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A GIS-Based Approach to Determining Optimum Logistics Center Locations: The Example of Southeastern Anatolia Region

Year 2025, Volume: 8 Issue: 4, 583 - 620, 25.10.2025
https://doi.org/10.33723/rs.1772759

Abstract

Effective location strategies for logistics and supply chain centers are critical for uninterrupted transportation and the sustainability of supply chain operations, especially in densely populated and strategically important regions. This study focuses on the Southeastern Anatolia Region of Türkiye, a vital corridor for east-west logistics flows, to optimize the location of logistics centers. To achieve this objective, Geographic Information Systems (GIS)-based spatial analysis was implemented using ArcMap 10.5 software and various open-source geographic datasets. Weighted Overlay Analysis was employed to integrate topographic, climatic, infrastructural, demographic, and seismic data. The datasets include variables such as elevation, slope, fault lines, population density, road networks, climate conditions, and energy infrastructure, enabling a comprehensive assessment of site suitability. The findings revealed that Gaziantep (Sam-Aktoprak region) and Mardin (near Ilısu) are the most suitable locations for logistics centers due to their proximity to major transportation corridors, mild climate, and minimal seismic risk. A total of 230 sites were identified as suitable, while areas with steep slopes, extremely hot climates, and proximity to active fault lines were considered less suitable.

References

  • Akkaya, O., & Şentürk, M. (2023). Güneydoğu Anadolu Bölgesinde Mülteci ve Göç Lojistiğinin Gelir Dağılımı Üzerindeki Etkileri. Toplum Ekonomi ve Yönetim Dergisi, 4(1), 131-144.
  • Alumur, S. A., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190(1), 1-21. https://doi.org/10.1016/j.ejor.2007.06.008
  • Aydın, N. Y. (2009). GIS-based site selection approach for wind and solar energy systems: a case study from Western Turkey (Master's thesis, Middle East Technical University (Turkey)).
  • Cesur, E., & Kesici Ocak, F. (2024). GIS-based service network optimisation for location of postal delivery system. Scottish Geographical Journal, 140(3-4), 581-598.
  • Church, R., & Murray, A. (2009). Business site selection, location analysis, and GIS. John Wiley & Sons.
  • Çakmak, E., Önden, İ., Acar, A. Z., & Eldemir, F. (2021). Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm. Case Studies on Transport Policy, 9(1), 59-67.
  • Çetinkaya, C., Özceylan, E., & Keser, I. (2022). A GIS-based AHP approach for emergency warehouse site selection: a case close to Turkey-Syria border. Journal of Engineering Research, 10(3A).
  • Erdin, C., & Akbaş, H. E. (2019). A comparative analysis of fuzzy TOPSIS and geographic information systems (GIS) for the location selection of shopping malls: a case study from Turkey. Sustainability, 11(14), 3837.
  • Hesse, M., & Rodrigue, J. P. (2004). The transport geography of logistics and freight distribution. Journal of Transport Geography, 12(3), 171-184. https://doi.org/10.1016/j.jtrangeo.2003.12.004
  • Huifeng, J., & Aigong, X. (2008). The method of warehouse location selection based on GIS and remote sensing images. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII. Part B, 2(3), 545-548.
  • Jayaraman, V., & Luo, Y. (2007). Creating competitive advantages through new value creation: A reverse logistics perspective. Academy of Management Perspectives, 21(2), 56-73. https://doi.org/10.5465/amp.2007.25356846
  • Larimi, N. G., Azhdari, A., Ghousi, R., & Du, B. (2022). Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages. Socio-Economic Planning Sciences, 82, 101250.
  • Ma, Z., Zheng, X., Liang, H., & Luo, P. (2024). Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors, 24(6), 1878.
  • Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726. https://doi.org/10.1080/13658810600661508
  • Önden, İ., Acar, A. Z., & Eldemir, F. (2018). Evaluation of the logistics center locations using a multi-criteria spatial approach. Transport, 33(2), 322-334.
  • Önden, İ., & Eldemir, F. (2022). A multi-criteria spatial approach for determination of the logistics center locations in metropolitan areas. Research in Transportation Business & Management, 44, 100734.
  • Rikalović, A., Soares, G. A., & Ignjatić, J. (2017). Analysis of logistics center location: a GIS–based approach. In VI International Symposium New Horizons of Transport and Communications, November (pp. 18-28).
  • Rikalovic, A., Soares, G. A., & Ignjatić, J. (2018). Spatial analysis of logistics center location: A comprehensive approach. Decision Making: Applications in Management and Engineering, 1(1), 38-50.
  • Ritchie, W. J., Kerski, J., Novoa, L. J., & Tokman, M. (2023). Bridging the gap between supply chain management practice and curriculum: A location analytics exercise. Decision Sciences Journal of Innovative Education, 21(2), 83-94.
  • Sarıkaya, M. Ş., Yanalak, M., & Karaman, H. (2022). Site selection of natural gas emergency response team centers in Istanbul metropolitan area based on GIS and FAHP. ISPRS International Journal of Geo-Information, 11(11), 571.
  • Soylu, N. (2021). Southeastern Anatolia Project (Gap) in Turkey and Food Security in the Middle East. International Journal of Water Management and Diplomacy, 1(2), 23-34.
  • Tatlı, F., & Ergün, M. (2025). Küresel Tedarik Zincirlerinde Son Adım Teslimat Optimizasyonu İçin Coğrafi Bilgi Sistemleri (CBS) ve Karar Destek Sistemleri (KDS) Kullanımı. Amasya Üniversitesi Ekonomi Ticaret ve Pazarlama Dergisi, 2(1), 1-11.
  • URL-1: https://www.basarsoft.com.tr/raster-veri/, (Erişim Tarihi: 26 Şubat 2025).
  • Yaman, A. (2024). A GIS-based multi-criteria decision-making approach (GIS-MCDM) for determination of the most appropriate site selection of onshore wind farm in Adana, Turkey. Clean Technologies and Environmental Policy, 1-24.

