Development of a parallel computing-based Futureland model for multiple land-use simulation: a case study in Shanghai

References

• Al-Sharif AA, Pradhan B. 2015. A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto Int. 30(8):858–881.
   Web of Science ®Google Scholar
• Chen S, Feng Y, Tong X, Liu S, Xie H, Gao C, Lei Z. 2020a. Modeling ESV losses caused by urban expansion using cellular automata and geographically weighted regression. Sci Total Environ. 712:136509.
   PubMed Web of Science ®Google Scholar
• Chen S, Feng Y, Ye Z, Tong X, Wang R, Zhai S, Gao C, Lei Z, Jin Y. 2020b. A cellular automata approach of urban sprawl simulation with Bayesian spatially-varying transformation rules. GISci Remote Sens. 57(7):924–942.
   Web of Science ®Google Scholar
• Chen Y, Liu X, Li X. 2017. Calibrating a land parcel cellular automaton (LP-CA) for urban growth simulation based on ensemble learning. Int J Geog Inf Sci. 31(12):2480–2504.
   Web of Science ®Google Scholar
• Cloern JE, Abreu PC, Carstensen J, Chauvaud L, Elmgren R, Grall J, Greening H, Johansson JOR, Kahru M, Sherwood ET, et al. 2016. Human activities and climate variability drive fast‐paced change across the world’s estuarine–coastal ecosystems. Glob Chang Biol. 22(2):513–529.
   PubMed Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Feng Y, Tong X. 2018. Calibration of cellular automata models using differential evolution to simulate present and future land use. Trans GIS. 22(2):582–601.
   Web of Science ®Google Scholar
• Feng Y, Tong X. 2020. A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. Int J Geog Inf Sci. 34(1):74–97.
   Web of Science ®Google Scholar
• Feng Y. 2017. Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules. Int J Geog Inf Sci. 31(6):1198–1219.
   Web of Science ®Google Scholar
• Firozjaei MK, Sedighi A, Argany M, Jelokhani-Niaraki M, Arsanjani JJ. 2019. A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model. Cities. 93:120–135.
   Web of Science ®Google Scholar
• Gao C, Feng Y, Tong X, Lei Z, Chen S, Zhai S. 2020. Modeling urban growth using spatially heterogeneous cellular automata models: comparison of spatial lag, spatial error and GWR. Comput Environ Urban Syst. 81:101459.
   Web of Science ®Google Scholar
• Gomes E, Inácio M, Bogdzevič K, Kalinauskas M, Karnauskaitė D, Pereira P. 2021. Future land-use changes and its impacts on terrestrial ecosystem services: a review. Sci Total Environ. 781:146716.
   PubMed Web of Science ®Google Scholar
• Guan Q, Shi X, Huang M, Lai C. 2016. A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures. Int J Geog Inf Sci. 30(3):494–514.
   Web of Science ®Google Scholar
• Güneralp B, Seto K. 2013. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ Res Lett. 8(1):014025.
   Web of Science ®Google Scholar
• Hagen A. 2003. Fuzzy set approach to assessing similarity of categorical maps. Int J Geog Inf Sci. 17(3):235–249.
   Web of Science ®Google Scholar
• Halmy MWA, Gessler PE, Hicke JA, Salem BB. 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr. 63:101–112.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Jennings V, Johnson Gaither C, Gragg RS. 2012. Promoting environmental justice through urban green space access: a synopsis. Environ Justice. 5(1):1–7.
   Google Scholar
• Jennings V, Larson L, Yun J. 2016. Advancing sustainability through urban green space: cultural ecosystem services, equity, and social determinants of health. Int J Environ Res Public Health. 13(2):196.
   PubMed Web of Science ®Google Scholar
• Kanniah KD. 2017. Quantifying green cover change for sustainable urban planning: a case of Kuala Lumpur, Malaysia. Urban For Urban Greening. 27:287–304.
