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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING https://doi.org/10.1080/13467581.2022.2049277 Spatial distribution of public elementary schools: a case study of Najran, Saudi Arabia Saad AlQuhtani Architectural Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia ABSTRACT ARTICLE HISTORY Received 6 April 2021 Spatial equity in the provision of educational services is a major component to provide Accepted 21 February 2022 a healthy and cheerful living environment in cities. Experts, accordingly, set many standards for selecting school locations. This study has used many of those standards to investigate the KEYWORDS spatial distribution of boys’ public elementary schools in Najran city. Statistical techniques such Spatial distribution; spatial as Locational Quotient, Lorenz Curve, and Geographic Information System tools were equity; elementary schools; employed to show the spatial distribution and analysis of elementary schools. In addition, school locations standards; GIS evaluates the current locations of schools and suggests suitable locations for future GIS; Najran city schools. The results indicate that the population number and schools within the city are not equidistributed. Some districts are experiencing a glut and concentration of schools, especially in old, fully developed, and highly populated districts, while most of the new eastern districts suffer a lack and have no adequate access to schools. Also, half of the city districts do not have elementary schools. Furthermore, many schools are located close to sources of danger or nuisance sources. Consequently, the study concluded by showing suitable locations for future schools and recommended that planners provide elementary schools in deficient districts and enhance equitable distribution of elementary schools throughout the city. 1. Introduction and the distribution of services (Cohen 2006; Wu et al. In recent decades, overpopulation growth and increas- 2020a). This necessitates the intervention of planners ing urbanization in many developing countries have and decision-makers to take the required measures to put excessive pressure on city services (Wazzan 2017). provide the efficient and high-quality public services In many cases, the capacity of existing services cannot that residents are looking for in terms of amount and meet the increased demand of the population, espe- adequate spatial distribution (Carvalho, 2010; Cohen cially in the absence of pre-planning for the number 2006) CONTACT Saad AlQuhtani firstname.lastname@example.org Architectural Engineering Department, College of Engineering, Najran University, PO Box 1988, Najran, Saudi Arabia © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 S. ALQUHTANI One of the most essential public services that use, urban development, and population growth, residents need on a continual basis is educational resulting in more pressure on existing educational services, which are important to any city’s popula- services (Belarem et al. 2018). As a result of these tion. Many countries around the world have created changes, many issues have arisen. One of these systems and policies to achieve educational equity. issues is how the schools’ sites are chosen (Alrehili Assessing the efficiency of educational service is 2015). No systematic way was used to select many a multidimensional task that includes many factors school locations, despite having established stan- such as availability, quality, quantity, distribution, dards, leading to some common concerns. For geographic and financial accessibility, and client instance, many schools in the Saudi cities have satisfactions (Dawod et al. 2013). Nevertheless, the been placed by highways or congested roads or spatial distribution of schools is the most important are in proximity to dangerous or nuisance sources; factor. Urban planners are responsible for the equi- another problem is that some neighborhoods have table spatial distribution of schools within the cities clustered public elementary schools while others so that schools can cover the current and targeted lack schools (Alharbi 2018; Alrehili 2015). In addi- population, thus allowing students to go to schools tion, it is common in Saudi Arabia that the residents safely, with the lowest cost and effort in terms of of a small village or a district may convince deci- time and distance. Besides that, school locations sion-makers to establish a new school even if their should follow the standards of distance from desir- request is not justifiable (Al-Zeer 2005). able (e.g., parks) and non-desirable land uses (e.g., Therefore, this paper tries to detect the spatial factories) (Al-Meteer 1999; General Directorate of degree of inequality and adequacies in the provi- Military Survey, 2002). However, many public ser- sion of boys’ public elementary schools in Najran vices in general, and schools in particular, are and whether the schools’ locations are chosen placed rather haphazardly; at many locations and based on the standards, whether they are distribu- times, sites are undertaken according to need (Al- ted randomly or follow a specific pattern, and Zeer 2005). Therefore, the spatial analysis of school whether they cover all populated neighborhoods. distribution has gained increased attention in the To achieve this aim, the author examines the spatial last few decades from an urban planning, geogra- evolution of elementary schools’ current system in phical, and environmental perspective (Elzahrany terms of number and locations, patterns, density, 2003). They have been trying to achieve what is and proximity to desirable or non-desirable land called spatial justice (Dadashpoor and Rostami use, then evaluates the spatial distribution and sug- 2011). gests suitable locations for future schools. However, In order to evaluate and analyze the spatial dis- this study only focuses on boys’ public elementary tribution of public elementary schools in Najran city, schools and does not cover girls’ public elementary Saudi Arabia, this study tries to assess the relation- schools due to the lack of spatial and statical data ship between the spatial distribution of boys’ ele- about girls’ schools; additionally, girls’ schools are mentary schools and population distribution served by an organized, timely, accurate, and safe through some statistical techniques. Then, it will school bus network. rely on the construction of geographical informa- tion systems (GIS) that many studies concerning 3. The significance of the study educational services adopt as a technical tool for analyzing and evaluating the spatial distribution This study is important since it is the first study in Saudi and network, measuring accessibility, and managing Arabia using statistical and spatial techniques to and presenting data related to educational facilities address the spatial distribution of public elementary (Alrasheed and Elgamily 2013; Dawod et al. 2013). schools in Najran and tries to find out whether the This study also provides suggestions for educational distribution of boys’ elementary schools is based on service agencies about current and future suitable acceptable standards related to educational service locations in Najran or another city with similar distribution. Also, this study investigates the relation- characteristics. ships between school locations and the surrounding and impactful desirable and non-desirable land uses. This study is a unique one since it evaluates the current 2. The problem statement locations of schools and suggests suitable locations for Throughout the last four decades, many educational future schools. Additionally, this study tries to fill in changes have occurred in Saudi Arabia; the imple- some gaps in the theory and literature review of urban mentation of the five-year development plans that education spatial distribution in developing countries. began in the early 1970s was the main driving force Eventually, the results of this study should shed light behind these changes (Al-Zeer 2005). Most Saudi on several essential issues related to the locations of cities have witnessed major rapid changes in land this type of school. Finally, the study’s contributions JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3 can serve as a point of reference for research regarding and the school building construction (Earthman the spatial distribution of schools in developing coun- 2017). However, in this study, the concentration will tries in general and Saudi Arabia in particular. be on the equity of the spatial distribution of schools and the factors affecting it. Al-Awadhi and Mansour (2018) defined spatial 4. Literature review inequity as the variation among geographic zones regarding the distances from home to the desired service