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Introduction
A network can be defined as a system of interconnected technological devices and elements that facilitate the transfer of data between locations (Bazargan 2018). It constitutes one of the most useful aspects of a GIS application in various fields. A network can be used in analysis processes, commonly referred to as Routing or pathfinding. For instance, it can be applied in the computation of distances between two locations. In that regard, it is widely used in identifying cost-effective network paths between two locations (Pun-Cheng, Chan & society 2016). In the field of health studies, this analysis can adequately identify the shortest path between two locations: a patient’s position and a health center (Muhsen & Hassan 2018). Network analysis can also be applied in the decision-making processes within a health care organization. For instance, finding the most appropriate route, allocating vehicle routing, identifying nearby facilities, and choosing a suitable service area and location (ESRI 2016). The results of an analysis can guide an ambulance driver through a direction file that designates the best routes to reach a patient within the shortest time possible. Allocation is an invaluable component of GIS Network analysis because it provides a spatial network that enhances the distribution of resources and facilitates service zoning. For instance, user-defined rules that govern maximum travel distance can be used to allocate pupils in a certain district to schools after their successful assignment to the nearest network connection (Murad 2004). GIS is an effective way of measuring distance using the road network, even though a standard measurement range is non-existent. Researchers can develop suitable working ranges based on their experience or familiarity with various areas. (It is important to consider factors such as population density and topography). For instance, a study conducted by Gibson et al. (2011) in the Shaanxi Province (China) measured the distances between rural health centers and homes using two sets of ranges: 5-kilometer and 10-kilometer. According to Boothby and Drummer (2003), analysis models developed through the use of GIS are important in facilitating mobility. Studies conducted using GIS provide useful information to planners regarding areas that receive inadequate services from health care institutions (Dell’Ovo et al. 2017). Moreover, they can use GIS to choose the most suitable locations to construct new facilities. The major goals would be the allocation of an appropriate number of users to specific locations and the minimization of distances between the facilities and patients’ homes (Gholami-Zanjani, Pishvaee & Torabi 2018). Moreover, planners can use GIS to identify changes in the demand for medical services in certain areas and implement mitigation strategies (Murad 2004). This could increase accessibility to medical services because the population’s needs for medical care necessitate a better allocation of resources (Gatrell and Senior 2005). Topography, weather patterns, and the condition of the land can affect the accessibility of roads. Travel time can invariably change, depending on the degree of traffic congestion. The capacity of the facilities should be put into consideration because it can affect the forces of supply and demand (Połednik et al. 2018). Network analysis encompasses two main factors: impedance (Lyod 2010) and supply-demand values ((Heywood, Cornelius and Carver 2006). Network analysts must compute impedance and effectiveness values. Effectiveness value refers to the distance between two nodes.
The Measurement of Service Accessibility using Floating Catchment Analysis
A catchment area can be defined as the zone surrounding a location where people travel to access certain services (Delamater, Shortridge & Kilcoyne 2019). The catchment area method involves the creation of a ring with time and distance elements around the facilities. Allan (2014) provides a detailed literature review of facilities that offer health care services within the community about the needs of the people. Allan (2014) motes that the two-step floating catchment area (2SFCA) was the most preferred by researchers working in the field of medical health to measure the ease with which the people could access various health facilities. The majority of health studies chose the aforementioned method to compute distances and the accessibility of facilities by people living in surrounding areas (Allan 2014). Catchment areas share facilities because of the extensions of regions that emanate from the location of different service facilities in the same region (Pan et al. 2018). Wang, Y et al. (2018) argues that the range and quality of services offered in different catchment areas determine how attracted the people are to facilities in specific locations. Radke and Mu (2000) advocate the use of the floating catchment area technique. However, in 2003, Luo and Wang (2003a.b) made some improvements on the method to enhance its accuracy and effectiveness as referenced in (Luo, W. & Qi 2009). The two-step floating catchment method is an effective analysis technique because it computes the ratios of service facilities about population density (Luo and Wong, 2003 b; (Wang, Fahui, Luo & place 2005). Luo, W., and Qi (2009) have referred to the 2SFCA as an excellent gravity model that gets reliable results. It comprises two steps: first, the location of the residents within a specific catchment area that contains service facilities is calculated as well as the ratio of the faculties about the number of people living in the area is computed (Luo and Qi 2009). Second, the location of the residents and facilities within the catchment area is determined, and compare the ratio of the residents to the number of facilities as well as the time distance between the two (Luo and Qi 2009). Allan (2014) notes that many researchers use the 2SFCA method and incorporate different variables (cultural and social components) to determine areas that are disadvantaged about accessibility. Luo, Wei, et al. 2003; Wang, Fahui & Minor 2002 have furthered the application of the basics of the aforementioned method in research studies. In particular, Luo, Wei et al. (2003) have created a variation of the 2SFCA method that combines gravity model and FCA, and it has been used to identify areas in Illinois, USA that