## 4 Summary and Conclusions  
The study proposed an interpolation method for observation data collected in 3D, such as altitude, depth, or water level, and as an example, it showed the results of the seismic liquefaction hazard assessment and mapping using borehole data. As a result, the study showed that the interpolation of 3D observation data was possible through the integration of IDW, the most widely used interpolation method, and the B-rep-based topological relationship in 3D GIS. Such an approach is advantageous in the interpolation  
Figure 11: Comparison of IDW interpolation results: (**a**) NP\(-\)III; (**b**) FD\(-\)I.  
Figure 12: 3D seismic liquefaction hazard web mapping application. and visualization of various data observed in the real world and can contribute to the expansion of 3D GIS applications. The following is the proposal for improvement plans for the comparison and review of the developed outcomes to the previous research and further application to future research. Concerning the 3D IDW interpolation, this study is significant in that it expanded the neighborhood search, the distance calculation, and the interpolation performed in the 2D space to the 3D space. However, the study did not quantitatively evaluate the results of the improved IDW interpolation, and therefore, it is necessary to discuss such results in the future. Concerning 3D search neighborhoods, this study performed a searching topology relationship with the observed data by visualizing the spheroid or box according to the given search distance in the B-rep structure. This is an expansion of the 2D search neighborhoods to a 3D environment and was proposed by considering the characteristics of data densely populated in a vertical direction. A similar concept can be found in the Empirical Bayesian Kriging 3D offered by Esri [45]. This interpolation method determines neighbors by visualizing a sphere that has an identical distance to all axes, and based on the user's definition, it can also determine neighbors by identifying sectors. Such an approach is used in identifying neighbors in an independent space and can be efficiently applied to the observation data affected by direction. Such an approach is used in identifying neighbors in an independent space and can be efficiently applied to the observation data affected by direction. For example, local atmospheric environment data has various directions of wind angles, and wind field estimation based on this data can be useful in various simulations [46]. Concerning seismic liquefaction hazard assessment, generally, it assesses FL to evaluate the occurrence of liquefaction by depth on one spot. However, in the case of the liquefaction hazard map that deals with a small-scale map, it uses the liquefaction potential index (LPI), which is produced by multiplying and integrating FL with the inverse weighting value of depth. Since LPI provides liquefaction potential on the ground surface, it can evaluate the liquefaction potential of facilities located on the ground. However, in South Korea, where underground space is actively used, it is necessary to conduct liquefaction potential assessments not only on facilities on the ground but also on underground facilities. Therefore, this study performed 3D liquefaction hazard mapping through improved interpolation based on FL by depth. Such 3D mapping results can be used as the criteria showing the subsurface liquefaction potential and also in selecting the target regions for an earthquake preparedness/response plan. Concerning interpolation and mapping workflow, the study proposed the workflow aiming to integrate the functions of various S/W through the link among widely used file systems and standard data formats, such as Excel spreadsheet, Shapefile, and SLPK among others. While such an approach can be effectively used when no standard workflow exists, it is limited in terms of data duplication and S/W maintenance.