# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 7 Conclusions  
This study employed the attribute information of six types of underground facilities and the history information of ground subsidence in the target area to select factors influencing ground subsidence through correlation analysis and present a model of ground subsidence vulnerability through OLS and spatial regression analysis. In addition, a map of ground subsidence vulnerability in the target area was created through the selected optimal model.  
Figure 2: Comparison of ground subsidence vulnerability maps. (**a**) A map of ground subsidence vulnerability; and (**b**) location of actual ground subsidence occurrences.  
The global Moran's I was checked through OLS analysis and the results show that the spatial data exhibited autocorrelation. Thus, the model of ground subsidence vulnerability in the target area employed spatial regression models (SEM and SLM). The results of analysis using the spatial regression models show that the model estimated by SLM was more fit. The results of an analysis of correlation coefficients regarding the attribute information of six types of underground utilities in the SLM model show that the correlation of the density of all six types of underground utilities was the highest. This is a similar finding to those of previous studies, which showed that the density of underground utilities is strongly correlated with ground subsidence [10; 33]. Furthermore, the \(\rho\) value of the SLM model verified that the ground subsidence is influenced by underground utilities and spatial ripple effects.  
Using the attribute information of underground utilities in the same target area, the map of ground subsidence risk prediction based on machine learning and the map of ground subsidence vulnerability in this study were compared [15]. The comparison results show that the central and eastern parts of the target area where ground subsidence frequently occurred had the same high vulnerability to ground subsidence. Thus, the central and eastern parts of the area and regions with a high density of underground utilities should be checked as a priority to prepare for a ground subsidence accident in the target area.  
In this study, a model to predict ground subsidence vulnerability was developed using the attribute information of only six types of underground utilities. However, ground subsidence can have more complex causes. Thus, a model that more reliably predicts ground subsidence vulnerability can be developed in the future by securing high-quality ground subsidence, influencing factor data (subway, underpass, high-rise building information, etc.).  
Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Methodology, S.L.; Software, S.L.; Writing--original draft, S.L.; Writing--review & editing, S.L., J.K. ([PERSON]) and J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Research for this paper was carried out under the KICT Research Program (project no. 20230116-001, Underground Utilities Diagnosis and Assessment Technology (4/4).) funded by the Ministry of Science and ICT.  
Not applicable.  
The authors declare no conflict of interest.