An Enhanced Earthquake Risk Analysis using H3 Spatial Indexing

Risk assessment is the first step in disaster risk reduction and crucial for types of disasters with potentially significant impacts, such as earthquakes. Besides the direct impact of very devastating shocks, earthquakes can also trigger cascading events that are no less damaging such as landslides, liquefaction, and tsunamis. This study aims to apply the H3 spatial indexing system to improve earthquake risk assessment using Sorong City as a case study, and compare the result of H3-based risk assessment with the normative risk assessment in Indonesia as defined by relevant Perka BNPB (Badan Nasional Penanggulangan Bencana/National Disaster Management Authority) 2/2012. H3 is an open-source geospatial indexing system developed by Uber, which utilizes a hierarchical hexagonal grid for indexing, calculations, and visualization. The hexagonal cells in the grid have equidistant neighbors and possess the property of expanding neighboring rings, approximating circles and optimizing space filling. The H3 spatial index finds extensive applications in earthquake hazard, vulnerability, capacity, and risk assessment systems. The value of each parameter is entered into each hexagon so that in the end each hexagon has its risk index value. The study shows that the use of H3 in earthquake risk assessment can provide a more detailed level of analysis and indirectly does not depend on regional administrative boundaries. Technically, this advantage provides a more efficient calculation method and is also easier to modify partially both spatially and in terms of the parameters used. Based on the performance evaluation results on the spatial join aspect, H3 provides much faster performance compared to traditional indexing systems. Hence, the H3 spatial index will be very useful to meet the need for an earthquake risk assessment on a larger scale with a higher level of resolution.


Introduction
Earthquake risk assessment is an important component of disaster management.It helps identify areas and populations most at risk of earthquakes and guides the development and implementation of effective mitigation and response strategies.Earthquakes are one of the most destructive and frequent natural disasters, causing heavy casualties and damage to infrastructure and the built environment.

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Carrying out regular earthquake risk assessments allows disaster management authorities and municipalities to identify areas of high risk and prioritize the allocation of resources for risk reduction actions.Such measures may include retrofitting existing buildings to make them more earthquakeresistant, strengthening critical infrastructure such as hospitals and emergency services, and establishing evacuation plans to ensure the safety of residents in the event of an earthquake.[1] Risk assessment plays a crucial role in identifying vulnerable areas and populations, as well as discerning the diverse needs and required resources in each region to effectively mitigate the impacts of earthquake disasters.It serves as an essential tool for strategic planning, capable of both saving lives and minimizing economic losses resulting from earthquake events.Conducting thorough earthquake assessments also proves instrumental in the formulation and implementation of effective risk reduction measures.[1] Various methods have been developed to evaluate the potential impact of earthquakes on the human population and the environment.Two methods commonly used for seismic risk assessment are based on raster data and administrative polygons.The raster-based method represents the study area as a grid of cells, each with an assigned value representing a specific attribute, such as population density or building type.This approach allows the integration of multiple data sources, such as remote sensing and Geographic Information System (GIS), to create a detailed and accurate representation of the study area [2].Raster-based methods have been used for hazard and vulnerability assessments, as well as for damage and loss prediction [3].The administrative polygon-based method represents the study area as a set of geographically defined areas, which are then assigned values that represent certain attributes.This approach enables the integration of socio-economic data, such as population demographics and land use, with geospatial data to understand the potential impact of earthquakes on certain areas or population groups [4].
Grid systems in vector format are gaining popularity for use in earthquake risk assessment because of their ability to efficiently organize and analyze large amounts of data.The hexagon grid, in particular, is advantageous for earthquake risk assessment as it preserves the relationships of adjacent cells while reducing the "points-in-polygon" problem thereby increasing spatial accuracy compared to traditional square grids.[5] A specific hexagon grid system that is increasingly being used is the Uber H3 system.H3 is a hierarchical hexagonal grid system that divides the world into equal-sized hexagons, providing a stable and efficient way of indexing and querying large-scale geospatial data.It can be used for hazard and vulnerability assessments, as well as for damage and loss prediction.In addition, it is also possible to evaluate the accessibility of emergency services and critical infrastructure, and to simulate evacuation scenarios.[6]