   Web of Science ®Google Scholar
• Karimi F, Sultana S, Babakan AS, Suthaharan S. 2019. An enhanced support vector machine model for urban expansion prediction. Comput Environ Urban Syst. 75:61–75.
   Web of Science ®Google Scholar
• Kumari N, Saco PM, Rodriguez JF, Johnstone SA, Srivastava A, Chun KP, Yetemen O. 2020. The grass is not always greener on the other side: seasonal reversal of vegetation greenness in aspect‐driven semiarid ecosystems. Geophys Res Lett. 47(15):e2020GL088918.
   Web of Science ®Google Scholar
• Li D, Li X, Liu X, Chen Y, Li S, Liu K, Qiao J, Zheng Y, Zhang Y, Lao C. 2012. GPU-CA model for large-scale land-use change simulation. Chin Sci Bull. 57(19):2442–2452.
   Google Scholar
• Li Q, Feng Y, Tong X, Zhou Y, Wu P, Xie H, Jin Y, Chen P, Liu S, Xv X, et al. 2022. Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios. Sustainable Cities Soc. 76:103444.
   Web of Science ®Google Scholar
• Li X, Chen Y, Liu X, Xu X, Chen G. 2017. Experiences and issues of using cellular automata for assisting urban and regional planning in China. Int J Geog Inf Sci. 31(8):1606–1629.
   Web of Science ®Google Scholar
• Li X, Liu X, Yu L. 2014. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. Int J Geog Inf Sci. 28(7):1317–1335.
   Web of Science ®Google Scholar
• Liang X, Liu X, Li D, Zhao H, Chen G. 2018a. Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. Int J Geog Inf Sci. 32(11):2294–2316.
   Web of Science ®Google Scholar
• Liang X, Liu X, Li X, Chen Y, Tian H, Yao Y. 2018b. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landscape Urban Plann. 177:47–63.
   Web of Science ®Google Scholar
• Lin Y-P, Chu H-J, Wu C-F, Verburg PH. 2011. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling–a case study. Int J Geog Inf Sci. 25(1):65–87.
   Web of Science ®Google Scholar
• Lipowski A, Lipowska D. 2012. Roulette-wheel selection via stochastic acceptance. Physica A. 391(6):2193–2196.
   Web of Science ®Google Scholar
• Liu S, Zhang X, Feng Y, Xie H, Jiang L, Lei Z. 2021. Spatiotemporal dynamics of urban green space influenced by rapid urbanization and land use policies in Shanghai. Forests. 12(4):476.
   Web of Science ®Google Scholar
• 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. Landscape Urban Plann. 168:94–116.
   Web of Science ®Google Scholar
• Mansour S, Al-Belushi M, Al-Awadhi T. 2020. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy. 91:104414.
   Web of Science ®Google Scholar
• Martín-Ortega P, García-Montero LG, Sibelet N. 2020. Temporal patterns in illumination conditions and its effect on vegetation indices using Landsat on Google Earth Engine. Remote Sens. 12(2):211.
   Google Scholar
• Mas J-F, Kolb M, Paegelow M, Olmedo MTC, Houet T. 2014. Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Modell Software. 51:94–111.
   Web of Science ®Google Scholar
• Mesta C, Cremen G, Galasso C. 2022. Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley. Sci Rep. 12(1):6152.
   PubMed Web of Science ®Google Scholar
• Mirbagheri B, Alimohammadi A. 2017. Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models. Trans GIS. 21(6):1280–1297.
   Web of Science ®Google Scholar
• Mustafa A, Rienow A, Saadi I, Cools M, Teller J. 2018. Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. Eur J Remote Sens. 51(1):391–401.
   Web of Science ®Google Scholar
• Nagendra H, Bai X, Brondizio ES, Lwasa S. 2018. The urban south and the predicament of global sustainability. Nat Sustain. 1(7):341–349.
   Web of Science ®Google Scholar
• Newland CP, Maier HR, Zecchin AC, Newman JP, Van Delden H. 2018. Multi-objective optimisation framework for calibration of cellular automata land-use models. Environ Modell Software. 100:175–200.
