVdtJm8MVLYNPNXto7pOWTdABKS15NkfcaMdE4lJKNzeJYO8IPQ8UUjnZ2WfJVe9a/oH2yd38ixjnEKts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYet6DD6OZRhmcPyUrWOYAfNTOeI5A88wXYRnmH7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+c2Drep6LnApBPjBDGYic48DWnbnbOG1ttPT0/Opu4+WedsgA2rpw5uskSdYrqS0v86j9wjBypI11lLWMGx/pQrekQpAPTLeByI6jrQvPf9VKko3ZPXZICh80foluLDSTuI110QZus/TkeBCty0K3YIdsXc9R1pXiNdzGcoY239sFZZ3cjy+P2NfPLnSJ3p/vqehNL4eNsq64vXZqBNTbjt/c9a2vrMNC27FucqN+gup1oUvC+zzW9R5pXTmWuR+Fenqr2Y50sk7vgZnZgnZP2DqDtG56+71ROp7cSF7r6r3fybpoo35MFNbr3Y90AFvXD4R11q7MpThtk4xBzjE01smk5KFCWBdt1I6I46C4lZ45Tl8XOmZh64YCYV06gMDEXeEZEwiSYlisU0nJQ4WwrtI0A9ztlhz2iErzDls3xJB1MiJSUIprRyvrGG8wtVb/DNdEQEnKRkYbS/N4dIMyi8m6VGoykLFOhyaL5RVaYLGClO/FJy/EaxOyQ8sXkc9MWcnYprEmNke3gpODwpxqIgmvN1QvOg1ZJSWbeORhgKwLl6UpSHFrdeLydzD2hWe+rX9Of6lzI+HNZhKjjcWbTTi6to1/fbKOzvxBjZQ564p34V/qMdzgieUVWmApfiHraN3y7a3Hx8MPqUO2rueQdalhSehRC8MaKiIiRGgGikmsuJV6OVoDZQprZF0mNdnqSxR0aHKEqzUwkMKgqnOQVfZyPgSZAgsKm/FZWtv8oJWIvHE729g0kdi96DRklTJiKoYBsk6bgkQb+F9l9Ui43Erqx6Z3cKyDGeh4UFkchaEPHJx4uHZiZPIhjXXRxp0jwfmdT+juEMlYV1kZB4+hFD3E5gvgLNQ9HxR/qB8fwXXLKzvJ+y98Izpk63pOO+vq0asUmoUlYV0ZZ50lusMDbzBdS80wM6nJeBG+PQiyDi6n05QaJHqjOpoJhiuUvZwLQQ5XyA+ZrJXU9BhlQkfsJgK7F5OGrKzTFcNAO+vqx17ZXoXRC0vCuukdGOeKdIe30Dq3iL7JGebCJlxpzzbFswMCnxtUFp/D0y+PRPeh4TTpSXU0nwzvt1ZhzFvYpjJb13vaWyei6ox1FPAjWEo+oJPCtGxqstWX6VRLWcJBT6Csw1dhju1EqUGhOlSVcsVYZzchcr2IokrU0hXDQHvrSltp64pbcVn+3RZaV7ZXx+GvSKbBdBTKYggT6L7MfR1ZhwUc4KgG6qR1+Fr8YfV5VcP0FnlfZ62m4Gwya11wAyN56PQS5qTCT6FSNjVZXGQXdGiyukTXgQFKDBTIdkLU4KPzs2lXjHV2E0LopVrrNGRlna4YBuR9XcuspuB6JsiUsi641kw2xIO8hdZHzTt4DVk3vSP/q3S0bvK3X7XkHZu2T9cJ06Z+xBGQresHZF1qpRJnkznrgqm1RJSy1pkLBRnrTGgylsXqqJ2DLMTouXUmDVnNMHXFMEDWpVYqp5vvjeasC85820qolLXO3NEJMtZVdm6dGpO2VXbep76ozhrr2Lr+IayL6nIUgn9mXM/MW4fTQ2qTtk7OMC3S1tF5aR2mTOIJqDNRxUKMrHXqbg1v1VKu2DNM04SwraOTqnOstiqGAWFdtBGfku94Ws/MW0ezTLqRS1knZ5gWaevovBzjpndEW5pQCsfYun4jrAvK+tk4PbrLWkcLKbSIAtZdIK+EStnUZHGRKdDloqk4Ea2ILoUB4hUlUmWBkAtOZF0x1tlNCLsXUs0q2hXDgLAumN5W2tGju6x1tJBym5ZMFlqXvkevhHXhg5aeWwrS1tFYKKwTY190H7rEOsu6o3BGlZneIq3DkaxyFO5+5mO0KKonM0GhRk8OMAa5tKbGP1w4gfloCHaShJnUZCimPocJqqnQZLHiAj1BHeUgY5DySvxPUAkdjmGHxolSI54OwsX6i7qNwFhnNxHMtu6oKGVooNKQRVLyhKkYBqR1QXE7rsL/VU3ebOAdXrSRvDkyudxC61bHC5dPfkvjHy2ctM7NNj85AnbS0AcjINzvTd5UQ2Xmc5ig2onwWjN+a1SuuBQfHoO6n499SHVg2j9DZWXnk3F44ed1vUdZl/nOQQlu4+5N1ZPkzxSDXPg8SeIbcIqij+vzcbJ+ATOLoSadmqy/aqAKM7HMPFbrLnBO14nsZdDoiupQjlvQ7ZfwG+fhkOKMaVgDRN6xCE4e000Uv2/oKOXzDZmGPCKTkk2FbOw0yrrMdw6KcBt3v/x9kvz3G83ko5cKn7aS+I8w/OHzuuTJR41k49JKK0ENJ+/CZffVCmfuOwfnt5K4utj45lJZrrvAudepbvubS1Wq+/Vo8Ltt6HAZOnxedMP0Cm0d4xDaOsZL2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hp6RMozPOJjVyjCewwnJTBfwDNNv2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8pjfWUfpBdxwo47U3Oa7DA1vnN9I6iveI74lAyH1D6QeyvAcdMl53pzc5rkMEW+c3wrpSjHEDr6joyH0TWluu70Ekt6TdF8Ozp15vYOv8hqyTSVph7aDWyW33ukFtBL0v2DrGJ8g6udVlsATWdcpm3R22rqewdX4jrdMmdMxm3R22rqewdX4jZ5i0EzNwg74ZAQaZCFZdwuzWt9eSBHejFegEV7JOxKvixs+plpkwV7DudUxbLVkxrpv0WxPcMV1U4n7OKqM1vA411mqKSV2d+lKmuIZzrSS+OmafG27YOr8RqykFUGSN9m+WaSImglWXMLs1uRKGS3o01AmuZB1NUwv1uJxumQ1zhZPzR2FIrY3pyNboESolwnl0pcpozeW46tRVbPF7THGNNpfGghlMMhiuRNbOsHV+I6wLwgoOEuCdtM5EsFphrAWRD6Lzf1IJrjNiRUbcIlots2GuoohJBTqyNVoGTWQyVhVexXwyFBmtuRxXk7oqWwSLqBlVDVcia2fYOr+R1sG7FieXVWmdSRIxJbSIxNL3cJkEV9JVSqVb2tcTxjo6UorYI5RyCl9zCayE3UI2CRZbIgwh23YYYev8RlsHAxTc3ZWEdSaC1Q5jzVrXJsFVnjUtc2GulnU6stVWZc8c12wLaCJ/BVhndTnMsHV+Y1kHQqAlwjqhkF1qY10+wbWNdeZ6wpph6shWXDqVjwr3znGdbc3L1FVZKxLqiOFKZO0MW+c3ZF20go5Ylsh5YaqkXBLaKHSC6weyIWVLmpb29YS2bspEtoI4NEmUs0XbqZx1uRZqhpk+N9ywdX4jrKvT6IUfURHW4aiHNXZJuVQSN3BAKsF1RjSkdU2rpX09oa27gL9JKCLu8nBRhSSznRKnLJNyLeCHircbskTWzrB1fiOtw+dpBfRBZLPiECYjWK0wVnCpBK3WxMAIVqUSXGltJVyipFarpXU9oa07ifGpIrJVPDQoPZrAeqp8g5yiHNfWkkpgJagFpq7qFjDE4VFh7qQ+p2acwwpb5zdkXXhF57TiUuY8iGMiWE2J8lcpslWQSXB9BRrG83TWbpkOc6WmtSv4CsgYV7kcgqPaTEPluFIl2Ig5rjKBlcAWmLpqWlCwa3Jv2pwbjkTWzrB1fmOvpuyFmDd2Q/ctmXawdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN91bF+JnmrPPvNvSfUumPWyd3+xnrGMGBVvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1frMf6/bOQe4cf8zByPuBrfMb2zoRFNKRvXOQO8cfczDyvmDr/MayTia2dmbvHOTO8cccjLwf2Dq/sawrY7rIruxpXXpfaJvOZ3aBrWO8xLJuaS67O3oWtm5AsHV+Y6wrrdHmz7vB1g0Its5vjHXVqkrWod2GaiH+qIU6xxjAfdX14dR8UsGoYyiHlfWEUpDBLRl/3DkYuW0y8t7ByG2SkcM5aOBTMLKGrfMbbV2ESeGxXE+R+Vc3ZkBGmHWqerBOH+KKJpSrpCemR87AEYgq4o/DjsHIcElNJyPj8LeEm6bvHYycT0aWwchL/gQja9g6v9HWlVEQNYUUy5nRZ/BCOcZyeojW6UPwCBrjzpdVuiqap7EOzmBdh2Bk3CfTJCNjgFe5hb9472DkfDKyf8HIGrbOb5R1YQ3N0RmPNNcsqyVNyzpzqK0rUXgWod2a0lE/4oQ5Q4OmccQaopRS+Coy6XIm5ZvIPKA2bYcZts5vlHUlvIvCGynSC/SAiePnVBahw1REifShtm5KB9oZty6I3nLByGSdFWOsXcnEHucjWts0kb8CrLN69AK2zm+UdUtCD51UDIJRKCTUzJeEMlRpDrV1F/BmTWBZp00UWNa1S0beOxgZk5HbNJGn/AhG1rB1fiOtUxnJOtC4FC/dQBGpwlhHOcgZ66bUGozllpxhGox10GMuGTkXe5y3LtdEzjDTpzyBrfMbaV1VDnFCJVEgb+hOz1hHmf8Z6wo1/ZkW7RbUiX4UxjrRIzki4o+jlQmSzFZKnLFNyjXxMBhZw9b5jbCuIG7hgGqyJAapslAJDZI5xmjdBXOorYPRK74QUgiyPaK1DUaGM/A/lYxMDw2C0tpEh2DkqglGbpOMnA9GFi09gK3zG7SO8oxpllim5Qka5NSccyaWkcUgpEAmGGOx9md6Lc3D670p0ULEH9dKnYKRaxdUj1YysglGphqQEYORL1jByG2Skb0LRtawdX6jVlMYl2Dr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb9g6F2Hr/IatcxG2zm/YOhdh6/yGrXMRts5v2DoXYev8hq1zEbbOb9g6FzmIdWfuNk7vewcLuOhlX7a9GCL2YV0m5Didl7xLAvKBwpGfbci64jbuD1M/Juv2YKGVxPu2bqGZxGzd4CHrxMbPelfL9mRCjnFboXJYi1+lo10SkA8UjvyMI8e6V7ZXX6LjbogeHGCsCx/wWHcIyLHu1bgmwrJ2QWyup8G8ZG1d9qTNLqeY9kjriturR+i4G0Agtm5IkNbpPZ93IWuPiQ4B2Lpewtb5DVvnImyd37B1LpK3bvJmI0nuvzQShMstuAU/ga9PjoUPWsnquGgBAs3MNZPHp0aKd6Ht4xMjQbQh9vl9cizQdWKx8+2tx8fCa7hcw6sph0DOOisR2c5AvoELLmgPJR/TZuxgnVjHNCezucj2qbbZyoU1OJ0kGGGgrlQRyXS9lX2so5O9zUU25KwrbkFpciN+DRSZ3kHRxGtw+w0lDRh4ayKoNFfHKyvjcEH9WBA9xNFvoXluJJB1x8W65ZVm8v4LD9dOjEw+5LHuEMhZZxKRcZUSDijveA0UKayBPRhsR2mraN3X0KIcmJNwNp2LbJ0yHVO/Y9jvGKbxFDbrYI6OO1YRyaIDnZxsopP9zUU2ZK0Ll+kBArknp5LRBj4oiB68QA0AVV0/Nrv4nDiK7sNV0zufQGcVVUd6rsKgt4C+QRVbN3jy1pkMH5MfsoTHVEuhIyLZDmeY9D99MsjmIlunrI5FGg/G9ohMcuzSxB3L/GOBSU5W9R7nIhuy1hW37ozCj3CZtKk03xsFmRJ4rVSxnjDWmSOABj2Btg5/gMEwVrJ1h0Lb+zrpibZOpNFRbSnG5H9CWiculSepFzxBWKcEGeterKEzaJ0Mo8O445xI2jr8YTW0zvlG1rrpJlkX3G7BZFGYdfvi9/Vj4aentDOWdZO/+0o9M9f2Tf5W1YkqMQSydYdC3jodgWysE8mrQpkbSbJOsVrKOpFlTidFJBAgrbNOAbrj3Fh3NrDijtMimexjUe9zLrJhd+vwR+nTI/BaXtRDnWXdbPPWqTFpG42L9FPXsXWHTn6GaRKR21oXTK0l4satnXV0QpOyzspW1tZhVmthE0YvK+44ZV1VxyJr63TD2da8X7nIhox1tz9ryIJQaXpn9Y9nR2CO+OezRhltXRnnjuIIW9ISJ80nZR1bd+hkrFv6DAu7WoeNqZC3zhoxCds6Ope1DlRMknm4xsQdp6yzs4/FD9PQPucbaevC5asrLVy9jDZqJF/4oAXzSFwVkU8NEG3dJfRJH8FNXXT/GDmWsi5axwVNtu5QSFsX1r5GUbLWlep6lYQWUmiRRFkH59VJcYHBOiUMzFpXVgqJKlk0IlmxyPKHaWif8w1p3SvCumjj3ZNbcXkkvL1JD9zUvFHNHgXas9Mb9RPhtWb85qh4aBAUHx6Daqp7Cy6Ahr+E2srOJ+Pwws/rDgFp3avCuqh+3UpE1tZFlJtcxZuy0lqkFJXWQb06iaNgKhfZOgXd6GxlZd2LNT3CybhjTFkW+ccEtgmvU3Kyqvc4F9lA1onvHBDvjU7ebSXxRzTSAeKBgfXYABWEdqtX8RWIq4uNby6Vd8TRk2PB61uybrRClWDjB9vJxqVl85idGRhknfjOAVHNJCLLDOTrKq+49Dn8J70B0tFZpB7pMOMwKKRyka2c49BELVv9CujhuLpS9KsGPp2cDHdxsj6Xi3xBtvUIOdYxLtCHb6rJse6QKDVQJXBdHjMCtm7w0Meo4nvyOzQG+U01uDnKnTowh2pdOI+jZnjFRP4zBFs3cOC2qQB30uLDjinkYoQ31lXFUkipoZ8GMARbN2jCGi78wY9O1vWUw7UuwbGusCY/6swo2LpBox56LT29dWX6vOSuHO593Qx+fvrrGfr7Mga2btDIZ9Dt2Kd1VXpIvTuHax3THrZu0IQ18bSMyubrauabavSlNni5ADXxzNS9JFmDWvP9NPXttRu0PJh6bJ2HrXMRtm7g4Dc91+iRl/V1NfNNNXygVcYXqJihJ2EXknqkm+IZqMNvxWU/FtkOts5F2LrBE1bwS9LgnfV1NfubaviBEPEiPj2C1aap+UQJWzessHWHQYizw6r1yStRzFmn3IvfNk3ZuuGHrTscCnB39wGoR5TT35lpY53VlK0bfti6QwLkmTPW7G2dbsrWDT9s3aCJVvAhOQr1gXxyB+xlnZxhImzd8MPWDZqojjqBPHWYZVIRQIl2s+4V05StG37YukET1emLLyiY9XU165tq7awrmaa2dbWwUKFOO8LWuQhbN2jCK/gxqfUKzhitr6vpb6pdoVUTpH4BX0VFrWR/P018e21tPknmaT/ZzrB1LsLW+Q1b5yJsnd+wdS7C1vkNW+cibJ3f7MO6zP4R3SQkH2jLCYwRkcVnFbbOb8i6fiUkHygcGWNE2Dq2zmfkWNenhOSobe0eeLrH5X5g6/xGWqe+wL4bWbHEE0NJe+va1+4BW8fWeQ5b5yJsnd+wdS7C1vlNzrqeJiSDda9jDyWr26iOSzdJUp+wQ5Mx/jhem5Bdaeue7YRkxlNy1vU0ITmsJfNHqQfTbYS7vMDFZ/F3yYxjij9Okmr0CCwqPDLWPdsJyYyn5K0DZeTE0HyO+qAJyaKIPZhu6ZtM5RaaSZU0nwxl/LEoW9bZmT7PXEIy4ylt7+ukN9q6gyYkK8/oa0q6W8AaopR1+CrC6bImyWO7yTOSkMx4St66HiYk29aZbi1XdMaxqBFBrCmTnumEZMZT8jPMHiYkW9ZZ3eLvEPtaUyXJ1Mm6ZzshmfGUjHW9TUg21tEcVXWAaeT408o4tpWyTLJTkMUPOcPMnPMNts5v0tb1OCFZdqS8ld3CIdzURSsRLakIb8QrRZXbJpGG8lg1VImS9jnfYOv8RlrXp4RkbR14q7ulhwbQ0QRlHKvQZIo/hq7GsCtl0jOdkMx4C1nXr4Rk0YPackJ2q5ZcYFhLhyajjLorOY3khGTGQ+RYxzgFW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3bJ2LsHV+w9a5CFvnN2ydi7B1fsPWuQhb5zdsnYuwdX7D1rkIW+c3e1h3oIhjm6fu4JmErfMbaR1l6cT3ZPCqRkUchzqTdZ8cKCOZYev8RlhXooDkV0RClo3cwvLA1qkOmH3B1vkNWRfWKP4jrHWy7uCwdQeBrfMbsk7vt/7U1pUpY8uCrTsIbJ3fSOs6qbFPaarZeBG27kCwdX4jZ5hiL3TEZBxbwceYhAz/uwA18czUPco8Ni11kvIN3J7ZRB3YHZgsZBO7XFij9hh2oH+pykHG660M5FQ88pUx+5SPsHV+I1ZT8P2/Rvukm4xjK/gYN0T/Gv4HFTO0sfqFpB7plngW6jAHORWlZXVgcpepMWYZ1MYwlKewWQdzTOqxjEp+V/SQTlB+VuKRAbbOb4R1QVjBoQO8MxnHVvAxPgEQSSI4LNJrXDItTQpJyjq7AxOQLBtv1idENDlG2ZnUY8zswRxkgclHfpbikQG2zm+kdfBexulh1cT1iFLOOuVe/LYJ9mlvXboDIGPdizV0Bq2TmXSUepwzSVmHr3ZDfcpH2Dq/0dbBNBPu7mB2J7DDIIG8daalEiljXboDHZCcG+vOBnbqcdqkXIKyaehvPDLA1vmNZR0KMae12ds6LVgX1pmAZGMdRrYWNmH0slKP09aZfGRlnW7ocTwywNb5DVkXreBDcjTqA/nkbm/r5AwT2ds6mm1mrQMVk2QeOjGpx3DaMsnKQBavpqF1ykfYOr8R1tXRJxCiDrNMKgqJlDTtrHtFt1QiZayzO6D6rHVlpZBJPcayMcnKQFavqqHP8cgAW+c30jp8VlZAK0zGsRV83M66kmlpW1cLCxW8ALA6gHMqIFlb92JNj3Ay9RiDlcEkzEEmsJHMR5bVz0Q8MsDW+Q1ZF17Bp3HrFZwymoxjE3wsAoyB+gV8FZnHtZJsSWdFkvLaPMwZ8Vk4YSUnp7OQRWMBPRzXv1T8JjXy6Xzk6rMUjwywdX5jr6YMmFIDVUrwgR2Thq3zm8OzLpy/AaNceEUn/zMats5vDs+6qlgKKTXMAgwjYev85hCtS3CsK6zJTzozFmyd3xzifd0MLuF8PYNLOEwats5vDtE6piNsnd+wdS7C1vkNW+cibJ3fiEdmDOMxP5Vvdmfgsc5FeKzzG7bORdg6v2HrXISt8xu2zkXYOr9h61yErfMbts5F2Dq/YetchK3zG7bORdg6v2HrXISt85s9rBvqrNbSXEvuBTH15QH2NTpzkIt6A1vnN9K6fme1HriD/WHvdRTMttQOLIutA+xXe6CLegRb5zfCulK/s1oPw7ogXFRjXXSQPfwOceM/ts5vyDpfs1q1dQcSiK1j+gRZJzNFDjOrNXdhL2DrGCeR1nVSY5/WHTirFS7UUQe9g61jnETOMPue1QodUHscTU1wq6oSF4Iipk87s5USWt8dCcKpR/ULa0nyR9rLj2qvYjG8/J0o4qqlleJqW3d+vpWsgUX6dLRJvzSpT1jBr7huSZmw13GzTsu6VJNT0OSFcK4lfr049ZJo1xvYOr8Rqyn9zmrFJhfq2AjP6wjWCBN9qEqm9egsVhXOiv2IZrNLI+EKdHN1LFykgdHktoJTuBv0yhitWureU9YlH0XBbAv70b/8EToFTUaC2RVZh+uWv4dfO/EIBCs8sqyjJtv1F0yT7++MBedbd/DqI+JU72Dr/EZY1/esVrh4Djd5LuHtGwW34vSt3FJV0jqTxSrDWYlyC8a50g1oUMAoIPANdbByW0muaA7GHZDIBLza1uExBQnpkNdoGa6fRm2s4Ff5axcbVKZOBLLJKaxurWKTTdAsXIYaOoWF3sHW+Y20Dt5A/c1qxZCumEZTAb2lSw1VJayzsljttzw0g7EMIW9EA9OWhkKJ9Exe3c46cwhYyeaiTryKvnUrBVSQdfha2q4dgarF1mv4/xZsHbMftHUwkPQzqxUuA63Xxd2jzlnVVcI6K4s19Za/3krWKb3Hss60LTXwWgF6ZlJc21pX+J06bX5J4XdfiTpRI0bA1B9BNtHWTTflrwfr4BRMO9k6plss69CefmW10mVTcPeIdRTcKt7SqkpZp/1Jv+WnHkEzOJWyTrXFzFdRIs+qrY9Uims762bNaSjD1JV+qjrx2sY62cSyDpuYUzzWMd1D1vU9q1VeJh5RwNTTylmF6SMUUjNMJPWWB0QzYR2dM22taSJ49nusl1e3sY4uk6enpa5WnXjNWwcTSmqirZMzTHHqCM8wmf0grOt3Vite9gGumuAKCzWitzQtpCzhe1tYZ7JY0295XEhZxGNhHU0pTVsoiSELWGzNozHy6jbWlc1p0Vm0bNfJM9/jiqTshCAPoUJbFy7LOzox6rF1zD6Q1vU5q5WsW4vECgs0fFEEt5bWJuSYhbNEuFBmsWKf1icqS49UMxDlZFBYocd4su0cLcpcDKkEnl1UKa5oat46+J86DWehDXRu1clfO9u6MwYvxjrw8CXT5H/F67bxAGNjzamewdb5DVnX96xW1UGMMT4muLXwuarCFVS8UP12Hc6KFD7DZjgZpCfbogjVKrcV3vvQHEr4vA6hFNeL1AdNPqm+doVeL1LIa+Pri2o5BlqcV3Xy144Ev28k6xdXWsmq+FVBtgmuoXwJpfvTI+ZU77Rj6/zGXk1xHzEp9J9urDtzt3Ha9jx73BVw0cs9HKOZ7mDrXISsizZwVK0f04VwuYFzW8HtVhLbli1kjrtioZnEbN3gYetcRI51BbhlpOPJjfoJXLKxrAui5fTYFj04wFgXPuCx7hBg61xEWhdtwEiXKliAMCnLssddwdYdCsNkXYiffu7LV4Jcg63zm+Ea654V2Dq/YetcpL11xbnWuZHJb+nRxZ1REGZmrpk8PiWtMcfFu40keQw3gtGGeDry5Fig68Ri59tbj4+F17bhHK+mHAJsnYu0ta7STJJzpa3VIwWxtvKgdWsCKlfHqYl1XFkZD4pbcEX0EEe/hea5kUDWHRfrlleayfsvPFw7MTL5kMe6Q4CtcxFtHQ1VCOl3u3VuEYY7UOu9UTmjNHNPczy7+Jw4iu7DuemdT6CziqojPVfB2gX0DarYusHD1rlIh/u62603V1CcPawzRwANegJtHf4obuEgCWW2bvCwdS7S0To11sFL2rKUdZP0hT+yTpoWBJO/VXWiSgyBbN2hwNa5SGfryjt4X1cTw5SyjDDHs81bp8akbTQs0k9dx9YdOmydi3S2bgTXVD7Cr/Z1tK6Mc0dxBG6JxRaaT8o68YOtO0TYOhfZZaxbll+mNZZlrbuEPtlno/vHyDFRp06t44ImlNm6wcPWuYi0LvM5TFpNUV9hT3mVOj4NjcNrzfjNUfHQICg+PAbVVPcWTDeh4S+htrLzyTi88PO6Q4CtcxGyLvudgxtqg6Qkif9wBGeayepVesVJpHUMxNXFxjeXyjvi6Mmx4PUtWTdaoUqw8YPtZOPScktczgwSts5F5FjXhuIWeZSINRJmOGHrXKSjdeHNGzDDDD/Qn0hhhhG2zkU6WlfZxvUQGPHu8Fg3xPTMOswPkcW9gKZqK7A2YK6ILCLpxpmTTwdmkYilhAOFIWPMiCz2ms7WNf8NxrrJb9fkEgozlEjrOiQkdw/uR9Slddi0s3WYK2IZgJsDndUbhmVO7ht75zHaw0hYd+AE5YFbF5z/D/hTf/Om2kSJGUqEdaVOCcndE4qN+7oBmu4y1old9DThomVd9uR+SVlHCcpirDtYgnJv98C06Wwd4wNkXeeE5H3QvXXQtHvraHt1zVNal0Fbd6Bu2TrmgJB1In4HbADrDhxVzNb1DrbOb6R1YoNmIJ9x3C1sXe9g6/xGzjBVQrKIKgZ9rKBkVQqn1upvryUJbdas0LHHZF1hPk6Se3CLmG5rOhOAdSYlWeUrY0akzFQGAyj+mPoF68Q6ZnhdnbTCkQkKK1YJyo9kgrJOMIZi5wRlbZ1MUDZno+/p3yKpv9AuHlkkKBvrTExymwRl3DxhXwnKbJ3fiNUUk5As80GsoGRVAjWT5EoYLtmjYaRjj9E6mqkW6nE53dZ0JgHrdI4xrmhCuYovkQ4mltnJ6MQihTOftU7CWRWOTOQSlDEKhNpQgjE6NR0E59smKCvrVIKyDEt+AXrNJCin4pG/FX8WY52OSU4nKDfvjNKp4v4SlNk6vxHWmYRkaZ0VlGySeQqU0hPV9XQUm8N4RNE8oNKMWJSR7pm2VhcCtA5+jZjXiXAe2umScnuottx6F/sV1p0V/9MnrXBkom2CstVmtwRlZR0e45/BhCXnE5TBMRWP3MCyPcM0McnhMjW5bSUoj1JB/KquYOv8RloHbxBKSJbWyeUVNXxRCd6HZJIoS+COUMUeY/gdGUt+WW3tLgTyvk68u411IreHaks6FFlZlzqJRTpBmMZyl1qwSbUB4XZNUM5aZ44QMeoRUEnW4asIr8vd1ynrTJPgtpWgzNYxEm0dDBRwd1cS1pXBQKJsSrZ1NINMUCYTe4x5/2Qd+WVZZ3chQOt0jrGxToXYYe116JdCkZV11kkrSFmQS1BuQXPdJpegrCOS21mnw5J1NZCLR6aJa8o6HZMsqtskKOs/xN6wdX5jWYd3bjKOXM4zUyXbOnFMqIzj3awzXQhwhqlTkttbJ/rFN2o764xGAkxQRhFs6/CY2DVBWTQSx3i1CUsGMgnKu1lnYpKVdfqTkuqU+vN0AVvnN2SdSUiW1slJYaqkrLNv0Aj53MHEK2Mgq9XW7kJATU0ocVvroF8KRW5jnZw9phCNhXXYyGqza4KyUEEcw9XT1lm4qRPxdbl45Lx1VkyyeJUzTPsUW8dIhHU6IVlmHOOohzV2SVlXEndvAplxLKybEW1pXdNqa3chkMOieHcb64wzViiyss46Ka4wtEtQttpAsXOCsjghjuHqi9ZZkZNsJShr62SCsmWdFZOsXq0EZV5NYVJI63RCssw4NvHHpoQmlaDVmhgYiZKKPUaVyrS2Ei7VC+m2VheCpeSCTkmGN7KyDh/R4zMEeM+bUGRlHUzU4KRILYaBTYYjE20TlLENJRhT8eJYhwTljHUnc2cxQTkVj/xPUKkSlLV1dpPl9gnKb4pf1RVsnd+QdVZCcjaq2C5FdSiJRGOFzjimDOT6K9A2nscGqbamC0THJVNKMh2JfOXamM5UPkn9gkcikBioT+DJi3gSDNLhyAglKNOTcZGgTEW7TZsEZZV1TJNPK0EZkHHH0/IQhrVdEpTlNLLHCcpsnd/Yqyl7IWaN3bGftr1DzDCHH7bOb9g6F2Hr/IatcxG2zm/YOhdh6/yme+vE51Gspwa7sJ+2vSOkBGV8lDbssHV+s5+xjhkUbJ3fsHUuwtb1GJwEMQwzOH7K1jHMgGHrGGbQ/FTOMxmGGRBB8P8DYojVVmTBFbsAAAAASUVORK5CYII=\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eMethod\u003c/h2\u003e\n \u003cp\u003eThis study followed a two-pronged approach to the analysis, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The first analysis is titled \u0026quot;Analysis on the Relationship between Monument Location and Disaster Hazard.\u0026quot; The first step in this analysis involved classifying the 2,049 disaster monuments extracted from the database into four categories based on the types of disasters noted: flood, tsunami, storm surge, and sediment. Note that disaster monuments like floods and sediments may cover multiple disasters. In such cases, they are treated as separate disaster monuments. The second step involved plotting the disaster monuments\u0026apos; location and then matching the disaster hazard to the monument\u0026apos;s location using a hazard map portal site. The third step was to examine the relationship between the disaster monuments\u0026apos; targeted disasters and the current hazard. For example, the current degree of flood hazard at the location of flood-related disaster monuments was examined.\u003c/p\u003e\n \u003cp\u003eThe second analysis assesses the possibility of using a Voronoi map consisting of the locations of disaster monuments as a disaster hazard map. Unlike the first analysis, which concentrated on specific areas where disaster monuments existed, this analysis covered the entire land area of Japan, including regions where no disaster monuments were present. First, mesh data with a 1 km resolution were obtained from the National Land Numerical Information website (National Land Information Division 2024), and point data for the center of gravity of each mesh were generated. This dataset comprises 178,347 meshes with a population greater than zero in 2015. Subsequently, the latitude and longitude coordinates of the point data for the center of gravity of each mesh were obtained using GIS functionality. Access to the hazard map portal site was then used to extract the disaster hazard corresponding to the center of gravity point. In other words, 178,347 accesses to the portal site were required for this extraction. Maps were created for each disaster hazard, resulting in the current national disaster hazard map. In parallel, Voronoi maps were generated for each disaster based on the location information of the disaster lore monuments. A Voronoi map divides multiple points positioned arbitrarily at a specific distance into regions based on the proximity of other points at the same distance to the points. This map is commonly used in facility layout planning. In this study, a Voronoi map was created using GIS. Finally, the Voronoi map was compared with the disaster hazard map to determine whether the distribution of disaster lore monuments could serve as a risk map to replace the disaster hazard map.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis on the Relationship between Location of Monuments and Disaster Hazard\u003c/h2\u003e\n \u003cp\u003eTables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e through \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e show the aggregate results for the current disaster hazard for each heritage monument. These tables indicate that over half of monument types are still at risk. Notably, 70% of the locations around tsunami-related monuments continue to face tsunami hazards. It may be because of the challenges of implementing hardware measures to mitigate hazards. For example, the percentage of flood-related monuments built before the 1990s, which are expected to be inundated by more than 2 m (14\u0026thinsp;~\u0026thinsp;19%), is lower than those built after the 2000s (22\u0026thinsp;~\u0026thinsp;50%). However, this trend was not observed for tsunami-related monuments. It reveals that, although flood control projects can help reduce the damage caused by floods, constructing tsunami breakwaters to prevent tsunamis remains difficult.\u003c/p\u003e\n \u003cp\u003eTo examine the differences in disaster hazards according to location, we analyzed the urbanization status of disaster monuments. The data for urban conditions were systematically arranged by cross-referencing the geographic coordinates of disaster monument sites with vector data for urbanized zones, as defined by Japan\u0026apos;s City Planning Law (2018). The information was obtained from the National Land Information Download Service (National Land Information Division 2024). The results indicate that a more significant proportion of flood and storm surge monuments are situated in areas currently at risk of disaster when they are located in urbanized areas. It suggests that urbanization exacerbates disaster hazards.\u003c/p\u003e\n \u003cp\u003eThe sediment-related monuments\u0026apos; years of construction and location do not differ. It suggests that the unique characteristics of Japanese topography make it challenging to mitigate hazards effectively through landslide countermeasures.