GIS location-allocation models in improving accessibility to primary schools in Mansura city-Egypt

Abstract

Egypt governmental reports shows non-optimal geographical distribution of educational facilities and the difficulty in reaching school locations leads to high rates of early school drop-out. Reports showed that 2.75% of school dropout in Mansura city-Egypt (the case study) was due to spatial factors related to the difficulty of reaching school. Urban Planning Authority's report endorse that, the primary school location and the residential building must range between 500–750 m. However, such recommendations failed to take into attention or abided by a developing country like Egypt. The study goes through two Scenarios: (i) Scenario No. 1. Evaluating current school locations and (ii) Scenario No. 2. Analyzing proposed school locations and assess accessibility effectiveness after the process of schools’ geographical redistribution, it was carried out using GIS environment. The study objectives are about examined the role of spatial modeling and location analysis for improving the accessibility to public primary schools in Egypt: The specific aim of the study (i) Showing geographic distribution of public primary Schools in Mansura city. (ii) Examining the accessibility to Public Primary Schools in Mansura city and (iii) Using GIS tools to propose optimum locations where future public primary schools ought to be located. The study manipulated location-allocation models in GIS being one of the location analyses tools. It supports the process of spatial decision-making through several models and various scenarios. These provides varied options and great flexibility during planning. For analyzing the current situation of primary schools' distribution, GIS spatial analysis tools were used such as: Average Nearest Neighbor, Hotspot analysis and Grouping analysis. Study results concluded that number of demand points exceeds the distance of arrival at the optimal standard was about 54.96%. Schools concentration ratio in the city was 71.74% fell to 19.77% after the process of geographical redistribution of school locations. The proposed map of primary school locations in Mansura city can give a clear view for the decision maker on what location-allocation models can contribute for improving accessibility to educational facilities through applying the reliable standards.

Appendix 1 The technical report for study analysis tools.

Analysis tool	Tool inputs	Tool outputs	Tool description
Standard distance	School locations (point layer) Residential blocks (point layer) Circle Size (1_STANDARD_DEVIATION—1 standard deviation)	For measuring the compactness of primary schools and residential blocks in Mansura city	Measuring the compactness of a distribution provides a single value representing the dispersion of features around the center. The value is a distance, so the compactness of a set of features can be represented on a map by drawing a circle with the radius equal to the standard distance value. The Standard Distance tool creates a circle polygon
Directional distribution	School locations (point layer) Residential blocks (point layer) Circle size (1_STANDARD_DEVIATION—1 standard deviation)	For measuring directional trends of primary schools and residential blocks in Mansura city	Creates standard deviational ellipses to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends
Optimized hot spot analysis	Residential blocks (point layer)	For creates a map of statistically significant hot and cold spots for primary schools and residential district in Mansura city	Given incident points or weighted features (points or polygons), creates a map of statistically significant hot and cold spots using the Getis-Ord Gi* statistic. It evaluates the characteristics of the input feature class to produce optimal results
Buffer analysis	The central feature of residential blocks Distance [value or field]: 1000 M	For creating multiple buffers for primary schools and residential blocks in Mansura city	The feature class containing the output buffers
Grouping analysis	Mansura layer (Polygon Layer) Number of groups (2) Analysis fields (Schools No., population, Illiterates, industry employees, residential district Area.) Spatial constraints (contiguity edges only)	For dividing Mansura city to groups (residential districts) based on feature attributes	Groups features based on feature attributes and optional spatial or temporal constraints. The output feature class created containing all features, the analysis fields specified, and a field indicating to which group each feature belongs
Location–allocation models/Problem type must be in this area
Problem type	Facilities to choose (38 Schools) Impedance cutoff (None). Belongs to minimize impedance	For determining the effectiveness of primary schools' network in Mansura city	It chooses facilities such that the total sum of weighted impedances (demand allocated to facility multiplied the impedance to the facility) is minimized
Minimize impedance	Impedance transformation (linear). Impedance (length-meters) Travel from (Demand to facility). Output shape type (straight line)
Maximize coverage	Facilities to choose (38 schools). Impedance cutoff (750 m). Impedance transformation (linear). Impedance (length-meters) Travel from (demand to facility). Output shape type (straight line)	For determining the covered and non- covered facilitate demand points in Mansura city	It chooses facilities such that all or the greatest amount of demands is within a specified impedance cutoff
Minimize Facilities	Facilities to choose (None). Impedance cutoff (750). Impedance transformation (linear). Impedance (length-meters) Travel from (demand to facility). Output shape type (straight line)	For determining the minimum facilities for primary schools in Mansura city	It chooses the minimum number of facilities needed to cover all or the greatest amount of demand within a specified impedance cutoff