A review of geospatial-based urban growth models and modelling initiatives

References

• Adejoke AO, Ok OK, Badaru YU. 2014. Analysis of change detection of Birnin-Kudu land cover using image classification and vegetation indices. J Environ Earth Sci. 4(21):1–10.
   Google Scholar
• Ahmed B, Ahmed R. 2012. Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS Int J Geo-Inform. 1:3–31.10.3390/ijgi1010003
   Web of Science ®Google Scholar
• Al-shalabi M, Billa L, Pradhan B, Mansor S, Al-Sharif AA. 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. Environ Earth Sci. 70:425–437.10.1007/s12665-012-2137-6
   Web of Science ®Google Scholar
• Alsharif AA, Pradhan B. 2014. Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. J Indian Soc Remote Sens. 42:149–163.10.1007/s12524-013-0299-7
   Web of Science ®Google Scholar
• Álvarez Martínez J-M, Suárez-Seoane S, De Luis Calabuig E. 2011. Modelling the risk of land cover change from environmental and socio-economic drivers in heterogeneous and changing landscapes: the role of uncertainty. Landsc Urban Plan. 101:108–119.10.1016/j.landurbplan.2011.01.009
   Web of Science ®Google Scholar
• Araya YH, Cabral P. 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sens. 2:1549–1563.10.3390/rs2061549
   Web of Science ®Google Scholar
• Arentze T, Hofman F, Mourik H, Timmermans H, Wets G. 2000. Using decision tree induction systems for modeling space-time behavior. Geogr Anal. 32:330–350.
   Web of Science ®Google Scholar
• Arsanjani JJ, Helbich M, de Noronha Vaz E. 2013. Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities. 32:33–42.10.1016/j.cities.2013.01.005
   Web of Science ®Google Scholar
• Arsanjani JJ, Helbich M, Kainz W, Boloorani AD. 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf. 21:265–275.10.1016/j.jag.2011.12.014
   Web of Science ®Google Scholar
• Arsanjani TJ, Javidan R, Nazemosadat MJ, Arsanjani JJ, Vaz E. 2015. Spatiotemporal monitoring of Bakhtegan Lake’s areal fluctuations and an exploration of its future status by applying a cellular automata model. Comput Geosci. 78:37–43.
   Web of Science ®Google Scholar
• Arsanjani JJ, Kainz W 2011. Integration of spatial agents and Markov chain model in simulation of urban sprawl. Paper presented at: Proceedings of the 4th AGILE International Conference on Geographic Information Science, April 18–22, Utrecht, The Netherlands.
   Google Scholar
• Arsanjani JJ, Kainz W, Mousivand AJ. 2011. Tracking dynamic land-use change using spatially explicit Markov chain based on cellular automata: the case of Tehran. Int J Image Data Fusion. 2:329–345.10.1080/19479832.2011.605397
   Google Scholar
• Athanassopoulos S, Kaklamanis C, Kalfoutzos G, Papaioannou E. 2012. Cellular automata: simulation using Matlab. Paper presented at: The Sixth International Conference on Digital Society (ICDS); 2012 Jan 30–Feb 4; Valencia, Spain.
   Google Scholar
• Babanyara Y, Usman H, Saleh U. 2010. An overview of urban poverty and environmental problems in Nigeria. J Human Ecol. 31:135–143.
   Google Scholar
• Baja S, Arif S. 2014. GIS-based modelling of land use dynamics using cellular automata and Markov chain. J Environ Earth Sci. 4:61–66.
   Google Scholar
• Basnet P. 2011. An analysis of urbanization trend, pattern and policies in Nepal. Sonsik J. 3:64–71.