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eDisaster\u0026nbsp;hazard\u0026nbsp;at the location of flood disaster monuments\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eDisaster\u0026nbsp;hazard\u0026nbsp;at the location of tsunami disaster monuments\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eDisaster\u0026nbsp;hazard\u0026nbsp;at the location of storm-surge disaster monuments\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eDisaster\u0026nbsp;hazard\u0026nbsp;at the location of sediment disaster monuments\u003c/p\u003e\n \u003cp\u003e\u003cimg 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YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6Yevs5A2tC8XU5WFMk5oDXiOuuT3bI9HjEk6R3Mo5jpgauTHveHUxabFAmXVlON9quiqmQC+0xczIblXJrOQqrVuPZhy30o6/eQuexqGYGdmtLCt5GmqtW4vmcKpkmhnZ3bo5FZ9Tb2QdqoHWhSBaQJpFcdS3PRpdl9xNH97roB1OSx7DnfrNwAm62qmyzm+C7IuPciB5lPJ3ckETzHfKiqRTZ13xaGPG/fpaytvbmMn+sBC8WIBn8FDR01Bq3cpPX85lv79eeH4dHvza8lRI96axzm+BdV4D1YtAtYhU626PSNclfxFvmiEtnTBwIrS5LCbnB1RZdw8lczNwE1TTpfq8V4dgVygrOpsy67J/eArW4dMobL9VfHbZ24NgVygrmkhfpXXZf/1uY25l74q38+Gsk1+fk7sNZxzW+bvoGgS3QOSbnW3cGIUBl7w63Z3jVT2x2g12iqyDULch77m85EjrvAeQbKpBkXVe5d1tyDCR9aWUtM777ALtGT8Krcts/d2/dazLfPq23G06Y7GuFcJq0LpRqz2GgshNtt/UOn9TRMsgpAQTVijfQxRZh1UdVpMknSMyTBfyTVUosu6TXFpaB9I5IsNMQ75JB8ePQut+cy29tSEzzPRXmCZPBWewzmtQ8ebcBifIOreGgkWNfOvRIuyJkm2Pmo/AgEv3pbawlOFTtXVA0IaAWgL7IOhRb0opTCW9KmNHjXXFpZRL1hWP4GlAaom9KcVwJulVGTfqrFtZmnHBOtGbshLOJr0qpnMG60CCMIbwcwfWyTrqVYEgd4PUCFu+3H6Dug4J8K4B73N3gtZBWIUwB9mtiHlY1Hn1Jdip4oxKrMveTjnCOngaey0RIArlGW9v2T+4qeBpKLMue3vWIesQKOoyO7dy+7eUfFJNltGtcz9Cm6KYQpmwDoG1vnque2R4+lzy78uVEPJVUdeFqus6ohzinUvPvO15pwBpZpV2jhsl1q3TxZY4JsGyB3S1wKtccgqQZlZWFZxRmXVrEKuRD+BBZ7beSeUhzdy6OwXBbhx1HeI1Qgh5VM+Rjrg9Mr0uUVG3CALjRQSwfDJ9mICs47zvcEEbZF3n1ONEUV0HjzvpTfH+hOUcXkQQ1kUK3rMK6zp46CLWuVjUkXUy9BnNmKzDlBOEiwN52UBsj0qPS34LP+TwUrXoU8E4OoHrdYAIdXIBRR28c83KMJGOdesU3KCog6dhXIaJSOugqIN4Z2+G2Qtew6654vsoSADbKN4Zajqk6xLeEYDqRsJrv6n+uyluFc65JL4Pg8WdUxIRDs5tUm8KgNYVDuFpkGXFMp0ne2BabwogrFspU4TL7rdt7U1Rx/AuKYx1k0SZdRNFqXVTCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZyZDWhTHNmBrGccuXE6k2PJyUlSYh7yLaiWbddnL7VE5wyavHtUxNzJXcg0LraPLzUhvnai20xRytblXJ/MjqrFtvx2JaVm8v/uatwqHYcCvLSp6GOuvcraP424uOk38Z0xyt7tbNqficGso6F972ZFPNdYKWj/Mix3FEk5KLuf8lsp1sRvMnY7tk+1SOuxTBn7ubPsgXyT0CddaV2hspnCkZrPN3ckEzB/vKiqRTZV32AD7mFuAxr7c3ZpzsDwvBC9x6qOhpKLPO/Wohldn5Yi77/fXC8+vw4NeWp0K6YWMdzfovJvqPZNgLnAhNoJsO2E42q91YxD1hkGyfPlX5MZeiFrzp3QysBf3BTpl1/h+fYqzzm2BdqT7v1SHYFcqqzqbIuk9y4gGvN1G24rPL3h4Eu0JZ0ZTe6qzLwMPPVy6u7F3xdj6cdfLrNDu5+Yxinb+L8UpELa/qCZv6gh21622G7fq2X8ugSwHleUREqndQZZ23/fPqoHXeg/7sdoyosQ5CHYQ4SJAPb+LLJK3zPrtAR8eP2roO4pu0LvPp23Kf6YxkXSuEVaFPEIp9AzaRdT3NRLue7dcy4JJbrT2WFd2AdMqsu5dzu9aJDNN9hEmmGtRYh1UdJJhupfa4DWWdyDDTX19T9JqptQ6TSpFhpiHflDtNZxTr3BqaE1Fwu+93Ylq+AcUXcBtSSGzX2wzb9W2/lgGX/OajRbiBwpDKw14UWVeCR9+1TvSmlMJU0qsydlT1pkCYa1z2Dzb/PpU92Jih3pRiKJaywThRaN3KT3H85ZzoTVkJZ5NeFdMZxTrqLYHgA3Z5n7sd6574oAWEtDtJu24zate7/XoGraMThFjbeXW87aLGOv9Oqs86BIo6r74E2yrOqMw6yDKX/R8xuhWpIwWexoy3t+wfUM45ZpTGusxOC/tRoL5bn8vs3Mrt31L2mk2OkazrroWQNIq6LmyhWE4kriF02skVbEd07+A1nGhdQL75zb6/V2NdGVIzZKlrnbc97xQgzayGKs6ozjpnfTVH1hXIOq9yCdfSlVUFZ1Rb12WFZ5mtd1J5SDO37k5BsBvVOq+BHmFnPph2ch8mIprJdoDcPoUBl+CtDrdBYx5uve/kPQnUWIf0xzoq6sg63Dt21Fnnfn1NKFZ4dpm2UsK6SMF7Vq11mSfvwUuPFxEcsm5jCvoxR7QOU0nc3MT4hrGovwuzE+JEM9muu30Kgy5hH6ZXp3uM+u9gUtZBUQfGG5hhroNx2IcJaSU8cCjq4GkYmWE6a6v4QQFFHWabVmWYeL275nqNpDiTXYp+a+C7KdTuaqeZaNf9s9M45lIAKd8S3uBF614UW4epJvadlkSE85sm9aa42/Doya7CoVgpluk82QPDelPyL+H//Af4kFfKFOGy+22belMmw/AuqbNuoiis6yaI2lg3jbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7GRI68JYzjcet3xa0ETjXmNghmTZTjbrtEu2T+O4SzjlcuhW4e9zco9AoXXlKJWcsNAWMyO7VSWzkquzbr0di8mQvb34m7cKh2LDreAc5eNHnXXu1lH81+spnCqZZkZ2t25OxefUUNa5tVjYVHOdoOWHoJBbC2nq/6BXO9lONnOSdsn2qRxzyW/C239xE+6m2q+dOutK7Y30oxyd0N/JBU08bVmRdKqsyx7Ay7YAj3m9vTHjZH9YCF7g1kNFT0OZde5XCyl3q3nd//564TnI56wtT4V0w8Y6FAxCG6oX1R7j5P5h5EQRbuJNB2yXNHPhP2zX2aYWr+OYS/dRVX8Rb5oYNDsos87/49MoJ064GtbnvToEu0JZ1dkUWfdJTjzg9SbKVnx22duDYFcoK5pIX5l12UV4fbL7H4Z7V7ydD2ed/PqcPGI4o1jn76IEQesjSBu9qids6gt21C5p5geiXWebWryOQZcg1CVOe3U8WQdV1nnbP69GdN9efUla5z1I0zEFqLEOQh2EOEiQD2/iU5HWeZ9doKPjR21dl9m5VRLWZT59W+4znZGsa2G8wRQzxnqO9g3YRNYlzbCeo3bJ9mkMuoRVncws/c2+UKnKuns5V1rnP0qLDNOFfFMVaqzDqg4STLdSe9yGsk5kmOmvryl6zRRbl/1/b2cpw0xDvin3mc4o1rk1NCdqeFC/wVoSw/INEZFugxfYrtMM6zxY7W6fxgkuBe3GPC4p1+yiyLoSPHpp3b2kN6UUppJelbGjqjcFwlzjsn+w+fep7MHGDPWmFEOxlA3GiVrrfgOfFtSbshLOJr0qpjOKdWgRULv61Ie1ILHuCQa10PHvJO1kM9cT7TrbeEev5SSX/CZFVLzpQY11/p1UYl3nhFDUQbLpN5X0RCizDrLMZf9HjG5F6kiBpzHj7S37B5Rzjhml1uUD+RpBUQfJZm7/lrLXbHKMZJ1cE70otatU14Wtz1GnSFxD6LTDFdmObOvewWs40aUI8lP/vtxIUGNdGVIzZMnx78kTeNvzTgHSzGqo4ozqrHPWV3NkXYGs8yqXcC1dWVVwRpXWZT+RHSiZrXdSeUgzt+5OQbAb1TqvEbo1tCmoua/ow0SgGYQ42U5un85JLrmbkMtiUbfYm2OqsQ6hWAdFnTghFXVkncg7x4w669yvrwnFCs8u01ZKWBcpeM8qtA6KOsf5jz6kIFjUkXUbU9CPOaJ1mErCDWaNkFNCitnfhZm0o2addsn2qZzkEoQ6vGoHGWpvT6Ja67onhKIOPjIMzDDXwTjsw4S0Eh44FHXwNMzLMLP7mH18e9FxoKij7kybMkzsTIQ6rSGLs+Q7J35r4Lsp1O5q0ky26/zZqQy4hF8RgWwvEHlfn7dKrVsSJ1xNOSXZn9k0qTfF3YbHT3YVDsVKsUznyR4Y1puSf0n/I/551lkpU4QDC23qTZkMw7ukzrqJorCumyBKe1OmErZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbN70ENPD5ybB1OmHrzAFH4opoMcQo5jQipZhq4CTYOp2wdQYhJQqHGmRyYJzYAdg6nbB1JhHg5IzHhiF/BWzduYWtMwm3VnNxKHJETFeFxRuq6ES1X8pJ38QYsH5L5KOvgK3TCVtnFH4rCER+GeGEAjguc4ATebi1ePc+zkgM0sFtAL6J6QZeBVunE7bOLKLdp9ST4uMCEk6ccyDEmEc3eIBmHAhbPlt3fmHrzEJMTywSS8okQUTKNEWggwM4VSP6yNadX9g6s+hYl0zcEcWBiHWJdVTlQcRj684vbJ1ZJNaJCVNBMjCuzzq/hbLBbrbu/MLWmUVinZjvOwyggJOrPcGPZo9j684vbJ1R4FUBKRN+U4UmiYsfxC2/5wsr2CagA1TinQxbpxO2zk7YOp2wdXYypHU42TEtKG7KBcbRJLBKRLvk+ODyNAZdwslaxcTIXn1CMySX2zhJK5yuMe84hbaYo9WtKpkfWaV165G4a1wWDsUcrW4FZ0seP0qty+y0v72Yf4kTtsIz2Lo5FZ9TQ1nn1sT3XujCe8uXC7ok2DcvuWyXHB9cnsqAS/jtG6+OiXQ0+O0aVdbhjOStnN9ccoL6vL+TC5o52FtWJJ0664pHG3TXuMz+sBC8WIBn8FDR01Bp3drRF3NO9vvrhefX4cGvLU+FdMPGOrrmLnpwol26/h7VXPqCZ/+3PLGdbFa7OrCUvTyvYdC6DNwEEOOiFr73e1Fl3T06URmnIy9HYX3eq0OwK5QVnU2Zddk/PCXraFl8dtnbg2BXKCua0luhdWsvULaVvSvezoezTn6dZic3n1GsE1cpApEtBq082dQX7KidbNa6PbDE29dzkksRxNP2ktzooMg6CHUbKYiveL6gfl9Y5z3ozW3HiiLrvMq725hhiqW0zvvsgjw8btRZl3/5Ab5C0rrMp2+L3cYzknX0A6NAfBMGbKKY128TWSeawfH+5