## 3 Materials and Methods
### Data Preparation  
To assess suitable sites for locating hospitals in the Gaza Strip, the datasets collected in digital format from different organizations were assembled in a GIS environment and evaluated for quality and suitability for the purpose by comparing them with the Landsat 8 imagery and high-resolution Google Earth map. The conditioning factors used are  
Figure 3: Overall methodological flowchart.  
categorized under three classifications: environmental (land use related), topographic, and geodemographic factors. In the literature, there is no consensus among researchers on specific factors to be used for location suitability assessments. However, some specific factors that have been widely used by researchers indicate their importance in the location-based decision-making process [58; 59]. From each of the classifications, the relevant data layers were organized for further processing. All of the conditioning factors were transformed to 10 m resolution raster products using ArcGIS 10.5.  
The topography of an area has a direct impact on site selection because it controls a number of natural processes that constitute environmental concerns, such as flooding and erosion. In this study, the 6 topographical factors identified to be relevant to assessing hospital site suitability are slope, altitude, plan curvature, topographic wetness index, topographic roughness index, and stream power index (Figure 4a-f). These data layers were derived from the 20 m resolution DEM obtained from the UNRWA.  
Figure 4: _Cont._  
Figure 4: _Cont_.  
Human settlement is largely controlled by natural phenomena, such as the physical landscape, water availability, vegetation/forest distribution, and fertile soil for crop production. For the environmental variables, 6 data layers, including roads, main road, river network, residential area, agricultural land, and refugee camps, were extracted from the land use data. Using the Euclidean distance tool, the distance from any road, the distance from a main road, the distance from the river network, the distance from a residential area, the distance from an agricultural area, and the distance from a refugee camp, the conditioning factors were generated (Figure 4g-l). The Euclidean distance analysis allows  
Figure 4: Conditioning factors considered for the hospital site suitability; (**a**) elevation altitude, (**b**) slope surface, (**c**) plan curvature, (**d**) topographic wetness index, (**e**) topographic roughness index, (**f**) stream power index, (**g**) distance from road, (**h**) distance from river, (**i**) distance from main road (**j**) distance from residential, (**k**) distance from agriculture, (**l**) distance from refugee camps (**m**) population size, (**n**) population density, and (**o**) no\(-\)go zone.  
quantifying the spatial relationship between the factors and the suitable location in linear distance [13; 20; 60; 61; 62].  
In any community, the inhabitants are not spread evenly; this accounts for the variation in population density along different societal dimensions that influence the choice of siting a new hospital [6; 63; 64; 65; 66]. In terms of the cost and response time, locating a hospital close to where people live has a direct implication on the emergency response during disasters. Combining the demographic parameters to the geographic factors allows for the addition of population size and density as important factors to provide a comprehensive foundation for the analysis and planning of the health service. In the current study, the population data obtained from the Ministry of Interior 2018 census data of districts and governor area were interpolated using the inverse distance weightage (IDW) algorithm [13] to generate a raster data layer and were combined with the vector map of each district in ArcGIS 10.5, thus the population size and the population density geodemographic factors (Figure 4m,n) were produced.