   Google Scholar
• Batty M, Longley P. 1986. The fractal simulation of urban structure. Environ Plan A. 18:1143–1179.10.1068/a181143
   Web of Science ®Google Scholar
• Bhatta B. 2010. Analysis of urban growth and sprawl from remote sensing data. New York, NY: Springer Science & Business Media.10.1007/978-3-642-05299-6
   Google Scholar
• Chen L. 2012. Agent-based modeling in urban and architectural research: a brief literature review. Front Arch Res. 1:166–177.10.1016/j.foar.2012.03.003
   Google Scholar
• Chen CF, Son NT, Chang NB, Chen CR, Chang LY, Valdez M, Centeno G, Thompson CA, Aceituno JL. 2013. Multi-decadal mangrove forest change detection and prediction in Honduras, Central America, with Landsat imagery and a Markov chain model. Remote Sens. 5:6408–6426.10.3390/rs5126408
   Web of Science ®Google Scholar
• Cheng W, Wang K, Zhang X. 2010. Implementation of a COM-based decision-tree model with VBA in ArcGIS. Expert Syst Appl. 37:12–17.10.1016/j.eswa.2009.01.006
   Web of Science ®Google Scholar
• Colwell RN. 1997. History and place of photographic interpretation. In: Philipson WR, editor. Manual of photographic interpretation. 2nd ed. Maryland (MD): American Society for Photogrammetry and Remote Sensing; 33–48.
   Google Scholar
• Crooks AT. 2010. Constructing and implementing an agent-based model of residential segregation through vector GIS. Int J Geogr Inform Sci. 24:661–675.10.1080/13658810903569572
   Web of Science ®Google Scholar
• Dadras M, Mohd Shafri HZ, Ahmad N, Pradhan B, Safarpour S. 2014. Land use/cover change detection and urban sprawl analysis in Bandar Abbas City, Iran. Sci World J. 2014:1–12.
   Web of Science ®Google Scholar
• de Noronha Vaz E, Nijkamp P, Painho M, Caetano M. 2012. A multi-scenario forecast of urban change: a study on urban growth in the Algarve. Landsc Urban Plan. 104:201–211.
   Web of Science ®Google Scholar
• Deep S, Saklani A. 2014. Urban sprawl modeling using cellular automata. Egypt J Remote Sens Space Sci. 17:179–187.10.1016/j.ejrs.2014.07.001
   Google Scholar
• Deka J, Tripathi OP, Khan ML. 2011. Urban growth trend analysis using Shannon Entropy approach – a case study in North-East India. Int J Geom Geosci. 2:1062–1068.
   Google Scholar
• Deng X, Huang J, Rozelle S, Uchida E. 2010. Economic growth and the expansion of urban land in China. Urban Stud. 47:813–843.10.1177/0042098009349770
   Web of Science ®Google Scholar
• Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards. 65:135–165.10.1007/s11069-012-0347-6
   Web of Science ®Google Scholar
• Dimitrios P. 2012a. Urban growth modelling using determinism and stochasticity in a touristic village in western Greece. Open J Civil Eng. 2:42–48.
   Google Scholar
• Dimitrios P. 2012b. Urban growth prediction modelling using fractals and theory of Chaos. Open J Civil Eng. 2:81–86.
   Google Scholar
• Eyoh A, Olayinka DN, Nwilo P, Okwuashi O, Isong M, Udoudo D. 2012. Modelling and predicting future urban expansion of lagos, nigeria from remote sensing data using logistic regression and GIS. Int J Appl. 2(5):116–124.
   Google Scholar
• Green SB, Salkind NJ. 2010. Using SPSS for windows and Macintosh: analyzing and understanding data. New Jersey (NJ): Prentice Hall.
   Google Scholar
• Guan Q, Clarke KC. 2010. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model. Int J Geogr Inform Sci. 24:695–722.10.1080/13658810902984228
   Web of Science ®Google Scholar
• Haibo Y, Longjiang D, Hengliang G, Jie Z. 2011. Tai’an land use analysis and prediction based on RS and Markov model. Proc Environ Sci. 10:2625–2630.10.1016/j.proenv.2011.09.408
   Google Scholar
• Hashim M, Mohd Noor N, Marghany M. 2011. Modeling sprawl of unauthorized development using geospatial technology: case study in Kuantan district, Malaysia. Int J Digit Earth. 4:223–238.10.1080/17538947.2010.494737
   Web of Science ®Google Scholar
• He C, Zhao Y, Tian J, Shi P. 2013. Modeling the urban landscape dynamics in a megalopolitan cluster area by incorporating a gravitational field model with cellular automata. Landsc Urban Plan. 113:78–89.10.1016/j.landurbplan.2013.01.004
   Web of Science ®Google Scholar
• Hosseinali F, Alesheikh AA, Nourian F. 2013. Agent-based modeling of urban land-use development, case study: simulating future scenarios of Qazvin city. Cities. 31:105–113.10.1016/j.cities.2012.09.002
   Web of Science ®Google Scholar
• Hove M, Ngwerume ET, Muchemwa C. 2013. The urban crisis in sub-Saharan Africa: a threat to human security and sustainable development. Stability. 2:1–14
   Google Scholar
• Hui-Hui F, Hui-Ping L, Ying L. 2012. Scenario prediction and analysis of urban growth using SLEUTH model. Pedosphere. 22:206–216.