ZmsA+ncau1xUt8lKLIOqzqRYMIDbt6hDNN9NBhox4ci6z7Jpck6WFYimWGmv76m5jVTaJ27VXt8FH97UWSY6a8wTZ4KRrHOraE5UWuXFo28jGH5hqi6bkMKie1ks8bVgWV/v8tJHHMpxAskfvPRIkShvkxWkXVA0G4UdtAzEC7A3pRSmEp6VcaOGuuKSykXrcMlWCd6U4rhTNKrMm6UWZfd//0/zGT3v5ij3pSVcDbpVTGdUayj3hKIOhmxuCGte+KDHaHj30nayWb4A4i+Jd7RaznBJa8uM9Swr7ZTZx34/evEOtyEog4yTgh/Ks6oxLrs7ZSD1tESrUMK5Rlvb9k/uKngaaiz7s/vwcNdOcDaDou6zM6t3P4tJZ9Uk2Uk63rWYCF6TcLW56iT+Op1t12yMrh8LSe55Dc/knZPyDqnfI/qulJ9Hm697XmnAGlmNVRxRiXWrUOa3AuK5lUuOQVIMyurCs6o2Lo8dV9mtt5JwVp66+4UBLtRrfMaWKSJxSv6MBHZ7Njy9ZzkkvfdjRr+bYAX0DootA7qONmHCTdU1JF1uDV2FNV18LiproOliHUuFnVkXaTgPauwrkPF8rtXIGhjUUfWbZzXfsyhAotgROswlewssLOyvwszaSebHVuewkkuRSH1YXr1vqek0LpymPIhxQwo1EFRB+KblWEiA9ZBUQdPw7gMk/owIa2EBw9FHV4xP6cZpvuR7NMYkuGsw+964u/VqTiTC8BvDXw3hdpdlceTdt32pzHgUoCpEnUnwkr/xQM11uF3YehEfpO+m+KURISDTZN6U4B+64pl2sgeGNabgtrFMV48WClThMvut89lb4q/OdwbPGHIWDcZhndJYaybJMqsmygKrTOEYb4C0gtbpxO2ziBC+jmPHA1MbNCy5dMFLsowcY1+XAdp4GvyTbZOJ2ydOdCwYE+fitHA5BhhGOTcWuMGfdM4jgOxrDbi3U33dfGPrdMJW2cMybBgNGBD7xhh2KGItkGs+4j6U8KWj8bRvlfA1umErTOGZFgwkinZSHrwpXUP8EtYaCNeTmPrzitsnTEkw4IJ6+RG8uXixDqq5aC+Y+vOM2ydMSTDggnB5Ia4ROcvSus+SkYIY+vOM2ydOSTDgpFMcsOJMNh9jHXd+xTjINjhCGFs3XmGrTMIGhYsGQ2MNuQSf9wWBzihSIBXDsDFOI5g7dXasXU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dDGWdW8NZumDFa9AYnHLRWSYk7QaP0yRf1OL1DLqEc6e2crCSjPnZQZ11Xj2upeEG52ottMUcrW5VyfzIKq1bxzlavb24cRmexqGYo9WtLCt5Ggqty+y0/3IlhVO20hyt7tbNqficGsY6d9MHocAbHNY9aHhykWzKVt12g8dx7OlkHobXMuAS3V8VtPOb0r4OyqwrtzdScL4lnJfc38kFTTxtWZF06qwrHm3MONmD5VTh2WX/h4XgxQI8g4eKnoY667L7t2byu1ey318vPL8OD35teSqkG8o6fxFvWmL4driRi2QTbgRJu4HjXgOnYBhmEtkBl8T9NUPn/jFjVVlXJrnLOB15OQrr814dgl2hrOhsyqzL/uEpWEfxbj1afXbZ24NgVygrmtJbnXVr0RzerO5d8XY+nHXy6zQ7ufkMXdeBO0IfnJMSFw1fbPYEO8Rr3JH7k+N5mgBlmGB3kktePYBQ11VboMi6oL0Et14db4P6fWGd9yBNBxWgyDqv8u52NOPtYUJZeHa/TtZ5n12Qh8eNMusyO7fgBcrv/vYZWZf59G15wHSGts7fdDFjhHejtK51W2wO2ORv3pD7k+O3IUoeb3cSJ7kE58Wqrj/BVGSdW609bse1RUgw4QE371CG6T7qP/U4UWTdJ7k0WIcJJlj34lc/YoaZ/vqamk8qhdZhggnWPf+nHzDDTH+FafJUMLR1kOWJWbsC0TMCNskYlm+IUHSbUsj7vmzWOX4DS0Ix99ApnOSSyC6DNvZudFFjnd989Au42d1Fz0C4AHtTSmEq6VUZO2qsKy6lXLTuR/Ss8GIhwN6UYjiT9KqMG3XW/fk9eAb559cL2JuyEs4mvSqmM6x1AcStV1j3xHfCOHT8O7LdoHU+dm2+bl6hDie4hOdF/KZcESiybgd1K7UpsIqOFCzqIOP0m0p6IpRYl72dcvqsw0deKGPG6R/cVPA01FuHDxqKOsg4cxT+TGdI6/z7cCPqulBkmKGs28LW56hTJK4RYDvZLDkuYpzITk/huEt0XiKawJUDYR1EOMwwS3WMrt72vFOANLMaqjijEuvW2/ghF8f/4xlmmMVnl2GfV7mE/qUrqwrOqLiuW9m7As8js/VOCvxLb92dgmA3nHVQXDnOoi87JQf6KLGBRLTbPX7ca/RJ8wqOuSTPC2t4FaEHNdYJt4J6VfRhwg0VdWQdbo0dRXUdPG6IdUkfJty4WNSRdZGC96wy65I+TLhxsagj6zamoB9zKOtwGkpMETF3DBqeXFAq2deFKdvdOHYcM9AhGHQJr9PB/WEXYn+oU2Qd9WFiPgkxL6BQB0UdfGSYlWEiZB2mmAUKdVDUwdMwLsOkFDO/i6EOijqKffZkmAG+9+nLIaAVWiQXnaWk067/uNcYpqZDBlyCVA/5GMtCzPl6UGQdnRJOBbpT701JRDjYNKk3BSDrnOwBfTfFKZbpPLBpVm8KPOR9+m6Ks1KmCAebVvWmTIThXVJl3YRRZt1EUWjdlMLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOTtg6nbB1dsLW6YSts5OhrHNrcdzC2fi9hpj6OIwD3J9sJiTtOvtlu1D++WkcdynCGZfFTMk0ZXGCQuvKNCuyV49raTgxzYzsVpXMSq7OunV4xXAy5OIRzkNeOBQzI7uVZSVPQ611a9Gck38Z08zI7tbNqficGsY6d9MHocAbvxXg9P4oF9okN0UjIGmX7E/ahTXXCYbR7phLfhN8zaF6cRzJfYQ660rtDbjvMt76O7mgmYN9ZUXSqbIuewAv20LKrbRjsC77w0LwYgGewUNFT0OpdSs/fTmX/f564fl1ePBry1Mh3VDW+Yt40wqdCN/5eINeiTVxI0jadfdTO69BjUG90zjm0n1Ule7WCfFOOiizzv/jU4h15RbKVqrPe3UIdoWyqrMpsu6TnHzA2QOIbsVnl709CHaFsqKJ9FVal/3X7zbmVvaueDsfzjr59Tm523CGruvAHaEPRrEem3qDHeI17nT3Uzt/F90ZJtgNugShLnHaq/adRpV13vbPq1EqaC/hhrTOe5CmYwpQYx2Eug1xx73WeZ9doH3jR6F1ma2/+7eOdZlP35a7TWdo6/xNV0Q41EfY1Nnsxd+80d0v24XJ9ikMuoSpJUUd+HO8jy6qrLuXc6tRulp73IayTmSY7iPxCFSgxjqs6iDBhDWyTmSY6a+vKXrNVFr3m2vprQ2ZYaa/ouc0DQxtHSR73aDVF8PyDRGRblMKed/viW3UjkpCJxqIiSdxgktBW/SiUK7ZRZF1pcAB63LNR7+AOBulCtibUgrFUjYZK6p6U5zCYeMyLMg60ZtSDGeSXpVxo866laUZF6wTvSkr4WzSq2I6w1oX9Gg2YN0T3wnj0PHvyHaD1lGvShyfpa4D/Cbehfd5/1+rsc6/kyLrdqiqo44ULOq8+pLfVNITocw66Zu4RQrlGW9v2T/APs1xo8y67O1Zh6xDoKjL7NzK7d9S9ppNjiGt8+/DjSjkwv66LmyREJG4VoDtus2kdUh37TWc6FKEuWXYn2Aqsq5MlyhkViu6L73teacAaWY1VHFGddY566tw3x3rvMolpwBpZgX3jhtl1q0dif8fH8CDzmy9k8pDmrl1dwqC3XDWQVHnOIt+p3NSONTtq0wQ7XY7+zuueY0BbU7kJJfwcoS46UWNdQjVdahYUIfUloo6so6u4o0bdda5VMYl1tEWWRcpeM8qrOvgoYtY52JRR9bJ0Gc0Q1nnt/ATp+Zi7tjtm6RUsq8LU7a70dmfWIcZ6BCc5BKFOrK5F6XWUR8mpJVwDijq4CPDwAyTQl3HOijq4GkYl2Ei0joo6iDe2ZRhBuhSjOKAVmgT9i1imSY3Ezrtkv2indcYpqZDBlxyq/D31IcfDaanaq1z8NsweOKSiHB+06TeFHcbHj3ZhZ2Z37wF0pXpPNkD03pTAGHdSpkiXHa/bVVvykQY3iV11k0UhXXdBFFq3VTC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXbC1umErbMTtk4nbJ2dsHU6YevshK3TCVtnJ2ydTtg6O2HrdMLW2QlbpxO2zk7YOp2wdXZyFuvcWhy3cKJwr0Fzsg5un5mTXArl5Pz9KLOu3I5pXlavHtfSQVvM0epWlcyPrNK69UjcNS4Lh2KOVrci5kseNwqty+y0/3Il5eRfxjRHq7t1cyo+p85gHU7N79ZAM5x1PGh4g9uy2Rk4waWwlnaC49qpss5vCsnL7Y2U4+/kgiaeuqxIOnXWFY826K5xmf1hIXixAM/goaKnoc667P6tmfzulez31wvPr8ODX1ueCunOYp2/iDet0IkiWImiwW24OSPHXfLqOCN5BOr1o8q6e8LvMnleqs97dQh2hbKisymzLvuHp2QdLYvPLnt7EOwKZUVTequzbi2aw5vVvSvezoezTn6dZic3n7PWdV4jgP9gRQa3we0zcdwlfxcz1+PBTpF1EOogxjlBewm3pHXeg0Hnx4Yi67zKu9uYYYqltM777II8PG6UWZfZuQXPIr/722dkXebTt+UB0zmrdf6miwklGoFeHNs+EydY1wzhdmLWYVXXyrnV2uM2lHUiw3QfDZ58fCiy7pNcmqyDZSWSGWb662tqXjOF1mGCCdY9/6cfMMNMf4Vp8lRwVuvuQxkn4xBZ1t2O45iSzNsu3o7EcZfcKgoXNebldoIi64Cg3Sg0H/0CfI9SBexNKYViKY+PFTXWFZdSLlqHS7BO9KYUw5mkV2XcqLPuz+/By55/fr2AvSkr4WzSq2I6Z7QugLDWa13Pdhw6Ifzz72C70TjBJbcKFkPckZsJ6qwD3X69Q1UddaRgUefVl/ymkp4IJdZlb6cctI6WaB1SKM94e8v+wU0FT0O9dfigoaiDjDNH4c90zmadfx9uRB0XYh3Xux1jXRed6RLCK1zym3jHfSi0zik/2kXfRPeltz3vFCDNrIYqzqjEunVIk3tB0bzKJacAaWZlVcEZFdd1K3tX4Blktt5JgX/prbtTEOzOZB0UcY6z6Hf6LAe3z8rJLnl1rO36UWid+2iRFAvqkNZSUUfWRSrOqKiug8dNdR0sRaxzsagj6yIF71ll1iV9mHDjYlFH1m1MQT/mWazzW/gRWnMxpcQuy8HtM3OiS5iuHkOhdeUwhX2YkFbCOaCoQ+2NyjCRAeugqIOnYVyGSSlmfhdDHRR1FPuszTCxvwQAF0A3kGxw++wcd4m+IiLXe1FjHRWReNEggCQNlyUR4fymUb0pQL91xTJtZA8M602Bh7xP301xVsoU4WDT5t4UNQzvksJYN0mUWTdRFFo3pbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHV2wtbphK2zE7ZOJ2ydnbB1OmHr7ISt0wlbZydsnU7YOjth63TC1tkJW6cTts5O2DqdsHXnC/GbNYZhJsXPpHvq4VinE451dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnY7BOzG/guDUxEqbcPAtsnZGwdaMysnU4RWQchHDT8HBKxxrOKuJ+5HoNMb0BWzcCbJ2dnCHWRTFo5bdqOHsIjfosF28y6rOArTMStm5UzmAdThAJjtF4s6GIaxEqyNaNDFtnJ2ep60i4ACMeZJZo4RPINyOwzm/BTkw4a2J1VNg6I2HrRuUs1pFwEZZxOF1WEMdhSLFud9ONar+M4zuNmFYp+RwFts5I2LpROYt1XiNyvI/Dlo9zRWKsC4R1cAMr2JsiV0Xz4WHrjIStG5WzWOeAcEEA5Z33MW51rIPgl1gnVqnxCLB1RsLWjcqZrPNbH3/uQUD7JZVubN2ZYevs5EzWQVEXYkEnps1i684MW2cnZ7OOJiP3GmI6R2Hd+2zd6LB1dnI267zP0ag72JcC0sXxR604iOI4wq+sCHB1ZO3YOiNh60blbNYpgq0zErZuVM6xdThNcRw35nGaZLjtQ5l1ZTgpzoZMUzPDI6CZkd2qklnJVVnnbrfjby7ASvEobi2knMKhmBnZrSwreRpKrcvstL+9mH8Z08zI7tbNqficOsfWQcqKqarjNwMnGNBOlXV+M45bOZRvI+X4O7mgCRtOWZF0aqxzv76W8vY2Zpxi9S2n8GLB/2EheAHyOQ8VPQ2V1q0dfTHnZL+/Xnh+HR782vJUSHeOrfMX8TYUXTPiposq6+6hZKAZmueU6vNeHYJdoazobIqsy8BNYfuCt4ehbb32v+uXvT0IdoWyoon0FVq39gJlW9m74u18OOvk1+fkfsM553WdV/W8Ol4VHAh2iqyDUAcxDnLbJdyS1nkP0nRQAerquvWlVPbHa/BcCi82hXXeZ5h0qkCddfmXH+ArJK3LfPq22G0859y6IKQEE69ViCgkUWQdVnWtnFutPW5DWScyTPdR35nHijLrQDone4DVXOHFr37EDDMNeac8OG6UWedu1R4fxd9eFBlm+itMk6eCc27dfd/xd/ECxWSsA4J2o9B89AuQPUoVsDelFIqlPD5WFFlXPIrjjRm30oS36Xr9MvWmFMOZpFdl3CizLrv/+3+Yye5/MUe9KSvhbNKrYjrn2zq8Ljhh60C3X+9QVUcdKVjUefUlv6mkJ0JZrPP2Wgspt4K9wN+8hTsK5Rmo8/yDmwqehjrr/vwePNyVA6ztsKjL7NzK7d9S9ZpNkPNtXRhiHz5mmOFE6jqi/GgXfRPdl972vFOANLMaqjijuroueyCuEsilV7mE3ZnpyqqCMyq2Lk/dl5mtd1Kwlt66OwXB7lxb525imJtkHybgPlokxYI6eE5FHVkXqTijOuu8P1EZR52X8DSwqCPrIgXvWYV1HSqW372Sclws6si6DV39mMk3IAfBr0Ae5+S9knNtnRiVBVPMSV2vA8phCvswIa2Ec0BRBy+3eRmms05BrXgkCjko6tBA0zJM6sOEtBIePBR1eMV8YhkmDcoFn/ReI/lmI6wNax0OsTBO60bzPRQ/ShiaQZcicad+c0LfTXGr8FLjRQP8WgwuSyLCwQMwqDelcAjPAuzy9mRN5xTLdJ7sgWG9KahdHOPFg5UyRbjsfntivSn0DX9ADg2EIp783j+OHMPr1Yxm3Qi+A5H8KdDQDO+Swlg3SdTFukmi0DqNvIF1pw5eMmKsG/7MyJvGulfD1p0jpt86HA4ogvd+GOPOAHKJAFSQQ3L5rY9asAcSUXEAR4mN4ZhcC08Yu0updQFb93rYuvNL1zqo8Xbvh7VM7QkY1PC8RgAaXk2G5ALVPlqk97o44Dp/G9LegH5iiiYeG7trwLoBjQEa0zkCkYf0Hf0m2aI4fiKtE43lR0bSINnZA1tnJNMf6yhpgzc7lVLYu4c7kiG5/FbxKmgSULcftRR7A1F2ha1d2bBLv3UDGtM+Oln49OlwvjtRCH8Bfwp7vQY+hOReXfmR8Vg0SD4YemHrjMQC6/CNSnme6E6kHotkwASUiyRJDiR7xT2AG7Jhl37rBjVGhHU1d0jfn0KrIKYGQvzuvdJNbwN5vANbZyTTbR2OmEAudKyL4oDe+z3WCVnkgWQvjRsLK6dZB/RpjHSsw+1Tfce8EQippOvWdd3PgKRBZ2cPbJ2RTKd1+JYG3M/lG1e89yFY4Lt6wDqKMe8nBzp7W9ACJKjKhl0GrBvQGDnZulf5Hsi/orMn1vV+BiQNOjt7YOuMZDqtcyIMdvSGF29tXEUZcPhlnHSg+96/A0me495ODuDe99EIeIvjX/XaJOi3blBjQm7Q9qm+0y1A0VVa1/8ZkDRIdvbA1hnJlFpH/f6iF1D0/0GuiDkafl3lQdzalUNy3aBuRCCUB3xsiJ2LEIhgdzKMV692/dYNakxA7QZ307iXWPda36XsAfWkiO7OTmNxl7JBZ2cPbJ2RTKt16ui3bkBjoR0oV/u4UR3Od/pgSFo8IemSxr78yBANOh8MPbB1RsLWjcqx3hSdsHVGwtaNClunE7bOTtg6nbB1dsLW6YStsxO2TidsnZ2wdTph6+yErdMJW2cnbJ1O2Do7Yet0wtbZCVunE7bOUHAUsITe9SEZs3X4TWtg8GvNQ8LWGYmF1vWOAnbqiGDHGd06+oEcuZXQI7uw7tgP54aErTMSC63rHQVhcESEIRjZOvrVkd/qPdNx2TnWDQdbZxo4HsNtF3/BE2H4gRiD69VkcCFxWLZ9NaNaR9KhdhFtEsdllz+sGxW2zkgssg6nuwlqLo4Clgz8g+v06zcnrF0Vh2XjVzOidV5D3Kdba+UTvxPxUXLxC1oxOBjsH1E+ts5ILLKOKih4n4Np+PPszm+1pXU35OHTGNG6AH86h4RxkAztQOILycG9APynn7Qmg4WNAFtnJDZlmBBS0AA0DTcGrHPl4dM4u3U4pq04JfxLPgP81kc4QgN8BiRjgYnmw8HWGYlVvSluDVXDt33vaEAdF8Th0xjZOrh3pCfWyYdAPvqtB3hSqOuw1gSGMb8DW2ckNtV11JVII3P1jQYkhzK5Kg+fxojWdTovo5bfZ52U3G9RSYfWyYajwNYZiU3WPfXhnU7W1ZKBf2D9fTm4EI5IgodPY0TrMKzigvowk6GK4F/yGQCxDhuAdclYYKPA1hmJTda936DLBWDYnWTgH1jHNz4OLuTLw6cxqnWkOJwDhJN+Nzyw7o78DMCxwjDy4thE9DkwxEPowtYZiVV13VgY1Tos7QAZ8NBvTCvjO0JyWKPPABocTIwFNgpsnZGwdaMyunUKGXAJM9hkidOotnB6foFC68pRyim16WSFtpij1a0qmR9ZpXXrEdx18SjG2VkLh2KOVreyrORpqLPO3TqKv71IU7bSHK3u1s2p+Jw6x9aFsr8Ul+4mZLDVrnbqrCu1N1KlWtoJmjl/Jwe3sK+sSDp11hWPNmac4ouFlFtZzf2wEMCa4zxU9DSUWed+tZDK7Hwxl/3+euH5dXjwa8tTId05ts7fekrW0dJfxLVmJ2FVZp3/x6fRfB3nJC/XPq7Pe3UIdoWyqrOpsi77h6dg3XrtLZyTfPXZZW8PQ15Z0ZTe6qzLwAufr1xc2bvi7Xw46+TXaXZy8zm31nnVq3hlorPE1Xqnb0aVdd72z6tRbgcDXNB81CDrvAdpcXD8KLLOq7y7DRlm4fDujFe5VBLWeZ9dkIfHjdq6DuKbtC7z6dtyn+mcW+vu+y7OQNRZAv5m5+2vyrp7OThbronVXNC8Qxmm+6hbTo4bRdZ9kkujdVjXNS47Wcow019fU/SaqbUOk0qRYaYh35Q7Tee8WhcGUMeBbckSud+9AqjIuhKdLU0FZLkxH2BvSilMJb0qY0eNdcWllEvWuZV2C96p1JtSDGeSXpVxo9C6lZ/i+Ms50ZuyEs4mvSqmc06t8+/AewZsS5ZI0HPtT411/p0Uni2FHaZxXKPICkWdV1/ym0p6IpRYl70NzwKt8/50LV1p36QHXijPeHvL/oHYGi9KY11mp4X9KFDfrc9ldm7l9m8p+aSaLOfUOrzW18sSKHFfHkPUWFdud87mSM+87XmnAGlmNVRxRiXWrctncbOM8Y66VKDSu+QUIM2srCo4o9q6Lis8y2y9k8pDmrl1dwqC3bmt67oxTizpB+uLSY6pxjoEYx0sqPMStrCoI+to77hRVNfB44ZY51bQusI2WOdiUUfWRQres2qtyzx5D156vIjgkHUbU9CPaYp1fhM/vkXSB6i2rtQWhRwUdWigURkmQhlm8egmXq+Dc0BRB0/DyAzTWVvFDwoo6jDbtDPDxCnJT+LknzeMOnzKq60LRNak/nodnm2+nuhdEhEOlDepNwUQvSnyuylOsUznyR4Y1puSfwn/zz/Ah7xSpgiX3W9b2JviNV7xzcoTrRt5qLDhXVJn3URRZt1EURvrppERYx3OSz48bxbrXgdbd45g60ZFqXWjDhXG1hkJWzcqA9bhz3gCcCvy8ad6CCSVYiiwqPbLOALr8Ldzxxr6rY9aWHjhb1zl0GCRHCoMEI1hF95F0iDZ2QNbZyRs3aj0WyfG+LvaiHc3wTIxBIMYqOHp03j3fljL1J6Ebq3hDTQEgz5aFLEtGRosGSqsc6+uW6O7eCwaJMMJ9sLWGQlbNyr91iVj/KEOODYDkgyPQkUabfitYLCh3ypeJbuSocFoAAdZ13VHDoSb3gbyeAe2zkjYulE5VtdRx2MyIgrSsQ63qa4Tlw/6GmIfJjXEvBEIqaTr1nXdkQOTBp2dPbB1RsLWjcqAdXKMv9OtG2iI1lGDZGgwCmOJdb0jB3bGDkt29sDWGQlbNyr91qEmg9YlG7RN1kFyONhQJp3O+3QL0Piz0rqksWiZNEh29sDWGQlbNyr91oUtMcZfv3ViKLB7iXUY/AYb4thgoJl7Ww4RGFBPiuju7DQWdykbdHb2wNYZCVs3Kv3WeWKMv904jkK6DICAcrWPG1XR34+jgFGS2dfwBl02EOWaHBoMW9BQYZ3GPhzBuxANkp10CglbZyRs3agc603RCVtnJGzdqLB1OmHr7ISt0wlbd76gKolhmInxM7aOYSbMz2TEYxhmQjjO/weuAgWuiqdsOAAAAABJRU5ErkJggg==\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePotential Use of Voronoi Diagrams as Disaster Hazard Maps Based on the Location of Disaster Monuments\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe Voronoi diagrams in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e depict the spatial distribution of the monuments for each disaster. These diagrams reveal that smaller areas exist in regions with higher monument concentrations. Nevertheless, peninsulas and islands exhibit smaller areas, regardless of their traditional monuments, a characteristic inherent to Voronoi diagrams. When evaluating these areas, it is essential to determine whether there are associated disaster lores.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the current disaster hazard, measured in units of a 1 km mesh, and categorizes the hazard levels based on the severity of the hazard. The legends in the figures correspond to Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. There are three levels for flood, tsunami, and storm surge, and meshes without colors are outside the area or have no data. There are two colors for sediment: high-hazard areas are purple, and low-hazard areas are light blue. The distribution of flood hazards is widespread across Japan, with particularly high-hazard areas in the Tokyo and Tokai metropolitan areas. As expected, tsunami hazards were limited to the coastal regions. The concentration of hazards is noteworthy in the Kanto and Tohoku regions and in the cities of Nagoya, Osaka, Tokushima, and Kochi, which were affected by the Great East Japan Earthquake. Storm surge hazards are focused on several key locations, including Tokyo, Nagoya, Osaka, Saga, and Kumamoto. On the other hand, landslide hazards are less prevalent and not present in Hokkaido or the Tokyo metropolitan area.