A new type of dual-scale neighborhood based on vectorization for cellular automata models

References

• Abolhasani, S., and M. Taleai. 2020. “Assessing the Effect of Temporal Dynamics on Urban Growth Simulation: Towards an Asynchronous Cellular Automata.” Transactions in GIS 24 (2): 332–354. doi:10.1111/tgis.12601.
   Web of Science ®Google Scholar
• Abouali, M., F. Daneshvar, and A. P. Nejadhashemi. 2016. “MATLAB Hydrological Index Tool (MHIT): A High Performance Library to Calculate 171 Ecologically Relevant Hydrological Indices.” Ecological Informatics 33: 17–23. doi:10.1016/j.ecoinf.2016.03.004.
   Web of Science ®Google Scholar
• Aburas, M. M., Y. M. Ho, M. F. Ramli, and Z. H. Ash’aari. 2016. “The Simulation and Prediction of Spatio-temporal Urban Growth Trends Using Cellular Automata Models: A Review.” International Journal of Applied Earth Observation and Geoinformation 52: 380–389. doi:10.1016/j.jag.2016.07.007.
   Web of Science ®Google Scholar
• Azari, M., A. Tayyebi, M. Helbich, and M. A. Reveshty. 2016. “Integrating Cellular Automata, Artificial Neural Network, and Fuzzy Set Theory to Simulate Threatened Orchards: Application to Maragheh, Iran.” Giscience & Remote Sensing 53 (2): 183–205. doi:10.1080/15481603.2015.1137111.
   Web of Science ®Google Scholar
• Azari, M., and M. A. Reveshty. 2013. “Interference of Human Impacts in Urban Growth Modelling with Transition Rules of Cellular Automata, GIS and Multi-Temporal Satellite Imagery: A Case Study of Maraghe, Iran.” Journal of the Indian Society of Remote Sensing 41 (4): 993–1008. doi:10.1007/s12524-013-0275-2.
   Web of Science ®Google Scholar
• Barredo, J. I., M. Kasanko, N. McCormick, and C. Lavalle. 2003. “Modelling Dynamic Spatial Processes: Simulation of Urban Future Scenarios through Cellular Automata.” Landscape and Urban Planning 64 (3): 145–160. doi:10.1016/S0169-2046(02)00218-9.
   Web of Science ®Google Scholar
• Barreira-Gonzalez, P., M. Gomez-Delgado, and F. Aguilera-Benavente. 2015. “From Raster to Vector Cellular Automata Models: A New Approach to Simulate Urban Growth with the Help of Graph Theory.” Computers Environment and Urban Systems 54: 119–131. doi:10.1016/j.compenvurbsys.2015.07.004.
   Web of Science ®Google Scholar
• Berberoglu, S., A. Akin, and K. C. Clarke. 2016. “Cellular Automata Modeling Approaches to Forecast Urban Growth for Adana, Turkey: A Comparative Approach.” Landscape and Urban Planning 153: 11–27. doi:10.1016/j.landurbplan.2016.04.017.
   Web of Science ®Google Scholar
• Birkbeck, N., J. Levesque, and J. N. Amaral. 2007. A Dimension Abstraction Approach to Vectorization in Matlab. International Symposium on Code Generation and Optimization (CGO'07), San Jose, CA, USA, pp. 115–130. doi:10.1109/CGO.2007.1.
   Google Scholar
• Cao, M., G. A. Tang, Q. F. Shen, and Y. X. Wang. 2015. “A New Discovery of Transition Rules for Cellular Automata by Using Cuckoo Search Algorithm.” International Journal of Geographical Information Science 29 (5): 806–824. doi:10.1080/13658816.2014.999245.
   Web of Science ®Google Scholar
• Chang, C. Y., and C. C. Ma. 2017. “Increasing the Computational Efficient of Digital Cross Correlation by a Vectorization Method.” Mechanical Systems and Signal Processing 92: 293–314. doi:10.1016/j.ymssp.2017.01.027.
