Rural Schools Consolidation of Study on Optimization-karthividhyalayaicse

Rural Schools Consolidation of Study on Optimization-karthividhyalayaicse

To study the relationship between school district size and bus transportation costs. Sohoni & Saporito (2009) used GIS to link maps of elementary, middle, and high school attendance boundaries, examine student enrollment in non-neighborhood schools changes levels of racial segregation in public schools across urban school districts by comparing the racial composition of schools and their corresponding attendance area. Results showed that segregation levels in school catchment areas become lower from elementary to middle to high schools. Nayati (2008) established a GIS based school transport management system to helps bus-stop allocation, specifically to design the fastest and safest bus route with AVL facility. Mulaku (2011) used GIS technology in the Kenyan Schools Mapping Project, to collect the geographic location of schools, the number of existing schools of different levels in the public and private sectors, their enrolment and the number of teachers data for all Kenyan learning institutions to provide useful information for educational planners.

Recently in China, with the development of school layout adjustment, more and more researchers began to pay attention to the layout of the school, and use the GIS technology to study the rationality and fairness of the adjustment of the school layout. Kong, Li and Zhang (2008) used GIS to assess the educational equality and spatial accessibility in school redistricting. Kong & Lv (2010) used GIS spatial models such as nearest school model, gravity model and Huff model to test by linear regression of actual. CBSE Schools in Kumbakonam

and estimated student enrollments of all schools. Zhao, Wu and Parolin (2012) used GIS and Ordinal Logit Model analyzed the impact of school closure on students’ schooling distance in rural area. Zhao, Shao, Guo et al. (2016) presented an analysis of the spatial pattern evolvement characteristics of rural schools and their development level in a mountainous area over the past 10 years. Various methods, including trend surface analysis, spatial hot spot detection, kernel density estimation, principal component analysis and clustering analysis were used in the study. Dai (2017) used GIS technology to construct a quadratic programming model to minimize the variance of all students’ expected values on educational quality under constraints of the maximum distance and schools’ capacities. This new allocation model can significantly improve the spatial equity of educational resources compared with the way of allocating students to the nearby school.