Depreme dirençli kentler: Bursa ili Yıldırım ilçesi örneği

Year 2023, Volume: 4 Issue: 2, 47 - 57, 28.12.2023

Büşra Şener Anil Akın

https://doi.org/10.59751/agacorman.1310296

Abstract

Bu çalışma, kentsel dirençlilik ve afet dirençliliği kavramlarını tanımlamayı ve Coğrafi Bilgi
Sistemleri (CBS)’ni kullanarak depreme dirençli kentler oluşturmayı amaçlamıştır. Çalışmada
kent planlama çalışmalarının CBS ile entegre bir şekilde yürütülmesiyle, deprem ve diğer
afetlere karşı dirençli kentler oluşturulmanın ve olası afetlerde meydana gelebilecek kayıpları
önlemenin ya da minimum seviyeye indirmenin önemi vurgulanmaktadır. CBS’ye dayalı
sistemlerin bu konudaki katkılarını gösterebilmek amacıyla, Bursa’nın ilk yerleşim yerlerinden
olan, çok fazla göç alan, birçok fay hattının üzerinde ve yakınında konumlanan ve plansız ve
çarpık kentleşme yapısına sahip Yıldırım ilçesi çalışma alanı olarak seçilmiştir. Yıldırım
ilçesinin jeolojik, demografik, yapısal ve çevresel özelliklerine göre depreme olan dirençliliği
analiz edilerek yüksek ya da düşük dirence sahip bölgeleri tespit edilmiştir.

Keywords

dirençlilik, kentsel dirençlilik, deprem dirençliliği, CBS, mekansal analizler

Resilient Cities to Earthquakes: The Case of Yıldırım District, Bursa province

Abstract

This study aimed to define the concepts of urban resilience and disaster resilience, and to create earthquake-resistant cities using Geographic Information Systems (GIS). By integrating GIS into urban planning efforts, the importance of creating resilient cities against earthquakes and other disasters to prevent or minimize potential losses in case of emergencies is emphasized. To demonstrate the contributions of GIS-based systems in this regard, the district of Yıldırım, which is one of the earliest settlements in Bursa, experiences significant migration, is located near multiple fault lines, and has an unplanned and haphazard urban structure, was chosen as the study area. The earthquake resilience of Yıldırım district was analyzed based on its geological, demographic, structural, and environmental characteristics, identifying areas with high or low resilience.

Keywords

Resilience, urban resilience, earthquake resilience, GIS, spatial analysis.