   Web of Science ®Google Scholar
• Chen, J. Z., M. Q. Du, X. J. Qin, and Y. W. Miao. 2018. “An Improved Topology Extraction Approach for Vectorization of Sketchy Line Drawings.” Visual Computer 34 (12): 1633–1644. doi:10.1007/s00371-018-1549-z.
   Web of Science ®Google Scholar
• Dahal, K. R., and T. E. Chow. 2015. “Characterization of Neighborhood Sensitivity of an Irregular Cellular Automata Model of Urban Growth.” International Journal of Geographical Information Science 29 (3): 475–497. doi:10.1080/13658816.2014.987779.
   Web of Science ®Google Scholar
• Ewing, R., G. Tian, and T. Lyons. 2018. “Does Compact Development Increase or Reduce Traffic Congestion?.” CITIES 72 (A): 94–101. doi:10.1016/j.cities.2017.08.010.
   Google Scholar
• Feng, Y. J., and X. H. Tong. 2018. “Dynamic Land Use Change Simulation Using Cellular Automata with Spatially Nonstationary Transition Rules.” Giscience & Remote Sensing 55 (5): 678–698. doi:10.1080/15481603.2018.1426262.
   Web of Science ®Google Scholar
• Feng, Y. J., and X. H. Tong. 2019. “Incorporation of Spatial Heterogeneity-weighted Neighborhood into Cellular Automata for Dynamic Urban Growth Simulation.” Giscience & Remote Sensing 56 (7): 1024–1045. doi:10.1080/15481603.2019.1603187.
   Web of Science ®Google Scholar
• Feng, Y. J., Y. Liu, and M. Batty. 2016. “Modeling Urban Growth with GIS Based Cellular Automata and Least Squares SVM Rules: A Case Study in Qingpu-Songjiang Area of Shanghai, China.” Stochastic Environmental Research and Risk Assessment 30 (5): 1387–1400. doi:10.1007/s00477-015-1128-z.
   Web of Science ®Google Scholar
• Feng, Y. J., Y. Liu, X. H. Tong, M. L. Liu, and S. S. Deng. 2011. “Modeling Dynamic Urban Growth Using Cellular Automata and Particle Swarm Optimization Rules.” Landscape and Urban Planning 102 (3): 188–196. doi:10.1016/j.landurbplan.2011.04.004.
   Web of Science ®Google Scholar
• Feng, Y. L., and Y. Qi. 2018. “Modeling Patterns of Land Use in Chinese Cities Using an Integrated Cellular Automata Model.” Isprs International Journal of Geo-information 7 (10): 403. doi:10.3390/ijgi7100403.
   Web of Science ®Google Scholar
• He, H. B., and E. A. Garcia. 2009. “Learning from Imbalanced Data.” Ieee Transactions on Knowledge and Data Engineering 21 (9): 1263–1284. doi:10.1109/TKDE.2008.239.
   Web of Science ®Google Scholar
• He, J., X. Li, Y. Yao, Y. Hong, and Z. Jinbao. 2018. “Mining Transition Rules of Cellular Automata for Simulating Urban Expansion by Using the Deep Learning Techniques.” International Journal of Geographical Information Science 32 (10): 2076–2097. doi:10.1080/13658816.2018.1480783.
   Web of Science ®Google Scholar
• Kazemzadeh-Zow, A., S. Z. Shahraki, L. Salvati, and N. N. Samani. 2017. “A Spatial Zoning Approach to Calibrate and Validate Urban Growth Models.” International Journal of Geographical Information Science 31 (4): 763–782. doi:10.1080/13658816.2016.1236927.
   Web of Science ®Google Scholar
• Kennedy, J., and R. Eberhart. 1995. “Particle Swarm Optimization.” Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948 vol.4, doi:10.1109/ICNN.1995.488968.
   Google Scholar
• Kocabas, V., and S. Dragicevic. 2006. “Assessing Cellular Automata Model Behaviour Using a Sensitivity Analysis Approach.” Computers Environment and Urban Systems 30 (6): 921–953. doi:10.1016/j.compenvurbsys.2006.01.001.
   Web of Science ®Google Scholar
• Li, X., and A. G. O. Yeh. 2000. “Modelling Sustainable Urban Development by the Integration of Constrained Cellular Automata and GIS.” International Journal of Geographical Information Science 14 (2): 131–152. doi:10.1080/136588100240886.