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the correlation between the size of the Voronoi diagram area and the disaster hazard. The vertical axis of the figure represents the percentage of hazard to the number of 1 km meshes corresponding to each area within the Voronoi diagram. The hazard count was 1 for minor hazards, 2 for moderate hazards, and 3 for significant hazards for floods, tsunamis, and storm surges. The number of sediment disasters is 1 for minor and 2 for significant hazards. The data presented herein pertain only to areas that are not exceedingly small, possess at least one 1 km mesh, and do not have a disaster hazard of zero, which is often the case in island regions. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows an example of this calculation for floods, tsunamis, and storm surges. A Voronoi diagram comprises four 1 km mesh areas: one mesh had more than 2m of inundation, one had more than 1m and less than 2m of inundation, one had less than 1m of inundation, and one had outside hazards. Because the diagram consists of four meshes and the total disaster hazard is six (equivalent to 3\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;1), the hazard ratio for the Voronoi diagram was calculated as 1.5 (=(3\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;1)/4). However, in the case of sediment hazards, the maximum value of the ratio of disaster hazard is 2, given the existence of two distinct hazard levels. Note that the sample sizes for each hazard category differed from those in the previous analyses, as the count was based on the number of Voronoi maps and not the number of disaster monuments.\u003c/p\u003e\n \u003cp\u003eThe relationship between the area and the hazard ratio depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates that the disaster hazard ratio tends to decrease as the area increases for all disasters. The regression model in Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and 8 illustrates this relationship. In each model, the negative and statistically significant relationship between the size of the area and the hazard ratio indicates that the Voronoi diagram based on the disaster tradition monument partially accurately represents the current disaster hazard. In other words, the areas with small areas in the Voronoi diagram are where the disaster monuments are concentrated, which means that the current disaster hazard is high.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the results expanding the regions where the number of samples exceeds a certain level, and the accuracy is relatively high. Floods are on the upper left of the figure, and it shows that the Voronoi area is small in the coastal areas of the Hokuriku region, where the current flood hazard is severe. The relationship between the two is solid around the Shogawa River in Takaoka City, Toyama Prefecture (the circle in the figure). The tsunami is on the Tohoku region\u0026apos;s Pacific side in the figure\u0026apos;s upper right. The tsunami hazard is high on the Pacific side of the Tohoku region in the upper right of the figure, which was greatly affected by the Great East Japan Earthquake, and the accumulation of monuments and high tsunami hazards are seen along the Sanriku coast. In the case of storm surges, the relationship between the two is particularly evident in the Tokai region. The area circled by the circle was severely damaged by Typhoon Isewan in 1959, and the storm surge hazard remains. In the case of sediment, the Kinki region has a deep relationship. The hazards are widely distributed inland. The areas with tiny Voronoi areas and serious landslide hazards are the Rokko, Kizu River, and Kiiyama.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Result of linear\u0026nbsp;regression\u0026nbsp;model\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e Result of linear\u0026nbsp;regression\u0026nbsp;model\u0026nbsp;(continue)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnalysis of the relationship between the monument's location and the disaster hazard revealed that the monument's location does not necessarily correspond with the current hazard map. Specifically, 50% of the monument locations for flood and sediment hazards were outside the hazard area. Recognizing that being designated a hazardous area does not necessarily guarantee safety is essential. Although hazard maps raise residents' awareness of disaster prevention, they have certain limitations (Saiga et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For example, in the 2019 typhoon that hit eastern Japan, victims were found in areas not designated as landslide hazards. Even accurate information is not trusted if it goes against people's desire for safety, and even inaccurate information is believed if it is embedded in the local body of knowledge (Isoda et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reassurance that residents would trust the measures to protect them, even though they were aware of the warnings, is misplaced (Van Luik et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Assessing hazards through lessons learned from past disasters, such as disaster monuments, aside from surveys and simulation\u003cb\u003es\u003c/b\u003e, is crucial.