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Defining, Evaluating and Reducing Scenarios for Locating and Allocating Different Types of Educational Spaces using the Depth-First Search Algorithm
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Zahed Khateri   , Mohammad Karimi * , Parastoo Pilehforooshha |
| K.N.Toosi University of Technology |
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Abstract: (17 Views) |
| Population growth and limited availability of usable land have underscored the importance of the optimal placement of service centers, particularly the educational facilities, within the urban environments. The proper siting of educational spaces plays a crucial role in enhancing the educational quality and ensuring the spatial equity. Decision-making regarding the location of these facilities involves two fundamental steps: site selection and optimal allocation, aiming to ensure the desirability of the selected locations, appropriate spatial distribution, and equitable access for urban residents. One of the main challenges in this process is the large number of potential scenarios for siting and allocation of the educational spaces, making comprehensive analysis time-consuming and complex. To address this issue, this study presents a four-stage process for defining, evaluating, and reducing the number of possible scenarios. In the first stage, the integrated suitability of the educational spaces is assessed. In the second stage, a novel scenario reduction model based on the Depth-First Search (DFS) algorithm is developed and implemented. The third stage involves ranking the selected scenarios, and finally, in the fourth stage, the allocation of the educational spaces is carried out using the reduced set of scenarios. The results indicate that the average distance students need to travel to access to the educational facilities has decreased by 9% for all educational levels. Additionally, the coverage of the educational facilities has increased from 28% to 42%, and the overall suitability index has improved from 0.60 to 0.69. These findings demonstrate the effectiveness of the proposed model in improving the siting and allocation of the educational spaces, making it a practical and efficient approach for urban planning. |
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| Keywords: Location and allocation, Educational spaces, Scenario reduction, depth-first search algorithm |
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Full-Text [PDF 2438 kb]
(10 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2025/02/9 | Accepted: 2025/05/28 | ePublished ahead of print: 2026/01/14 | Published: 2026/01/14
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