   Web of Science ®Google Scholar
• Jantz CA, Goetz SJ, Donato D, Claggett P. 2010. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst. 34(1):1–16.10.1016/j.compenvurbsys.2009.08.003
   Web of Science ®Google Scholar
• Jiang B, Yao X. 2010. Geospatial analysis and modelling of urban structure and dynamics, Vol. 99. Berlin: Springer Science & Business Media.
   Google Scholar
• Jiang B, Yin J. 2014. Ht-index for quantifying the fractal or scaling structure of geographic features. Ann Assoc Am Geogr. 104:530–540.10.1080/00045608.2013.834239
   Web of Science ®Google Scholar
• Jin H, Mountrakis G. 2013. Integration of urban growth modelling products with image-based urban change analysis. Int J Remote Sens. 34:5468–5486.10.1080/01431161.2013.791760
   Web of Science ®Google Scholar
• Kim D, Batty M. 2011. Calibrating cellular automata models for simulating urban growth: comparative analysis of SLEUTH and metronamica. Centre for Advanced Spatial Analysis, Paper, 176.
   Google Scholar
• Li D. 2010. Remotely sensed images and GIS data fusion for automatic change detection. Int J Image Data Fusion. 1:99–108.10.1080/19479830903562074
   Google Scholar
• Li X, Lao C, Liu X, Chen Y. 2011. Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape. Int J Geogr Inform Sci. 25:575–593.10.1080/13658816.2010.481262
   Web of Science ®Google Scholar
• Li W, Wu C, Zang S. 2014. Modeling urban land use conversion of Daqing City, China: a comparative analysis of ‘top-down’ and ‘bottom-up’ approaches. Stoch Env Res Risk Assess. 28:817–828.10.1007/s00477-012-0671-0
   Web of Science ®Google Scholar
• Li X, Zhang X, Yeh A, Liu X. 2010. Parallel cellular automata for large-scale urban simulation using load-balancing techniques. Int J Geogr Inform Sci. 24:803–820.10.1080/13658810903107464
   Web of Science ®Google Scholar
• Liao FH, Wei YD. 2014. Modeling determinants of urban growth in Dongguan, China: a spatial logistic approach. Stoch Env Res Risk Assess. 28:801–816.10.1007/s00477-012-0620-y
   Web of Science ®Google Scholar
• Liu X, Li X, Shi X, Zhang X, Chen Y. 2010. Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. Int J Geogr Inform Sci. 24:783–802.10.1080/13658810903270551
   Web of Science ®Google Scholar
• Magliocca N, McConnell V, Walls M, Safirova E. 2012. Zoning on the urban fringe: results from a new approach to modeling land and housing markets. Reg Sci Urban Econ. 42:198–210.10.1016/j.regsciurbeco.2011.08.012
   Web of Science ®Google Scholar
• Maithani S, Arora MK, Jain RK. 2010. An artificial neural network based approach for urban growth zonation in Dehradun city, India. Geocarto Int. 25:663–681.10.1080/10106049.2010.524313
   Web of Science ®Google Scholar
• Maliene V, Grigonis V, Palevičius V, Griffiths S. 2011. Geographic information system: old principles with new capabilities. Urban Des Int. 16(1):1–6.10.1057/udi.2010.25
   Web of Science ®Google Scholar
• Mandelbrot BB. 1983. The fractal geometry of nature, Vol. 173. London: Macmillan.
   Google Scholar
• Melillo JM, Richmond T, Yohe GW. 2014. Climate change impacts in the United States: the third national climate assessment. US Global change research program, 841.
   Google Scholar
• Memarian H, Balasundram SK, Talib JB, Sung CTB, Sood AM, Abbaspour K. 2012. Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. J Geogr Inf Syst. 4:542–554.
   Google Scholar
• Shafizadeh Moghadam H, Helbich M. 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Appl Geogr. 40:140–149.10.1016/j.apgeog.2013.01.009
   Web of Science ®Google Scholar
• Murcio R, Rodríguez-Romo S. 2013. Modeling urban patterns across geographical scales by a fractal diffusion-aggregation approach. Paper presented at: Proceedings of the European Conference on Complex Systems 2012; Cham, Switzerland.