Figure 1. Sorong City
To demonstrate the potential application of H3, Sorong City (Figure 1) is chosen as a case study for comparing the process and results of earthquake risk assessment using H3 and the ones based on Head of BNPB (Badan Nasional Penanggulangan Bencana/National Disaster Management Authority) Regulation 2/2012 on disater risk assessment.Sorong City is located in the western part of Papua Province, Indonesia, is an area at high risk of earthquakes due to its proximity to several active faults.The most significant fault is the Sorong Fault, which crosses the city and has the potential to cause moderate to large earthquakes.In its history, the city of Sorong has experienced several earthquakes, the last and most significant being the 2009 Sorong earthquake with a magnitude of 7.2 which caused damage to buildings, and infrastructure, and resulted in casualties [7].The city is also located near the boundary of the Bird's Head plate, where the Australian plate is subducting under the Sunda plate, which can cause tsunamis.
Given the high risk of earthquakes, it is very important to carry out regular and comprehensive earthquake risk assessments for Sorong City.Thus, the H3 hexagonal grid system will be applied in this study to help assess earthquake risk in Sorong City, and the results will be compared with the results of risk assessments that are common in Indonesia.The use of H3 is expected to support disaster management authorities in mitigating the negative impact of earthquakes, especially by identifying high-risk areas and populations and providing appropriate actions for those areas.

Literature Review
Earthquake risk assessment (ERA) is an important tool for identifying, analyzing, and mitigating the potential impacts of seismic events on human and natural systems.The goal of ERA is to determine the likelihood and consequences of earthquakes occurring in a given area.It is done to inform decision making related to disaster risk reduction and management.[3] One key aspect of ERA is the unit of analysis, or the level at which risk is being assessed.Common units of analysis include individual buildings, communities, cities, and regions.The choice of unit of analysis will depend on the specific goals and objectives of the risk assessment, as well as the available data and resources.[8] Another important aspect of ERA is the type of data used in the assessment.Data can be divided into two main categories: hazard data, which relates to the likelihood of an earthquake occurring, and vulnerability data, which relates to the potential impacts of an earthquake on human and natural systems.Hazard data can include information on historical earthquake activity, seismic hazard maps, and probabilistic seismic hazard analyses.Vulnerability data can include information on building codes and construction practices, population density and demographics, and critical infrastructure.[3] There are several different methods used for earthquake risk assessment, depending on the unit of analysis and the data type used.Some common methods include: a. Building-specific methods: These methods focus on the risk to individual buildings and structures.
They typically involve detailed engineering analyses and inspections to assess the seismic performance of the building and identify potential vulnerabilities.Examples include performancebased engineering and fragility analysis.[9] b.Community-based methods: These methods focus on the risk to neighborhoods, cities, or regions.
They typically involve the use of GIS data and spatial analysis techniques to map the distribution of hazards and vulnerabilities across the community.Examples of this method include its usage in the HAZUS and RAPID.[10] c. Probabilistic methods: These methods use statistical and probabilistic techniques to estimate the likelihood of earthquakes occurring and the potential impacts on human and natural systems.
Examples include probabilistic seismic hazard analysis (PSHA) and seismic loss estimation.[11] The grid system is a method of dividing a geographical area into a regular grid of cells, typically in a square or hexagonal shape, for the purpose of spatial analysis and mapping.The grid system has a long history in geographical information science and has been widely used in a variety of applications, including land-use planning, environmental modeling, and risk assessment.[6] One recent development in the grid system is the implementation of the H3 hexagonal grid system.H3 was developed by Uber Technologies, Inc. as a way to index and spatially partition geographical data in a hierarchical manner.The hexagonal shape of the H3 grid allows for more efficient spatial analysis and mapping than traditional square grid systems.[6] In recent years, the application of the grid system and H3 to earthquake risk assessment has gained significant attention.This is due to the ability of the grid system and H3 to efficiently and accurately map and analyze the spatial distribution of seismic hazards and vulnerabilities.The use of the grid system and H3 in earthquake risk assessment allows for the development of detailed and accurate maps that can be used to identify areas of high risk and inform decision making related to disaster risk reduction and management.[6] One of the advantages of using the H3 grid system in earthquake risk assessment is that it allows for more precise and accurate mapping of seismic hazard and vulnerability data.The hexagonal shape of the H3 grid allows for better representation of areas with complex shapes and irregular boundaries.
Additionally, the hierarchical nature of the H3 grid allows for more efficient storage and processing of large datasets.[6] Another advantage of using the H3 grid system in earthquake risk assessment is that it allows for more flexible and adaptable spatial analysis.The hierarchical structure of the H3 grid allows for the analysis of data at different levels of resolution, from a global scale down to a local scale.This can be useful in identifying patterns and trends in seismic hazard and vulnerability data that may not be visible at a single scale.[6] 3. Materials and Methods