References

• AFAD, 2022, İl Afet Risk Azaltma Planı (İRAP), Bursa.
• Akın, A., 2007. Çukurova Deltası kıyı alanında arazi örtüsü değişimlerinin belirlenmesinde farklı uzaktan algılama yöntemlerinin değerlendirilmesi (Yüksek Lisans Tezi), Çukurova Üniversitesi Fen Bilimleri Enstitüsü.
• Akın Tanrıöver, A., Berberoğlu, S., Atanur,G., Polat, S., 2016. Yoğun kentleşme baskısı altında kalan Bursa Kenti'nde kentsel arazi kullanımı değişimlerinin belirlenmesi ve 2040 yılı için modellenmesi, Bilimsel Araştırma Projesi, Proje No: 2015-01-012.
• Alexander, D.E., 2013. Resilience and disaster risk reduction: an etymological journey, Natural Hazards and Earth System Sciences Discuss, (1), 1257-1284p.
• Aplin, P., Smith, G.M., 2008. Advances in object-based image classification, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 725-728p.
• Ainuddin, S., Routray, J.K., 2012. Community resilience framework for an earthquake prone area in Baluchistan, International Journal of Disaster Risk Reduction, 2, 25-36p.
• Baboo, S.S., Devi, M.R., 2011. Geometric correction in recent high resolution satellite imagery: A case study in Coimbatore, Tamil Nadu, International Journal of Computer Applications, 14(1), 32-37p.
• Bailey, T.C., Gatrell, A.C., 1995. Interactive Spatial Data Analysis, Essex: Longman.
• Basabe, P., 2013. Hyogo Framework for Action 2005-2015, Encyclopedia of Natural Hazards, Springer.
• Bastaminia, A., Safaeepour, M., Tazesh, Y., Rezaei, M.R., Saraei, M.H., Dastoorpoor, M., 2018. Assessing the capabilities of resilience against earthquake in the city of Yasuj, Iran, Environmental Hazards, 17(4), 310-330p, DOI: 10.1080/17477891.2018.1456397.
• Berberoğlu, S., Altunkasa, M.F., Sirel, B., Uslu, C., EvrenDilek, F., Özkan, C., Erginkaya, C., 2009a. Farklı yönetim politikaları doğrultusunda Adana kentsel gelişiminin geleceğe yönelik modellenmesi, TÜBİTAK Araştırma Projesi Gelişme Raporu, Çukurova Üniversitesi Peyzaj Mimarlığı Bölümü, Proje No: 107Y112, Rapor No: 2.
• Berberoğlu, S., Altunkasa, M.F., Sirel, B., Uslu, C., EvrenDilek, F., Özkan, C., Erginkaya, C., 2009b. Farklı yönetim politikaları doğrultusunda Adana kentsel gelişiminin geleceğe yönelik modellenmesi, TÜBİTAK Araştırma Projesi Gelişme Raporu, Çukurova Üniversitesi Peyzaj Mimarlığı Bölümü, Proje No: 107Y112, Rapor No: 3.
• Boltižiar, M., Chrastina, P., 2018. Application of Geographical Information System (GIS) in geography (Digital data pre-processing for land-use changes analysis), DIVAI 2018 – The 12th international scientific conference on Distance Learning in Applied Informatics, 29-36s.
• Bonham-Carter, G.F., 2014. Geographic Information Systems for geoscientists: modelling with GIS, Pergamon, Elsevier.
• Bracken, I., 1991. A surface model approach to small area population estimation, Town Planning Review, 62(2), 225-237p.
• Bracken, I., Martin, D., 1989. The generation of spatial population distributions from census centroid data source, Environment and Planning A, 21(4), 537-543p.
• Brassett, J., Vaughan-Williams, N., 2015. Security and the performative politics of resilience: Critical infrastructure protection and humanitarian emergency preparedness, Security Dialogue, 46, 32-50p.
• Bruneau, M., Chang, S.E., Eguchi, R.T., Lee, G.C., O'Rourke, T.D., Reinhorn, A.M., Shinozuka, M., Tierney, K.T. , Wallace W.A., Von Winterfeldt, D., 2003. A framework to quantitatively assess and enhance the seismic resilience of communities, earthquake spectra, 19(4), 733-752p.Bursa Büyükşehir Belediyesi, 2014, Bursa şehir sağlık profili, Bursa.
• Burrough, P.A., McDonnell, R.A., 1998. Creating continuous surfaces from point data, In Principles of Geographic Information Systems, Oxford University Press, Oxford, UK.
• Campbell, J.B., 1996. Introduction to Remote Sensing (2nd ed.), New York: Guilford Press.
• Carver, S.J., 1991. Integrating multi-criteria evaluation with Geographic Information Systems, International Journal of Geographical Information Systems, 5, 321-339p.
