###### Abstract
There are several spatial factors (e.g., supply and demand locations, distance or travel time) and non-spatial factors (e.g., socioeconomic variables of population) that inevitably affect how people access and utilize healthcare. Many studies have used and combined such factors to obtain more accurate and realistic results of measuring spatial accessibility to healthcare [20; 21; 22; 23; 24; 25]. Measuring spatial accessibility to healthcare helps in formulating planning scenarios that indicate where the healthcare services should be allocated to improve poor spatial accessibility to services. That eventually contributes to minimizing spatial disparities in access to healthcare [26; 27]. Several studies have identified and analyzed spatial disparities in access to healthcare by measuring spatial accessibility. For example, [PERSON] and [PERSON] [28] analyzed a location-based spatial accessibility to identify regional disparities in access to dental services in the State of Ohio. The results revealed disparities in distribution of dental services. For reducing spatial disparity in access to healthcare, [PERSON] et.al. [29] measured and analyzed the spatial accessibility to healthcare services using the travel time technique (i.e., drive time) to assess an inequality of spatial access to healthcare in Deqing County, China. Furthermore, [PERSON] and [PERSON] [30] integrated spatial and nonspatial factors (i.e., socioeconomic status) to assess spatial disparities in access to community care resources for providing a comprehensive consideration of equal allocation. The two-step floating catchment area (2 SFCA) method is one of the most recent and common methods used to measure a spatial accessibility to healthcare. It is considered a special case of the gravity model. At first, [PERSON] and [PERSON] [31] proposed the 2 SFCA method in the form of a spatial deconstruction method, and then [PERSON] and [PERSON] [32; 33] developed it to be an appropriate method for measuring the spatial accessibility, taking into consideration supply and demand together. Studies have proven that the 2 SFCA method is easier to use and can calculate scores of spatial accessibility compared to the gravity model [18; 34; 35; 36]. Subsequently, many improvements were made to the original 2 SFCA method to better deal with the effect of distance decay within the catchments of demand and supply (i.e., considering disparities in spatial accessibility within catchments as a function of distance). Improvements were started by [PERSON] and [PERSON] [37] who introduced the enhanced 2 SFCA (E2 SFCA), and then [PERSON] and [PERSON] [38] who provided the kernel density 2 SFCA (KD2 SFCA), and so on [39]. Nevertheless, many studies have used the 2 SFCA method to measure the spatial accessibility to healthcare [22; 36; 40; 41; 42; 43]. The geographical information systems (GIS) play an important role in spatial decision-making related to public health. Furthermore, it can effectively deal with healthcare planning issues by applying statistical and analytical methods. The GIS could be used to enhance the outputs of measuring the spatial accessibility to healthcare. This could be performed using different GIS-based measures and models depending on spatial and nonspatial factors to assess spatial inequalities, identify shortage areas of healthcare, and determine the optimal allocation of health resources [6; 32; 33; 44]. However, the GIS technology has been used in many studies concerned with measuring spatial accessibility to healthcare using the 2 SFCA method. For example, [PERSON] and [PERSON] [22] used the GIS technology to execute the 2 SFCA method to measure spatial accessibility based on travel time, which was calculated by building the (O-D) cost matrix between the physician location and the population location. Areas with poor accessibility to primary healthcare were defined by integrating spatial and nonspatial factors.