   Google Scholar
• Murray-Rust D, Rieser V, Robinson DT, Miličič V, Rounsevell M. 2013. Agent-based modelling of land use dynamics and residential quality of life for future scenarios. Environ Model Softw. 46:75–89.
   Web of Science ®Google Scholar
• Nour AM. 2011. The potential of GIS tools in strategic urban planning process; as an approach for sustainable development in Egypt. J Sustain Develop. 4:284–298.
   Google Scholar
• Olusina J, Abiodun E, Oseke J. 2014. Urban sprawl analysis and transportation using cellular automata and Markov chain. Phys Sci Int J. 4:1191–1210.10.9734/PSIJ
   Google Scholar
• Ongsomwang S, Saravisutra A. 2011. Optimum predictive model for urban growth prediction. Suranaree J Sci Technol. 18:141–152.
   Google Scholar
• Owoeye J, Ogundiran A. 2014. A study on housing and environmental quality of Moniya Community in Ibadan, Nigeria. J Environ Earth Sci. 4:51–60.
   Google Scholar
• Ozturk D. 2015. Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Remote Sens. 7:5918–5950.10.3390/rs70505918
   Web of Science ®Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Dubourg V. 2011. Scikit-learn: machine learning in Python. J Mach Learn Res. 12:2825–2830.
   Web of Science ®Google Scholar
• Poelmans L, Van Rompaey A. 2010. Complexity and performance of urban expansion models. Comput Environ Urban Syst. 34:17–27.10.1016/j.compenvurbsys.2009.06.001
   Web of Science ®Google Scholar
• Prakasam C. 2010. Land use and land cover change detection through remote sensing approach: a case study of Kodaikanal taluk, Tamil nadu. Int J Geomat Geosci. 1:150–158.
   Google Scholar
• Qiu R, Xu W, Zhang J. 2015. The transformation of urban industrial land use: a quantitative method. J Urban Manage. 4:40–52.10.1016/j.jum.2015.07.001
   Google Scholar
• QuanLi X, Kun Y, GuiLin W, YuLian Y. 2015. Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China. Nat Hazards. 75:95–118.10.1007/s11069-014-1303-4
   Web of Science ®Google Scholar
• Rodrigue J-P, Comtois C, Slack B. 2013. The geography of transport systems. New York (NY): Routledge.
   Google Scholar
• Samat N, Hasni R, Elhadary YAE. 2011. Modelling land use changes at the peri-urban areas using geographic information systems and cellular automata model. J Sustain Develop. 4:72–84.
   Google Scholar
• Santé I, García AM, Miranda D, Crecente R. 2010. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plan. 96:108–122.10.1016/j.landurbplan.2010.03.001
   Web of Science ®Google Scholar
• Shuaibu MA, Sulaiman IM. 2012. Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res. 5:43–55.
   Google Scholar
• Spekkers M, Kok M, Clemens F, ten Veldhuis J. 2014. Decision-tree analysis of factors influencing rainfall-related building structure and content damage. Nat Hazards Earth Sys Sci. 14:2531–2547.10.5194/nhess-14-2531-2014
   Web of Science ®Google Scholar
• Subedi P, Subedi K, Thapa B. 2013. Application of a hybrid cellular automaton – Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Appl Ecol Environ Sci. 1:126–132.10.12691/aees-1-6-5
   Google Scholar
• Syphard AD, Clarke KC, Franklin J, Regan HM, Mcginnis M. 2011. Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data. J Environ Manage. 92:1882–1893.10.1016/j.jenvman.2011.03.014
   PubMed Web of Science ®Google Scholar
• Tannier C, Thomas I, Vuidel G, Frankhauser P. 2011. A fractal approach to identifying urban boundaries. Geogr Anal. 43:211–227.10.1111/j.1538-4632.2011.00814.x
   Web of Science ®Google Scholar
• Tayyebi A, Pijanowski BC, Tayyebi AH. 2011. An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landsc Urban Plan. 100:35–44.10.1016/j.landurbplan.2010.10.007
   Web of Science ®Google Scholar
• Tewolde MG, Cabral P. 2011. Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sens. 3:2148–2165.10.3390/rs3102148
   Web of Science ®Google Scholar
• Thapa RB, Murayama Y. 2012. Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landsc Urban Plan. 105:140–148.10.1016/j.landurbplan.2011.12.007
   Web of Science ®Google Scholar
• Torrens PM. 2000. How cellular models of urban systems work (1. Theory). Paper presented at: Working Paper Series 28, Centre for Advanced Spatial Analysis, University College London, UK.
