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Articles

Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion

Pages 217-237 | Received 02 Nov 2015, Accepted 09 Jan 2017, Published online: 14 Feb 2017

References

  • Agboola, T. (1986). City profile: Kaduna. Cities, 3, 283–352. doi: 10.1016/0264-2751(86)90069-7
  • Agunbiade, M. E., Rajabifard, A., & Bennett, R. (2012). The dynamics of city growth and the impact on urban land policies in developing countries. International Journal of Urban Sustainable Development, 4(2), 146–165. doi: 10.1080/19463138.2012.694818
  • Almeida, C. M., Gleriani, J. M., Castejon, E. F., & Soares-Filho, B. S. (2008). Using neural networks and cellular automata for modelling intra-urban land-use dynamics. International Journal of Geographical Information Science, 22(9), 943–963. doi: 10.1080/13658810701731168
  • Alonso, W. (1964). Location and land use. Cambridge, MA: Harvard University Press.
  • An, L., Linderman, M., Qi, J., Shortridge, A., & Liu, J. (2005). Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers, 95(1), 54–79. doi: 10.1111/j.1467-8306.2005.00450.x
  • Arsanjani, J. J. (2011). Dynamic land use/cover change modelling: Geosimulation and multiagent-based modelling. Berlin: Springer Science & Business Media.
  • Arsanjani, J. J., Helbich, M., & Vaz, E. D. N. (2013). Spatiotemporal simulation of urban growth patterns and modeling: The case of Tehran. Cities, 32, 33–42. doi: 10.1016/j.cities.2013.01.005
  • 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. doi: 10.1080/19479832.2011.605397
  • de la Barra, T. (1989). Integrated land use and transport modelling: Decision chains and hierarchies. Cambridge: Cambridge University Press.
  • Barredo, J. I., Demicheli, L., Lavalle, C., Kasanko, M., & McCormick, N. (2004). Modelling future urban scenarios in developing countries: An application case study in Lagos, Nigeria. Environment and Planning B: Planning and Design, 31(1), 65–84. doi: 10.1068/b29103
  • Barredo, J. L., Kasanko, M., McCormick, N., & Lavalle, C. (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
  • Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning and Design, 21, S31–S48. doi: 10.1068/b21S031
  • Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233. doi: 10.1016/S0198-9715(99)00015-0
  • Brail, R. K., & Klosterman, R. E. (2001). Planning support systems: Integrating geographic information systems, models, and visualization tools. Redlands, CA: Esri Press.
  • Brown, D. G., Walker, R., Manson, S., & Seto, K. C. (2004). Modeling land cover and land use change. In G. Gutman, A. C. Janetos, C. O. Justice, E. F. Moran, J. F. Mustard, R. R. Rindfuss, … B. L. Turner II (Eds.), Land change science: Observing, monitoring, and understanding trajectories of change on the earth’s surface (pp. 395–409). Norwell, MA: Kluwer.
  • Brown, D. G., Walker, R., Manson, S., Seto, K. C., Verburg, P., Kok, K., … Veldkamp, A. (2006). Modeling land-use and land-cover change. In E. F. Lambin & H. Geist (Eds.), Land-use and land-cover change (pp. 117–35). Berlin: Springer.
  • Bununu, Y. A., Ludin, A. N. M., & Hosni, N. (2015). City profile: Kaduna. Cities, 49, 53–65. doi: 10.1016/j.cities.2015.07.004
  • Chen Zeng, M. Z., Cui, J., & He, S. (2015). Monitoring and modeling urban expansion – A spatially explicit and multi-scale perspective. Cities, 43, 92–103. doi: 10.1016/j.cities.2014.11.009
  • Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7), 699–714. doi: 10.1080/136588198241617
  • Couclelis, H. (1985). Cellular worlds: A framework for modeling micro – Macro dynamics. Environment and Planning A, 17, 585–596. doi: 10.1068/a170585
  • Couclelis, H. (1987). Cellular dynamics: How individual decisions lead to global urban change. European Journal of Operational Research, 30, 344–346. doi: 10.1016/0377-2217(87)90080-4
  • Cullingworth, J. B., Cherry, G. E., Sheail, J., Wannop, U., Ravetz, A., Hall, P., … Cullingworth B. (1994). Fifty years of post-war planning. Town Planning Review, 65, 277–290. doi: 10.3828/tpr.65.3.kg916n32571085r4
  • Dubovyk, O., Sliuzas, R., & Flacke, J. (2011). Spatio-temporal modelling of informal settlement development in Sancaktepe district, Istanbul, Turkey. ISPRS Journal of Photogrammetry and Remote Sensing, 66(2), 235–246. doi: 10.1016/j.isprsjprs.2010.10.002
  • Eastman, J. R. (2014). Idrisi TerrSet 18.00. Worcester, MA: Clark University.