Urban planning and geography fields have become facility, such as a school or hospital. Zenk, Tarlov, and Sun a major contributor to solving the population’s every- (2006) added that spatial inequity describes how service day problems, especially when practitioners choose facilities are not distributed evenly throughout the spe- the most appropriate public services locations (Al- cific area. Therefore, it can be an interpretative method of Sheikh 2010). Planners can choose the most suitable analyzing imbalance and unequal allocation of services sites for public services by understanding the relation- across a geographic area (Barbieri et al. 2019). According ship between humans, the environment, and land uses to the geographic theoretical basis, the spatial equity or within the city or region; furthermore, they study phe- spatial justice of desirable public services such as schools nomena distribution and whether the distribution con- means a shorter distance between residents and the stitutes a specific pattern or is random. So, suppose the public services; in other words, the public services are in distribution constitutes a particular pattern; in this reach and they are not unevenly distributed over the case, it means there are some factors behind the for- space. However, the distance to non-desirable services mation of this pattern that the researchers seek to find such as landfills and factories should be longer, so the and understand, but if the distribution is random, this population is not affected (Al-Awadhi and Mansour indicates the weakness of the accuracy that created 2018). On the other hand, some authors asserted that this pattern (Haggett 1979). the concept of spatial justice does not have a clear defini - The geography of education is defined by tion. It features the role of space in producing justice and Kučerová and Kučera (2012) as “a scientific discipline injustice (Williams 2013). Also, Barbieri et al. (2019) added consisting of the study of spatial variations in the that if spatial justice is a spatially dependent issue, it provision, uptake, and output of educational facilities cannot be determined only through traditional indicators and resources.” They also added that when inequality at an administrative level but also through diverse exists in a city, urban planners and geographers need aspects of socioeconomic, demographic, and social fac- to examine the differences in school distribution tors of the residents, as well as the differences and simila- across the city or region. Malczewski and Jackson rities from one location to another. (2000) show that schools must be organized in such Studies of the spatial distribution of elementary a way to maximize accessibility to those within schools in western countries began as early as 1929 a school district; they explained that the equity con- when Clarence Perry devised the famous theory of the cept could be achieved through minimizing the varia- “neighborhood unit” where the elementary school is bility of the access to schools. In other words, spatial located in the neighborhood center with a maximum equity in educational services is the expectation of one-quarter mile distance for pupils walking to school. human beings for the distribution of public service Then, in the 1950s, scholars began to study school facilities, including providing convenient and quick district deviation and location planning to understand access to educational facilities; all students should social impacts and other factors, rather than just eco- be treated equally (Wu et al. 2020b). In addition, the nomic factors. This can be called “location theory” (Yan same space separation should exist between all edu- et al. 2018). Furthermore, the interest in studying spa- cational facilities and population residents (Rosa tial distribution of schools began in the early 1960s, 2014). Therefore, one of the factors to evaluate spatial but many studies found problems associated with equity is accessibility (Tsou, Hung, and Chang 2005), school locations. and this is mainly based on physical distance mea- Selecting the appropriate location for schools is surements (Hewko, Smoyertomic, and Hodgson 2002; a significant factor for authorities responsible for the Fan et al. 2017). However, this factor is often imprac- spatial equity of schools. The location, size, and proxi- tical since it ignores many other indicators such as mity to positive or negative externalities can materially proximity to other land uses, service scope, construc- affect pupils’ educational achievements. Thus, many tion of the buildings, road network structure, and agencies responsible for education have developed population density. Thus, many scholars introduced selection site criteria not only for current needs but some other factors that can evaluate the quality of also for projected needs. For example, UNESCO (1985) facilities, such as service range and size (Tsou, Hung, issued norms and standards for the selection of pro- and Chang 2005), population differences among dif- spective school sites, with the main factors being: the ferent locations (Chang and Liao 2011), funding, since school must not be located in front of railways or major it can improve the quality of schools (Adams 1994), roads; the location must be more than 200 m from teacher-student ratio (Rodriguez and Elbaum 2014), 4 S. ALQUHTANI noisy and noxious industries and more than 400 m to school; 150 m from the closest highway or main the leeward of factories; students will not have to cross road; 75 m from the nearest road intersection or dangerous roads; the land is level and well-drained; gas station; 3 km from the closest airport; 150 m availability of water and other services; and easy access from power transmission lines and 500 m from any to a playing field. power transmission plant; 150 m from factories and In the U.S.A., selection criteria were also developed by warehouses; 1 km from cooking gas cylinder distri- agencies. For example, the important criteria of the butors or hazardous materials warehouses and fac- California Department of Education CDE (2004) are: tories; 300 m from valleys; 100 m from water there should be safe sidewalks and bike lanes to the catchment areas; and the land slope must be less school, especially within the walking or biking distance than 18%. Riyadh Municipality (2001) issued to schools (½ mile); the school site should not be subject a guideline for educational services. There should to flooding, and the maximum noise in the area sur- be an elementary school for boys and another for rounding schools is 50 decibels. The distance to the girls for each 3,600 population and it should be closest airport runway should be at least 3.2 km, 100 m connected to students’ homes by safe sidewalks. to power transmission lines, 400 m to hazardous air The buffer zone for each elementary school is emissions or handled hazardous materials or wastes, 550 m, where students can walk to school easily; it 450 m to major roads, railroad tracks, and aboveground should be far away from noisy and congested or underground pipelines that can pose a safety hazard streets. (e.g., pressurized gas, gasoline, sewer, high-pressure The selection criteria can help the responsible water pipelines). Furthermore, the U.S. Environmental team to select proper locations that provide a safe Protection Agency [EPA] (2011) issued some standards and supportive environment for instructional pro- such as the accepted maximum walking or biking dis- grams and the learning process. However, many tance is a ½ mile (800 m) to the closest elementary school; elementary schools do not follow these criteria, walking distances should be less than a ½ mile to the since many are located in improper locations. For community facilities (e.g., libraries, parks, museums). The example, they are either located immediately distance to hazardous waste sites, landfills, and solid beside highways, next to a congested road inter- waste should be more than 1 mile (1.6 km); a ½ mile section, or in proximity to noisy factories or (800 m) to high-traffic roads and highways, rail lines, and a valley. large industrial facilities; 300 m to gas stations and other In Riyadh city, the capital of Saudi Arabia, Alquraini fuel-dispensing facilities and small sources (e.g., auto (2001) found that 104 out of 167 wards did not have body shop, furniture, wood, electronic manufacturing); schools, and most of those schools were distributed in 3.2 km to the airport runway; 150 m to power lines; 450 m a convergent way. In addition, the author found that the to hazardous material pipelines and water or fuel storage distance between homes and schools varied between 1 tanks; and 400 m to geologic hazards (e.g., landslide zone, to 25 km. Similarly, the study by Al-Zeer (2005) revealed