Kentsel Arazi Kullanım Talebinin Tahmini: Türkiye Üzerine Bir Çalışma

Yıl 2024, Cilt: 17 Sayı: 2, 626 - 650, 17.03.2024

Eda Ustaoglu

https://doi.org/10.35674/kent.1339840

Öz

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.

Anahtar Kelimeler

Kentsel Arazi, Arazi Kullanım Talebi, Arazi Kullanım Projeksiyonu, İstatistiksel Yöntemler, Türkiye

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

Öz

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.

Anahtar Kelimeler

Urban land, Land use demand, Land use projection, Statistical methods, Turkey

Kaynakça

• Akintoye, A., Skitmore, M. (1994). Models of UK private sector quarterly construction demand. Construction Management and Economics, 12(1), 3-13.
• Arauzo-Carod, J. M. (2005). Determinants of industrial location: an application for Catalan municipalities. Papers in Regional Science, 84, 105-120.
• 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.
• Barredo, J. I., Kasanko, M., McCormick, N., Lavalle, C. (2003). Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64(3), 145-160.
• 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.
• Burgi, M., Hersperger, A. M., Schneeberger, N. (2004). Driving forces of landscape change-current and new directions. Landscape Ecology, 19, 857–868.
• 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.
• Dökmeci, V., Berkoz, L. (2000). Residential location preferences according to demographic characteristic in Istanbul. Landscape and Urban Planning, 48, 45-55.
• Ergun, N. (2004). Gentrification in Istanbul. Cities, 21(5), 391-405.
• Erman,T. (2001).The politics of squatter (gecekondu) studies in Turkey: The changing representations of rural migrants in the academic discourse. Urban Studies, 38(7), 983-1002.
• Ersoy, M. (2015). An introduction to the administrative structure and spatial planning in Turkey. ODTU MF Cep Kitaplari No. 18, ODTU, Ankara.
• European Environment Agency (EEA) (2022). Data and Maps: Sharing European Environmental Datasets, Maps, Charts and Applications; Copenhagen, Denmark: EEA, http://www.eea.europa.eu/data-and- maps Accessed: 12 April 2022.
• Eurostat (2023). Eurostat Regional Yearbook 2022. Brussels: EC, https://ec.europa.eu/statisticalatlas/viewer/?mids=BKGCNT,C99M01,CNTOVL&o=1,1,0.7&ch=C02,TRC,TYP&center=38.59975,40.06913,4&lcis=C99M01& Accessed: 01 August 2023.
• Fan, Y. C. R., Ng, T. S., Wong, J. M. (2011). Predicting construction market growth for urban metropolis: an econometric analysis. Habitat International, 35, 167-174.
• FAO (1995) Planning for sustainable use of land resources: Towards a new approach. FAO Land and Water Bulletin 2, Rome, Italy.
• Fischer, G., Sun, L. X. (2001). Model based analysis of future land-use development in China. Agriculture, Ecosystems and Environment, 85, 163-176.
• Garcia, A. M., Sante, I., Boullon, M., Crecente, R. (2012). A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, NW Spain. Computers, Environment and Urban Systems, 36(4), 291-301.
• Gokce, D., Chen, F. (2019). A methodological framework for defining ‘typological process’: the transformation of the residential environment in Ankara, Turkey. Journal of Urban Design, 24(3), 469-493.
• He, C., Okada, N., Zhang, Q., Shi, P., Zhang, J. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Applied Geography, 26(3-4), 323- 354.
• Hua, B. G., Pin, H. T. (2000). Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach. Construction Management and Economics, 18, 607-618.
• Heistermann, M., Muller, C., Ronneberger, K. (2006) Land in sight? Achievements, deficits and potentials of continental to global scale land-use modeling. Agriculture Ecosystems and Environment, 114, 141–158.
• Hoymann, J. (2011). Quantifying demand for built-up area-A comparison of approaches and application to regions with stagnating population. Journal of Land Use Science, 7(1), 67-87.