   Web of Science ®Google Scholar
• Nor ANM, Corstanje R, Harris JA, Brewer T. 2017. Impact of rapid urban expansion on green space structure. Ecol Indic. 81:274–284.
   Web of Science ®Google Scholar
• Pontius RG Jr, Huffaker D, Denman K. 2004. Useful techniques of validation for spatially explicit land-change models. Ecol Modell. 179(4):445–461.
   Web of Science ®Google Scholar
• Şalap-Ayça S, Jankowski P, Clarke KC, Kyriakidis PC, Nara A. 2018. A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model. Int J Geog Inf Sci. 32(4):637–662.
   Web of Science ®Google Scholar
• Saputra MH, Lee HS. 2019. Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability. 11(11):3024.
   Web of Science ®Google Scholar
• Schweitzer C, Priess JA, Das S. 2011. A generic framework for land-use modelling. Environ Modell Software. 26(8):1052–1055.
   Web of Science ®Google Scholar
• Seto KC, Güneralp B, Hutyra LR. 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci U S A. 109(40):16083–16088.
   PubMed Web of Science ®Google Scholar
• Shahbazian Z, Faramarzi M, Rostami N, Mahdizadeh H. 2019. Integrating logistic regression and cellular automata–Markov models with the experts’ perceptions for detecting and simulating land use changes and their driving forces. Environ Monit Assess. 191(7):1–17.
   Web of Science ®Google Scholar
• Siddiqui A, Siddiqui A, Maithani S, Jha A, Kumar P, Srivastav S. 2018. Urban growth dynamics of an Indian metropolitan using CA markov and logistic regression. Egypt J Remote Sens Space Sci. 21(3):229–236.
   Web of Science ®Google Scholar
• Srivastava A, Kumari N, Maza M. 2020. Hydrological response to agricultural land use heterogeneity using variable infiltration capacity model. Water Resour Manage. 34(12):3779–3794.
   Web of Science ®Google Scholar
• Tong X, Feng Y. 2019. How current and future urban patterns respond to urban planning? An integrated cellular automata modeling approach. Cities. 92:247–260.
   Web of Science ®Google Scholar
• Valbuena D, Verburg PH, Bregt AK, Ligtenberg A. 2010. An agent-based approach to model land-use change at a regional scale. Landscape Ecol. 25(2):185–199.
   Web of Science ®Google Scholar
• Van Vliet J, Bregt AK, Hagen-Zanker A. 2011. Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol Modell. 222(8):1367–1375.
   Web of Science ®Google Scholar
• Verburg PH, Overmars KP. 2009. Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecol. 24(9):1167–1181.
   Web of Science ®Google Scholar
• Visser H, De Nijs T. 2006. The map comparison kit. Environ Modell Software. 21(3):346–358.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Wei P, Chen S, Wu M, Deng Y, Xu H, Jia Y, Liu F. 2021. Using the InVEST model to assess the impacts of climate and land use changes on water yield in the upstream regions of the Shule River Basin. Water. 13(9):1250.
   Web of Science ®Google Scholar
• Wu Z, Chen R, Meadows ME, Sengupta D, Xu D. 2019. Changing urban green spaces in Shanghai: trends, drivers and policy implications. Land Use Policy. 87:104080.
   Web of Science ®Google Scholar
• Xing W, Qian Y, Guan X, Yang T, Wu H. 2020. A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Comput Geosci. 137:104430.
   Web of Science ®Google Scholar
• Zhou L, Dang X, Sun Q, Wang S. 2020. Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities Soc. 55:102045.
   Web of Science ®Google Scholar
• Zhuang H, Liu X, Liang X, Yan Y, He J, Cai Y, Wu C, Zhang X, Zhang H. 2022. Tensor-CA: a high-performance cellular automata model for land use simulation based on vectorization and GPU. Trans GIS. 26(2):755–778.
   Web of Science ®Google Scholar