volcanic activity, flood zone). a shortage of schools in the north part of Riyadh, resulting In Saudi Arabia, the government took into consid- in overcrowding in most schools. Regarding the spatial eration the importance of improving education quality, distribution of schools in northern Riyadh, Alharbi (2018) and it considered that implementing planning stan- also concluded that the distribution of schools takes dards for selecting schools’ sites is an important tool a clustered pattern heading towards randomness; this for achieving the goals of their development strate- pattern is characterized by the concentration of schools gies. Therefore, the government commissioned many around one another in a small area and buffer zones of relevant government agencies to set the standards, schools overlapping between many schools, while other such as the Ministry of Municipal and Rural Affairs areas had a shortage. Regarding proximity to the danger (MOMRA), General Directorate of Military Survey area, Al-Meteer (1999) found that 42% of schools in (GDMS) (Zabedi 2010), and Riyadh Municipality. Riyadh were located next to unsafe roads. MOMRA sets standards for selecting elementary In Jeddah, the second-largest city in Saudi Arabia, school sites, and they include: each school serves many studies were conducted to examine schools’ a neighborhood (3,600 residents); the service buffer spatial distribution. Belarem et al. (2018) and Zabedi zone is 500 m; all students can reach the school (2010) concluded that schools’ spatial distribution walking on safe sidewalks or local streets; the site showed a great imbalance between the districts; should be away from noise, pollution, dust, and many schools were concentrated in the city center, natural hazards; and it is preferable to be in proxi- where the population density is greater, while the mity to a park (MOMRA 2006). GDMS (2002) also sets north and south districts had few schools. This distri- standards for selecting elementary school locations, bution was relatively proportional to the distribution of although this agency is not related to the education population numbers and densities. However, a large field. Those standards are: the elementary school proportion of residents had problems with accessibility should be 500 m away from another elementary to schools, and it became more difficult with the weak JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5 network of school buses and public transportation from walking a long distance to schools, and 42% of services. Zabedi (2010) added that most of the schools students pointed out that they cross a highway or main in Jeddah were clustered in some wards, especially roads during their walk. However, there was a positive areas with high population density; also, based on relationship between the schools’ numbers and the MOMRA and GDMS standards, buffer zones of schools population density of the districts. overlapped between many schools. School site selec- Most of the previous studies used GIS techniques to tion did not follow set standards; some were located investigate the spatial distribution of schools within close to main roads, gas stations, or noisy areas. Pasha cities; however, very few studies were found using (2004) showed that 4.5% of elementary schools were Location Quotient (LQ) and Lorenz Curve. For example, located next to main roads, and around 34% of the Wazzan (2017) studied the spatial degree of inequality schools were near collector streets. Recent work by in the provision of the first and second stage of basic Murad, Dalhat, and Naji (2020) investigated the loca- education schools in Lattakia, Syria, and the results tion distribution and accessibility of the elementary revealed that the population and the number of schools in Jeddah, and the authors found a shorter schools are not equidistributed since schools are con- commuting distance to denser schools that are mainly centrated in some districts while other lack schools. LQ located in the center of the city while other districts of values are different between both types of schools, the city need more schools. where it is from 0 to 2.54 for the first stage and from 0.60 to 1.94 for the second stage; this means as the In Makkah, Alrehili (2015) evaluated the current number increases above one, the schools become locations of schools and the results showed that more concentrated. The results of the Lorenz Curve many schools did not follow school site selection stan- showed that about 50% of first-stage schools are dards; they were located in certain districts only while enjoyed by around 50% of the residents, and 50% of others lacked schools, and many were in proximity to the second-stage schools are enjoyed by 60% of the dangerous land uses. On the other hand, Dawod et al. residents. Another study by Musa and Mohammed (2013) found a strong positive relationship between 2012) in Bida City, Nigeria, investigated the schools’ the number of schools and population density. distributions and found that schools were not guided However, the city still needed many schools to cover by population distribution in the districts. The LQ for the shortage in some districts. In Abha, Alhajri (2016) elementary schools varied between 0–6.8 and 0–27.2 found that schools were clustered in districts near the for high schools, which means some districts are defi - city center and western districts where the population cient in elementary schools, and some of the popula- density is higher, and most schools were distributed tions did not have adequate access to these facilities. randomly. Similar results were also found by Alqahtani From studying the school site selection standards (2018), and he added that the city is still in need of and previous studies, it is found that in Saudi Arabia, many elementary schools since the current schools some agencies who are not responsible for educa- only cover 8% of the Abha area. tional services have issued some standards, while the In some cities in Saudi Arabia, only one study was Ministry of Education or any of its branches have not found which dealt with the schools’ distribution within issued any published guidelines. Also, it is found that the city, such as Buraidah, Hail, and Najran. For example, many existing standards either nationally or interna- Alsagri and Aldagheiri (2013) examined the equity in the tionally did not cover most aspects and distances spatial distribution of schools in Buraidah city and found related to suggested school locations, except for EPA that schools were unevenly distributed, with many and GDMS standards that consider many criteria in schools concentrated in wards near the city center. Also, detail and proper distances to surrounding elements there was a positive relationship between the number of or land uses. In addition, inequity in the spatial distri- schools and population density. In Hail, Hail Region, the bution of schools is visible in most schools in Saudi results of Alshammari’s (2011) study revealed that schools cities. Most schools were clustered in the city center followed a randomly dispersed pattern, and some highly districts, where there is a higher population density populated districts were not covered by schools. The only and lower vacant lands, whereas distant districts of study in Najran was done by Alsalem (2011), and it was the city center – especially the new districts with few designed to analyze the equity of spatial distribution in population densities – lack schools. Many schools are schools when the city had forty-six districts and before also located in proximity to danger or nuisance sources the planning of the eastern districts located near Najran that negatively affect educational achievements. University. This study used questionnaires and interviews Parts of the studies mentioned in the literature to measure the accessibility to schools. The results support this study in terms of the examined educa- showed that schools were distributed among only thirty- tional service and its goals. They aimed to examine the five districts within the city, with a dispersed distribution equity in the spatial distributions of schools, search for that tended to be random; 70% of the students suffered 6 S. ALQUHTANI inappropriate distribution and amend it, examine the studies related to education facilities’ spatial distribu- proximity to desirable and non-desirable land used, tion since the 1980s and is demonstrated in many of and choose suitable locations for schools. Therefore, the previous studies such as (Alharbi 2018; Alqahtani this study benefits from the standards and previous 2018; Al-Zeer 2005; Zabedi 2010). This technology studies in support of the theoretical side, determining contributes to reaching accurate and fast statistical the appropriate methods for