## 1 Introduction  
Monitoring and analyzing subsidence in urban areas provides actionable intelligence for the prevention of potential loss of life and property, given the high rates of urbanization in many parts of the world. A variety of factors could produce urban subsidence including natural and anthropogenic causes such as lithology [1; 2; 3; 4], dewatering [4; 5], tectonic activities [6; 7], building loads [3; 6; 8], Mining activities [9], and groundwater consumption [6; 10; 11; 12]. One of those factors could individually influence the surface subsidence. Thus, the causes of urban subsidence within a study area are normally interpreted individually, but subsidence may be caused by interaction of several factors acting together in the same area and, normally, the interaction between several subsidence causes increases potential subsidence-related hazards in urban areas such as building collapse, and could influence the inhabitants living in these urban areas. The interaction between causal factors is clear in many cases [13]. Nevertheless, the interaction between different factors is normally treated descriptively in large areas as in [PERSON] et al. [8], or mathematically in very small areas such as at building scale [13], and relatively few studies integrate spatially two or more causes when interpreting a pattern of subsidence in a city scale or larger areas of interest [14]. Hence, in this study, we propose a new model to interpret and map the complex subsidence causality (CSC) based on the number of spatially dependent factors that contribute to subsidence in a given area of interest. This interpretation is helpful in defining potentially hazardous areas, for better management in existing urban areas, and planning new expansion. Since factors causing surface subsidence are varied in each area, this model was designed to be flexible in the matter of subsidence inputs based on the unique characteristics of a study area. The causes of subsidence and the integration and interaction and correlations between these causal factors, therefore, need to be analyzed in detail to reach this goal. Accurate surface velocity estimations can reduce the uncertainty in models of subsidence phenomena. Microwave remote-sensing data gives the opportunity to measure surface motion precisely. This model depends on the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) technique [15], to detect subsidence in the study area. PS-InSAR is a SAR based deformation monitoring technique designed to overcome temporal and geometrical decorrelation problems of differential interferometric SAR (D-InSAR), by identifying and exploiting stable targets in a temporal series of interferograms as permanent scatterers. This technique has the capability to detect surface deformation in wide area coverage at millimeter-level precision [16]. The reliability of results derived from the PS-InSAR technique was confirmed by continuous global positioning system (GPS) data measurements [17]. In this study, ascending and descending data of Sentinel-1 [18], were used to estimate surface motion in the line of sight (LOS) in Alexandria, Egypt. The two results of LOS were integrated to decompose the surface vertical velocity of Alexandria. Alexandria was selected as a test area due to its location in a delta margin. Cities in delta margins are well known for their complex patterns of urban subsidence. The decrease in the Nile annual water and sediment discharge over the last two centuries related to barrages and dam construction on the lower River Nile has resulted in a subsidence rate increase across the city [4]. subsidence and sea-level rise make this city vulnerable to coastal flooding, potentially exposing a large urban population to risk [19].