Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2022, Cilt: 7 Sayı: 1, 58 - 70, 15.04.2022

Abdullah Fadhıl , Tuba Kurban

https://doi.org/10.29128/geomatik.852900
Cited By: 3

Öz

Kaynakça

  • Avrupa Çevre Ajansı (2006). Kopenhag, Lüksemburg: Avrupa Toplulukları Resmi Yayınlar Ofisi, ISBN: 978–92–9167–370–4.
  • Al-shalabi M, Billa L, Pradhan B, Mansor S & Al-Sharif A A (2013). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental earth sciences, 70(1), 425-437.
  • Alsharif A A & Pradhan B (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149-163.
  • Anderson J R (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964): US Government Printing Office.
  • Araya Y H & Cabral P (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549-1563.
  • Arsanjani J J, Kainz W & Mousivand A J (2011). Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. International Journal of Image and Data Fusion, 2(4), 329-345.
  • Atşan H A (2011). Irak'ta nüfus politikası ve 1977-2007 dönemi nüfus artışına etkisi. Kadisiyah sosyal Bilimler, 14(1-2), 347-360.
  • Barnes K B, Morgan III J M, Roberge M C & Lowe S (2001). Sprawl development: Its patterns, consequences, and measurement. Towson University, Towson, 1-24.
  • Başlık S (2008). Dinamik Kentsel Büyüme Modeli: Lojistik Regresyon ve Cellular Automata (İstanbul ve Lizbon Örnekleri). Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü.
  • Bugliarello G (2003). Large urban concentrations: A new phenomenon. Earth Science in the City: A Reader, 56, 7-19.
  • Carruthers J I & Ulfarsson G F (2002). Fragmentation and sprawl: Evidence from interregional analysis. Growth and change, 33(3), 312-340.
  • Coppedge B R, Engle D M & Fuhlendorf S D (2007). Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology, 22(9), 1383-1393.
  • Dadhich P N & Hanaoka S (2011). Spatio-temporal urban growth modeling of Jaipur, India. Journal of Urban Technology, 18(3), 45-65.
  • Dai F, Lee C & Zhang X (2001). GIS-based geo-environmental evaluation for urban land-use planning: a case study. Engineering geology, 61(4), 257-271.
  • Fan F, Wang Y & Wang Z (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental monitoring and assessment, 137(1-3), 127.
  • Forte B, Cerreta M & De Toro P (2019). The human sustainable city: challenges and perspectives from the habitat agenda: Routledge.
  • Gordon P & Richardson H W (1997). Are compact cities a desirable planning goal? Journal of the American planning association, 63(1), 95-106.
  • Grekousis G, Manetos P & Photis Y N (2013). Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area. Cities, 30, 193-203.
  • Guan D, Li H, Inohae T, Su W, Nagaie T & Hokao K (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological modelling, 222(20-22), 3761-3772.
  • Guerriere M R & Detsky A S (1991). Neural networks: what are they? : American College of Physicians.
  • Harris P M & Ventura S J (1995). The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogrammetric engineering and remote sensing, 61(8), 993-998.
  • Hu Z & Lo C (2007). Modeling urban growth in Atlanta using logistic regression. Computers, environment and urban systems, 31(6), 667-688.
  • Jacobs J (1961). The Death and Birth of Great American Cities: London: Penguin.
  • Jadkowski M A, Howard R R & Brostuen D E (1990). Application of SPOT data for regional growth analysis and local planning [J]. Photogrammetric engineering and remote sensing, 56(2), 175-180.
  • Jensen J R (1996). Introductory digital image processing: a remote sensing perspective: Prentice-Hall Inc.
  • Kahn M E (2000). The environmental impact of suburbanization. Journal of policy analysis and management, 19(4), 569-586.
  • Kamusoko C, Aniya M, Adi B & Manjoro M (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
  • Li X & Gar-On Yeh A (2004). Data mining of cellular automata's transition rules. International journal of geographical information science, 18(8), 723-744.
  • Liu W & Seto K C (2008). Using the ART-MMAP neural network to model and predict urban growth: a spatiotemporal data mining approach. Environment and Planning B: Planning and Design, 35(2), 296-317.
  • Liu X, Li X, Yeh A G-O, He J & Tao J (2007). Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D: Earth Sciences, 50(10), 1578-1588.
  • Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, . . . Pei F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.
  • López 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(4), 271-285.
  • Méaille R & Wald L (1990). Using geographical information system and satellite imagery within a numerical simulation of regional urban growth. International Journal of Geographical Information System, 4(4), 445-456.
  • Muller M R & Middleton J (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151-157.
  • Myint S W & Wang L (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32(6), 390-404.
  • Nechyba T J & Walsh R P (2004). Urban sprawl. Journal of economic perspectives, 18(4), 177-200.
  • Osman T, Arima T & Divigalpitiya P (2016). Measuring urban sprawl patterns in Greater Cairo Metropolitan Region. Journal of the Indian Society of Remote Sensing, 44(2), 287-295.
  • Ozturk D & Batuk F (2011). Implementation of GIS-based multicriteria decision analysis with VB in ArcGIS. International Journal of Information Technology & Decision Making, 10(06), 1023-1042.
  • Pendall R (1999). Do land-use controls cause sprawl? Environment and Planning B: Planning and Design, 26(4), 555-571.
  • Pontius Jr R G (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10), 1041-1050.
  • Sarı H & Özşahin E (2016). CORINE Sistemine Göre Tekirdağ İlinin AKAÖ (Arazi Kullanımı/Arazi Örtüsü) Özelliklerinin Analizi/Analysis of LULC (Landuse/Landcover) Characteristics of Tekirdag Province based on the CORINE System. Alınteri Zirai Bilimler Dergisi, 30(1), 13-26.
  • Seto K C, Fragkias M, Güneralp B & Reilly M K (2011). A meta-analysis of global urban land expansion. PloS one, 6(8), e23777.
  • Simmons C (2007). Ecological footprint analysis: A useful method for exploring the interaction between lifestyles and the built environment: London: Routledge.
  • Staff J R C, Forschungsstelle E K G & Centre E C J R (2006). Urban sprawl in Europe: The ignored challenge: Office for Official Publications of the European Communities.
  • Steininger M (1996). Tropical secondary forest regrowth in the Amazon: age, area and change estimation with Thematic Mapper data. International Journal of Remote Sensing, 17(1), 9-27.
  • Sudhira H, Ramachandra T & Jagadish K (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29-39.
  • Sutton P C (2003). A scale-adjusted measure of “urban sprawl” using nighttime satellite imagery. Remote sensing of Environment, 86(3), 353-369.
  • Triantakonstantis D & Mountrakis G (2012). Urban growth prediction: a review of computational models and human perceptions.
  • Tu J V (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11), 1225-1231.
  • Verburg P H, Soepboer W, Veldkamp A, Limpiada R, Espaldon V & Mastura S S (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3), 391-405.
  • Verhagen P (2007). Case studies in archaeological predictive modelling (Vol. 14): Amsterdam University Press.
  • Wakode H B, Baier K, Jha R & Azzam R (2014). Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad, India. Arabian Journal of Geosciences, 7(1), 109-121.
  • Wang S, Zheng X & Zang X (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238-1245.
  • Wey W (2013). Smart growth principles combined with fuzzy AHP and DEA approach to the transit-oriented development (TOD) planning in urban transportation systems. Journal of Energy Technologies and Policy, 3(11), 251-258.
  • White R & Engelen G (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and planning A, 25(8), 1175-1199.
  • Wu F (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. International journal of geographical information science, 16(8), 795-818.
  • Wu H, Li Z, Clarke K C, Shi W, Fang L, Lin A & Zhou J (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040-1061.
  • Xu T, Gao J & Coco G (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain–cellular automata. International Journal of Geographical Information Science, 33(10), 1960-1983.
  • Yang X, Zheng X-Q & Lv L-N (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological modelling, 233, 11-19.
  • Youssef A M, Pradhan B & Tarabees E (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arabian Journal of Geosciences, 4(3-4), 463-473.
  • Zhang Q, Ban Y, Liu J & Hu Y (2011). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems, 35(2), 126-139.

Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği

Yıl 2022, Cilt: 7 Sayı: 1, 58 - 70, 15.04.2022

Abdullah Fadhıl , Tuba Kurban

https://doi.org/10.29128/geomatik.852900
Cited By: 3

Öz

Kentsel yayılma küresel bir olgudur ancak Irak gibi gelişmekte olan ülkelerde çok daha hızlı bir şekilde gerçekleşmektedir. Kerkük, kentsel alanlarda hızla artan bir genişlemeye tanık olunan Irak kentlerinden biridir. Bu kentsel yayılma; hızlı nüfus artışı, plansız büyüme ve göç gibi çeşitli faktörlerden kaynaklanmaktadır. Bunun sonucunda kentsel ekosistem bu süreçten önemli ölçüde etkilenmektedir. Bu çalışmada, 2002-2018 yılları arasında Kerkük ilindeki arazi kullanımı/arazi örtüsü değişiklikleri CBS ve uzaktan algılama teknikleri kullanılarak incelenmiştir. Tespit edilen değişimler göstermektedir ki en büyük değişiklik %130 oranında bir artışla kentsel alanlarda gerçekleşmiştir. Bununla birlikte tarım arazilerinin %22 ve su alanlarının da %8 oranında genişlediği tespit edilmiştir. Buna karşılık, açık arazi alanında %3 oranında bir azalma olduğu da görülmüştür. Bu aşamadan sonra, gelecekte yaşanacak kentsel yayılma oranının öngörülmesi amacıyla karma bir Hücresel Otomata Markov Zincir modeli oluşturulmuştur. Model mevcut arazi kullanım haritası ile karşılaştırılmıştır ve Kappa istatistikleri kullanılarak doğrulanmıştır. Kappa standart, kappa konum ve kappa nicelik katsayıları sırasıyla 0.8799, 0.9143 ve 0.9154 olarak bulunmuştur. Bu sonuçlar, referans haritası ile karşılaştırma haritası arasında iyi bir örtüşme olduğunu göstermektedir. Ayrıca bu model, 2030 ve 2035 yıllarında gerçekleşmesi muhtemel kentsel yayılmanın tahmin edilmesi için kullanılmıştır.