   Web of Science ®Google Scholar
• Li, X., Y. Chen, X. Liu, X. Xu, and G. Chen. 2017. “Experiences and Issues of Using Cellular Automata for Assisting Urban and Regional Planning in China.” International Journal of Geographical Information Science 31 (8): 1606–1629. doi:10.1080/13658816.2017.1301457.
   Web of Science ®Google Scholar
• Li, X. C., X. P. Liu, and L. Yu. 2014. “A Systematic Sensitivity Analysis of Constrained Cellular Automata Model for Urban Growth Simulation Based on Different Transition Rules.” International Journal of Geographical Information Science 28 (7): 1317–1335. doi:10.1080/13658816.2014.883079.
   Web of Science ®Google Scholar
• Liang, W., and M. Yang. 2019. “Urbanization, Economic Growth and Environmental Pollution: Evidence from China.” Sustainable Computing-informatics & Systems 21: 1–9. doi:10.1016/j.suscom.2018.11.007.
   Web of Science ®Google Scholar
• Liao, J. F., L. N. Tang, G. F. Shao, and X. D. Su. 2014. “A Neighbor Decay Cellular Automata Approach for Simulating Urban Expansion Based on Particle Swarm Intelligence.” International Journal of Geographical Information Science 28 (4): 720–738. doi:10.1080/13658816.2013.869820.
   Web of Science ®Google Scholar
• Liao, J. F., L. N. Tang, G. F. Shao, X. D. Su, D. K. Chen, and T. Xu. 2016. “Incorporation of Extended Neighborhood Mechanisms and Its Impact on Urban Land-use Cellular Automata Simulations.” Environmental Modelling and Software 75 (SI): 163–175. doi:10.1016/j.envsoft.2015.10.014.
   Google Scholar
• Lin, J. Y., X. Li, S. Y. Li, and Y. Y. Wen. 2020. “What Is the Influence of Landscape Metric Selection on the Calibration of Land-use/cover Simulation Models?.” Environmental Modelling and Software 129: 104719. doi:10.1016/j.envsoft.2020.104719.
   Web of Science ®Google Scholar
• Liu, D. Y., X. Q. Zheng, H. B. Wang, C. X. Zhang, J. Y. Li, and Y. Q. Lv. 2018. “Interoperable Scenario Simulation of Land-use Policy for Beijing-Tianjin-Hebei Region, China.” Land Use Policy 75: 155–165. doi:10.1016/j.landusepol.2018.03.040.
   Web of Science ®Google Scholar
• Liu, J. Y., M. L. Liu, X. Z. Deng, and D. Luo. 2002. “The Land Use and Land Cover Change Database and Its Relative Studies in China.” Journal of Geographical Sciences 12 (3): 275–282. doi:10.1007/BF02837545.
   Google Scholar
• Liu, J. Y., W. H. Kuang, Z. X. Zhang, and W. F. Chi. 2014a. “Spatiotemporal Characteristics, Patterns, and Causes of Land-use Changes in China since the Late 1980s.” Journal of Geographical Sciences 24 (2): 195–210. doi:10.1007/s11442-014-1082-6.
   Web of Science ®Google Scholar
• Liu, J. Y., Z. X. Zhang, X. L. Xu, and N. Jiang. 2010a. “Spatial Patterns and Driving Forces of Land Use Change in China during the Early 21st Century.” Journal of Geographical Sciences 20 (4): 483–494. doi:10.1007/s11442-010-0483-4.
   Web of Science ®Google Scholar
• Liu, X. P., L. Ma, X. Li, and Z. J. He. 2014b. “Simulating Urban Growth by Integrating Landscape Expansion Index (LEI) and Cellular Automata.” International Journal of Geographical Information Science 28(1):148–163. doi:10.1080/13658816.2013.831097.
   Web of Science ®Google Scholar
• Liu, X. P., X. Li, Y. M. Chen, and B. Ai. 2010b. “A New Landscape Index for Quantifying Urban Expansion Using Multi-temporal Remotely Sensed Data.” Landscape Ecology 25(5):671–682. doi:10.1007/s10980-010-9454-5.