the study and using the results for the distribution of geographical phenom- statistical and GIS techniques to produce and analyze ena, such as the spatial distribution of elementary the maps. schools, through creating a spatial and descriptive database which can help to find the relationship between school locations and the surrounding and 5. Methodology and data processing influencing geographical phenomena and to deter- mine their patterns and characteristics in a way that This study utilizes the quantitative methods related to traditional methods are incapable of (Dawod 2012). the statistical and spatial analytical approach, which In addition, GIS technology can also be used in could help recognize the spatial distribution of ele- choosing future suitable school sites (e.g., Alqahtani mentary schools in different locations within the city 2018; Alrehili 2015; Zabedi 2010) or redistributing and highlight the spatial differences in the distribu- some of the current schools (e.g., Alharbi 2018; tions (Meselhi 2008). So, in the next processing stage, Alrehili 2015). the equity issue of elementary schools’ distribution In this study, first, GIS is used to show the schools’ with respect to population has been investigated. In numbers, locations, and density per district and popu- this regard, the author utilized Location Quotient (LQ), lation in the maps provided by Najran Municipality. Lorenz Curve, and the ratios and percentages. This is Then, some important spatial tools available in GIS followed by using a unique tool, GIS, to analyze and are used to measure the spatial geographic distribu- display geographically referenced information. tions. These methods measure and compare school First, the Location Quotient (LQ) shows the extent of distribution by calculating the values that represent spatial concentration or inequity of elementary schools the characteristics of distribution such as concentra- in the districts, and it can be computed by using tion, dispersion, and directions (Esri 2020a); these equation (1). The LQ of each district is expected to be methods are also known as measures of spatial disper- 1.0. However, if the value is greater than 1.0, it means sion and spread, and they include, for example, mean that the district has a higher concentration of elemen- center, central feature, standard distance, and direc- tary schools than the city. tional distribution, which are explained in detail in the analysis section. No: of commodity X in district A No: of commodity X in the city The third GIS tool used is the density analysis tool, LQðX; AÞ ¼ (1) Population of district A which can determine the extent of density change of Population of the city the phenomenon distribution throughout the study Another method is the Lorenz Curve, which is widely area (Dawod 2012). It has two major techniques, used in urban planning and geography to measure the point density and kernel density, which are explained equality distribution through a diagonal line where the and used below. Fourthly, analyzing patterns is an greater the deviations of the Lorenz Curve means the incredibly effective method in revealing elementary greater the inequality. In contrast, zero means com- schools’ distribution patterns and showing whether plete equality, and 1 means complete inequality. By they have a specific pattern or are distributed ran- putting the cumulative proportion of elementary domly. Analyzing patterns has some techniques that schools in the y-axis and the proportion of the popula- help find the spatial distribution type of elementary tion in the district on the x-axis, the area of equality is schools, such as nearest neighbor analysis, Ripley’s being calculated. Besides that, ratios and percentages K function, and Moran Index. This is followed by are used to explain the relationships between some using GIS proximity tools that show the extent of the variables, such as the ratio of schools to the population phenomenon’s proximity to similar geographic vari- by the district. ables, desirable and non-desirable land uses. The major techniques in proximity tools are buffer zone, In the last two decades, GIS school mapping has point distance, and Thiessen polygons; those techni- often been used for educational planning (Burrough ques are discussed and used in the results section. The et al. 2015); it provides a mapping tool for the rela- sixth important GIS method is the spatial interpolation tionships between school distribution, different sur- that predicts values for cells in a raster form for rounding land uses, and age of the population, and it a limited number of sample data points (Esri 2020b). is an efficient tool in managing and planning the The most important techniques in spatial interpolation accessibility to schools (Hite 2006). As stated by Yan that can show the concentration or higher and lower et al. (2018), GIS technology has been widely used in JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7 density of schools are Inverse Distance Weighted (IDW) distributed equally within the city districts. Also, one and kriging. Additionally, a GIS tool that determines would assume that each district has at least one ele- the statistically significant spatial clusters of high mentary school; in addition, one would hypothesize values (hot spots) and low values (cold spots) is used, that in a district of a greater number of populations, and it is called hotspot analysis. we would observe more elementary schools within Finally, some GIS techniques are used to examine that district. In other words, there is an elementary school for each 3,600 population as requested by the the suitability of current school locations and sug- standards. Furthermore, it is assumed that boys’ ele- gest proper future elementary school locations. The mentary schools are located within 500 m of students’ reference standards for all of the processes are residences. Regarding choosing elementary school mainly CDE, EPA, MOMRA, GDMS, and Riyadh locations, it is hypothesized that schools follow the Municipality; those standards assist in examining standards of school site selection, which have been whether elementary school sites in Najran were set by authorities regarding the distances from other chosen based on the standards or not, especially elementary schools and desirable or non-desirable in terms of the spatial distribution pattern, coverage land uses. of the population, density, and proximity to desir- able and undesirable land use; those standards also help to suggest prospective suitable boys’ elemen- 6. Study area tary school sites. In this study, the directional hypothesis is used, and Najran city is located in the southwestern part of it is a prediction made by the author regarding the Saudi Arabia, as shown in Figure 1 (General relationships between two variables. So, the main Authority for Statistics [GASTAT], 2010). This city hypothesis is that all boys’ elementary schools are has a stripe shape in a flat land, and its area is Figure 1. (A): administrative division of Saudi Arabia; (B): Najran Region; (C): Najran city; (D): population density in Najran city. This number includes the population of the city’s districts, the villages, and districts located out of the city administrative boundaries. However, this study only covers the districts located within the city boundaries, which is 316,379 inhabitants, and this is because of the availability of the data. 8 S. ALQUHTANI about 885 km ; the city has seventy-eight residen- 7.1. Spatial distribution and density of tial districts, as shown in Figure 1) (Najran elementary schools Municipality 2019). The population number of The analysis of elementary school distribution has Najran increased sharply; it was 192,325 inhabitants been conducted in both statistical and spatial in 1992 (GASTAT 1992), and the number increased scales within Najran districts. As shown in Figure by more than double to 454,035 inhabitants in 2) and Table 1, Najran city had fifty-three elemen- 2019 (Najran Municipality 2019). This may be due tary schools in 2019, and those schools were dis- to the increase in immigration rates to Najran, espe- tributed among thirty-nine out of seventy-eight cially after the opening of Najran University, the districts. This means only 50% of the districts had high rates of investment in the city, and the elementary schools. It can also be noticed from increased financial allocations directed to the city. Figure 2) and Table 1 that two districts have four In Najran, the average population density in 2019 schools for each district (Alfahad and Alghwela), varied between districts of the city; it is higher in Dahdha district has three schools, six districts have proximity to the city center and some old districts, two schools each, and thirty districts have one which