OPTİMUM LOJİSTİK MERKEZİ YERLERİNİN BELİRLENMESİNE YÖNELİK CBS TABANLI BİR YAKLAŞIM: GÜNEYDOĞU ANADOLU BÖLGESİ ÖRNEĞİ

Year 2025, Volume: 8 Issue: 4, 583 - 620, 25.10.2025
https://doi.org/10.33723/rs.1772759

Abstract

Lojistik ve tedarik zinciri merkezlerinin etkin yerleşim stratejileri, özellikle yoğun ve stratejik öneme sahip bölgelerde, kesintisiz ulaşım ve tedarik zinciri operasyonlarının sürdürülebilirliği açısından kritik bir öneme sahiptir. Bu çalışmada, lojistik merkezlerinin optimal konumlandırılması amacıyla, doğu-batı lojistik akışları için hayati bir koridor teşkil eden Türkiye'nin Güneydoğu Anadolu Bölgesi'ne odaklanmaktadır. Bu amaca ulaşmak için, ArcMap 10.5 yazılımı ve çeşitli açık kaynak kodlu coğrafi veri setleri kullanılarak Coğrafi Bilgi Sistemleri (CBS) tabanlı mekansal analiz uygulanmıştır. Çalışmada, topografik, iklimsel, altyapısal, demografik ve sismik verilerin entegrasyonu için Ağırlıklı Çakıştırma Analizi yöntemi uygulanmıştır. Veri setleri, yükseklik, eğim, fay hatları, nüfus yoğunluğu, yol ağları, iklim koşulları ve enerji altyapısı gibi değişkenleri içermekte olup, alan uygunluğunun kapsamlı bir şekilde değerlendirilmesini sağlamaktadır. Elde edilen bulgular, ana ulaşım koridorlarına yakınlık, ılıman iklim koşulları ve minimum sismik risk faktörleri nedeniyle, Gaziantep (Sam-Aktoprak bölgesi) ve Mardin (Ilısu yakınları) lokasyonlarının lojistik merkezler için en uygun alanlar olduğunu ortaya koymuştur. Toplamda 230 adet alan uygun olarak tanımlanırken, dik eğimli, aşırı sıcak iklim koşullarına sahip ve aktif fay hatlarına yakın alanlar daha az elverişli olarak değerlendirilmiştir.