Data
The data used includes physical and socio-economic data, which are collected from various official sources, namely government agencies.The seismic hazard index data was taken from InaRisk BNPB.The obtained data is in raster format.The data used to assess vulnerability includes demographic, infrastructure, and economic characteristic data in the study area.The capacity index was taken from the latest IKDn (Indeks Ketahanan Daerah/Regional Resilience Index) assessment results by BNPB.Supporting data was collected from observations via Google Earth.
Specifically, vulnerability assessment includes the assessment of social, physical, environmental, and economic vulnerability.In assessing social vulnerability, the data used includes vulnerable groups, population density, and demographic structure.The assessment of physical vulnerability uses data on critical facilities, support facilities, roads, and buildings.The assessment of environmental vulnerability uses data on built and non-built areas.Meanwhile, the assessment of economic vulnerability includes data on productive land, productive land GDP, and economic activity centers.

H3 Spatial Indexing
H3 Spatial Indexing is a hierarchical geospatial indexing system that partitions the world into hexagonal cells.This system, which is open source under the Apache 2 license, was specifically developed to address the challenges faced by Uber's data science needs.One of the key advantages of H3 is its ability to join disparate data sets, making it a versatile tool for analysis.[6] The hexagonal shape of the grid employed by H3 offers several benefits.Firstly, it provides smooth gradients, allowing for accurate measurement of differences between cells.Additionally, the equidistant relationship between hexagonal neighbors simplifies the analysis of movement, as hexagons exhibit expanding rings of equidistant neighbors that approximate circles.Moreover, hexagons are optimally space-filling, allowing for a more precise tiling of polygons compared to other shapes like squares.[6] As a standard unit of analysis, H3 serves as a basis for joining various data sets.The H3 library supports the indexing of points, lines, and regions into the grid.This means that different data formats, such as rasters, can be easily indexed into H3 using combinations of these basic operations.Consequently, once data is indexed, it can be seamlessly joined with other datasets based on the H3 index.[6] Beyond its indexing capabilities, H3 offers additional features for modeling flow.It can create indexes that represent movement from one cell to a neighboring cell, allowing for the association of weights with specific movements.This facilitates the analysis of movement patterns within the grid.[6] Furthermore, H3 is well-suited for the application of machine learning techniques to geospatial data.The hexagonal grid defined by H3 can be treated as a pixel grid, enabling the use of computer vision techniques like convolution.The H3 library includes functions for finding neighbors (kRing) and transforming indexes into a two-dimensional coordinate space, enabling the implementation of various computer vision algorithms.[6] The hierarchical nature of H3 indexing is an essential characteristic of the system.Each hexagonal cell in the grid, up to the maximum supported resolution, has seven child cells below it.This hierarchical subdivision, known as aperture 7, enables efficient traversal and organization of data within the grid.Although hexagons do not perfectly subdivide into seven finer hexagons, the alternate orientation of grids approximates this subdivision, allowing for precision truncation and determination of children cells.[6] While geographic containment is approximate, logical containment in the index is exact.This means that H3 can be used as an exact logical index on top of data indexed at a specific resolution.The hierarchical structure of H3 also facilitates the efficient relation of datasets indexed at different resolutions, as functions for changing precision (h3ToParent, h3ToChildren) are implemented with minimal computational overhead.Furthermore, due to the numerical proximity of geographically close locations, the H3 index structure enables fast operations on spatially related data points.[6] In analysis scenarios where precision or uncertainty of a location needs to be encoded in the spatial index, the hierarchical structure of H3 can be leveraged.For instance, a GPS receiver's point can be indexed at a coarser resolution when the precision of the signal is lower, or cells can be aggregated to a parent cell when there are insufficient data points in each finer cell.This flexibility allows for adaptable and contextually appropriate indexing based on the specific analysis requirements. [6]