• Hussain, S., Karuppannan, S. (2023). Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan. Geology, Ecology, and Landscapes, 7(1), 46-58.
• Jackson, L., Bird, S., Matheny, R., O’Neill, R., White, D., Boesch, K., Koviach, J. (2004). A regional approach to projecting land-use change and resulting ecological vulnerability. Environmental Monitoring and Assessment, 94, 231-248.
• Jacobs-Crisioni, C., Rietveld, P., Koomen, E. (2014). The impact of spatial aggregation on urban development analyses. Applied Geography, 47, 46-56.
• 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.
• Jiang, H., Liu, C. (2014). A panel vector error correction approach to forecasting demand in regional construction markets. Construction Management and Economics, 32(12), 1205–1221.
• Jiang, H., Guo, H., Sun, Z., Xing, Q., Zhang, H., Ma, Y., Li, S. (2022). Projections of urban built-up area expansion and urbanization sustainability in China’s cities through 2030. Journal of Cleaner Production, 367, 133086.
• Jin, Y., Wilson, A., (1993). Generation of integrated multispatial input-output models of cities (GIMIMoC) I: initial stage. Papers in Regional Science, 72, 351-367.
• Jun, M-J., (2004). A metropolitan input-output model: multisectoral and multispatial relations of production, income formation, and consumption. Annals of Regional Science, 38, 131-147.
• Jun, M-J. (2005). Forecasting urban land-use demand using a metropolitan input-output model. Environment and Planning A, 37, 1311-1328.
• Kabisch, N., Haase, D. (2011). Diversifying European agglomerations: evidence of urban population trends for the 21st century. Population, Space and Place, 17(3), 236-253.
• 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.
• Klosterman, R. E., Siebert, L., Kim, J-W., Hoqua, M. A., Parveen, A. (2006) What if evaluation of growth management strategies for a declining region. International Journal of Environmental Technology and Management, 6, 79-95.
• 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.
• Le Mouel, P., Boitier, B., Zang, N. A., Chevallier, C., Zagame, P., Kaae, B. C., Ortiz, R. A., Nielsen, T. S., Jansson, T., Bakker, M., Verkerk, H. (2009). NEMESIS adapted to SENSOR sectors, extension to EU-25, and inclusion of land supply module, forecast simulation of baseline scenarios and policy cases. SENSOR Project Priority Area 1.1.6.3 Global Change and Ecosystems, Deliverable 2.1.3.
• Liang, X., Liu, X., Li, X., Chen, Y., Tian, H., Yao, Y. (2018). Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landscape and Urban Planning, 177, 47-63. Lopez, E., Bocco, G., Mendoza, M., Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe. A case in Morelia city, Mexico. Landscape and Urban Planning, 55, 271-285.
• Ministry of Development (MD) (2014). The Tenth Development Plan 2014–2018, Ankara, Turkey: Ministry of Development.
• 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.
• OECD (2016). Boosting regional competitiveness in Turkey-Assessing regional competitiveness in Turkey. Paris: OECD.
• OECD (2017). The governance of land use: Country fact sheet Turkey. Paris: OECD.
• OECD (2010). The 2008-09 crisis in Turkey: Performance, policy responses and challenges for sustaining the recovery. Working paper no. 819. Paris: OECD.
• Parker, D. C., Meretsky, V. J. (2004). Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agriculture, Ecosystems and Environment, 101(2), 233-250.
• 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.
• Sing, F. T. (2003). Dynamics of private industrial space demand in Singapore. Journal of Real Estate Research, 25(3), 301-324.
• 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.
