The best school site choosing for rural areas of the Husseiniya district in Karbala province using GIS (Model Builder) Techniques

  • Qusai Neama Jaleel Department of Physics, College of Science, University of Baghdad, Baghdad, Iraq.
  • Nawal K. Ghazal Department of Remote Sensing and GIS, College of Science, University of Baghdad. Baghdad, Iraq.

Abstract

With the increments number of population nowadays, one of the most essential problems in rural areas is the number of students in each school which exceed the school capacity. Thus, it is important to increase the number of schools and to determine the suitable location of them. The aim of this study is to determine the most suitable sites for school using Geographic Information System (GIS 10.2) (ModelBildur)in Husseiniya district, Karbala province.  Land classification, slope, distance from the single schools, proximity to the inflated schools, work buffer 100 meters around the main roads and railways and removing them from the map, represent the main criteria used to evaluate the location suitability and the most crowded school. Schools with high number of students are calculated and evaluated (above 1000) used point density tool to calculate the largest schools inflated from students. The weighted overlay tool is also employed to weight the criteria. The results demonstrate that the developed GIS 10.2 is successfully able to determine the best school location in Husseiniya district depending on the criteria with high efficiency. The excellent performance of the developed program shows its high efficiency for best locations detection in various applications such as, Popular clinics and police stations.

Published
Jan 8, 2018
How to Cite
JALEEL, Qusai Neama; GHAZAL, Nawal K.. The best school site choosing for rural areas of the Husseiniya district in Karbala province using GIS (Model Builder) Techniques. Iraqi Journal of Science, [S.l.], v. 58, n. 4C, p. 2473-2485, jan. 2018. ISSN 2312-1637. Available at: <http://scbaghdad.edu.iq/eijs/index.php/eijs/article/view/55>. Date accessed: 17 jan. 2018.
Section
Remote Sensing