  • Eastman, J. R., Van Fossen, M. E., & Solorzano, L. A. (2005). Transition potential modeling for land cover change. In D. J. Maguire, M. Batty, & M. F. Goodchild (Eds.), GIS, spatial analysis and modeling (pp. 357–386). Redlands, CA: ESRI Press.
  • Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.
  • Ettema, D., de Jong, K., Timmermans, H., & Bakema, A. (2007). PUMA: Multi-agent modelling of urban systems. 45th Congress of the European Regional Science Association, 23–27 August 2005, Vrije Universiteit, Amsterdam.
  • Fischer, M. M. (2006). Computational neural networks – Tools for spatial data analysis. In M. M. Fischer (Ed.), Spatial analysis and geocomputation (pp. 79–102). Berlin: Springer.
  • Fix, E., & Hodges, J. L. (1989). Discriminatory analysis. Nonparametric discrimination: Consistency properties. International Statistical Review/Revue Internationale de Statistique, 57(3), 238–247.
  • Gahegan, M. (2000). On the application of inductive machine learning tools to geographical analysis. Geographical Analysis, 32(1), 113–139.
  • Geoghegan, J., Schneider, L., & Vance, C. (2004). Temporal dynamics and spatial scales: Modeling deforestation in the Southern Yucatán Peninsular region. GeoJournal, 61(4), 353–363. doi: 10.1007/s10708-004-5052-x
  • Gong, Z., Tang, W., & Thill, J.-C. (2012). Parallelization of ensemble neural networks for spatial land-use modeling. Proceedings of the 5th international workshop on location-based social networks – LBSN ‘12, 48. New York, NY: ACM Press.
  • Gong, Z., Thill, J. C., & Liu, W. (2015). ART-P-MAP neural networks modeling of land-use change: Accounting for spatial heterogeneity and uncertainty. Geographical Analysis, 47(4), 376–409. doi: 10.1111/gean.12077
  • Haruna, M. (2012). A brief history of Kaduna: The city of crocodiles. Kaduna: People and Politics.
  • He, C., Okada, N., Zhang, Q., Shi, P., & Zhang, J. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Applied Geography, 26(3), 323–345. doi: 10.1016/j.apgeog.2006.09.006
  • Huang, B., Zhang, L., & Wu, B. (2009). Spatiotemporal analysis of rural–urban land conversion. International Journal of Geographical Information Science, 23(3), 379–398. doi: 10.1080/13658810802119685
  • Hunt, J. D., & Simmonds, D. C. (1993). Theory and application of an integrated land-use and transport modelling framework. Environment and Planning B: Planning and Design, 20(2), 221–244. doi: 10.1068/b200221
  • Irwin, E. G., & Geoghegan, J. (2001). Theory, data, methods: Developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85(1–3), 7–24. doi: 10.1016/S0167-8809(01)00200-6
  • Kaduna State Government. (1985). 1963 census population and projections from 1978–1985 by districts and local government council areas. M. o. E. D. Statistics division. Kaduna: Statistics Division, Ministry of Economic Development.