Earthquake Risk Assessment with IRBI Method
IRBI (Indeks Risiko Bencana Indonesia/Natural Disaster Risk Index) is a method developed by the Indonesian National Disaster Management Authority (BNPB) to assess earthquake disaster risks in Indonesia.The method uses a risk index that combines several factors, including hazard, exposure, and vulnerability.The IRBI method considers several factors that may affect earthquake risk, including the seismicity rate, ground motion intensity, population density, and building quality.
The IRBI method is a multilevel index that assesses risks on a scale of 0 to 7, with higher numbers indicating higher risks.The index is based on three main components: hazard, exposure, and vulnerability.The hazard component assesses the potential for earthquakes in a given area.The exposure component assesses the number of people and buildings that may be affected by an earthquake.The vulnerability component assesses the susceptibility of people and buildings to damage from an earthquake.
To calculate the hazard component of the IRBI index, BNPB Indonesia considers several factors, including seismicity rates, ground motion intensity, and geological characteristics of the region.The exposure component considers population density, building density, and other demographic factors.
Finally, the vulnerability component assesses the quality of buildings, infrastructure, and other factors that may impact the ability of people and communities to cope with an earthquake.
The IRBI method also takes into account the potential consequences of an earthquake disaster.For example, it considers the potential impact on critical infrastructure such as hospitals, schools, and transportation systems.The method also considers the potential for secondary disasters such as landslides, fires, and tsunamis.

Earthquake Risk Assessment with H3
Earthquake risk assessment with H3 spatial index involves several steps.Firstly, the study area is divided into a grid system using the H3 spatial index.Each grid cell contains a unique H3 index, which is used to represent the location of the cell.The H3 resolution used is 11, following Tobler's principle at a 1:50,000 administrative boundary scale.
The seismic hazard index is then calculated for each grid cell.The seismic hazard index provides information about the probability and intensity of earthquakes.The obtained data is in raster format, and subsequently, zonal statistics operation with the mean measure is applied to acquire index values at each hexagon point (resulting from the conversion of polygons to points).Furthermore, a spatial join is conducted to incorporate hazard values into the attributes of the hexagons (polygons).
The vulnerability index is obtained from social, physical, environmental, and economic vulnerability indices.The weight values are adjusted using the IRBI method of BNPB.Mathematically, the formula to calculate the vulnerability index can be seen as follows: where VI represents the Vulnerability Index, S represents the Social Vulnerability Index, F represents the Physical Vulnerability Index, L represents the Environmental Vulnerability Index, and E represents the Economic Vulnerability Index.
Each type of vulnerability includes components and sub-components, which can be seen in detail in Table 1.For data analyzed at the district level, the methodology employed is akin to the IRBI method.
For data pertaining to sub-components subjected to spatial analysis at the hexagonal grid level, each distance-related sub-component is computed utilizing the Euclidean distance.The capacity index is taken from the IKDn data.This involves evaluating the availability and effectiveness of emergency services, evacuation routes, and other critical infrastructure.The capacity index value is given the same value for each grid cell as the IKDn assessment is done at the district/city level.
The results of seismic hazard, vulnerability, and capacity assessments are combined to generate a comprehensive earthquake risk index for each grid cell.This index provides a comprehensive measure of the risk of damage due to earthquakes in each study area location.Mathematically, the formula to calculate the risk index can be seen as follows: where RI represents the Risk Index, HI represents the Hazard Index, VI represents the Vulnerability Index, and CI represents the Capacity Index.