was around 174 inhabitants/hectare, and then, school each. Thirty-nine districts do not have it declined to about forty-eight inhabitants/hectare in schools; only two of them are unpopulated. further districts. It decreases to less than five inhabi- The density of schools helps to identify districts tants/hectare in half of the city’s districts, especially the with a more significant or lower number of schools city’s eastern districts, as shown in Figure 1) (Najran than the others; this can be counted based on the Municipality 2019). Regarding the number of boys’ area or the population size. Measuring school dis- elementary schools, in 1965, there were only four ele- tribution per district area may not give mentary schools in Najran; however, the number a comprehensive idea of the school distribution increased to fifty-three schools by 2019 (Ministry of in residential districts, so looking at the relation- Education 2020). ship between the number of schools and the population of each district is a better way (Alsagri and Aldagheiri 2013), since the population is the 7. Results and discussions main factor in the educational process. The Pearson correlation result is (0.68), which means The data and maps of elementary school locations a positive correlation between school numbers within Najran districts help to interpret the spatial and the population size in each district. Many pre- distribution. So, this section begins with presenting vious studies found a positive relationship some statistical techniques that can show the dis- between the number of schools in the district tribution of elementary schools with respect to and the population size (Alsagri and Aldagheiri population and districts. This is followed by using 2013; Alsalem 2011; Dawod et al. 2013). This some GIS spatial analysis functions that can assist in study considers an elementary schools’ average identifying the center of schools, the distribution per 3,600 people in districts, since this is the stan- pattern of schools, directional trends, the relation- dard issued by MOMRA and Riyadh Municipality. In ship between schools and other land uses, the clus- Najran, as shown in Table 2, with the exception of ters of the schools, and, finally, the suitability of districts not having schools – which represent half current school locations and suitable locations for of the city’s districts – the study finds 20.5% of future schools. Figure 2. (A): elementary schools in Najran city; (B): number of schools per district. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 9 Table 1. Distribution of schools in Najran based on population and districts. Number of Number of Schools/ % of % cumulative % of % cumulative District Name population Schools L Q population population population schools school Alfahad 20,557 4 1.16 5139.25 6.4976 6.4976 7.5472 7.5472 Alghwela 13,488 4 1.77 3372 4.2632 10.7608 7.5472 15.0943 Dahda 18,895 3 0.95 6298.33 5.9723 16.7331 5.6604 20.7547 AbaAlsaud 18,262 2 0.65 9131 5.7722 22.5053 3.7736 24.5283 (CBD) Nohogah 17,941 2 0.67 8970.5 5.6707 28.1760 3.7736 28.3019 BaniSalman 10,874 2 1.10 5437 3.4370 31.6130 3.7736 32.0755 Alkhaldia 7914 2 1.51 3957 2.5014 34.1145 3.7736 35.8491 ShabReer 7208 2 1.66 3604 2.2783 36.3927 3.7736 39.6226 Alshorfa 5504 2 2.17 2752 1.7397 38.1324 3.7736 43.3962 King Fahad 0 1 – – 0.0000 38.1324 1.8868 45.2830 Park Alfaisalia 19,233 1 0.31 19,233 6.0791 44.2115 1.8868 47.1698 Almonajem 15,050 1 0.40 15,050 4.7570 48.9685 1.8868 49.0566 Aldubat 13,572 1 0.44 13,572 4.2898 53.2583 1.8868 50.9434 Aldyafa 10,842 1 0.55 10,842 3.4269 56.6852 1.8868 52.8302 Prince 8652 1 0.69 8652 2.7347 59.4199 1.8868 54.7170 Meshaal Alhazan 6440 1 0.93 6440 2.0355 61.4554 1.8868 56.6038 Athayba 5777 1 1.03 5777 1.8260 63.2814 1.8868 58.4906 Almoratah 5199 1 1.15 5199 1.6433 64.9247 1.8868 60.3774 Alamlah 5051 1 1.18 5051 1.5965 66.5212 1.8868 62.2642 Algabel 4177 1 1.43 4177 1.3203 67.8414 1.8868 64.1509 Alorysa 3373 1 1.77 3373 1.0661 68.9075 1.8868 66.0377 Alrowekbah 3261 1 1.83 3261 1.0307 69.9383 1.8868 67.9245 ZorWada 3064 1 1.95 3064 0.9685 70.9067 1.8868 69.8113 Almasmah 2730 1 2.19 2730 0.8629 71.7696 1.8868 71.6981 Alhamar 2647 1 2.26 2647 0.8367 72.6063 1.8868 73.5849 Alkharaa 2626 1 2.27 2626 0.8300 73.4363 1.8868 75.4717 Nehegah 2273 1 2.63 2273 0.7184 74.1547 1.8868 77.3585 Regla 1723 1 3.46 1723 0.5446 74.6993 1.8868 79.2453 Fowaz 1675 1 3.56 1675 0.5294 75.2288 1.8868 81.1321 Moneef 1638 1 3.64 1638 0.5177 75.7465 1.8868 83.0189 ZorAlhareth 1611 1 3.71 1611 0.5092 76.2557 1.8868 84.9057 Reman 1505 1 3.97 1505 0.4757 76.7314 1.8868 86.7925 Almofejah 1202 1 4.97 1202 0.3799 77.1113 1.8868 88.6792 BaniKolaib 1008 1 5.92 1008 0.3186 77.4299 1.8868 90.5660 Alkholaif 894 1 6.68 894 0.2826 77.7125 1.8868 92.4528 Alekam 548 1 10.89 548 0.1732 77.8857 1.8868 94.3396 Sagher 511 1 11.68 511 0.1615 78.0472 1.8868 96.2264 Aljameah 252 1 23.69 252 0.0797 78.1269 1.8868 98.1132 AbaAlreshash 247 1 24.17 247 0.0781 78.2049 1.8868 100.0000 Alokdood area 0 0 – – 0.0000 78.2049 0.0000 Aldarah 0 0 – – 0.0000 78.2049 0.0000 Alyasameen 5 0 – – 0.0016 78.2065 0.0000 Alsalam 10 0 – – 0.0032 78.2097 0.0000 Alzohor 10 0 – – 0.0032 78.2128 0.0000 Aloroba 15 0 – – 0.0047 78.2176 0.0000 Sohaifat 48 0 – – 0.0152 78.2328 0.0000 Alsanaea 264 0 – – 0.0834 78.3162 0.0000 Algheda 432 0 – – 0.1365 78.4527 0.0000 AbaAlkhareet 432 0 – – 0.1365 78.5893 0.0000 Alan 448 0 – – 0.1416 78.7309 0.0000 Magan 480 0 – – 0.1517 78.8826 0.0000 Airport Area 490 0 – – 0.1549 79.0375 0.0000 BearAbean 511 0 – – 0.1615 79.1990 0.0000 Alsafa 564 0 – – 0.1783 79.3773 0.0000 Alnahda 700 0 – – 0.2213 79.5985 0.0000 Alfaji 710 0 – – 0.2244 79.8229 0.0000 Albatha 720 0 – – 0.2276 80.0505 0.0000 Almarkab 815 0 – – 0.2576 80.3081 0.0000 Sahaban 1050 0 – – 0.3319 80.6400 0.0000 Awera 1102 0 – – 0.3483 80.9883 0.0000 Alnaslaah 1169 0 – – 0.3695 81.3578 0.0000 ShabWabran 1329 0 – – 0.4201 81.7779 0.0000 Almoamer 1526 0 – – 0.4823 82.2602 0.0000 Alawakel 1550 0 – – 0.4899 82.7501 0.0000 ZorBaniAmeer 1758 0 – – 0.5557 83.3058 0.0000 Alrowdah 1830 0 – – 0.5784 83.8842 0.0000 Almakhbah 2074 0 – – 0.6555 84.5397 0.0000 Berk 2217 0 – – 0.7007 85.2405 0.0000 Alkhadra 2253 0 – – 0.7121 85.9526 0.0000 Alhaira 2374 0 – – 0.7504 86.7030 0.0000 Segam 2806 0 – – 0.8869 87.5899 0.0000 Algasoom 3102 0 – – 0.9805 88.5704 0.0000 AbuGhubar 4242 0 – – 1.3408 89.9112 0.0000 (Continued) 10 S. ALQUHTANI Table 1. (Continued). Number of Number of Schools/ % of % cumulative % of % cumulative District Name population Schools L Q population population population schools school Alshahban 4680 0 – – 1.4792 91.3904 0.0000 Alhossain 5154 0 – – 1.6291 93.0194 0.0000 Almakheam 5964 0 – – 1.8851 94.9045 0.0000 Taslal 6657 0 – – 2.1041 97.0087 0.0000 Jourbah 9464 0 – – 2.9913 100.0000 0.0000 neighboring districts. They suffer when commuting Table 2. Number of schools versus the population size. to and from schools, especially with the lack of Schools compliant with the standard Number of (1:3,600) districts % school buses and public transportation. No school 39 50 To be more specific, Location Quotient (LQ) Schools less than the standard 16 20.5 shows the extent of spatial concentration or Schools meeting with the standard 1 1.3 Schools above the standard 22 28.2 inequity of elementary schools in the districts Total 78 100 regarding the number of populations. If the LQ value is less than 1.0, the local concentration of elementary schools is less than expected given the districts have schools but less than the standard trends in the city as a whole; if your LQ value is (1:3,600) meaning, for each school, the average of 1.0, then the local concentration of elementary population ranges from 3957 to 19,233 persons. schools is as expected given the trends in the Most of those districts can be considered as old city as a whole; and if your LQ value is greater districts that have been fully developed and have than 1.0, then the local concentration of elemen- a higher population density; however, 28.2% of the tary schools is more than expected given the districts have more schools than the standard, trends in the city as a whole. As shown in which means that the average population per Table 1, the LQ of twenty-nine (55%) districts var- school ranges between 247 to 3,373 persons, and ies between 1.03 to 24.17, meaning that those most of those districts are located on the fringes of districts have a higher concentration of elementary the city; they are villages in the southern, western, schools than the whole city, and it can be higher if and northern parts or new districts with a low districts have a lower number of populations per population size. The study found that elementary school. Most of these districts are distributed in all schools’ average per population in Najran is parts of the city. However, the LQ of nine (17%) 1:5,970 people; however, there are thirty-nine dis- districts varies between 0.31 to 0.95, which means tricts without schools, where most of them are that those nine districts have a lower concentra- new districts that have started growing and need tion of elementary schools than Najran city; this schools urgently. Population size in some of them can be noticed in districts that have a higher num- began to increase sharply, so some of them require ber of populations per school, and most of those more than one elementary school. Students living districts are the old and fully developed districts in districts without schools join schools in that do not have enough schools. In this Lorenz Figure 3. The shape of lorenz curve for elementary schools in Najran city. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 11 Figure 4. Geographical distribution analysis of primary schools in Najran. Curve, and as shown in Figure 3, 50% of elemen- elementary school covers 3,600 persons, and each tary schools are enjoyed by around 50% of the district has at least one elementary school, can be population, but 100% of the schools are only rejected. enjoyed by 78% of the population. This shows that the city lacks elementary schools in some 7.2. Methods of measuring geographic districts, resulting in more pressure on some of distributions the available schools. This outcome is supported by previous studies which found that in many These methods measure the spatial concentration, Saudi Arabian cities, districts in proximity to the direction, dispersion, and spread of elementary schools city center have a greater number of schools than and include the tool of mean center, central feature, what is located further away due to the higher standard distance, and directional distribution. Using number of populations within those schools, and GIS tools revealed that the mean center is close to some of the districts located in the city fringes had central feature with a slight deviation of 2.7 km towards a shortage in schools (Alharbi 2018; Alqahtani the west, which means a higher number of schools in 2018; Alrehili 2015; Belarem et al. 2018; Murad, the city center and western districts (Figure 4). Standard Dalhat, and Naji 2020; Zabedi 2010). Based on the distance is an important GIS tool that can measure the above results, the author’s hypotheses that each Figure 5. (A): point density of elementary schools; (B): kernel density of elementary schools. 12 S. ALQUHTANI degree to which school locations are concentrated or districts and they are distributed in a random way dispersed around their center (Esri 2020a). As shown in (Alhajri 2016; Alsagri and Aldagheiri 2013; Alharbi Figure 4, the standard circle radius is 13,608 meters, and 2018; Alquraini 2001; Al-Zeer 2005). Directional distribution (Standard Deviational the circle is located in the central parts of the city; it Ellipse) is another significant function that can sum- includes 37 out of 53 schools, representing 69.8% of the marize the spatial characteristics of geographic fea- total number of schools. This indicates that elementary tures including central tendency, dispersion, and schools are concentrated in the residential districts near directional trends (Esri 2020a). As shown in Figure the city center, and there are fewer schools towards the 4, the directional trend distribution shows that the peripheries. The standard area covered 202 km , repre- standard distance is in the X-axis direction is senting 52% of residential districts’ total area (386 km ). 18.6 km, Y-axis is 4.7 km, and the distribution’s This can be an indicator that schools are clustered in deflection value is 71.6°. This means that the distri- densely populated residential districts and vital areas in bution of schools in Najran takes a direction trend Najran, but most of those schools are distributed ran- from southwest to northeast, and this could be due domly. Those observations align with many of the pre- to the city’s linear shape within a flat land sur- vious studies done in Saudi Arabia, since most of them rounded by rugged mountains in the north, south, found that schools are clustered in highly populated and west. Figure 6. Nearest neighborhood analysis, ripley’s K function, and moran index. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 13 Ripley’s K function is another important GIS techni- 7.3. Density analysis que that determines whether elementary schools’ dis- This method helps determine the locations of ele- tribution patterns exhibit clustering or dispersion over mentary school concentrations in Najran and their a range of distances. Figure 6 shows that the observed relevance to surrounding land uses or roads. Point K value is greater than the expected K value by around density produces a surface map that shows the 12,800 meters; then, the observed K value becomes distribution density extent of elementary schools smaller than the expected K value for a short distance. within the area (Dawod 2012). Figure 5) shows This shows that the distribution of elementary schools that the highest density is in two areas in the city: is more clustered in most parts of the city, which can the first is the old, highly populated and developed be consistent with the nearest neighbor analysis find - districts, and the second one is located in both ing. This can be due to a higher number of schools in previous areas, C.B.D districts, and some areas in specific locations at an extremely high confidence level the southern part of the city that can be considered since the observed K value is larger than the upper a village, where residents could convince the offi - confidence envelope value. These findings are consis- cials to establish new schools. The lowest density is tent with many previous studies that found the dis- noticed in most parts of the city, especially towards tance between schools located in the city centers and the eastern part. students’ homes are shorter than the distance from Kernel density is one of the GIS functions that schools located in districts far from city centers to shows a circular neighborhood link that reflects the students’ homes. elementary school density in each circle, and sur- In addition, Moran Index is +0.26, as shown in Figure face trends are determined based on the geogra- 6, which means that the distribution pattern of ele- phical spread of schools, so it is concentrated in mentary schools in Najran is clustered. This agrees with areas with high school density while it recedes in the previous results, which show that most elementary low-density areas. Figure 5) shows that schools’ schools are clustered mainly in old and highly popu- higher density is in old and fully developed residen- lated districts. However, the main hypothesis of this tial districts and the residential districts near the city study that assumed all boys’ elementary schools are center. Then, it declines moving east towards the distributed equally within the city districts can be fringes; it shows similarity with point density results. rejected. Most of the previous studies showed that a greater density of schools was found in proximity to the city centers where the districts were full of 7.5. Proximity residents. Proximity determines the extent of the phenomenon’s proximity to similar or different geographic variables. Table 3. shows the comparison of professional authori- 7.4. Analyzing patterns ties’ standards, mentioned in the literature section, and Knowing the patterns of elementary schools leads to the current situation of Najran elementary schools a search for the factors affecting its formation or deter- regarding the coverage and distances to other elements mining if the distribution is random. Some GIS techni- or land uses. The section below shows some types of ques help to find the spatial distribution type of proximity analyses, such as the buffer zone, proximity to elementary schools in Najran. Nearest neighbor analy- other elements or land uses, and Thiessen polygons. sis is an important and accurate quantitative technique Based on GDMS, MOMRA, and Riyadh Municipality that attempts to determine the distribution patterns of standards, elementary schools’ buffer zones should be a phenomenon spatially and whether the distribution 500 m. It means that this is the maximum walking dis- is clustered, random, or dispersed. As shown in Figure tance for students. As shown in Figure 7, twelve elemen- 6, the nearest neighbor ratio is 0.85, which means the tary schools’ buffer zones overlay with each other. This is distribution pattern of elementary schools is clustered, mainly found in old, highly populated districts and some but it tends towards the random pattern, and there is southwestern villages. Also, the buffer of some schools a less than 5% likelihood