Anahtar Kelimeler

Arazi Kullanımı Arazi Örtüsü Kentsel Yayılma Hücresel otomat Markov zincir

Kaynakça

  • Avrupa Çevre Ajansı (2006). Kopenhag, Lüksemburg: Avrupa Toplulukları Resmi Yayınlar Ofisi, ISBN: 978–92–9167–370–4.
  • Al-shalabi M, Billa L, Pradhan B, Mansor S & Al-Sharif A A (2013). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental earth sciences, 70(1), 425-437.
  • Alsharif A A & Pradhan B (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149-163.
  • Anderson J R (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964): US Government Printing Office.
  • Araya Y H & Cabral P (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549-1563.
  • Arsanjani J J, Kainz W & Mousivand A J (2011). Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. International Journal of Image and Data Fusion, 2(4), 329-345.
  • Atşan H A (2011). Irak'ta nüfus politikası ve 1977-2007 dönemi nüfus artışına etkisi. Kadisiyah sosyal Bilimler, 14(1-2), 347-360.
  • Barnes K B, Morgan III J M, Roberge M C & Lowe S (2001). Sprawl development: Its patterns, consequences, and measurement. Towson University, Towson, 1-24.
  • Başlık S (2008). Dinamik Kentsel Büyüme Modeli: Lojistik Regresyon ve Cellular Automata (İstanbul ve Lizbon Örnekleri). Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü.
  • Bugliarello G (2003). Large urban concentrations: A new phenomenon. Earth Science in the City: A Reader, 56, 7-19.
  • Carruthers J I & Ulfarsson G F (2002). Fragmentation and sprawl: Evidence from interregional analysis. Growth and change, 33(3), 312-340.
  • Coppedge B R, Engle D M & Fuhlendorf S D (2007). Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology, 22(9), 1383-1393.
  • Dadhich P N & Hanaoka S (2011). Spatio-temporal urban growth modeling of Jaipur, India. Journal of Urban Technology, 18(3), 45-65.
  • Dai F, Lee C & Zhang X (2001). GIS-based geo-environmental evaluation for urban land-use planning: a case study. Engineering geology, 61(4), 257-271.
  • Fan F, Wang Y & Wang Z (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental monitoring and assessment, 137(1-3), 127.
  • Forte B, Cerreta M & De Toro P (2019). The human sustainable city: challenges and perspectives from the habitat agenda: Routledge.
  • Gordon P & Richardson H W (1997). Are compact cities a desirable planning goal? Journal of the American planning association, 63(1), 95-106.
  • Grekousis G, Manetos P & Photis Y N (2013). Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area. Cities, 30, 193-203.
  • Guan D, Li H, Inohae T, Su W, Nagaie T & Hokao K (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological modelling, 222(20-22), 3761-3772.
  • Guerriere M R & Detsky A S (1991). Neural networks: what are they? : American College of Physicians.
  • Harris P M & Ventura S J (1995). The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogrammetric engineering and remote sensing, 61(8), 993-998.
  • Hu Z & Lo C (2007). Modeling urban growth in Atlanta using logistic regression. Computers, environment and urban systems, 31(6), 667-688.
  • Jacobs J (1961). The Death and Birth of Great American Cities: London: Penguin.
  • Jadkowski M A, Howard R R & Brostuen D E (1990). Application of SPOT data for regional growth analysis and local planning [J]. Photogrammetric engineering and remote sensing, 56(2), 175-180.
  • Jensen J R (1996). Introductory digital image processing: a remote sensing perspective: Prentice-Hall Inc.
  • Kahn M E (2000). The environmental impact of suburbanization. Journal of policy analysis and management, 19(4), 569-586.
  • Kamusoko C, Aniya M, Adi B & Manjoro M (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
  • Li X & Gar-On Yeh A (2004). Data mining of cellular automata's transition rules. International journal of geographical information science, 18(8), 723-744.
  • Liu W & Seto K C (2008). Using the ART-MMAP neural network to model and predict urban growth: a spatiotemporal data mining approach. Environment and Planning B: Planning and Design, 35(2), 296-317.
  • Liu X, Li X, Yeh A G-O, He J & Tao J (2007). Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D: Earth Sciences, 50(10), 1578-1588.
  • Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, . . . Pei F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.
  • López 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(4), 271-285.
  • Méaille R & Wald L (1990). Using geographical information system and satellite imagery within a numerical simulation of regional urban growth. International Journal of Geographical Information System, 4(4), 445-456.
  • Muller M R & Middleton J (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151-157.
  • Myint S W & Wang L (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32(6), 390-404.
  • Nechyba T J & Walsh R P (2004). Urban sprawl. Journal of economic perspectives, 18(4), 177-200.
  • Osman T, Arima T & Divigalpitiya P (2016). Measuring urban sprawl patterns in Greater Cairo Metropolitan Region. Journal of the Indian Society of Remote Sensing, 44(2), 287-295.
  • Ozturk D & Batuk F (2011). Implementation of GIS-based multicriteria decision analysis with VB in ArcGIS. International Journal of Information Technology & Decision Making, 10(06), 1023-1042.
  • Pendall R (1999). Do land-use controls cause sprawl? Environment and Planning B: Planning and Design, 26(4), 555-571.
  • Pontius Jr R G (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10), 1041-1050.
  • Sarı H & Özşahin E (2016). CORINE Sistemine Göre Tekirdağ İlinin AKAÖ (Arazi Kullanımı/Arazi Örtüsü) Özelliklerinin Analizi/Analysis of LULC (Landuse/Landcover) Characteristics of Tekirdag Province based on the CORINE System. Alınteri Zirai Bilimler Dergisi, 30(1), 13-26.
  • Seto K C, Fragkias M, Güneralp B & Reilly M K (2011). A meta-analysis of global urban land expansion. PloS one, 6(8), e23777.
  • Simmons C (2007). Ecological footprint analysis: A useful method for exploring the interaction between lifestyles and the built environment: London: Routledge.
  • Staff J R C, Forschungsstelle E K G & Centre E C J R (2006). Urban sprawl in Europe: The ignored challenge: Office for Official Publications of the European Communities.
  • Steininger M (1996). Tropical secondary forest regrowth in the Amazon: age, area and change estimation with Thematic Mapper data. International Journal of Remote Sensing, 17(1), 9-27.
  • Sudhira H, Ramachandra T & Jagadish K (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29-39.
  • Sutton P C (2003). A scale-adjusted measure of “urban sprawl” using nighttime satellite imagery. Remote sensing of Environment, 86(3), 353-369.
  • Triantakonstantis D & Mountrakis G (2012). Urban growth prediction: a review of computational models and human perceptions.
  • Tu J V (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11), 1225-1231.
  • Verburg P H, Soepboer W, Veldkamp A, Limpiada R, Espaldon V & Mastura S S (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3), 391-405.
  • Verhagen P (2007). Case studies in archaeological predictive modelling (Vol. 14): Amsterdam University Press.
  • Wakode H B, Baier K, Jha R & Azzam R (2014). Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad, India. Arabian Journal of Geosciences, 7(1), 109-121.
  • Wang S, Zheng X & Zang X (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238-1245.
  • Wey W (2013). Smart growth principles combined with fuzzy AHP and DEA approach to the transit-oriented development (TOD) planning in urban transportation systems. Journal of Energy Technologies and Policy, 3(11), 251-258.
  • White R & Engelen G (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and planning A, 25(8), 1175-1199.
  • Wu F (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. International journal of geographical information science, 16(8), 795-818.
  • Wu H, Li Z, Clarke K C, Shi W, Fang L, Lin A & Zhou J (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040-1061.
  • Xu T, Gao J & Coco G (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain–cellular automata. International Journal of Geographical Information Science, 33(10), 1960-1983.
  • Yang X, Zheng X-Q & Lv L-N (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological modelling, 233, 11-19.
  • Youssef A M, Pradhan B & Tarabees E (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arabian Journal of Geosciences, 4(3-4), 463-473.
  • Zhang Q, Ban Y, Liu J & Hu Y (2011). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems, 35(2), 126-139.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Abdullah Fadhıl 0000-0002-5685-9207