• Chelleri, L., 2012. From the «resilient city» to urban resilience. A review essay on understanding and integrating the resilience perspective for urban systems, Documents d’Anàlisi Geogràfica, 58(2), 287-306p.
• Di Lisio A., Russo, F., 2010. Thematic maps for land-use planning and policy decisions in the Calaggio stream catchment area, Journal of Maps, 6, 68-83p.
• Djalante, R., Holley, C., Thomalla, F., Carnegie, M., 2013. Pathways for adaptive and integrated disaster resilience, Natural Hazards, 69, 2105-2135p.
• Dronova, I., Gong, P., Wang, L., 2011.Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China, Remote Sensing of Environment, 115, 3220-3236p.
• Düzgün, Ş., 2010. Uzaktan algılamaya giriş, http://www.acikders.org.tr/course/view.php?id =28. (Erişim: Aralık, 2013).
• Erden, T., 2009. Coğrafi Bilgi Sistemleri ile Analitik Hiyerarşi Yöntemi’ne dayalı itfaiye istasyon yer seçimi: İstanbul örneği (Doktora Tezi), İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü.
• Ergün Konukçu, B., 2020. Akut Şok ve Kronik Stresler Karşısında Kentsel Dayanıklılık & Sürdürülebilir Dayanıklılık Yaklaşımı, Resilience, 4(2), 323-345s.
• Ergünay, O., 2002. Afete Hazırlık ve Afet Yönetimi, Türkiye Kızılay Genel Müdürlüğü Afet Operasyon Merkezi, Ankara.
• Flahaut, B., Mouchart, M., Martin, E.S., Thomas, I., 2003. The local spatial autocorrelation and the kernel method for identifying black zones a comparative approach, Accident Analysis & Prevention, 35, 991-1004p.
• Gerçek, D., Güven, İ.T., 2016. Kentsel dirençliliğin Coğrafi Bilgi Sistemleri ile analizi: Deprem ve İzmit kenti, Harita Teknolojileri Elektronik Dergisi, 8(1), 51-64s.
• Godschalk, D.R., 2003. Urban hazard mitigation: creating resilient cities, Natural Hazards Review, 4(3), 136-143p.
• Guo, Y., 2012. Urban resilience in post-disaster reconstruction towards a resilient development in Sichuan, China, International Journal of Disaster Risk Science, 3(1), 45-55p.
• Güllüoğlu, S.S., Palabaş, T., Melek, C.G., 2014. Uydu görüntülerinden elde edilen bilgilerle yeryüzü şekillerinin tanımlanması ve değişimlerinin gözlenmesinde coğrafi bilgi sistemlerinden yararlanılması üzerine bir ön çalışma, AJIT-e: Online Academic Journal of Information Technology, 5(16), 37-42s., DOI: 10.5824/1309-1581.2014.3.003.x.
• Gündoğdu, G., 2010. Coğrafi bilgi teknolojileri kullanılarak trafik kaza analizi: Adana örneği (Yüksek Lisans Tezi), Uzaktan Algılama Ve Coğrafi Bilgi Sistemleri Anabilim Dalı, Çukurova Üniversitesi Fen Bilimleri Enstitüsü.
• Gürsoy, O., Sadioğlu, U., 2021. 21. yüzyılda kente ilişkin olarak ortaya çıkan yeni kavramlar, Ankara Üniversitesi SBF Dergisi, DOI: 10.33630/ausbf.995115.
• Hatipoğlu, Y., 2019. Kentsel dirençlilik ile açık ve yeşil alanların erişilebilirliğinin ilişkisi: İstanbul'un kentsel alanları üzerine bir değerlendirme (Yüksek Lisans Tezi), Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
• Heywood, I., Oliver, J., Tomlinson, S., 1995. Building an exploratory multicriteria modelling environment for spatial decision support, In: P. Fisher (Ed.), Innovations in GIS 2, London: Taylor & Francis, 127-136p.
• Holling, C.S., 1973. Resilience and stability of ecological systems, Annual Review of Ecology and Systematics, 4, 1-23p.
• Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H.M., Ali, M., Masood, N., Amin, A., Farid, H.U., Sultana, S.R., Fahad, S., Wang, D., Nasim, W., 2019. Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan, Environmental Science and Pollution Research, DOI: 10.1007/s11356-019-06072-3.
• Jankowski, P., 1995. Integrating Geographic Information Systems and Multicriteria Decision Making Methods, International Journal of Geographic Information Systems, 9(3), 251-273p.
• Keller, C.P., 1996. Decision making using multiple criteria, NCGIA Core Curriculum, Unit 57, Santa Barbara, CA: National Center for Geographic Information and Analysis.
• Khalili, S., Harre, M., Morley, P., 2015. A temporal framework of social resilience indicators of communities to flood, case studies: Wagga wagga and Kempsey, NSW, Australia. International Journal of Disaster Risk Reduction, 13, 248-254p.