   Web of Science ®Google Scholar
• Liu, X. P., X. Liang, X. Li, and F. S. Pei. 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. doi:10.1016/j.landurbplan.2017.09.019.
   Web of Science ®Google Scholar
• Luo, J., and Y. H. D. Wei. 2009. “Modeling Spatial Variations of Urban Growth Patterns in Chinese Cities: The Case of Nanjing.” Landscape and Urban Planning 91 (2): 51–64. doi:10.1016/j.landurbplan.2008.11.010.
   Web of Science ®Google Scholar
• McGarigal, K., S. A. Cushman, and E. Ene. 2012. FRAGSTATS V4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Amherst: Computer software program produced by the authors at the University of Massachusetts. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html.
   Google Scholar
• Menard, A., and D. J. Marceau. 2005. “Exploration of Spatial Scale Sensitivity in Geographic Cellular Automata.” Environment and Planning B-planning & Design 32 (5): 693–714. doi:10.1068/b31163.
   Web of Science ®Google Scholar
• Moreno, N., F. Wang, and D. J. Marceau. 2009. “Implementation of a Dynamic Neighborhood in a Land-use Vector-based Cellular Automata.” Computers Environment and Urban Systems 33 (1): 44–54. doi:10.1016/j.compenvurbsys.2008.09.008.
   Web of Science ®Google Scholar
• Müller, K., C. Steinmeier, and M. Küchler. 2010. “Urban Growth along Motorways in Switzerland.” Landscape and Urban Planning 98 (1): 3–12. doi:10.1016/j.landurbplan.2010.07.004.
   Web of Science ®Google Scholar
• Mustafa, A., A. Heppenstall, H. Omrani, I. Saadi, M. Cools, and J. Teller. 2018a. “Modelling Built-up Expansion and Densification with Multinomial Logistic Regression, Cellular Automata and Genetic Algorithm.” Computers Environment and Urban Systems 67: 147–156. doi:10.1016/j.compenvurbsys.2017.09.009.
   Web of Science ®Google Scholar
• Mustafa, A., I. Saadi, M. Cools, and J. Teller. 2018b. “A Time Monte Carlo Method for Addressing Uncertainty in Land-use Change Models.” International Journal of Geographical Information Science 32 (11): 2317–2333. doi:10.1080/13658816.2018.1503275.
   Web of Science ®Google Scholar
• Omrani, H., A. Tayyebi, and B. Pijanowski. 2017. “Integrating the Multi-label Land-use Concept and Cellular Automata with the Artificial Neural Network-based Land Transformation Model: An Integrated ML-CA-LTM Modeling Framework.” Giscience & Remote Sensing 54 (3): 283–304. doi:10.1080/15481603.2016.1265706.
   Web of Science ®Google Scholar
• Poelmans, L., and A. Van Rompaey. 2009. “Detecting and Modelling Spatial Patterns of Urban Sprawl in Highly Fragmented Areas: A Case Study in the Flanders-Brussels Region.” Landscape and Urban Planning 93 (1): 10–19. doi:10.1016/j.landurbplan.2009.05.018.
   Web of Science ®Google Scholar
• Pontius, R. G., and L. C. Schneider. 2001. “Land-cover Change Model Validation by an ROC Method for the Ipswich Watershed, Massachusetts, USA.” Agriculture, Ecosystems & Environment 85 (1–3): 239–248. doi:10.1016/S0167-8809(01)00187-6.
   Web of Science ®Google Scholar
• Pontius, R. G., W. Boersma, J.-C. Castella, and P. H. Verburg. 2008. “Comparing the Input, Output, and Validation Maps for Several Models of Land Change.” Annals of Regional Science 42 (1): 11–37. doi:10.1007/s00168-007-0138-2.
   Web of Science ®Google Scholar
• Rabbani, A., H. Aghababaee, and M. A. Rajabi. 2012. “Modeling Dynamic Urban Growth Using Hybrid Cellular Automata and Particle Swarm Optimization.” Journal of Applied Remote Sensing 6 (1): 063582. doi:10.1117/1.JRS.6.063582.