Comparative Analysis
Comparative analysis is a valuable method for comparing the results of different risk assessment methods.One approach involves using statistical analysis to compare the distribution of risk across various domains.Statistical analysis for comparing the distribution of risk across different domains using descriptive statistics is an approach used to delineate and present the fundamental characteristics of risk distribution within each domain under consideration [12].This method aids in gaining initial insights into how risk data is spread across all the relevant domains.
In addition to statistical methods, visual methods are also employed for comparative analysis [13].The approach entails creating maps that display the results of both methods side by side.This provides a visual representation of the disparities in risk scores between the two methods and can be instrumental in identifying regions that consistently exhibit high or low risks in both methods, or areas where the methods diverge.
Another analysis method that can be utilized is SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) [14].This method involves identifying and analyzing the strengths and weaknesses of each risk assessment method, as well as the opportunities and threats associated with the implementation of each method.This allows for a comprehensive evaluation of each method, considering not only technical aspects but also social, economic, and political factors that may impact the implementation and effectiveness of the methods.This study will present a SWOT analysis of the H3 application for risk assessment in the context of disaster management in Indonesia.

Results of Hazard, Vulnerability, and Risk Analysis using the IRBI Method based on Administrative Boundaries
Based on the 2020 IRBI, Sorong City is classified as high risk.The highest risk in Sorong City comes from the earthquakes, forest and land fires, and floods.Looking at the results of disaster incidence analysis, it is known that Sorong City has experienced 18 natural disasters since 2009 to 2021.Earthquakes are one of the disasters with significant impacts occurring in 2009, 2015, and 2021. 10 The maps of hazard, vulnerability, and capacity indices for Sorong City can be seen in Figure 2. The hazard index assessment is based on data from disaster incidents in Sorong City.Based on the hazard index assessment results, the Sorong Manoi District has the highest level of natural hazard compared to other districts and is classified as a high hazard class.The high level of hazard in the Sorong Manoi District is influenced by a combination of natural and anthropogenic hazards with both values considered high.The Sorong District is classified as a moderate hazard class.In addition, the Sorong Kepulauan and Sorong districts have a low hazard index value.

Figure 2. Results of Risk Assessment based on Administrative Boundary
Vulnerability assessment is a comprehensive evaluation of the condition and characteristics of a society and its living location to determine the factors that are susceptible to a disaster as well as the factors that can reduce it.The vulnerability index assessment in this research uses an approach of vulnerability assessment on the administrative scale unit aspect that combines exposure, sensitivity, and capacity aspects as a potential value of a disaster.Vulnerability studies in Indonesia are generally separated into social, physical, environmental, and economic components as regulated in BNPB Regulation No. 2 of 2012.Vulnerability assessment of the coastal areas of Sorong City is conducted by analyzing the characteristics of Sorong City from various government data sources and field results.Based on the vulnerability assessment results in Sorong City, the most vulnerable district is the Sorong Manoi District with an index value of 0.64, while the least vulnerable district is the Sorong Kepulauan District with an index value of 0.25.The average vulnerability value is 0.44, so the vulnerability level of Sorong City is in the medium range.
The physical vulnerability profile of coastal district in Sorong City is found in the Sorong Manoi District, which has an index value of 0.80.This is due to the location of the Sorong Manoi District, which has more supporting facilities and residential buildings compared to other districts.The physical aspect is assessed based on the number of critical and supporting infrastructure facilities in the study area, including accessibility facilities consisting of public facilities and airports.Then, supporting facilities are obtained from health and education facilities.
The environmental vulnerability profile is obtained from the calculation of the built-up area and the unbuilt-up area.The built-up area is viewed from residential and reclamation areas, while the unbuiltup area is calculated from the land-use area value such as forests, swamps, mangroves, and coral reefs.
The environmental vulnerability profile of coastal district in Sorong City is found in the Klaurung District with an index value of 0.48.This is due to the fact that this district has the highest forest area compared to other districts, which is 80.09 km2.In addition, the built-up area score in this district is also among the highest with a value of 0.60.On the other hand, the coastal district with the lowest environmental vulnerability index value is the Sorong Kota District.This is because this district does not have swamp and mangrove areas and has the third-lowest built-up area score after the Sorong Kepulauan and Maladum Mes districts.
The calculation of the Economic Vulnerability Index (EVI) involves the computation of the Gross Regional Domestic Product (GRDP) of productive areas and the number of economic centers within the study location.The GRDP score for each regency/city is based on the total value of the GRDP within the area divided by its district area.The economic aspect also encompasses the calculation of the highest GRDP within the economic index of each regency/city.The economic vulnerability profile of Sorong coastal district is found in the West Sorong district, with an index value of 0.58.This is because the GRDP value in this district is known to be the third-highest after Klaurung and Maladum Mes districts, at Rp19.13679 billions.West Sorong district also has the largest number of tourist spots, amounting to 13 points, and the second-largest agricultural land area, at 11.04 km 2 .
The risk index value is obtained by considering the evaluation of hazards and vulnerabilities.Based on the calculation results, it is known that the Sorong Manoi district has the highest risk index value compared to other districts.The high risk index value needs to be considered because the population and settlements in this area are the highest, thus in the event of a disaster, the resulting losses will also be greater compared to other districts.