• Kindu, M., Schneider, T., Teketay, D., Knoke, T., 2013. Land Use/Land Cover change analysis using object-based classification approach in Munessa-Shashemene landscape of the Ethiopian highlands, Remote Sensing, 5, 2411-2435p, DOI: 10.3390/rs5052411.
• Kulig, J.C., Edge, D., Joyce, B., 2008. Community resiliency as a measure of collective health status: Perspectives from rural communities, CJNR (Canadian Journal of Nursing Research), 40, 92-110p.
• Kundak, S., Türkoğlu, H., 2007. İstanbul’da deprem risk analizi, İTÜ Dergisi Mimarlık, Planlama, Tasarım, 6(2), 37-46s.
• Lee, A.H.I., Chen, W.C., Chang, C.J., 2008. A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan, Expert Systems with Applications 34, 96-107p.
• Lorens, D.F., 2013. The diversity of resilience: Contributions from a social science perspective, Natural Hazards, 67, 7-24p.
• Malczewski, J., 1996. A GIS-based approach to multiple criteria group decision making, International Journal of Geographic Information Systems, 10(8), 955-971p.
• Malczewski, J., 1999a. GIS and multicriteria decision analysis, John Wiley and Sons Inc. U.S.A.
• Manjula, K.R., Jyothi, S., Kumar Varma, S.A., 2010. Digitizing the forest resource map using ArcGIS, International Journal of Computer Science Issues, 7(6), 300-306p.
• Manyena, S.B., 2006. The concept of resilience revisited, Disasters, 30, 434-450p.
• Martin, D., 1989. Mapping population data from zone centroid locations, Transactions of the Institute of British Geographers, 14(1), 90-97p.
• Martin, D., Bracken, I., 1991. Techniques for modeling population-related raster databases, Environment and Planning A, 23(7), 1069-1075p.
• Mason, D.C., Corr, D.G., Cross, A., Hogg, D.C., Lawrence, D.H., Petrou, M., Tailor, A.M., 1988. The use of digital map data in the segmentation and classification of remotely-sensed images. International Journal of Geographical Information Systems, 2, 195-215p.
• Mc Daniels, T., Chang, S., Peterson, K., Mikawoz, J., Reed, D., 2007. Empirical framework for characterizing infrastructure failure interdependencies, Journal of Infrastructure Systems, 13(3), 175-184p.
• Mehmood, A., 2016. Of resilient places: planning for urban resilience, European Planning Studies, 24(2), 407-419p, DOI: 10.1080/09654313.2015.1082980.
• Okabe, A., Satoh, T., Sugihara, K., 2008. A Kernel density estimation method for networks, its computational method, and a GIS-based tool, Tokyo Üniversitesi Mekânsal Bilgi Bilimleri Merkezi.
• Özden, A.T., 2021. COVID-19 sonrası mekânın değişimi üzerine spekülasyonlar, Mimarlık Dergisi, 417, 26-30s.
• Özmen, H.B., Huseynova, T., Pekkan, E., Tün, M., 2017. Türkiye'de meydana gelen depremlerin mekânsal istatistiksel analizi, 4. Uluslararası Deprem Mühendisliği ve Sismoloji Konferansı.
• Page-Tan, C., Fraser, T., Aldrich, D.P., 2021. Mapping resilience: GIS techniques for disaster studies, Research Methods of Disaster and Emergency Management: Social Science Approaches in Application, 339-354p.
• Parizi, S.M., Taleai, M., Sharifi, A., 2022. A GIS-based multi-criteria analysis framework to evaluate urban physical resilience against earthquakes, Sustainability, 14 (5034), DOI: 10.3390/su14095034.
• Paton, D., Millar, M., Johnston, D., 2001. Community resilience to volcanic hazard consequences, Natural Hazards, 24, 157-169p.
• Rezaei, M.R., Bastaminia, A., Saraei, M.H., 2016. Evaluation of dimensions, approaches and concepts of resilience in urban societies with an emphasis on natural disasters, Journal of Fundamental and Applied Sciences, 8, 1630-1649p.
• Richards, J.A., Jia, X., 2006. Remote Sensing Digital Image Analysis, DOI: 10.1007/3-540-29711-1.
• Roy, B., 1996, Multicriteria methodology for decision aiding, Dordrecht, Kluwer Academic Publishers.
• Rüsmetov, V., 2014. Coğrafi bilgi sistemleri ve 3D modelleme, KMÜ Sosyal ve Ekonomik Araştırmalar Dergisi, 16(Özel Sayı II), 146-150s.
• Saaty, T.L., 1980. The analytic hierarchy process: planning, priority setting, resource allocation. New York, NY: McGraw-Hill; 437.