   Google Scholar
• Townroe S, Callaghan A. 2014. British container breeding mosquitoes: the impact of urbanisation and climate change on community composition and phenology. PLoS One. 9(4):1–7.
   Web of Science ®Google Scholar
• Triantakonstantis D, Mountrakis G. 2012. Urban growth prediction: a review of computational models and human perceptions. J Geogr Inform Syst. 4:555–587.
   Google Scholar
• Triantakonstantis D, Mountrakis G, Wang J. 2011. A spatially heterogeneous expert based (SHEB) urban growth model using model regionalization. J Geogr Inform Syst. 3:195–210.10.4236/jgis.2011.33016
   Google Scholar
• Triantakonstantis D, Prastacos P, Tsoukala A. 2014. Analyzing urban sprawl in Rethymno, Greece. J Indian Soc Remote Sens. 42:601–610.10.1007/s12524-013-0350-8
   Web of Science ®Google Scholar
• UN. 2014. World urbanization prospects. The 2014 revision. New York (NY): United Nations.
   Google Scholar
• Vaz E, Arsanjani JJ. 2015. Predicting urban growth of the greater Toronto area – coupling a Markov cellular automata with document meta-analysis. J Environ Inform. 25:71–80.10.3808/jei.201500299
   Web of Science ®Google Scholar
• Vermeiren K, Van Rompaey A, Loopmans M, Serwajja E, Mukwaya P. 2012. Urban growth of Kampala, Uganda: pattern analysis and scenario development. Landsc Urban Plan. 106:199–206.10.1016/j.landurbplan.2012.03.006
   Web of Science ®Google Scholar
• Wang J, Mountrakis G. 2011. Developing a multi-network urbanization model: a case study of urban growth in Denver, Colorado. Int J Geogr Inform Sci. 25:229–253.10.1080/13658810903473213
   Web of Science ®Google Scholar
• Wei YD, Ye X. 2014. Urbanization, urban land expansion and environmental change in China. Stoch Env Res Risk Assess. 28:757–765.10.1007/s00477-013-0840-9
   Web of Science ®Google Scholar
• Wray C, Cheruiyot K. 2015. Key challenges and potential urban modelling opportunities in South Africa, with specific reference to the Gauteng City-region. South African J Geomat. 4:14–35.10.4314/sajg.v4i1.2
   Web of Science ®Google Scholar
• Wray C, Musango J, Damon K, Cheruiyot K. 2015. Modelling urban spatial change: a review of international and South African modelling initiatives. Paper presented at: Gauteng City-Region Observatory, Ocassional Paper 6, Johannesburg, South Africa.
   Google Scholar
• Wu X, Hu Y, He H, Xi F, Bu R. 2010. Study on forecast scenarios for simulation of future urban growth in Shenyang City based on SLEUTH model. Geo-spat Inform Sci. 13:32–39.10.1007/s11806-010-0155-7
   Google Scholar
• Xie Y, Fan S. 2014. Multi-city sustainable regional urban growth simulation – MSRUGS: a case study along the mid-section of Silk Road of China. Stoch Env Res Risk Assess. 28:829–841.10.1007/s00477-012-0680-z
   Web of Science ®Google Scholar
• Xie C, Huang B, Claramunt C, Chandramouli C. 2005. Spatial logistic regression and GIS to model rural-urban land conversion. Paper presented at: Proceedings of PROCESSUS Second International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation Models: Frameworks, Models and Applications, University of Toronto, Ontario.
   Google Scholar
• Zhang Q, Ban Y, Liu J, Hu Y. 2011. Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Comput Environ Urban Syst. 35:126–139.10.1016/j.compenvurbsys.2010.12.002
   Web of Science ®Google Scholar
• Zhao S. 2011. Simulation of mass fire-spread in urban densely built areas based on irregular coarse cellular automata. Fire Technol. 47:721–749.10.1007/s10694-010-0187-4
   Web of Science ®Google Scholar