\u003c/p\u003e \u003cp\u003eThe relationship between the concentration of disaster monuments and the severity of disaster hazards can be observed from a macro perspective, as indicated by the analysis of Voronoi maps and actual disaster hazards based on disaster legacy monuments. Preparing for future disasters using traditional monuments has the potential to reduce the damage caused by disasters with a high probability of occurrence. Monuments in the Tohoku region, many of which were erected after the Great East Japan Earthquake of 2011, are still fresh in our minds. However, in areas likely to be affected by the Nankai Trough Earthquake (MLIT \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which has a 70\u0026ndash;80% probability of occurring within the next 30 years with a magnitude of 8\u0026ndash;9 on the Richter scale, there are differences between areas with and without traditional monuments. For instance, disaster monuments are concentrated in Tokushima, Kochi, and Nagasaki prefectures but rarely in Aichi Prefecture. In such areas, disaster preparedness that relies on more than just the traditions of past disasters is essential.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eVisualization techniques in disseminating disaster information have been demonstrated to enhance citizens\u0026rsquo; awareness of disasters (Andrienko et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, advancements in Geographic Information Systems have facilitated the comprehension of intricate disaster problems (Cariolet et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A project has begun to map past disaster cases (Suzuki \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In 2019, the Geospatial Information Authority of Japan (GSI) began including disaster lore monuments on topographic maps (Fujii et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This development enables citizens to access the most recent disaster analysis findings and historical records of disasters through a single platform.\u003c/p\u003e \u003cp\u003eThis study endeavored to quantify ancient disaster monuments' potential, which commemorate past catastrophes' lessons, to serve as contemporary disaster hazard maps. Utilizing the disaster-related monument database made available by the Geospatial Information Authority of Japan (GSI), we analyzed to elucidate the present disaster hazard at the location where each monument was erected while also examining the relationship between the concentration of monuments and the current distribution of disaster hazards.\u003c/p\u003e \u003cp\u003eThe findings of this analysis offer valuable insights into how to prepare for future disaster hazards effectively. Depending on the presence or absence of traditional monuments in areas with similar hazards, different approaches can be employed in disaster education. If a monument exists but there is no current hazard due to preventive measures, it may be beneficial to convey the limitations of hazard maps.\u003c/p\u003e \u003cp\u003eHowever, it is essential to acknowledge the limitations of the present study. The analysis is based solely on the monument's location, without considering the distance between the monument and the disaster site. This aspect should be addressed in greater detail for a more accurate analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003eJSPS KAKENHI Grant Number 22K04580 supported this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The author declares that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrienko G, Fabrikant SI, Griffin AL, Dykes J, Schiewe J (2014) Geoviz: Interactive maps that help people think. 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It is vital to gain a deeper understanding of disaster-prevention measures to mitigate these effects. One aspect of this understanding is the construction of natural disaster monuments to educate future generations about lessons learned from past disasters. This study focuses on the location and distribution of these monuments and examines their potential as surrogate indicators of disaster hazards. To achieve this, we employ a two-pronged approach. The first approach is to analyze the relationship between the location of disaster monuments and disaster hazards. It involves plotting the locations of monuments from a database and investigating the relationship between the disaster covered by each monument and the current hazard using a hazard map portal site. The second approach is to assess the potential for disaster hazard mapping based on the disaster monuments. It involves creating Voronoi maps based on the location of disaster monuments and applying them to the entire national land area. It produced a disaster hazard map for Japan, including areas with no disaster monuments. The results provide aggregate information on the relationship between the location of disaster monuments and disaster hazards and the effectiveness of the Voronoi diagram-based disaster hazard maps. In many cases, the current hazard at the location of disaster monuments still exists, and 70% of the areas around tsunami-related monuments are still exposed to tsunami hazards. Additionally, this study suggests that Voronoi maps are promising for disasters and can accurately represent specific disaster hazard areas.","manuscriptTitle":"Examining the Potential of Natural Disaster Monuments as Surrogate Indicators for Disaster Hazards in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:19:25","doi":"10.21203/rs.3.rs-3929158/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-05-12T05:08:16+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-02-28T00:29:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-19T23:43:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-06T13:15:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-02-04T04:44:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"209c32c6-9098-4970-85ba-794a8bce4b32","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-01T20:12:38+00:00","versionOfRecord":{"articleIdentity":"rs-3929158","link":"https://doi.org/10.1007/s11069-024-06705-y","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2024-05-28 00:00:00","publishedOnDateReadable":"May 28th, 2024"},"versionCreatedAt":"2024-02-22 21:19:25","video":"","vorDoi":"10.1007/s11069-024-06705-y","vorDoiUrl":"https://doi.org/10.1007/s11069-024-06705-y","workflowStages":[]},"version":"v1","identity":"rs-3929158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3929158","identity":"rs-3929158","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}