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 International

Volumn 30, Issue 8, 2015, Pages 858-881

  Al sharif, Abubakr A A ^a   Pradhan, Biswajeet ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

CHAID DT; GIS; remote sensing; Tripoli; urban expansion

Indexed keywords

CELLULAR AUTOMATON; GIS; MARKOV CHAIN; REMOTE SENSING; SATELLITE IMAGERY; URBAN DEVELOPMENT; URBAN GROWTH;

LIBYAN ARAB JAMAHIRIYA; TRIPOLI [LIBYAN ARAB JAMAHIRIYA];

EID: 84938416864     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049.2014.997308     Document Type: Article

References (74)

1. S.Abdullahi, B.Pradhan, M.N.Jebur. 2014. GIS-based sustainable city compactness assessment using integration of MCDM, Bayes theorem and RADAR technology. Geocarto Int. 1–23. Available from: http://dx.doi.org/10.1080/10106049.2014.91196710.1080/10106049.2014.911967
2. M.Al-Shalabi, L.Billa, B.Pradhan, S.Mansor, A.A.Al-Sharif. 2012. 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.
3. A.A.Al-sharif, B.Pradhan. 2014a. 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.
4. A.A.Al-Sharif, B.Pradhan. 2014b. Monitoring and predicting land use change in Tripoli metropolitan city using an integrated markov chain and cellular automata models in GIS. Arab J Geosci. 7:4291–4301.
5. A.A.Al-Sharif, B.Pradhan, H.Z.M.Shafri, S.Mansor. 2013. Spatio-temporal analysis of urban and population growths in Tripoli using remotely sensed data and GIS. Indian J Sci Technol. 6:5134–5142.
6. O.F.Althuwaynee, B.Pradhan, H.-J.Park, J.H.Lee. 2014. A novel ensemble decision tree-based chi-squared automatic interaction detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides. 11:1063–1078.
7. J.J.Arsanjani, W.Kainz, A.J.Mousivand. 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
8. S.Baker, R.D.Cousins. 1984. Clarification of the use of chi-square and likelihood functions in fits to histograms. Nucl Instrum Methods Phys Res. 221:437–442.10.1016/0167-5087(84)90016-4
9. J.I.Barredo, L.Demicheli. 2003. Urban sustainability in developing countries’ megacities: modelling and predicting future urban growth in Lagos. Cities. 20:297–310.10.1016/S0264-2751(03)00047-7
10. R.M.Basse, H.Omrani, O.Charif, P.Gerber, K.Bódis. 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 Geogr. 53:160–171.10.1016/j.apgeog.2014.06.016
11. N.Batisani, B.Yarnal. 2009. Urban expansion in centre county, Pennsylvania: spatial dynamics and landscape transformations. Appl Geogr. 29:235–249.10.1016/j.apgeog.2008.08.007
12. M.Batty, H.Couclelis, M.Eichen. 1997. Urban systems as cellular automata. Environ Plann B Plann Des. 24:159–164.
13. M.Batty, P.A.Longley. 1994. Fractal cities: a geometry of form and function. London(UK): Academic Press.
14. M.J.Berry, G.Linoff. 1997. Data mining techniques: for marketing, sales, and customer support. New York(NY): John Wiley & Sons.
15. M.J.Berry, G.S.Linoff. 2004. Data mining techniques: for marketing, sales, and customer relationship management. Wiley.
16. R.Bou Kheir, J.Chorowicz, C.Abdallah, D.Dhont. 2008. Soil and bedrock distribution estimated from gully form and frequency: a GIS-based decision-tree model for Lebanon. Geomorphology. 93:482–492.10.1016/j.geomorph.2007.03.010
17. R.Bou Kheir, M.H.Greve, C.Abdallah, T.Dalgaard. 2010. Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree model in Lebanon. Environ pollut. 158:520–528.10.1016/j.envpol.2009.08.009
18. H.-Y.Chen, T.-W.Hou, C.-H.Chuang. 2010. Applying data mining to explore the risk factors of parenting stress. Expert Syst Appl. 37:598–601.10.1016/j.eswa.2009.05.028
19. J.H.Cho, P.U.Kurup. 2011. Decision tree approach for classification and dimensionality reduction of electronic nose data. Sens Actuators B Chem. 160:542–548.10.1016/j.snb.2011.08.027
20. C.-J.F.Chung, A.G.Fabbri. 2003. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 30:451–472.10.1023/B:NHAZ.0000007172.62651.2b
21. K.Clarke, S.Hoppen, L.Gaydos. 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ Plann B. 24:247–261.10.1068/b240247
22. B.R.Coppedge, D.M.Engle, S.D.Fuhlendorf. 2007. Markov models of land cover dynamics in a southern Great Plains grassland region. Landsc Ecol. 22:1383–1393.10.1007/s10980-007-9116-4