   Google Scholar
• Salap-Ayca, S., P. Jankowski, K. C. Clarke, P. C. Kyriakidis, and A. Nara. 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.” International Journal of Geographical Information Science 32 (4): 637–662. doi:10.1080/13658816.2017.1406944.
   Web of Science ®Google Scholar
• Sante, I., A. M. Garcia, D. Miranda, and R. Crecente. 2010. “Cellular Automata Models for the Simulation of Real-world Urban Processes: A Review and Analysis.” Landscape and Urban Planning 96 (2): 108–122. doi:10.1016/j.landurbplan.2010.03.001.
   Web of Science ®Google Scholar
• Shabanov, B. M., A. A. Rybakov, and S. S. Shumilin. 2019. “Vectorization of High-performance Scientific Calculations Using AVX-512 Intruction Set.” Lobachevskii Journal of Mathematics 40 (5): 580–598. doi:10.1134/S1995080219050196.
   Web of Science ®Google Scholar
• Shafizadeh-Moghadam, H., A. Asghari, M. Taleai, M. Helbich, and A. Tayyebi. 2017. “Sensitivity Analysis and Accuracy Assessment of the Land Transformation Model Using Cellular Automata.” Giscience & Remote Sensing 54 (5): 639–656. doi:10.1080/15481603.2017.1309125.
   Web of Science ®Google Scholar
• Shi, Y. H., and R. Eberhart. 1998. A Modified Particle Swarm Optimizer. 1998 IEEE international conference on evolutionary computation proceedings, IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA, pp. 69–73. doi:10.1109/ICEC.1998.699146
   Google Scholar
• Silva, L. P. E., A. P. C. Xavier, R. M. da Silva, and C. A. G. Santos. 2020. “Modeling Land Cover Change Based on an Artificial Neural Network for a Semiarid River Basin in Northeastern Brazil.” Global Ecology and Conservation 21: e00811. doi:10.1016/j.gecco.2019.e00811.
   Web of Science ®Google Scholar
• Skog, K. L., and M. Steinnes. 2016. “How Do Centrality, Population Growth and Urban Sprawl Impact Farmland Conversion in Norway?.” Land Use Policy 59: 185–196. doi:10.1016/j.landusepol.2016.08.035.
   Web of Science ®Google Scholar
• Stock, K., L. N. Pouchet, and P. Sadayappan. 2012. “Using Machine Learning to Improve Automatic Vectorization.” Acm Transactions on Architecture and Code Optimization 8 (4): 50. doi:10.1145/2086696.2086729.
   Web of Science ®Google Scholar
• Tan, R. H., Y. L. Liu, Y. F. Liu, Q. S. He, L. C. Ming, and S. H. Tang. 2014. “Urban Growth and Its Determinants across the Wuhan Urban Agglomeration, Central China.” Habitat International 44: 268–281. doi:10.1016/j.habitatint.2014.07.005.
   Web of Science ®Google Scholar
• Tayyebi, A., P. C. Perry, and A. H. Tayyebi. 2014. “Predicting the Expansion of an Urban Boundary Using Spatial Logistic Regression and Hybrid Raster-vector Routines with Remote Sensing and GIS.” International Journal of Geographical Information Science 28 (4): 639–659. doi:10.1080/13658816.2013.845892.
   Web of Science ®Google Scholar
• Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (2): 234–240. doi:10.2307/143141.
   Web of Science ®Google Scholar
• Tong, X. H., and Y. J. Feng. 2019. “How Current and Future Urban Patterns Respond to Urban Planning? an Integrated Cellular Automata Modeling Approach.” CITIES 92: 247–260. doi:10.1016/j.cities.2019.04.004.
   Web of Science ®Google Scholar
• Trouve, A., A. J. Cruz, K. J. Murakami, M. Arai, T. Nakahira, and E. Yamanaka. 2016. “Guide Automatic Vectorization by Means of Machine Learning: A Case Study of Tensor Contraction Kernels.” Ieice Transactions on Information and Systems, E 99D (6): 1585–1594. doi:10.1587/transinf.2015EDP7440.
