# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 1 Introduction  
The causes of ground subsidence, which mainly occur in urban areas, are highly complex. However, the main reported cause of ground subsidence is damage to underground utilities (such as water and sewage). The occurrence of ground subsidence can lead to potentially catastrophic accidents, raising public anxiety [1]. For this reason, it is very important to analyze the causes of ground subsidence and be prepared. Several studies were conducted analyzing the causes of ground subsidence. Ground subsidence occurs when underground structures are damaged and develop discontinuities, which can be due to a range of causes, such as external impact, water pressure, and degradation. When this damage occurs, water paths are formed, primarily around the damaged areas, through infiltration and changes in groundwater levels. Surrounding soil particles are lost and transported into underground structures, leading to the formation of holes. As these holes progressively expand, the upper layers of the ground eventually collapse, demonstrating the mechanism of ground subsidence [2; 3; 4; 5; 6; 7; 8]. To support preparation for ground subsidence, studies on the prediction of ground subsidence risk were steadily conducted. To predict the risk level of ground subsidence, the main factors influencing ground subsidence must first be selected. According to the literature review and analytic hierarchy process analysis studies, the main factors influencing the occurrence of ground subsidence in urban areas are cavity, surcharge load, depth, thickness, soil type, shear strength, relative density, and soundness and history of underground utilities [9; 10; 11]. To predict ground subsidence caused by damage to underground utilities, finite element analysis was used to analyze the ground's behavior according to the amount of settlement and the affected area, and it was found that the location of damage, the relative density of the ground, and stratigraphic conditions were significant factors influencing the ground subsidence [12; 13]. Furthermore, the attribute data of underground utilities were built using a geographic information system (GIS) program and a model, etc., to predict the risk level of ground subsidence based on machine learning, and regression analysis was developed [14; 15; 16]. The occurrence of ground subsidence is affected by spatial characteristics such as the location of underground utilities and geotechnical information. Thus, spatial correlation should be considered in the analysis to systematically predict the vulnerability to ground subsidence. For this reason, research on ground subsidence focused on indoor model experiments for mechanism determination, AHP analysis, and ground subsidence risk factor analysis, as well as risk prediction using machine learning techniques. However, there is no research using statistical techniques for spatial characteristics. Therefore, this study used GIS to develop a database by selecting the attribute information of underground utilities that are reported as the cause of ground subsidence and the history information of ground subsidence as influencing factors through a spatial regression model analysis for the purpose of developing a ground subsidence occurrence prediction model. The study also removed missing data and outliers from the database that was built. Using the database, an independent t-test was performed on the spatial information of underground utilities and the occurrence of ground subsidence, and a correlation analysis was performed. In addition, an ordinary least squares (OLS) analysis was performed to determine the influence relationship between ground subsidence-related influencing factors (independent variables) and the history information of ground subsidence occurrences (dependent variable), through which spatial autocorrelation was verified.