suffer a shortage of physicians. The method differs from the gravity model because it factors inaccessibility as a dichotomous measure and not as a fixed boundary within catchment areas. The two-step floating method attains a higher degree of accuracy in the interpretation of results and is more reliable than other gravity-based methods. For instance, it considers the forces of supply and demand for services, and how they interact across administrative borders. In that regard, the degree of accessibility recorded in various catchment areas is different (Luo, Wei, et al. 2003) Luo & Qi 2009). McGrail and Humphreys (2009) have used the two-step floating method in Victoria, Australia to measure the accessibility of primate health care services, and uncovered variabilities that were unnoticed by previous researchers using other techniques. The application of the method in health care assessment has been successful under different settings (Chukwusa & Comber 2018; Daly, Mellor & Millones 2019; Long, McDermott & Meadows 2018). 2SFCA has a high degree of accuracy in measuring the accessibility of service facilities, however, it has several limitations (McGrail & Humphreys 2009). Luo and Qi (2009) have enumerated two main disadvantages of using the method: it operates on the assumption that people in a catchment area have complete access to identified service facilities, and the people outside the area do not have access to the facilities. Disregarding these elements introduces bias when conducting a study (Iieshout 2012). Guagliardo (2004) recommends the consideration of a plethora of factors that go beyond the population of a specific location in a catchment area when using 2SFCA to conduct research. For instance, researchers should consider the employment location of the people in that specific location (Allan 2014). Allan (2014) observes that insufficiency of experimental and observational data is one of the limitations of using the 2SFCA technique in studies. This sentiment is echoed by Migrai (2012) and Allan (2014) who concur that the majority of studies that measured the accessibility of service areas using the 2SFCA method relied heavily on the technique’s major assumptions to conduct data analysis.
Luo and Qi (2009) have suggested the creation of Enhanced 2-Step Floating Catchment Analysis (E2SFCA) as an upgraded version of the 2SFCA method. The enhancement includes a distance decay function that is based on the assimilation of a discrete stepped function that has been critical in studies conducted in Northern Illinois to determine the degree of accessibility to primary care physicians. The new method includes the assignment of geographical weights to distinguish travel time zone and retain certain aspects of the gravity model. In addition, it excludes the dichotomous measurement component that is present in 2SFCA. Many researchers have described it as a simple method that renders data interpretation easy. Many applications of FCA have been reported in the field of health studies (Doi et al. 2017; McGrail & Humphreys 2009; Ngui & Apparicio 2011), in the valuation of transport opportunities (Langford, Higgs, and Fry, 2012), and recreational services (Dai & Planning 2011). Langford, Higgs, and Fry (2012) have introduced additional elements to enhance the method to improve the study of intra-urban variability in public transportation systems. Luo and Qi (2009) have expounded on these changes through the application of Gaussian function (Wang, 2007; Kwan 1998; as cited in Luo and Qi 2009) by incorporating varied weights to represent travel time in network analysis in Arc GIS (Luo and Qi 2009). First, Luo and Qi (2009) dedicated 30 minutes to time consumed traveling to service facilities (Lee 1991 as cited in Luo Qi 2009). Second, they split individual catchment areas into three regions based on time: between 0-10 minutes, 10-2- minutes, and 20-3-0 minutes (Luo and Qi 2009). This step involves the identification of each individual’s location and the measurement of the services offered within each zone relative to the resident ratio (Luo and Qi 2009). The second step involves the computation of all service locations within each of the aforementioned regions, and the addition of the resident ratio (Luo and Qi 2009). Luo and Qi (2009) argue that the E2SFCA method has many strengths, compared to its predecessor: it disregards the assumptions incorporated in 2SFCA and its execution is easy in GIS (Luo and Qi 2009). On the contrary, Langford, Higgs, and Fry (2012) maintain that the method has limitations because researchers experience difficulties when identifying various variables (Langford, Higgs & Fry 2012). In 2012, Luo, Wei, Whippo, and Place (2012) introduced the variable two-step floating catchment areas (V2SFCA) method that is more accurate and reliable as its analysis excludes the assumptions included in the application of 2SFCA. Luo, Wei, Whippo, and Place (2012) included 4 steps in the method as a way of avoiding the bias introduced by the assumptions made. In that regard, it is possible to calculate the ratio of services and compute the number of residents within catchment areas with a higher degree of precision. The first step involves the determination of the number of residents in specific zones and their addition to base residents. The second step is the calculation and addition of service facilities to the resident ratio. It is expected that the time around the area will slightly increase after the zone is resolved. The third step computes the residential location and service facilities ratios and incorporates the distance decay function. The final step includes the deduction of the distance decay function from the sum of the ratios obtained in step three (Luo, Wei, Whippo & place 2012).
Conclusion
In conclusion, the development of the FCA has contributed immensely to increasing precision in the measurement of spatial accessibility. The widespread use of FCA in the field of medicine and health research is proof of its efficacy in the scheduling and apportionment of emergency medical service stations. The method has several advantages that explain its increased application in medical research. However, Langford, Higgs, and Fry (2012) recommend the inclusion of additional elements that will enhance its degree of sensitivity about social inequality and its contribution to accessibility planning functions.
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