   Google Scholar
• van Vliet, J., A. K. Bregt, D. G. Brown, and P. H. Verburg. 2016. “A Review of Current Calibration and Validation Practices in Land-change Modeling.” Environmental Modelling and Software 82: 174–182. doi:10.1016/j.envsoft.2016.04.017.
   Web of Science ®Google Scholar
• Vermeiren, K., A. Van Rompaey, M. Loopmans, E. Serwajja, and P. Mukwaya. 2012. “Urban Growth of Kampala, Uganda: Pattern Analysis and Scenario Development.” Landscape and Urban Planning 106 (2): 199–206. doi:10.1016/j.landurbplan.2012.03.006.
   Web of Science ®Google Scholar
• Wahyudi, A., and Y. Liu. 2016. “Cellular Automata for Urban Growth Modelling: A Review on Factors Defining Transition Rules.” International Review for Spatial Planning and Sustainable Development 4 (2): 60–75. doi:10.14246/irspsd.4.2_60.
   Web of Science ®Google Scholar
• Wang, H. J., B. Zhang, C. Xia, S. W. He, and W. T. Zhang. 2020a. “Using a Maximum Entropy Model to Optimize the Stochastic Component of Urban Cellular Automata Models.” International Journal of Geographical Information Science 34 (5): 924–946. doi:10.1080/13658816.2019.1687898.
   Web of Science ®Google Scholar
• Wang, H. J., S. W. He, X. J. Liu, L. Dai, P. Pan, S. Hong, and W. T. Zhang. 2013. “Simulating Urban Expansion Using A Cloud-based Cellular Automata Model: A Case Study of Jiangxia, Wuhan, China.” Landscape and Urban Planning 110: 99–112. doi:10.1016/j.landurbplan.2012.10.016.
   Web of Science ®Google Scholar
• Wang, Y. W., Z. Y. Sha, X. C. Tan, H. Lan, X. F. Liu, and J. Rao. 2020b. “Modeling Urban Growth by Coupling Localized Spatio-temporal Association Analysis and Binary Logistic Regression.” Computers Environment and Urban Systems 81: 101482. doi:10.1016/j.compenvurbsys.2020.101482.
   Web of Science ®Google Scholar
• White, R., and G. Engelen. 1994. “Urban Systems Dynamics and Cellular Automata: Fractal Structures between Order and Chaos.” CHAOS SOLITONS & FRACTALS 4 (4): 563–583. doi:10.1016/0960-0779(94)90066-3.
   Web of Science ®Google Scholar
• White, R., and G. Engelen. 2000. “High-resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems.” Computers Environment and Urban Systems 24 (5): 383–400. doi:10.1016/S0198-9715(00)00012-0.
   Google Scholar
• Wu, F. L. 2002. “Calibration of Stochastic Cellular Automata: The Application to Rural-urban Land Conversions.” International Journal of Geographical Information Science 16 (8): 795–818. doi:10.1080/13658810210157769.
   Web of Science ®Google Scholar
• Wu, H., L. Zhou, X. Chi, Y. Li, and Y. R. Sun. 2012. “Quantifying and Analyzing Neighborhood Configuration Characteristics to Cellular Automata for Land Use Simulation considering Data Source Error.” Earth Science Informatics 5 (2): 77–86. doi:10.1007/s12145-012-0097-8.
   Web of Science ®Google Scholar
• Wu, H., Z. Li, K. C. Clarke, W. Z. Shi, L. C. Fang, A. Q. Lin, and J. Zhou. 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. doi:10.1080/13658816.2019.1568441.
   Web of Science ®Google Scholar
• Wu, J., M. F. Lu, Y. C. Dong, M. Zheng, M. Huang, and Y. N. Wu. 2011. “Zero-order Noise Suppression with Various Space-shifting Manipulations of Reconstructed Images in Digital Holography.” Applied Optics 50 (34): H56–H61. doi:10.1364/AO.50.000H56.
   PubMed Web of Science ®Google Scholar
• Xia, C., A. Q. Zhang, H. J. Wang, and J. F. Liu. 2020a. “Delineating Early Warning Zones in Rapidly Growing Metropolitan Areas by Integrating a Multiscale Urban Growth Model with Biogeography-based Optimization.” Land Use Policy 90: 104332. doi:10.1016/j.landusepol.2019.104332.