that this clustered pattern almost intersected with other nearby buffers, which is could be the result of random chance. Also, this means noticed around six schools in the city. As we go towards that some schools are concentrated in small areas with the eastern side of the city, the distances increased a short distance between each other. The rest of the between schools, meaning that many parts of the city schools are spread over large areas and are far apart are not served by schools; specifically, half of the city from each other. This can support what has been districts do not have elementary schools. The minimum mentioned in the density section and previous studies distance between elementary schools is 402 m, while the that schools are concentrated in old residential dis- maximum distance is around 7 km, and the average tricts, the number of schools is not enough in some distance is 1.85 km, and most of the city sections lack districts, and, in some, there are no schools. safe sidewalks. This can be a significant indicator of the 14 S. ALQUHTANI Table 3. Comparison between school selection site standards and current locations of elementary schools in Najran. Current status of Najran schools Standards UNESCO CDE EPA MOMRA GDMS Riyadh Municipality Mean Median Walking distance to school ≤ 800 m ≤ 800 m ≤ 500 m ≤ 500 m ≤ 550 m 1,857 m 1,468 m Served population ≤ 3,600 ≤ 3,600 5,970 – Existing of safe sidewalks Yes Yes Yes Yes Yes No No Airport runway ≥ 3.2 km ≥ 3.2 km ≥ 3 km 19.8 km 19.9 km High-traffic roads, highways, main roads, rail lines Not close ≥ 450 m ≥ 800 m Not close ≥ 150 m Not close 390 m 114 m Gas stations ≥ 300 m ≥ 75 m 943 m 434 m Power lines ≥ 100 m ≥ 150 m ≥ 150 m – – Power plant ≥ 500 m 3.5 km 3.3 km Hazardous waste sites ≥ 400 m ≥ 1.6 km – – Hazardous elements (e.g., material pipelines, storage tank, gas cylinder distributors) ≥ 450 m ≥ 450 m Not close ≥ 1 km 1.68 km 1.16 km Large industrial facilities No close ≥ 800 m Not close ≥ 150 m – – Small factories (e.g., car maintenance, iron factories, wood factories) ≥ 200 m ≥ 300 m Not close ≥ 150 m 1164 m 533 m geologic hazards (e.g., valleys, water catchment) No close ≥ 400 m Not close ≥ 300 m 1.1 km 442 m JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 15 Figure 7. Buffer zone of elementary schools. randomness of school location selection, reflecting the Despite the importance of main roads and their inequality of school distribution among the populated significant role in facilitating access to services, they residential districts in the city. As stated in some of the represent a great danger to students when schools are previous literature, it is more common in some Saudi in their vicinity. Accordingly, specialists have set stan- cities that buffer zones of schools located in city centers dards related to the distance between elementary overlapped between many schools, while other far dis- schools and main roads with the surrounding areas; tricts had a shortage of schools (Alharbi 2018; Belarem the distance ranges between 150 m to 800 m, as men- et al. 2018; Zabedi 2010). tioned in the literature section. Figure 8) shows that There is only one airport in Najran; it is on the city’s fourteen schools are located immediately next to high- eastern side and surrounded by new residential dis- ways, and fourteen schools are next to main roads. This tricts. Based on CDE, EPA, and GDMS standards, the means that 53% of elementary schools were placed in distance between the airport and schools should be proximity to dangerous roads. Also, forty-seven more than 3 km. In Najran, two schools were located schools are located within 450 m of main roads or within the specified distance from the airport. Gas highways in the study area. This shows that many stations are distributed throughout Najran, and based students are in danger either in the morning or when on EPA and GDMS standards, schools should be 300 m they leave schools. Also, those roads produce air and and 75 m, respectively, away from gas stations. In noise pollution, which affect students’ health and Najran, only one elementary school was located within achievements negatively. As found in previous studies, 75 m of a gas station, but eighteen schools are located it is common in Saudi Arabia to choose elementary within 300 m of gas stations. Regarding power plants, schools in proximity to roads to increase the accessi- based on GDMS, the distance between a school and bility to schools’ locations (Pasha 2004; Zabedi 2010), power plants should be more than 500 m. Only two but those locations have elevated air pollution and schools are found in proximity to power plants. This noise, which affect the schools’ populations negatively. can be due to the local authorities’ interest in selecting Factories usually emit dangerous gases and dis- school locations as far away from gas stations and charge chemicals into the air, water, and ground, power plants as possible. Even if this distance to gas which causes serious health problems for surround- station can be considered a short distance, there is ing communities. Industrial machinery and processes a need to increase it to 300 m as in the EPA standards produce noise, which disturbs students’ attention. since gas stations are considered a major source of The UNESCO, EPA, and GDMS set a distance of danger. However, twenty-three schools are within more than 200 m, 300 m, and 150 m, respectively, 1 km from cooking gas cylinder distributors, as from the closest schools. In Najran, as shown in shown in Figure 8). This poses a significant danger to Figure 9), seven schools are located within school buildings as these distribution sites are perilous. a distance of less than 200 m, and thirteen schools 16 S. ALQUHTANI Figure 8. (A): elementary schools in proximity to gas cylinder distributors; (B): elementary schools in proximity to main roads and highways. Figure 9. (A): elementary schools in proximity to factories; (B): elementary schools in proximity to valley or streams of torrents. Figure 10. Thiessen polygons. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 17 are less than 300 m from industry activities (e.g., car schools is shown in the northern part of the old and maintenance, blacksmithing, factories, iron factories, fully developed districts, where the higher population wood factories). Floods are considered natural density is, and it can also be noticed in Alghwela district. hazards and disastrous, leading to the destruction This represents about 20% of the city area. However, the of both environmental elements and human lives. lower density could be observed in villages in the south GDMS sets 300 m and EPA sets 400 m as and west of the city, east of old districts, and in some parts a minimum distance between valleys or streams of of Alghwela and Almarkab districts, representing about torrents to schools. However, in Najran, nineteen 35% of the city area. Towards the city’s eastern side, the elementary schools are located less than 300 m school density decreases gradually until reaching Najran from Najran valley or torrent streams, as shown in University, where it is the lowest density. Kriging gener- Figure 9). This is because Najran is located on the ates an estimated surface from a scattered set of points edges of a big valley, and many torrent streams run with z-values (Esri 2020b). These geostatistical procedure through the urban area. Not many studies have been results are almost like the results of (IDW), where the found in the literature investigating the proximity to schools’ density is mainly higher in mid-northern districts, some dangerous or noisy land uses. These findings which are fully developed and have a higher population can reject this study’s assumption that school loca- density. The density becomes lower upon reaching the tions follow the standards of school site selection. eastern or western side of the city (see Figure 11) (. These Thiessen polygons show the catchment area of each results prove that schools are clustered in certain districts current school. The minimum Thiessen area is 1.2 km , and dispersed in others within Najran city. 2 2 the maximum is 350.3 km , and the average is 39.4 km (see Figure 10). The small catchment areas that are less 7.7. Hotspot analysis than 8 km are located in old and fully developed districts and close to the city center. They are sur- Applying hotspot analysis identifies hot and cold spots rounded by catchment areas ranging between 8 to of elementary schools in Najran. The results reinforce 2 2 35 km . However, catchment areas more than 60 km previous results, indicating that hot spots are clustered are noticed in new districts, which are in the city in mid-northern parts of the city where the old and fringes and eastern plains. In these, the catchment fully developed residential districts are (see Figure 12). areas are different and heterogeneous in their size in However, the cold spot spread across the new and less the city. This is due to school clusters in the old districts densely populated residential districts that are mainly and districts near the city center and few schools in the located in the eastern part of the city; also, there are fringes. So, it is suggested that large catchment areas some cold spots that can be noticed (90% confidence) need new schools since the number of residents are in the neighborhoods surrounding the city center and growing dramatically, and students suffer from com- villages located in western and southwestern parts of muting long distances to reach schools. the city. 7.6. Spatial interpolation 7.8. Current status of public elementary schools according to degrees of suitability Inverse Distance Weighted (IDW) and Kriging were used. IDW are valuable tools that can estimate values based on The current status of elementary school locations in the average values of the surroundings cells (Esri 2020b). Najran is not similar in all districts due to different As shown in Figure 11), the highest concentration of natural and human characteristics of areas Figure 11. (A): inverse distance weighted; (B): kriging. 