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Kentsel Arazi Kullanım Talebinin Tahmini: Türkiye Üzerine Bir Çalışma

Year 2024, Volume: 17 Issue: 2, 626 - 650, 17.03.2024
https://doi.org/10.35674/kent.1339840

Abstract

Arazi kullanım değişikliğinin toplum ve çevre üzerinde olumsuz etkileri olabilir ve bu nedenle bu, hükümetler üzerinde çok büyük bir baskı oluşturur. Gelecekteki kentsel genişlemenin doğru tahminleri, sürdürülebilir büyüme ve çevrenin korunması için gereklidir. Bu makale, kentsel kullanımlar için arazi kullanımı değişikliklerini incelemekte ve ayrıca seçilen örnek çalışma alanında, yani Türkiye'nin NUTS3 (istatistik için karasal birimlerin terminolojisi) bölgelerinde konut ve endüstriyel/ticari arazi kullanımlarının projeksiyonu için farklı yöntemler uygulamaktadır. Yoğunluk ölçümleri, trend ekstrapolasyonu ve regresyon analizi, arazi kullanımını tahmin etmek için kullanılan söz konusu istatistiksel yöntemlerdir. Bulgular, geçmiş değişiklikleri yansıtmak için seçilen metodolojileri kullanmanın önemli bir belirsizliğe yol açtığını göstermektedir. Sonuçlar, seçilen değişkenlerdeki varyasyondan ve çalışma bölgesinin mekansal organizasyonundan önemli ölçüde etkilenir. Bu nedenle, Türkiye'deki arazi kullanımı değişikliklerini tahmin etmek için kullanılabilecek en uygun modeli seçmek için gelecekteki bir araştırma odağı olarak doğrulama analizi temel olacaktır. Mevcut analizin sonuçları, Türkiye bölgesel bağlamında arazi yönetimi ve kentsel arazi kullanımının sürdürülebilir büyümesi için hükümet ve yerel makamlar tarafından benimsenebilir.

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Estimation of Demand for Urban Land Uses: A Case Study of Türkiye

Year 2024, Volume: 17 Issue: 2, 626 - 650, 17.03.2024
https://doi.org/10.35674/kent.1339840

Abstract

Land use change can have adverse impact on society and environment and therefore this puts enormous pressure on governments. Accurate estimates of future urban expansion are essential for sustainable growth and the preservation of the environment. This article examines the land use changes for urban uses, and further applies different methods for the projection of residential and industrial/commercial land uses in the selected case study area i.e. NUTS3 (nomenclature of terrestrial units for statistics) regions of Turkey. Density measures, trend extrapolation and regression analysis are the subject statistical methods used for projecting the land use. The findings show that using the chosen methodologies to project past changes leads in significant uncertainty. The results are significantly influenced by the variation in selected variables, and spatial organization of the study region. Therefore, validation analysis as a future research focus will be essential to select the most appropriate model that can be used to project the land use changes in Turkey. The results from the current analysis can be adopted by the government and local authorities for the land management and sustainable growth of urban land use in the Turkish regional context.

References

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  • Arsanjani, J. J., Helbich, M., de Noronha Vaz, E. (2013). Spatiotemporal simulation of urban growth patterns using agent-based modelling: The case of Tehran. Cities, 32, 33-42.
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  • Batista e Silva, F., Koomen, E., Diogo, V., Lavalle, C. (2014). Estimating demand for industrial and commercial land use given economic forecasts. Plos ONE, 9(3), e91991.
  • Boitier, B., Da Costa, P., Le Mouel, P., Zagame, P. (2008). Urban land claims sub-categories: transport infrastructures, housing and industrial and commercial areas at national level. PLUREL Project Module 1: Driving Forces and Global Trends, Deliverable 1.1.3.
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  • Dadashpoor, H., Azizi, P., Moghadasi, M. (2019). Land use change, urbanization, and change in landscape pattern in a metropolitan area. Science of the Total Environment, 655, 707-719.
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  • Jansson, T., Bakker, M., Boitier, B., Fougeyrollas, A., Helming, J., Meijl, H., Verkerk, P. (2008). Cross sector land use modelling framework. In Helmind, K, Pérez-Soba, M and Tabbush, P (Eds.) Sustainability Impact Assessment of Land Use Change. Berlin, Springer.
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  • Jun, M-J. (2005). Forecasting urban land-use demand using a metropolitan input-output model. Environment and Planning A, 37, 1311-1328.
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  • Karahasan, B.C. (2014). The spatial distribution of new firms: Can peripheral areas escape from the curse of remoteness? Romanian Journal of Regional Science, 8, 1–28
  • KB-Kalkınma Bakanlığı (2013). İllerin ve bölgelerin sosyo-ekonomik gelişmişlik sıralaması araştırması (SEGE 2011), Bölgesel Gelişme ve Yapısal Uyum Genel Müdürlüğü, Ankara, Turkey.
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  • Koomen, E., Diogo, V., Dekkers, J., Rietveld, P. (2015). A utility-based suitability framework for integrated local-scale land-use modelling. Computers, Environment and Urban Systems, 50, 1-14.
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  • Ng, T. S., Skitmore, N., Wong, F. K. (2008). Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment, 43(6), 1171-1184.
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  • Qiang, Y., Lam, N. S. N. (2015). Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environmental Monitoring and Assessment, 187, 57.
  • Reginster, I., Rounsevell, M. (2006). Scenarios of future urban land use in Europe. Environment and Planning B, 33, 619-636.
  • Rindfuss, R. R., Entwisle, B., Walsh, S. J., Mena, C. F., Erlien, C. M., Gray, C. L. (2007). Frontier land use change: synthesis, challenges, and next steps. Annals of the American Association of Geographers, 97, 739-754.
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  • Sing, C. P., Edwards, D., Liu, H., Love, P. E. D. (2015). Forecasting private-sector construction works: VAR model using economic indicators. Journal of Construction Engineering and Management, 04015037.
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Details

Primary Language English
Subjects Urban Analysis and Development
Journal Section All Articles
Authors

Eda Ustaoglu 0000-0001-6874-5162

Publication Date March 17, 2024
Submission Date August 9, 2023
Published in Issue Year 2024 Volume: 17 Issue: 2

Cite

APA Ustaoglu, E. (2024). Estimation of Demand for Urban Land Uses: A Case Study of Türkiye. Kent Akademisi, 17(2), 626-650. https://doi.org/10.35674/kent.1339840

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