  • Karolien, V., Anton, V., Maarten, L., Eria, S., & Paul, M. (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
  • Lambin, E. F., Rounsevell, M. D. A., & Geist, H. J. (2000). Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems & Environment, 82(1–3), 321–331. doi: 10.1016/S0167-8809(00)00235-8
  • Landis, I. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. doi: 10.2307/2529310
  • Li, L., Sato, Y., & Zhu, H. (2003). Simulating spatial urban expansion based on a physical process. Landscape and Urban Planning, 64(1–2), 67–76. doi: 10.1016/S0169-2046(02)00201-3
  • Li, X., & Yeh, A. G. O. (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
  • Li, X., & Yeh, A. G.-O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343. doi: 10.1080/13658810210137004
  • Lin, Y., Chu, H., Wu, C., & Verburg, P. H. (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – A case study. International Journal of Geographical Information Science, 25(1), 65–87. doi: 10.1080/13658811003752332
  • Liu, Y., & Phinn, S. R. (2003). Modelling urban development with cellular automata incorporating fuzzy-set approaches. Computers, Environment and Urban Systems, 27(6), 637–658. doi: 10.1016/S0198-9715(02)00069-8
  • 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. doi: 10.1068/b3312
  • Loibl, W., & Toetzer, T. (2003). Modeling growth and densification processes in suburban regions – Simulation of landscape transition with spatial agents. Environmental Modelling & Software, 18(6), 553–563. doi: 10.1016/S1364-8152(03)00030-6
  • 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. doi: 10.1016/S0169-2046(01)00160-8
  • Mas, J. F., Puig, H., Palacio, J. L., & Sosa-López, A. (2004). Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software, 19(5), 461–471. doi: 10.1016/S1364-8152(03)00161-0
  • Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22(10), 1447–1459. doi: 10.1007/s10980-007-9135-1
  • Miller, E. J., Hunt, J. D., Abraham, J. E., & Salvini, P. A. (2004). Microsimulating urban systems. Computers, Environment and Urban Systems, 28(1–2), 9–44. doi: 10.1016/S0198-9715(02)00044-3
  • Mitchell, M., Crutcheld, J. P., & Das, R. (1996). Evolving cellular automata with genetic algorithms: A review of recent work. Paper presented at the the first international conference on evolutionary computation and its applications (EvCA’96), Moscow, Russia.
  • Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. doi: 10.1016/j.apgeog.2013.01.009
  • Murakami, A., Zain, A. M., Takeuchi, K., Tsunekawa, A., & Yokota, S. (2005). Trends in urbanization and patterns of land use in the Asian mega cities Jakarta, Bangkok, and Metro Manila. Landscape and Urban Planning, 70(3), 251–259. doi: 10.1016/j.landurbplan.2003.10.021
  • Orishimo, I. (2010). An approach to urban dynamics. Geographical Analysis, 19(3), 200–210. doi: 10.1111/j.1538-4632.1987.tb00125.x
  • Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337. doi: 10.1111/1467-8306.9302004
  • Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using neural networks and GIS to forecast land use changes: A land transformation model. Computers, Environment and Urban Systems, 26, 553–575. doi: 10.1016/S0198-9715(01)00015-1
  • Pijanowski, B. C., Pithadia, S., Shellito, B. A., & Alexandridis, K. (2005). Calibrating a neural network-based urban change model for two metropolitan areas of the upper Midwest of the United States. International Journal of Geographical Information Science, 19(2), 197–215. doi: 10.1080/13658810410001713416
  • Poelmans, L., & Van Rompaey, A. (2010). Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34(1), 17–27. doi: 10.1016/j.compenvurbsys.2009.06.001
  • Pontius, R. G., Jr., Cornell, J., & Hall, C. A. S. (2001). Modeling the spatial pattern of land-use change with GEOMOD2: Application and validation for Costa Rica. Agriculture, Ecosystems and Environment, 85, 191–203. doi: 10.1016/S0167-8809(01)00183-9
  • Pontius, R. G., Jr., Huffaker, D., & Denman, K. (2004). Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179(4), 445–461. doi: 10.1016/j.ecolmodel.2004.05.010
  • Pontius, R. G., Jr., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachussetts, USA. Agriculture Ecosystems & Environment, 85(1–3), 239–248. doi: 10.1016/S0167-8809(01)00187-6
  • Putman, S. H. (1998). Results from implementation of integrated transportation and land use models in metropolitan regions. In L. Lundqvist, L.-G. Mattsson, & T. J. Kim (Eds.), Network infrastructure and the urban environment (pp. 268–287). Berlin: Springer.