   Web of Science ®Google Scholar
• Xia, C., B. Zhang, H. J. Wang, S. Qiao, and A. Q. Zhang. 2020b. “A Minimum-volume Oriented Bounding Box Strategy for Improving the Performance of Urban Cellular Automata Based on Vectorization and Parallel Computing Technology.” Giscience & Remote Sensing 57 (1): 91–106. doi:10.1080/15481603.2019.1670974.
   Web of Science ®Google Scholar
• Xia, C., H. J. Wang, A. Q. Zhang, and W. T. Zhang. 2018. “A High-performance Cellular Automata Model for Urban Simulation Based on Vectorization and Parallel Computing Technology.” International Journal of Geographical Information Science 32 (2): 399–424. doi:10.1080/13658816.2017.1390118.
   Web of Science ®Google Scholar
• Xu, B., J. P. Chen, and P. P. Yu. 2017. “Vectorization of Classified Remote Sensing Raster Data to Establish Topological Relations among Polygons.” Earth Science Informatics 10 (1): 99–113. doi:10.1007/s12145-016-0273-3.
   Web of Science ®Google Scholar
• Yao, F. M., C. Hao, and J. H. Zhang. 2016. “Simulating Urban Growth Processes by Integrating Cellular Automata Model and Artificial Optimization in Binhai New Area of Tianjin, China.” Geocarto International 31 (6): 612–627. doi:10.1080/10106049.2015.1073365.
   Web of Science ®Google Scholar
• Yeh, A. G. O., and X. Li. 2001. “A Constrained CA Model for the Simulation and Planning of Sustainable Urban Forms by Using GIS.” Environment and Planning B-planning & Design 28 (5): 733–753. doi:10.1068/b2740.
   Google Scholar
• Yeh, A. G. O., and X. Li. 2002. “A Cellular Automata Model to Simulate Development Density for Urban Planning.” Environment and Planning B-planning & Design 29 (3). doi:10.1068/b1288.
   Google Scholar
• Yeh, A. G. O., and X. Li. 2006. “Errors and Uncertainties in Urban Cellular Automata.” Computers Environment and Urban Systems 30 (1): 10–28. doi:10.1016/j.compenvurbsys.2004.05.007.
   Web of Science ®Google Scholar
• Zhang, B., H. J. Wang, S. W. He, and C. Xia. 2020a. “Analyzing the Effects of Stochastic Perturbation and Fuzzy Distance Transformation on Wuhan Urban Growth Simulation.” Transactions in GIS 12683. doi:10.1111/tgis.12683.
   Web of Science ®Google Scholar
• Zhang, D. C., X. P. Liu, X. Y. Wu, and Y. M. Chen. 2019. “Multiple Intra-urban Land Use Simulations and Driving Factors Analysis: A Case Study in Huicheng, China.” Giscience & Remote Sensing 56 (2): 282–308. doi:10.1080/15481603.2018.1507074.
   Web of Science ®Google Scholar
• Zhang, Y. H., X. P. Liu, G. L. Chen, and G. H. Hu. 2020b. “Simulation of Urban Expansion Based on Cellular Automata and Maximum Entropy Model.” Science China-earth Sciences 63 (5): 701–712. doi:10.1007/s11430-019-9530-8.
   Web of Science ®Google Scholar
• Zhong, T., Y. Chen, and X. Huang. 2016. “Impact of Land Revenue on the Urban Land Growth toward Decreasing Population Density in Jiangsu Province, China.” Habitat International 58: 34–41. doi:10.1016/j.habitatint.2016.09.005.
   Web of Science ®Google Scholar
• ZiaeeVafaeyan, H., M. H. Moattar, and Y. Forghani. 2018. “Land Use Change Model Based on Bee Colony Optimization, Markov Chain and a Neighborhood Decay Cellular Automata.” Natural Resource Modeling 31 (2): e12151. doi:10.1111/nrm.12151.
   Web of Science ®Google Scholar