• Song, X., Feng, Q., Xia, F., Li, X., Scheffran, J. (2021). Impacts of changing urban land-use structure on sustainability city growth in China: A population-density dynamics perspective. Habitat International, 107, 102296.
• Tekeli, I. (2009). An exploratory approach to urban historiography through a new paradigm: The case of Turkey. In: H. Sarkis, N. Turan (Eds.). A Turkish triangle: Ankara, Istanbul and Izmir at the gates of Europe. US: Cambridge, Mass.
• Tekeli, I. (2010). Urban land, infrastructure and urban services. Istanbul: Tarih Vakfı Yurt Yayınları.
• Tong, D., Yuan, Y., Wang, X. (2021). The coupled relationships between land development and land ownership at China’s urban fringe: A structural equation modelling approach. Land Use Policy, 100, 104925.
• Turkstat. (2014). Statistical Indicators 1923–2013; Online Report; Turkish Statistical Institute: Ankara, Turkey, 2014. Available online: http://www.turkstat.gov.tr/Kitap.do?metod=KitapDetay& KT_ID=0&KITAP_ID=160. Accessed on: 24 May 2023
• Turkstat (2021). Turkish Statistical Institute. Ankara, Turkey. https://biruni.tuik.gov.tr/ bolgeselistatistik/degiskenlerUzerindenSorgula.do. Accessed on: 21 November 2023
• Turkstat (2022). Turkish Statistical Institute. Ankara, Turkey. https://biruni.tuik.gov.tr/bolgeselistatistik/degiskenlerUzerindenSorgula.do?d-4326216-p=4. Accessed on: 10 January 2024
• Turkstat (2023). Turkish Statistical Institute. Ankara, Turkey. http://www.turkstat.gov.tr/. Accessed on: 15 April 2023.
• UN (2014a) Online Report: https://news.un.org/en/story/2014/07/472752, Accessed 03 August 2023.
• UN (2014b). World Urbanization Prospects, the 2014 Revision. New York: UN Department of Economic and Social Affairs-Population Division, https://esa.un.org/unpd/wup/, Accessed 23 May 2023.
• UNCED (1993). Agenda 21: Programme of Action for Sustainable Development. United Nations, New York. 294 p.
• UNFPA (2007, 2014) State of world population 2007, 2014. United Nations Population Fund. http://www.unfpa.org/swp/2007/ http://www.unfpa.org/swp/2014/.
• Ustaoglu, E., Batista e Silva, F., Lavalle, C. (2018) Quantifying and modelling industrial and commercial land-use demand in France. Environment, Development, and Sustainability, https://doi.org/10.1007/s10668-018-0199-7
• Ustaoglu, E., Aydınoglu, A. C. (2019) Regional variations of land-use development and land-use/cover change dynamics: A case study of Turkey. Remote Sensing, 11, 885.
• Van Bronkhorst, B., Glumac, B., Van Rhee, M., Kunen, T., Schaefer, W. (2014). The Dutch land market: A regional tool for policy impact on vacancy and grant rates. Paper presented at 21st Annual European Real Estate Society Conference. ERES Conference. Bucharest, Romania, 2014.
• Verburg, P. H., Schot, P. P., Dijst, M. J., Veldkamp, A. (2004). Land use change modelling: current practice and research priorities. Geo Journal, 61, 309-324.
• White, R., Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24, 383-400.
• World Bank (2018) Population estimates and projections. https://datacatalog. worldbank.org/dataset/population-estimates-and-projections, Accessed 14 June 2023
• Xu, L., Liu, X., Tong, D., Liu, Z., Yin, L., Zheng, W. (2022). Forecasting urban land use change based on cellular automata and the PLUS model. Land, 11(5), 652.
• Xu, Q., Zhu, A-X., Liu, J. (2023). Land-use change modelling with cellular automata using land natural evolution unit. Catena, 224, 106998.
• Zhao, X., Pu, J., Wang, J., Chen, L. E., Yang, Z. (2018). Land-use spatio-temporal change and its driving factors in an artificial forest area in Southwest China. Sustainability, 10(11), 1-19. Zorlu, F., Yoloğlu, A. C. (2022). Mekansal plan öngörülerindeki yanılma sorunu üzerine bir değerlendirme ve üç öneri. In: M. Oğuz, A. Yarış, A. Vural (Eds.) Mekana ve İnsana Dair: Güncel Yaklaşımlar, Tartışmalar, Çalışmalar, İdealKent Yayınları, Ankara, Turkey.