  • Sanders, L., Pumain, D., Mathian, H., Guérin-Pace, F., & Bura, S. (1997). SIMPOP: A multiagent system for the study of urbanism. Environment and Planning B: Planning and Design, 24(2), 287–305. doi: 10.1068/b240287
  • Sangermano, F., Eastman, J. R., & Zhu, H. (2010). Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling. Transactions in GIS, 14(5), 569–580. doi: 10.1111/j.1467-9671.2010.01226.x
  • Soares-Filho, B. S., Pennachin, C. L., & Cerqueira, G. (2002). DINAMICA: A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154, 217–235. doi: 10.1016/S0304-3800(02)00059-5
  • Toffoli, T., & Margolus, N. (1987). Cellular automata machines: A new environment for modeling. Cambridge, MA: MIT Press.
  • Torrens, P. M. (2000). How cellualar models of urban systems work. ( 1. Theory). London: Centre for Advanced Spatial Analysis, University College London.
  • Torrens, P. M. (2006). Geosimulation and its application to urban growth modelling. Complex artificial environments. Berlin: Springer.
  • Torrens, P. M., & O’Sullivan, D. (2001). Cellular automata and urban simulation: Where do we go from here? Environment and Planning B: Planning and Design, 28, 163–168. doi: 10.1068/b2802ed
  • Tsang, S., & Leung, Y. (2011). A theory-based cellular automata for the simulation of land-use change. Geographical Analysis, 43(2), 142–171. doi: 10.1111/j.1538-4632.2011.00817.x
  • Turner II, B. (2009). Land change science. International encyclopedia of human geography. a. N. T. R. Kitchen (pp. 107–111). Oxford: Elsevier.
  • UNDESA. (2009). World urbanization prospects: The 2009 revision. New York, NY: Department of Economic and Social Affairs, United Nations Organization.
  • Van Schrojenstein Lantman, J., Verburg, P., Bregt, A., & Geertman, S. (2011). Core principles and concepts in land-use modelling: A literature review. In E. Koomen & J. Borsboom-van Beurden (Eds.), Land-use modelling in planning practice (pp. 35–57). Dordrecht: Springer.
  • Verburg, P. H., de Koning, G. G. J., Kok, K., Veldkamp, A., & Bouma, J. (1999). A spatial explicit allocation procedure for modeling the pattern of land use change based on actual land use. Ecological Modelling, 116, 45–61. doi: 10.1016/S0304-3800(98)00156-2
  • Verburg, P. H. P., Schot, P. P. P., Dijst, M. M. J., & Veldkamp, A. (2004). Land use change modelling: current practice and research priorities. GeoJournal, 61(4), 309–324. doi: 10.1007/s10708-004-4946-y
  • Von Thünen, J. H. (1966). Isolated state: An English edition of der isolierte staat. New York, NY: Pergamon Press.
  • Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314. doi: 10.1080/01944360208976274
  • Wegener, M., Mackett, R., & Simmonds, D. (1991). One city, three models: Comparison of land-use/transport policy simulation models for Dortmund. Transport Reviews, 11(2), 107–129. doi: 10.1080/01441649108716778
  • White, R., & Engelen, G. (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
  • Wilson, A. (2010). Entropy in urban and regional modelling: Retrospect and prospect. Geographical Analysis, 42, 364–394. doi: 10.1111/j.1538-4632.2010.00799.x
  • Wu, F., & Webster, C. J. (1998). Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B: Planning and Design, 25, 103–126. doi: 10.1068/b250103
  • Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75(1), 69–80. doi: 10.1016/j.landurbplan.2004.12.005
  • Xie, Y. (1996). A generalized model for cellular urban dynamics. Geographical Analysis, 28(4), 350–373. doi: 10.1111/j.1538-4632.1996.tb00940.x
  • Yeh, A. G. O., & Li, X. (2001). A constrained CA model for the simulation and planning of sustainable urban forms by using GIS. Environment and Planning B: Planning and Design, 28, 733–753. doi: 10.1068/b2740

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