Analytic Hierarchy Process vs. Machine Learning: Mapping School Suitability in Batna City, Algeria with Spatial Analysis

Hafida Tebbi, Hadda Dridi, Abdelmadjid Bouder

Abstract


The present study considers the suitability of sites for elementary schools in Batna, Algeria. The Analytic Hierarchy Process (AHP) method is compared to the three machine learning (ML) models, namely Random Forest (RF), Gradient Tree Boosting (GTB) and Classification and Regression Trees (CART). Spatial analysis techniques based on Geographic Information System (GIS) such as K-Nearest Neighbors (KNN), Moran’s I, inverse distance weighting (IDW) and Buffer Analysis were conducted prior to comparison to detect principal deficiencies and showed that 44.6 % of the 83 schools are located in 52% of the residential sectors. Peripheral gaps were also highlighted Nearest Neighbors Ratio (NNR = 0.69, Moran’I = 0.32, p <0.001).  AHP prioritized population density: 51.99%, while RF with an Area Under the Curve (AUC = 0.75) emphasized environmental limitations (slope, river spacing), differing from GTB (AUC = 0.65) and CART (AUC = 0.61). The combined suitability maps showed complementarity and guide planners to effectively address Batna's educational inequalities.

Keywords: school site selection, analytic hierarchy process, machine learning, GIS spatial analysis, suitability mapping, Batna (Algeria)

© 2025 Serbian Geographical Society, Belgrade, Serbia.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Serbia.


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Abdullah, S. A., Abbas, K. H., Harif, A. H., Alaa, R. F., & Hiba, S. (2023). Sustainable urban distribution of educational institutions and population density in Baghdad City using remote sensing techniques. IOP Conference Series: Earth and Environmental Science, 1202(1), Article 012015. https://doi.org/10.1088/1755-1315/1202/1/012015

Abdollahi, M., Faizi, M., & Naghibi, M. (2024). Applying analytic hierarchy process for site selection of a recreational-educational children complex in Shiraz City, Iran. Landscape Online, 99, Article 1120. https://doi.org/10.3097/lo.2024.1120

AlQuhtani, S. (2023). Spatial distribution of public elementary schools: A case study of Najran, Saudi Arabia. Journal of Asian Architecture and Building Engineering, 22(2), 705–725. https://doi.org/10.1080/13467581.2022.2049277

Al-Sabbagh, T. A. (2022). GIS location-allocation models in improving accessibility to primary schools in Mansura city - Egypt. GeoJournal, 87(2), 1009–1026. https://doi.org/10.1007/s10708-020-10290-5

Bendib, A. (2022). The effects of spatial clustering of public facilities on social equity and urban congestion in the city of Batna (Algeria). GeoJournal, 87(2), 1009–1026. https://doi.org/10.1007/s10708-020-10289-y

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman and Hall/CRC. https://doi.org/10.1201/9781315139470

Casali, Y., Aydin, N. Y., & Comes, T. (2022). Machine learning for spatial analyses in urban areas: A scoping review. Sustainable Cities and Society, 85, Article 104050. https://doi.org/10.1016/j.scs.2022.104050

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings, San Francisco. https://doi.org/10.1145/2939672.2939785

Clark, P. J., & Evans, F. C. (1954). Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology, 35(4), 445–453. https://doi.org/10.2307/1931034

Deruyter, G., Fransen, K., Verrecas, N., & De Maeyer, P. (2013). Evaluating spatial inequality in pre-schools in Ghent, Belgium by accessibility and service area analysis with GIS. International Multidisciplinary Scientific GeoConference-SGEM, 717–727. Sofia, Bulgaria: Stef92 Technology.

Dridi, Hx., Bendib, A., & Kalla, M. (2015). Analysis of Urban Sprawl Phenomenon in Batna City (Algeria) By Remote Sensing Technique. Analele Universităţii din Oradea, Seria Geografie, 25(2), 211–220.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451

Han, Z., Cui, C., Kong, Y., Li, Q., Chen, Y., & Chen, X. (2023). Improving educational equity by maximizing service coverage in rural Changyuan, China: An evaluation-optimization-validation framework based on spatial accessibility to schools. Applied Geography, 152, Article 102891. https://doi.org/10.1016/j.apgeog.2023.102891

Hersous, K., Souiher, N., Farouk, N., & Aguejdad, R. (2023). Urban sprawl prediction in Batna City, Eastern Algeria, using the SLEUTH model. Bitácora Urbano Territorial, 33(3). https://doi.org/10.15446/bitacora.v33n3.106226

Huang, Q., Cui, X., & Ma, L. (2023). The Equity of Basic Educational Facilities from the Perspective of Space. Sustainability, 15(15), Article 12031. https://doi.org/10.3390/su151512031

Huang, Q., Zhou, C., Li, M., Ma, Y., & Hua, S. (2024). An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China. Land, 13(8), Article 1188. https://doi.org/10.3390/land13081188

Hwang, C-L., & Yoon, K. (1981). Methods for Multiple Attribute Decision Making. In C-L. Hwang & K. Yoon (Eds.), Lecture Notes in Economics and Mathematical Systems (pp. 58–191). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3