References

  • Akkaya, O., & Şentürk, M. (2023). Güneydoğu Anadolu Bölgesinde Mülteci ve Göç Lojistiğinin Gelir Dağılımı Üzerindeki Etkileri. Toplum Ekonomi ve Yönetim Dergisi, 4(1), 131-144.
  • Alumur, S. A., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190(1), 1-21. https://doi.org/10.1016/j.ejor.2007.06.008
  • Aydın, N. Y. (2009). GIS-based site selection approach for wind and solar energy systems: a case study from Western Turkey (Master's thesis, Middle East Technical University (Turkey)).
  • Cesur, E., & Kesici Ocak, F. (2024). GIS-based service network optimisation for location of postal delivery system. Scottish Geographical Journal, 140(3-4), 581-598.
  • Church, R., & Murray, A. (2009). Business site selection, location analysis, and GIS. John Wiley & Sons.
  • Çakmak, E., Önden, İ., Acar, A. Z., & Eldemir, F. (2021). Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm. Case Studies on Transport Policy, 9(1), 59-67.
  • Çetinkaya, C., Özceylan, E., & Keser, I. (2022). A GIS-based AHP approach for emergency warehouse site selection: a case close to Turkey-Syria border. Journal of Engineering Research, 10(3A).
  • Erdin, C., & Akbaş, H. E. (2019). A comparative analysis of fuzzy TOPSIS and geographic information systems (GIS) for the location selection of shopping malls: a case study from Turkey. Sustainability, 11(14), 3837.
  • Hesse, M., & Rodrigue, J. P. (2004). The transport geography of logistics and freight distribution. Journal of Transport Geography, 12(3), 171-184. https://doi.org/10.1016/j.jtrangeo.2003.12.004
  • Huifeng, J., & Aigong, X. (2008). The method of warehouse location selection based on GIS and remote sensing images. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII. Part B, 2(3), 545-548.
  • Jayaraman, V., & Luo, Y. (2007). Creating competitive advantages through new value creation: A reverse logistics perspective. Academy of Management Perspectives, 21(2), 56-73. https://doi.org/10.5465/amp.2007.25356846
  • Larimi, N. G., Azhdari, A., Ghousi, R., & Du, B. (2022). Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages. Socio-Economic Planning Sciences, 82, 101250.
  • Ma, Z., Zheng, X., Liang, H., & Luo, P. (2024). Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors, 24(6), 1878.
  • Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726. https://doi.org/10.1080/13658810600661508
  • Önden, İ., Acar, A. Z., & Eldemir, F. (2018). Evaluation of the logistics center locations using a multi-criteria spatial approach. Transport, 33(2), 322-334.
  • Önden, İ., & Eldemir, F. (2022). A multi-criteria spatial approach for determination of the logistics center locations in metropolitan areas. Research in Transportation Business & Management, 44, 100734.
  • Rikalović, A., Soares, G. A., & Ignjatić, J. (2017). Analysis of logistics center location: a GIS–based approach. In VI International Symposium New Horizons of Transport and Communications, November (pp. 18-28).
  • Rikalovic, A., Soares, G. A., & Ignjatić, J. (2018). Spatial analysis of logistics center location: A comprehensive approach. Decision Making: Applications in Management and Engineering, 1(1), 38-50.
  • Ritchie, W. J., Kerski, J., Novoa, L. J., & Tokman, M. (2023). Bridging the gap between supply chain management practice and curriculum: A location analytics exercise. Decision Sciences Journal of Innovative Education, 21(2), 83-94.
  • Sarıkaya, M. Ş., Yanalak, M., & Karaman, H. (2022). Site selection of natural gas emergency response team centers in Istanbul metropolitan area based on GIS and FAHP. ISPRS International Journal of Geo-Information, 11(11), 571.
  • Soylu, N. (2021). Southeastern Anatolia Project (Gap) in Turkey and Food Security in the Middle East. International Journal of Water Management and Diplomacy, 1(2), 23-34.
  • Tatlı, F., & Ergün, M. (2025). Küresel Tedarik Zincirlerinde Son Adım Teslimat Optimizasyonu İçin Coğrafi Bilgi Sistemleri (CBS) ve Karar Destek Sistemleri (KDS) Kullanımı. Amasya Üniversitesi Ekonomi Ticaret ve Pazarlama Dergisi, 2(1), 1-11.
  • URL-1: https://www.basarsoft.com.tr/raster-veri/, (Erişim Tarihi: 26 Şubat 2025).
  • Yaman, A. (2024). A GIS-based multi-criteria decision-making approach (GIS-MCDM) for determination of the most appropriate site selection of onshore wind farm in Adana, Turkey. Clean Technologies and Environmental Policy, 1-24.
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Tourism (Other)
Journal Section Articles
Authors

Mustafa Ergün 0000-0003-1675-0802

Early Pub Date October 25, 2025
Publication Date October 25, 2025
Submission Date August 26, 2025
Acceptance Date October 22, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Ergün, M. (2025). OPTİMUM LOJİSTİK MERKEZİ YERLERİNİN BELİRLENMESİNE YÖNELİK CBS TABANLI BİR YAKLAŞIM: GÜNEYDOĞU ANADOLU BÖLGESİ ÖRNEĞİ. R&S - Research Studies Anatolia Journal, 8(4), 583-620. https://doi.org/10.33723/rs.1772759