Results of Hazard, Vulnerability, and Risk Analysis using the H3 Method
The earthquake disaster risk assessment using H3 resulted in an index ranging from 0 to 1, indicating the level of risk ranging from low to high.A value of 0 indicates the lowest level of risk, while a value of 1 indicates the highest level of risk.The map above shows that this result can be used to identify areas with the highest risk of earthquake disasters and help prioritize disaster preparedness and response measures.Looking at the spatial distribution of risk indexes in Sorong City (Figure 3), the pattern and cluster of high-risk areas are found in Sorong Kota, Sorong Manoi, and Sorong Timur districts.Meanwhile, lowrisk areas are found in Klaurung district.These results can be used to inform specific and targeted risk management decisions and actions.This includes identifying priority areas for disaster preparedness and response measures, such as improving buildings, conducting public awareness campaigns, and strengthening emergency response capacity.

Comparative Analysis
To carry out this comparative analysis, we used three types of analysis: statistical, visual, and SWOT analysis.The statistical analysis involved calculating the mean, standard deviation, and correlation coefficient of the risk values obtained from each method.The visual analysis involved creating maps to identify similarities and differences in the spatial distribution of risk.The SWOT analysis involved identifying the strengths, weaknesses, opportunities, and threats associated with each method.

Statistical Analysis
When comparing earthquake risk assessment results using administrative boundary-based and H3based methods, descriptive statistical analysis can be conducted to obtain further information about the distribution of values in both methods.Several descriptive statistical components that can be used include mean, median, standard deviation, skewness, kurtosis, min, and max.
In the administrative boundary-based method, the mean value shows the average risk value obtained from all measured areas, while the median indicates the middle value of the data.Standard deviation indicates how far the data is spread from the mean, while skewness and kurtosis inform the shape of Meanwhile, in the H3-based method, the descriptive statistical values will be obtained from each hexagonal cell.The mean, median, and standard deviation values can provide information about the spread of risk values across all hexagonal cells.Skewness and kurtosis can also provide information about the shape of the risk value distribution in each hexagonal cell.The minimum and maximum values in each hexagonal cell can indicate the range of risk values in that area.A comprehensive analysis of the statistical results presented in the Table 2 reveals several key findings when comparing the risk assessment based on administrative boundaries and the H3-based approach.
In terms of the mean, the administrative boundary-based risk assessment yielded a value of 0.346, while the H3-based assessment resulted in a lower mean of 0.24.This suggests that, on average, the H3-based approach tends to estimate lower levels of risk compared to the administrative boundarybased method.
Looking at the median values, we observe that the administrative boundary-based assessment has a median of 0.2, while the H3-based assessment has a slightly higher median of 0.21.This indicates that the central tendency of the risk distribution is similar between the two approaches, with the H3-based approach showing a slightly higher median value.
In terms of variability, the standard deviation provides insights into the dispersion of the risk values.The administrative boundary-based assessment exhibits a higher standard deviation of 0.35, indicating a greater spread of risk values across the study area.On the other hand, the H3-based assessment demonstrates a lower standard deviation of 0.153, suggesting a more concentrated and less variable risk distribution.
Examining the minimum and maximum values, we find that both approaches yield the same range of risk values, ranging from 0 (indicating no risk) to 1 (indicating the highest level of risk).This indicates that both methods capture the full spectrum of risk levels present in the study area.Considering the skewness values, we observe that the administrative boundary-based assessment has a skewness of 0.89, indicating a moderate positive skew in the risk distribution.In contrast, the H3-based assessment exhibits a higher skewness of 1.32, indicating a more pronounced positive skewness and a potential presence of extreme values or a longer tail in the risk distribution.