• Saaty, T.L., Vargas, L.G., 2006. Decision Making With The Analytic Network Process, Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks, pittsburgh: Springer.
• Sabri, S., Yaakup, A., 2008. Multi-Criteria decision making for Urban Sprawl, using Analytic Network Process and GIS, case of Iskandar Malaysia Region.
• Sánchez-Lozano, J.M., Teruel-Solano, J., Soto-Elvira, P.L., García-Cascales, M.S., 2013. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain, Renewable and Sustainable Energy Reviews, 24, 544-556p.Sestras,
• P., Sălăgean, T., Bilașco, S., Bondrea, M.V., Naș, S., Fountas, S., Cîmpeanu, S.M., 2019. Prospect of a GIS based digitization and 3D model for a better management and land use in a specific micro-areal for crop trees, Environmental Engineering and Management Journal, 18(6), 1269-1277p.
• Siddayao, G., Valdez, S., Fernandez, P., 2014. Analytic Hierarchy Process (AHP) in spatial modeling for floodplain risk assessment, International Journal of Machine Learning and Computing, 4(5), DOI: 10.7763/IJMLC.2014.V4.453.
• Silverman, B.W., 1986. Density Estimation for Statistics and Data Analysis, Chapman Hall, London.
• Srivastava, P.K., Pandey, P.C., Petropoulos, G.P., Kourgialas, N.N., Pandey, V., Singh, U., 2019. GIS and remote sensing aided information for soil moisture estimation: A comparative study of interpolation techniques, Resources, 8(2), DOI: 10.3390/resources8020070.
• Şenel, G., 2018. Delineation of water bodies with Landsat 8 and Sentinel 2 satellite imagery using different image processing algorithms (Master of Science Thesis), Istanbul Technical University Graduate School of Science, Engineering and Technology.
• Şirin Dincer, Ş.E., 2016. Dayanıklı kentler için kentsel tasarım ilkeleri (Yüksek Lisans Tezi), Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
• Tağıl, Ş., Alevkayalı, Ç., 2013. Ege Bölgesi’nde depremlerin mekânsal dağılımı: jeoistatistiksel yaklaşım, Uluslararası Sosyal Araştırmalar Dergisi, 6 (28), 369-379p.
• Temiz, M.S., Doğan, S., 2005. Dijital görüntülerin rektifikasyonu: sensör modelleri, geometrik görüntü dönüşümleri ve yeniden örnekleme, TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı 28 Mart - 1 Nisan 2005, Ankara.
• Tepavcevic, B., Šijakov, M., Šiđanin, P., 2012. Gis technologies in urban planning and education, Pollack Periodica, 7, 185-191p, DOI: 10.1556/Pollack.7.2012.S.18.
• Udin, W.S., Zahuri Z.N., 2017. Land use and land cover detection by different classification systems using remotely sensed data of Kuala Tiga, Tanah Merah Kelantan, Malaysia, Journal of Tropical Resource Sustainable Science, 5, 145-151p.
• UNISDR, U., 2005. Hyogo framework for action 2005-2015: Building the resilience of nations and communities to disasters, Extract from the final report of the World Conference on Disaster Reduction (A/CONF. 206/6).
• UNISDR, 2009. United Nations, Terminology on disaster risk reduction.URL-1, Coğrafi bilgi sistemleri terimleri, https://web.itu.edu.tr/~coskun/contents/lessons/gismanagement/CBS%20TERIMLERI%20SOZLUGU.pdf. (Erişim: Mayıs, 2023).
• Uzunçıbuk, L., 2009. Doğal afetlerin kentsel ve bölgesel planlamada yeri, Jeodezi, Jeoinformasyon ve Arazi Yönetimi Dergisi, 2(101), 18-27s.
• Vahidnia, M.H., Alesheikh, A., Alimohammadi, A., Bassiri, A., 2008. Fuzzy analytical hierarchy process in GIS application, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 593-596p.
• Wu, S., Qiu, X., Wang, L., 2005. Population estimation methods in GIS and remote sensing: A review, GIScience & Remote Sensing, 42(1), 80-96p, DOI: 10.2747/1548-1603.42.1.80.
• Xie, Z., Yan, J., 2008. Kernel density estimation of traffic accidents in a network space, Computers, Environment, and Urban Systems, 35(5), 396-406p.
• Yaman, Z.D., Tezer, A., 2011. Dayanıklılık kuramının kent planlama ile ilişkilendirilmesi.
• Zaidi, S.M., Akbari, A., AbuSamah, A., Kong, N.S., Gisen, A., Isabella, J., 2017. Landsat-5 time series analysis for land use/land cover change detection using NDVI and semi-supervised classification techniques, Polish Journal Environmental Studies, 26(6), 2833-2840p, DOI: 10.15244/pjoes/68878.