18 S. ALQUHTANI Figure 12. Hotspot analysis. Table 4. Location suitability of current elementary schools. locations, but planners should do their best. The Suitability grade Location suitability Schools numbers % author investigated the current school sites in terms 0 Very suitable 0 0 of adherence to the standards mentioned above of 1–6 Suitable 0 0 CDE, EPA, and GDMS, since those standards cover 7–12 Good 14 26.3 13–18 Fair 28 53 many factors. Based on data availability, nine factors 19–25 Not suitable 11 20.7 are used to determine the suitability degree of schools’ Total 53 100 locations. The highest weight is given to variables related to students’ safety, such as proximity to roads, surrounding schools; some districts are very old, highly valley, and power plants, followed by proximity to populated, surrounded by mountains, or penetrated factories, gas stations, gas cylinder distributors, the by highways, main roads, or valleys. Therefore, it is airport, the neighborhood center, and the closest sometimes difficult to achieve suitability for service school. As shown in Table 4, no school is found in Figure 13. Suitable locations for boys’ elementary schools. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 19 very suitable or suitable locations, which means no educational agencies. In Najran city, it has been noticed school in Najran follows the nine factors; fourteen that, to some extent, there is a positive correlation schools are in good locations since three factors are between school numbers and the population density. not applied to the school’s sites. Twenty-eight schools Also, elementary schools follow a clustered distribution are in fair sites with lower suitability than the previous pattern in that they are concentrated in old and fully ones since four or five factors are not applicable. developed districts that are mainly located in proximity Eleven schools are in unsuitable locations since they to the city center; the buffer zones of twelve schools do not apply most factors. These results call for pessi- overlapped with other schools, but their distribution mism about the suitability of current school locations tends toward the random pattern. On the other hand, in Najran, as many schools are in proximity to some students in half of the city districts took a long time to sources of danger or nuisance, while other districts are reach their schools since their districts are not covered by without elementary schools. elementary schools, despite most of those districts devel- oping quickly and urgently needing schools. This situa- tion is also noticed in many cities in Saudi Arabia, as 8. Suitable locations for boys’ elementary shown in the literature review section. schools in Najran city Regarding the distances between boys’ elementary schools and impactful land uses, only very few schools Searching for suitable sites for boys’ elementary schools are located within the specified distance from the air- in Najran has emerged as a priority in light of what has port, power plants, and gas stations. However, 53% of been observed regarding the current situation of school boys’ elementary schools in Najran are located near sites and the urgent need by some residents to have highways, 25% are located in proximity to factories, schools within their residential district. The GIS system and 36% of the schools are located along the edge of has the advantage of finding the best suitable sites for the valley. Investigating the suitability of elementary any service. Therefore, the researcher uses this system schools’ current locations shows that no school is to build a suitability model that suggests suitable loca- found in a very suitable location, while around 53% tions for boys’ elementary schools in Najran. Twelve of schools are located in fair locations that follow a few criteria are considered to develop a map showing sui- of the standards, and 21% of the schools did not follow table and unsuitable locations for schools. Those criteria almost all of the school site selection standards. include population density, proximity to closest ele- Thus, local educations agencies and planners must mentary schools, the neighborhood center, the closest evaluate current school locations to find whether they highways or main roads, the airport, gas stations, gas cylinder distributors, factories, power plants, and valley constitute an actual or potential endangerment of or streams. Although the author intended to include school users’ health and safety, or corrective measures more variables, some data is not available. should be taken that will result in danger and noise Due to applying many factors, Figure 13 demon- mitigation to levels that will not constitute endanger- strates the scarcity of entirely suitable sites; the ten- ment. Also, it is recommended that they build barriers dency toward fair and good locations and suitable (e.g., berms or walls) between schools and highways to locations are on the city’s eastern side. However, it is reduce air pollution and noise levels and to protect still possible to find some suitable locations for elemen- students. Also, it is suggested that educational agen- tary schools in the western, northern, and middle parts cies need to clearly define and update the current of the city that comply with most of the criteria. By standards, combine all of them under one major guide- applying the MOMRA and Riyadh Municipality stan- line, and take advantage of international professional dards that state, “there should be a boys’ elementary standards for selecting elementary schools’ locations. school for each 3,600 population”, Najran city is in need Then, they should establish new schools to fill the of thirty-five new elementary schools; this is only based deficit in fully developed and far-located districts, on the population of each district. However, considering especially as the population is increasing sharply dur- the longest walking distance to school and that each ing this period. In most cities, many districts still have neighborhood should have an elementary school, this suitable locations for new elementary schools, and will require establishing around 130 schools. many districts have vacant lands designated for schools that are already in suitable locations. Future research can use the updated standards to investigate 9. Conclusions and recommendations the current spatial distribution of schools and the demand for new schools in the future. Also, future Elementary schools provide one of the basic educational research can add additional factors when investigating stages; it is the main nucleus of future studies. Therefore, current schools’ locations or suggesting locations for studying current educational service spatial distribution future schools. Finally, future research can compare the and suggesting future locations is very important for 20 S. ALQUHTANI spatial distribution of boys’ and girls’ schools, when the Alrasheed, K., and H. Elgamily. 2013. “GIS as an Efficient Tool to Manage Educational Services and Infrastructure in data becomes available. Kuwait.” Journal of Geographic Information System 5 (1): 75–86. doi:10.4236/jgis.2013.51008. Alrehili, B. (2015). 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Journal of Asian Architecture and Building Engineering – Taylor & Francis
Published: Mar 4, 2023
Keywords: Spatial distribution; spatial equity; elementary schools; school locations standards; GIS; Najran city
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