The H3-based kurtosis being twice as large as the administrative boundary-based can be attributed to several factors.Firstly, the H3-based analysis utilizes a finer-grained spatial partitioning system, resulting in a higher number of data points and a more detailed representation of the risk distribution.This increased granularity can lead to greater variability and higher kurtosis values in the H3-based assessment.Secondly, the H3-based analysis captures the inherent spatial heterogeneity and clustering patterns within the data, which can contribute to a wider distribution and heavier tails in the risk assessment.In contrast, the administrative boundary-based analysis tends to aggregate data within larger administrative units, potentially smoothing out localized variations and reducing the overall kurtosis of the risk distribution.Additionally, the choice of statistical measure and calculation methodology can also influence the kurtosis values.It is possible that the specific calculation method used for the H3-based analysis is inherently more sensitive to extreme values or exhibits a different skewness-kurtosis relationship compared to the administrative boundary-based approach.
These statistical results demonstrate that the H3-based approach tends to yield lower mean values, a slightly higher median, a lower standard deviation, and a more positively skewed risk distribution compared to the administrative boundary-based approach.

Visual Analysis
When comparing the results of earthquake risk assessments using administrative boundary-based and H3-based methods, visual analysis can provide more easily understandable information and help identify certain patterns or trends in the risk data.From the map of the risk assessment results based on administrative boundaries, it can be seen that the area with the highest risk is located in the Sorong Manoi District, while other areas have relatively low levels of risk.From the map of the risk assessment results based on H3, it can be seen that the highest risk values are located in the Sorong Manoi, Sorong Kota, and Sorong Timur Districts.
From the visual analysis (Figure 4), it can be seen that both methods yield similar results by showing that the area with the highest risk is located in the Sorong Manoi District.However, the H3-based method provides more specific information at the hexagonal cell level, which successfully identifies Sorong Kota and Sorong Timur Districts as high-risk areas.H3 can show finer risk patterns at the cell level.

SWOT Analysis
In this section, we conduct a SWOT analysis of the H3 method with the aim of comprehensively identifying the intrinsic strengths and weaknesses of this method, as well as recognizing the opportunities and threats that may be associated with its use in the context of risk analysis.This analysis will provide profound insights into the potential for the development and improvement of the H3 method in disaster risk modeling.

Strengths
The utilization of H3 for earthquake risk assessment offers several advantages and positive value compared to the traditional administrative boundary-based method.One of the significant advantages is that H3 can provide a higher resolution spatial unit for risk assessment compared to administrative boundaries.With the H3 method, it is possible to create a more precise spatial unit, allowing for more accurate and detailed risk assessment.The H3 method also enables the combination of various types of data and models, making it a useful tool for multi-hazard risk assessment.
Another advantage of H3 is that it can overcome the issue of spatial heterogeneity in risk assessment, which is a common challenge in administrative boundary-based methods.This is because H3 units have more uniform spatial distribution and size, allowing for a more standardized risk assessment approach.Additionally, H3 can also facilitate the integration of community-level data, which can improve the accuracy of risk assessment and increase community engagement in disaster risk reduction efforts.

