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Research Articles

A moving window-based spatial assessment method for dynamic urban growth simulations

ORCID Icon, , , , , & ORCID Icon show all
Pages 15282-15301 | Received 21 Jan 2022, Accepted 28 Jun 2022, Published online: 14 Jul 2022

References

  • Akin A, Berberoglu S, Erdogan MA, Donmez C. 2012. Modelling land-use change dynamics in a Mediterranean coastal wetland using CA-Markov chain analysis. Fresen Environ Bull. 21:386–396.
  • 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(1):425–437.
  • Basse RM, Omrani H, Charif O, Gerber P, Bódis K. 2014. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geograph. 53:160–171.
  • Benham T, Duan Q, Kroese DP, Liquet B. 2015. CEoptim: cross-entropy R package for optimization. arXiv Preprint arXiv:1503.01842. doi:10.48550/arXiv.1503.01842.
  • Berger P, Pascal L, Sartor C, Delorme J, Monge P, Ragon CP, Charbit M, Sambuc R, Drancourt M. 2003. Generalized additive model demonstrates fluoroquinolone use/resistance relationships for Staphylococcus aureus. Eur J Epidemiol. 19(5):453–460.
  • Cao Y, Zhang X, Fu Y, Lu Z, Shen X. 2020. Urban spatial growth modeling using logistic regression and cellular automata: a case study of Hangzhou. Ecol Indicat. 113:106200.
  • Chazal JD, Rounsevell MDA. 2009. Land-use and climate change within assessments of biodiversity change: a review. Global Environ Change. 19(2):306–315.
  • Chen S, Feng Y, Ye Z, Tong X, Wang R, Zhai S, Gao C, Lei Z, Jin Y. 2020. A cellular automata approach of urban sprawl simulation with Bayesian spatially-varying transformation rules. GISci Remote Sens. 57(7):924–942.
  • Congalton RG, Green K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC Press.
  • Feng Y, Tong X. 2017. Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change. Environ Monit Assess. 189(10):1–17.
  • Feng Y, Tong X. 2020. A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. Int J Geograph Inform Sci. 34(1):74–97.
  • Feng Y, Wang J, Tong X, Shafizadeh-Moghadam H, Cai Z, Chen S, Lei Z, Gao C. 2019. Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors. Environ Monit Assess. 191(5):1–20.
  • Feng Y, Wang R, Tong X, Zhai S. 2021. Comparison of change and static state as the dependent variable for modeling urban growth. Geocarto Int. 1–24. doi:10.1080/10106049.2021.1959657.
  • Galante G, Mandrone S, Funaro M, Cotroneo R, Panetta S. 2009. Spatial and temporal changes in Aniene river basin (Latium, Italy) using landscape metrics and moving window technique. Eur J Remote Sens. 41:157–172.
  • Gao C, Feng Y, Tong X, Jin Y, Liu S, Wu P, Ye Z, Gu C. 2020. Modeling urban encroachment on ecological land using cellular automata and cross-entropy optimization rules. Sci Total Environ. 744:140996.
  • Guisan A, Edwards TC, Jr, Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model. 157(2–3):89–100.
  • Guzman LA, Escobar F, Peña J, Cardona R. 2020. A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region. Land Use Policy. 92:104445.
  • Hagen-Zanker A, Martens P. 2008. Map comparison methods for comprehensive assessment of geosimulation models. International Conference on Computational Science and Its Applications, Springer, pp. 194–209.
  • Hamad R, Balzter H, Kolo K. 2018. Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability. 10(10):3421.
  • Ilyassova A, Kantakumar LN, Boyd D. 2021. Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan. Geocarto Int. 36(5):520–539.
  • Jantz CA, Goetz SJ, Shelley MK. 2004. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plann B Plann Des. 31(2):251–271.
  • Lei Z, Feng Y, Tong X, Liu S, Gao C, Chen S. 2022. A spatial error-based cellular automata approach to reproducing and projecting dynamic urban expansion. Geocarto Int. 37(2):560–580.
  • Lin J, Li X, Li S, Wen Y. 2020. What is the influence of landscape metric selection on the calibration of land-use/cover simulation models? Environ Modell Softw. 129:104719.
  • Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, Li S, Wang S, Pei F. 2017. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc Urban Plan. 168:94–116.