23. P.N.Dadhich, S.Hanaoka. 2011. Spatio-temporal urban growth modeling of Jaipur, India. J Urban Technol. 18:45–65.10.1080/10630732.2011.615567
24. M.Debeljak, S.Džeroski. 2011. Decision trees in ecological modelling. In: F.Jopp, H.Reuter, B.Breckling, editors. Modelling complex ecological dynamics. Berlin: Springer; p. 197–209.
25. A.Eyoh, D.N.Olayinka, P.Nwilo, O.Okwuashi, M.Isong, D.Udoudo. 2012. Modelling and predicting future urban expansion of Lagos, Nigeria from remote sensing data using logistic regression and gis. Int J Appl Sci Technol. 2:116–124.
26. W.Gong, L.Yuan, W.Fan, P.Stott. 2015. Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata—Markov modelling. Int J Appl Earth Obs Geoinf. 34:207–216.10.1016/j.jag.2014.07.005
27. N.Hamdi, R.Goethert. 1998. Urban development and urban design: deciding the parameters. Urban Des Int. 3:23–31.10.1057/udi.1998.4
28. C.He, N.Okada, Q.Zhang, P.Shi, J.Zhang. 2006. Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Appl Geogr. 26:323–345.10.1016/j.apgeog.2006.09.006
29. Z.Hu, C.Lo. 2007. Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst. 31:667–688.10.1016/j.compenvurbsys.2006.11.001
30. B.Jiang, X.Yao. 2010. Geospatial analysis and modelling of urban structure and dynamics. Berlin: Springer.10.1007/978-90-481-8572-6
31. G.V.Kass. 1980. An exploratory technique for investigating large quantities of categorical data. Appl Stat. 29:119–127.10.2307/2986296
32. J.Kim, Y.Kang, S.Hong, S.Park. 2006. Extraction of spatial rules using a decision tree method: A case study in urban growth modelinged. In: Knowledge-based intelligent information and engineering systems, 10th International Conference, KES 2006; 2006 Oct 9–11; Bournemouth (UK); Proceedings, Part I. Springer; p. 203–211.
33. P.Kowe, E.Pedzisai, W.Gumindoga, D.T.Rwasoka. 2014. An analysis of changes in the urban landscape composition and configuration in the Sancaktepe District of Istanbul Metropolitan City, Turkey using landscape metrics and satellite data. Geocarto Int. 1–14. Available from: http://dx.doi.org/10.1080/10106049.2014.90563810.1080/10106049.2014.905638
34. S.Lee, I.Park. 2013. Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J Environ Manage. 127:166–176.10.1016/j.jenvman.2013.04.010
35. J.Lin, B.Huang, M.Chen, Z.Huang. 2014. Modeling urban vertical growth using cellular automata Guangzhou as a case study. Appl Geogr. 53:172–186.10.1016/j.apgeog.2014.06.007
36. C.Linard, A.J.Tatem, M.Gilbert. 2013. Modelling spatial patterns of urban growth in Africa. Appl Geogr. 44:23–32.10.1016/j.apgeog.2013.07.009
37. W.-Y.Loh, Y.-S.Shih. 1997. Split selection methods for classification trees. Stat Sin. 7:815–840.
38. P.A.Longley. 2002. Geographical information systems: will developments in urban remote sensing and GIS lead to ‘better’ urban geography? Progr Hum Geogr. 26:231–239.10.1191/0309132502ph366pr
39. S.Maithani, M.K.Arora, R.K.Jain. 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
40. I.Masser. 2001. Managing our urban future: the role of remote sensing and geographic information systems. Habitat Int. 25:503–512.10.1016/S0197-3975(01)00021-2
41. D.Mitsova, W.Shuster, X.Wang. 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc Urban Plann. 99:141–153.10.1016/j.landurbplan.2010.10.001
42. M.R.Muller, J.Middleton. 1994. A markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landsc Ecol. 9:151–157.
43. A.J.Myles, R.N.Feudale, Y.Liu, N.A.Woody, S.D.Brown. 2004. An introduction to decision tree modeling. J Chemom. 18:275–285.10.1002/(ISSN)1099-128X
44. R.Nisbet, J.Elder Iv, G.Miner. 2009. Handbook of statistical analysis and data mining applications. Amsterdam: Academic Press.
45. L.B.J.F.R.Olshen, C.J.Stone. 1984. Classification and regression trees. Belmont(CA): Wadsworth International Group.
46. M.Pal, P.M.Mather. 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ. 86:554–565.10.1016/S0034-4257(03)00132-9
47. M.Park, Y.Kim, H.-S.Lee, S.Han, S.Hwang, M.J.Choi. 2013. Modeling the dynamics of urban development project: Focusing on self-sufficient city development. Math Comput Model. 57:2082–2093.10.1016/j.mcm.2011.05.058
48. S.Park, S.Jeon, C.Choi. 2012. Mapping urban growth probability in South Korea: comparison of frequency ratio, analytic hierarchy process, and logistic regression models and use of the environmental conservation value assessment. Landsc Ecol Eng. 8:17–31.10.1007/s11355-010-0137-9