Huu Duy, N., Tuan Pham, L., Xuan Linh, N., Van Truong, T., Dang, D. K., Quang Hai, T., & Bui, Q.-T. (2024). Flood risk assessment using machine learning, hydrodynamic modelling, and the analytic hierarchy process. Journal of Hydroinformatics, 26(8), 1852–1882. https://doi.org/10.2166/hydro.2024.033

Luo, H., Zhao, X., Zhang, Y., & Cai, B. (2024). A framework for measuring the implementation of the Sustainable Development Goal 4.2 (Quality Preprimary Education) at the local scale: an empirical study in Nanjing, China. Sustainable Development, 32(4), 3979–4000. https://doi.org/10.1002/sd.2876

Meena, D. K., Tripathi, R., & Agrawal, S. (2022). An evaluation of primary schools and its accessibility using GIS techniques: A case study of Prayagraj district, India. GeoJournal, 88(2), 1921–1951. https://doi.org/10.1007/s10708-022-10715-3

Moreno-Monroy, A. I., Lovelace, R., & Ramos, F. R. (2018). Public transport and school location impacts on educational inequalities: Insights from São Paulo. Journal of Transport Geography, 67, 110–118. https://doi.org/10.1016/j.jtrangeo.2017.08.012

Murad, A. A., Dalhat, A. I., & Naji, A. A. (2020). Using geographical information system for mapping public schools distribution in Jeddah City. International Journal of Advanced Computer Science and Applications, 11(5), 82–90. https://doi.org/10.14569/IJACSA.2020.0110513

Ramadan, M. S., Khairy, N., Alogayell, H. M., Alkadi, I. I., Ismail, I. Y., & Ramadan, R. H. (2022). Spatial Equity Priority Modeling of Elementary and Middle Schools through GIS Techniques, El-Taif City, Saudi Arabia. Sustainability, 14(19), Article 12057. https://doi.org/10.3390/su141912057

Rekha, R. S., Radhakrishnan, N., & Mathew, S. (2020). Spatial accessibility analysis of schools using geospatial techniques. Spatial Information Research, 28, 699–708. https://doi.org/10.1007/s41324-020-00326-w

Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073–1076. New York: McGraw-Hill.

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I

Samad, A. M., Hifni, N. A., Ghazali, R., Hashim, K. A., Disa, N. M., & Mahmud, S. (2012). A study on school location suitability using AHP in GIS approach. IEEE 8th International Colloquium on Signal Processing and its Applications (pp. 393–399). Malacca, Malaysia. https://doi.org/10.1109/CSPA.2012.6194756

Sharma, G., & Patil, G. R. (2022). Spatial and social inequities for educational services accessibility – A case study for schools in Greater Mumbai. Cities, 122, Article 103543. https://doi.org/10.1016/j.cities.2021.103543

Siagian, I. S., & Revida, E. (2024). Spatial analysis of school accessibility in Tarutung District and its policy implications. South Asian Journal of Social Studies and Economics, 21(12), 18–31. https://doi.org/10.9734/sajsse/2024/v21i12914

Srivastava, N., & Saxena, N. (2023). Applications of Artificial Intelligence and Machine Learning in Geospatial Data. In L. Gaur & P. Garg (Eds.), Emerging Trends, Techniques and Applications in Geospatial Data Science (pp. 196–219). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-7319-1.ch010

Taleai, M., Sliuzas, R., & Flacke, J. (2014). An integrated framework to evaluate the equity of urban public facilities using spatial multi-criteria analysis. Cities, 40, 56–69. https://doi.org/10.1016/j.cities.2014.04.006

UNESCO (2016). SDG4-Education 2030, Incheon Declaration (ID) and Framework for Action. For the Implementation of Sustainable Development Goal 4, Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning Opportunities for All, ED-2016/WS/28. UNESCO.

United Nations (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations General Assembly.

Villalba Nieto, P., Sánchez-Garrido, A., & Yepes, V. (2024). A review of multi-criteria decision-making methods for building assessment, selection, and retrofit. Journal of Civil Engineering and Management, 30, 465–480. https://doi.org/10.3846/jcem.2024.21621

World Commission on Environment and Development (1987). Our common future. Oxford University Press.

Wolf, J., Feitosa, F., & Marques, J. L. (2021). Efficiency and equity in the spatial planning of primary schools. International Journal of E-Planning Research (IJEPR), 10(1), 21–38. http://dx.doi.org/10.4018/IJEPR.2021010102

Yang, D. Y., Sun, Y., & Yang, Y. (2017). Analysis of Spatial Accessibility for Rural School Redistricting in West China: A Case Study of the Primary Schools in Zhenyuan County, Yunnan Province. 4th International Conference on Information Science and Control Engineering (ICISCE) (pp. 193–197). Changsha, China. https://doi.org/10.1109/ICISCE.2017.50

Zangana, D. D., Ibrahim, A. J., Yuan, H., & Amani-Beni, M. (2024). Educational inequality in urban settings: A spatial analysis of school distribution and double-shift system challenges – A case study. Journal of Urban Management, 13(4), 832–849. https://doi.org/10.1016/j.jum.2024.08.004

Zhang, W., Gu, X., Tang, L., Yin, Y., Liu, D., & Zhang, Y. (2022). Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Research, 109, 1–17. https://doi.org/10.1016/j.gr.2022.03.015


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