  • Mondal B, Das DN, Bhatta B. 2017. Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration. Geocarto Int. 32(4):401–419.
  • Murase H, Nagashima H, Yonezaki S, Matsukura R, Kitakado T. 2009. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. ICES J Marine Sci. 66(6):1417–1424.
  • Pontius RG, Boersma W, Castella J-C, Clarke K, de Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, et al. 2008. Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci. 42(1):11–37.
  • Pontius RG, Si K. 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int J Geograph Inform Sci. 28(3):570–583.
  • Roodposhti MS, Aryal J, Bryan BA. 2019. A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change. Environ Modell Softw. 112:70–81.
  • Rubinstein R. 1999. The cross-entropy method for combinatorial and continuous optimization. Methodol Comput Appl Probabil. 1(2):127–190.
  • Shafizadeh‐Moghadam H, Valavi R, Asghari A, Minaei M, Murayama Y. 2022. On the spatiotemporal generalization of machine learning and ensemble models for simulating built‐up land expansion. Trans GIS. 26(2):1080–1097.
  • Shafizadeh-Moghadam H. 2019. Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. Comput Environ Urban Syst. 76:91–100.
  • Shao W. 2010. Effectiveness of water protection policy in China: A case study of Jiaxing. Sci Total Environ. 408(4):690–701.
  • Shinzawa H, Morita S, Noda I, Ozaki Y. 2006. Effect of the window size in moving-window two-dimensional correlation analysis. J Mol Struct. 799(1–3):28–33.
  • Shojaei H, Nadi S, Shafizadeh-Moghadam H, Tayyebi A, Van Genderen J. 2022. An efficient built-up land expansion model using a modified U-Net. Int J Digital Earth. 15(1):148–163.
  • Solanki H, Bhatpuria D, Chauhan P. 2017. Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea. Geocarto Int. 32(1):30–43.
  • Tayyebi A, Pijanowski BC. 2014. Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Observ Geoinform. 28:102–116.
  • Tong X, Feng Y. 2020. A review of assessment methods for cellular automata models of land-use change and urban growth. Int J Geograph Inform Sci. 34(5):866–898.
  • Van Vliet J, White R, Dragicevic S. 2009. Modeling urban growth using a variable grid cellular automaton. Comput Environ Urban Syst. 33(1):35–43.
  • Veldkamp A, Verburg PH, Kok K, Koning G, Priess J, Bergsma AR. 2001. The need for scale sensitive approaches in spatially explicit land use change modeling. Environ Model Assess. 6(2):111–121.
  • Visser H, De Nijs T. 2006. The map comparison kit. Environ Modell Softw. 21(3):346–358.
  • Wang J, Feng Y, Ye Z, Tong X, Wang R, Gao C, Chen S, Lei Z, Liu S, Jin Y. 2022. Simulating the effect of urban light rail transit on urban development by coupling cellular automata and conjugate gradients. Geocarto Int. 37(8):2346–2364.
  • Wu F. 2002. Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geograph Inform Sci. 16(8):795–818.
  • Wu Q, Hu D, Wang R, Li H, He Y, Wang M, Wang B. 2006. A GIS-based moving window analysis of landscape pattern in the Beijing metropolitan area, China. Int J Sustain Develop World Ecol. 13(5):419–434.
  • Wu H, Li Z, Clarke KC, 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. Int J Geograph Inform Sci. 33(5):1040–1061.
  • Wu H, Lin A, Clarke KC, Shi W, Cardenas-Tristan A, Tu Z. 2021. A comprehensive quality assessment framework for linear features from volunteered geographic information. Int J Geograph Inform Sci. 35(9):1826–1847.
  • Yang J, Liu W, Li Y, Li X, Ge Q. 2018. Simulating intraurban land use dynamics under multiple scenarios based on fuzzy cellular automata: a case study of Jinzhou district, Dalian. Complexity. 2018:1–17.
  • Zhou X, Wang F, Huang K, Zhang H, Yu J, Han AY. 2021. System dynamics-multiple objective optimization model for water resource management: a case study in Jiaxing City, China. Water. 13(5):671.
  • ZiaeeVafaeyan H, Moattar MH, Forghani Y. 2018. Land use change model based on bee colony optimization, Markov chain and a neighborhood decay cellular automata. Nat Res Model. 31(2):e12151.

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