Weaknesses
Despite its advantages, the utilization of H3 for risk assessment also has several limitations and weaknesses.One of the primary limitations is the potential for data inconsistency and inaccuracy due to the complexity of the H3 method.This is because H3 involves the integration of various types of data and models, which can lead to errors in data processing and analysis.
Another limitation is the need for significant computational resources and technical expertise to perform H3-based risk assessment.This can be challenging for many local and regional disaster management agencies, which may not have access to the necessary resources or technical expertise.
Additionally, the H3 method requires careful consideration of the choice of resolution level to balance the accuracy and efficiency of risk assessment.Choosing too high of a resolution level can result in excessive computational requirements and potential data inconsistency, while too low of a resolution level may lead to inaccurate risk assessment results.

Opportunities
In terms of opportunities, the H3-based risk assessment offers several promising prospects.Firstly, it provides an enhanced spatial resolution compared to the administrative boundary-based approach.By utilizing hexagonal grids, the H3 method allows for a more detailed analysis and identification of localized risk patterns and hotspots within the study area.This finer granularity can greatly improve risk management strategies by targeting specific areas with higher vulnerability.
The H3-based approach presents an opportunity for improved accuracy in risk assessment.Leveraging geospatial algorithms and techniques specifically designed for hexagonal grid systems, this method has the potential to yield more precise estimations of risk levels.The incorporation of these techniques can enhance the reliability of the results and support better-informed decision-making processes.
Additionally, the H3-based approach offers scalability and flexibility in terms of data integration and analysis.The hexagonal grid structure enables seamless aggregation and disaggregation of data, allowing for comparisons across various scales.This flexibility facilitates the integration of different datasets or models, further enhancing the comprehensiveness of the risk assessment.

Threats
However, the H3-based approach also presents certain threats that need to be considered.One such threat is the compatibility and availability of data in the hexagonal grid format.Adapting existing datasets to the H3 framework may require additional efforts and resources, which could introduce challenges and potential inaccuracies in the analysis.Furthermore, the interpretation and understanding of the results derived from the H3-based approach may pose challenges for stakeholders and decision-makers.Familiarity with the new methodology and potential training may be necessary to fully comprehend and interpret the outcomes generated from the hexagonal grid system.
Lastly, the successful implementation of the H3-based risk assessment relies on stakeholder acceptance and adoption.Resistance to change or a lack of familiarity with the new approach may hinder its widespread acceptance and utilization, potentially limiting its impact and effectiveness.Considering these opportunities and threats is crucial when evaluating and selecting the most suitable risk assessment methodology.Proper assessment and mitigation of the identified threats, along with leveraging the opportunities, can contribute to the successful implementation of the chosen approach and ultimately enhance risk management and decision-making processes.
The results of this comparative analysis provide insights into the effectiveness of the administrative boundary-based method and the H3 method in identifying areas of risk.The statistical analysis showed that the mean risk values obtained from the two methods were significantly different, with the H3 method generally resulting in higher risk values.The visual analysis showed that the spatial distribution of risk was also different between the two methods, with the H3 method identifying more areas of high risk.The SWOT analysis identified the strengths and weaknesses of the H3 method, as well as the opportunities and threats associated with its implementation.

Conclusions
Risk assessment is a crucial stage in disaster risk management.The use of H3 for earthquake risk assessment offers several advantages and positive values compared to traditional administrative boundary-based methods, namely providing spatial units with higher resolution, creating more precise spatial units, allowing for more accurate and detailed risk assessment, and addressing spatial heterogeneity issues in risk assessment.However, the use of H3 also has some limitations, including potential inconsistency and inaccuracy of data, and the need for significant computational resources and technical expertise.Further development of the H3 method can be done using machine learning and big data.With these technologies, H3 can be used to analyze big data in a shorter time and provide more accurate results in earthquake risk assessment.Further development of H3 can also involve community participation in data collection and risk assessment.By involving communities, H3 can obtain more data and increase community awareness of earthquake disaster risks in their area.

Figure 3 .
Figure 3. Results of Risk Assessment based on H3 . The minimum and maximum values indicate the range of risk values from the measured areas.

Table 1 .
Component and Sub-Components of Vulnerability

Table 2 .
Statistical Analysis