49. S.Pathan, V.Shukla, R.Patel, B.Patel, K.Mehta. 1991. Urban land use mapping: a case study of Ahmedabad city and its environs. J Indian Soc Remote Sens. 19:95–112.10.1007/BF03008125
50. A.N.Petrov, R.Sugumaran. 2005. Monitoring and modeling cropland loss in rapidly growing urban and depopulating rural counties using remotely sensed data and GIS. Geocarto Int. 20:45–52.10.1080/10106040508542363
51. B.C.Pijanowski, D.G.Brown, B.A.Shellito, G.A.Manik. 2002. Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Comput Environ Urban Syst. 26:553–575.10.1016/S0198-9715(01)00015-1
52. R.G.PontiusJr, L.C.Schneider. 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ. 85:239–248.10.1016/S0167-8809(01)00187-6
53. J.R.Quinlan. 1986. Induction of decision trees. Mach Learn. 1:81–106.
54. J.R.Quinlan. 1993. C4. 5: programs for machine learning. San Francisco(CA): Morgan Kaufmann.
55. G.Sandhya Kiran, U.B.Joshi. 2013. Estimation of variables explaining urbanization concomitant with land-use change: a spatial approach. Int J Remote Sens. 34:824–847.10.1080/01431161.2012.720738
56. L.Sang, C.Zhang, J.Yang, D.Zhu, W.Yun. 2011. Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model. 54:938–943.10.1016/j.mcm.2010.11.019
57. H.Shafizadeh Moghadam, M.Helbich. 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
58. G.Shmueli, N.R.Patel, P.C.Bruce. 2007. Data mining for business intelligence. Hoboken(NJ): Wiley.
59. S.Siedentop, S.Fina. 2010. Monitoring urban sprawl in Germany: towards a GIS-based measurement and assessment approach. J Land Use Sci. 5:73–104.10.1080/1747423X.2010.481075
60. A.Soffianian, M.A.Nadoushan, L.Yaghmaei, S.Falahatkar. 2010. Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran. World Appl Sci J. 9:1370–1378.
61. P.Swarna Latha, K.Nageswara Rao. 2011. Use of geo-spatial information and technologies for analyzing morphological changes in the Greater Visakhapatnam coastal region, east coast of India. Geocarto Int. 26:391–411.10.1080/10106049.2011.586733
62. L.Tole. 2008. Changes in the built vs. non-built environment in a rapidly urbanizing region: a case study of the Greater Toronto Area. Comput Environ Urban Syst. 32:355–364.10.1016/j.compenvurbsys.2008.08.002
63. S.Wang, X.Zheng, X.Zang. 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environ Sci. 13:1238–1245.10.1016/j.proenv.2012.01.117
64. R.White, G.Engelen. 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst. 24:383–400.10.1016/S0198-9715(00)00012-0
65. I.H.Witten, E.Frank. 2005. Data mining: practical machine learning tools and techniques. Burlington (MA): Morgan Kaufmann.
66. K.-Y.Wu, X.-Y.Ye, Z.-F.Qi, H.Zhang. 2013. Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: the case of fast-growing Hangzhou metropolitan area, China. Cities. 31:276–284.10.1016/j.cities.2012.08.003
67. G.Xian, M.Crane. 2005. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sens Environ. 97:203–215.10.1016/j.rse.2005.04.017
68. X.Yang, X.-Q.Zheng, L.-N.Lv. 2012. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Modell. 233:11–19.10.1016/j.ecolmodel.2012.03.011
69. F.Yuan. 2010. Urban growth monitoring and projection using remote sensing and geographic information systems: a case study in the Twin Cities Metropolitan Area, Minnesota. Geocarto Int. 25:213–230.10.1080/10106040903108445
70. R.Zarkami, R.Sadeghi, P.Goethals. 2012. Use of fish distribution modelling for river management. Ecol Modell. 230:44–49.10.1016/j.ecolmodel.2012.01.011
71. Q.Zhang, Y.Ban, J.Liu, Y.Hu. 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
72. X.Zhang, H.Jiang, J.Jin, X.Xu, Q.Zhang. 2012. Analysis of acid rain patterns in northeastern China using a decision tree method. Atmos Environ. 46:590–596.10.1016/j.atmosenv.2011.03.004
73. Y.Zhao, Y.Murayama. 2011. Urban dynamics analysis using spatial metrics geosimulation. In: Y.Murayama, R.B.Thapa, editors. Spatial analysis and modeling in geographical transformation process. Berlin: Springer; p. 153–167.
74. Y.Zhao, Y.Zhang. 2008. Comparison of decision tree methods for finding active objects. Adv Space Res. 41:1955–1959.10.1016/j.asr.2007.07.020