Tuba Kurban 0000-0001-5590-5307

Yayımlanma Tarihi 15 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 1

Kaynak Göster

APA Fadhıl, A., & Kurban, T. (2022). Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik, 7(1), 58-70. https://doi.org/10.29128/geomatik.852900
AMA Fadhıl A, Kurban T. Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik. Nisan 2022;7(1):58-70. doi:10.29128/geomatik.852900
Chicago Fadhıl, Abdullah, ve Tuba Kurban. “Hücresel Otomata Markov Zincir yöntemi Ile Kentsel yayılmanın Modellenmesi: Kerkük Ili örneği”. Geomatik 7, sy. 1 (Nisan 2022): 58-70. https://doi.org/10.29128/geomatik.852900.
EndNote Fadhıl A, Kurban T (01 Nisan 2022) Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik 7 1 58–70.
IEEE A. Fadhıl ve T. Kurban, “Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği”, Geomatik, c. 7, sy. 1, ss. 58–70, 2022, doi: 10.29128/geomatik.852900.
ISNAD Fadhıl, Abdullah - Kurban, Tuba. “Hücresel Otomata Markov Zincir yöntemi Ile Kentsel yayılmanın Modellenmesi: Kerkük Ili örneği”. Geomatik 7/1 (Nisan 2022), 58-70. https://doi.org/10.29128/geomatik.852900.
JAMA Fadhıl A, Kurban T. Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik. 2022;7:58–70.
MLA Fadhıl, Abdullah ve Tuba Kurban. “Hücresel Otomata Markov Zincir yöntemi Ile Kentsel yayılmanın Modellenmesi: Kerkük Ili örneği”. Geomatik, c. 7, sy. 1, 2022, ss. 58-70, doi:10.29128/geomatik.852900.
Vancouver Fadhıl A, Kurban T. Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik. 2022;7(1):58-70.

Cited By

Determining future scenarios of urban areas with cellular automata/Markov Chain Model method; example of Ereğli District Konya-Türkiye (2030–2040)
Earth Science Informatics
https://doi.org/10.1007/s12145-024-01283-w
Climate-related spatial reduction risk of agricultural lands on the Mediterranean coast in Türkiye and scenario-based modelling of urban growth
Environment, Development and Sustainability
https://doi.org/10.1007/s10668-023-03774-0
Yeşil alan uygunluk analizinin CBS tabanlı AHP ve TOPSİS yöntemleriyle değerlendirilmesi
Geomatik
https://doi.org/10.29128/geomatik.1171069