Share This Article:

A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization

Abstract Full-Text HTML Download Download as PDF (Size:3366KB) PP. 195-210
DOI: 10.4236/jgis.2011.33016    4,946 Downloads   9,843 Views   Citations

ABSTRACT

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

D. Triantakonstantis, G. Mountrakis and J. Wang, "A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization," Journal of Geographic Information System, Vol. 3 No. 3, 2011, pp. 195-210. doi: 10.4236/jgis.2011.33016.

References

[1] United Nations Population Fund, “State of the World Population 2007: Unleashing the Potential of Urban Growth,” United Nations Population Fund, United Nations Publications, 2007.
[2] United Nations Population Fund, “The State of World Population,” United Nations Population Fund, United Nations Publications, 1999.
[3] A. Rahman, Y. Kumar, S. Fazal and S. Bhaskaran, “Urbanization and Quality of Urban Environment Using Remote Sensing and GIS Techniques in East Delhi-India,” Journal of Geographic Information System, Vol. 3, No. 1, 2011, pp. 62-84. doi:10.4236/jgis.2011.31005
[4] R. M. Mendes and R. Lorandi, “Geospatial Analysis of Geotechnical Data Applied to Urban Infrastructure Planning,” Journal of Geographic Information System, Vol. 2, No. 1, 2010, pp. 23-31. doi:10.4236/jgis.2010.21006
[5] H. Briassoulis, “Analysis of Land Use Change: Theoretical and Modeling Approaches,” West Virginia University: Regional Research Institute, Morgantown, 2000. http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm
[6] J. I. Barredo, M. Kasanko, N. Mccormick and C. Lavalle, “Modelling Dynamic Spatial Processes: Simulation of Urban Future Scenarios through Cellular Automata,” Landscape and Urban Planning, Vol. 64, No. 3, 2003, pp.145-160. doi:10.1016/S0169-2046(02)00218-9
[7] Veldkamp and P. H. Verburg, “Modelling Land Use Change and Environmental Impact,” Journal of environmental management, Vol. 72, No. 1-2, 2004, pp. 1-3. doi:10.1016/j.jenvman.2004.04.004
[8] M. Batty, “Agents, Cells, and Cities: New Representational Models for Simulating Multiscale Urban Dynamics,” Environment and Planning A, Vol. 37, No. 8, 2005, pp. 1373-1394. doi:10.1068/a3784
[9] P. H. Verburg and A. Veldkamp, “Introduction to the Special Issue on Spatial Modeling to Explore Land Use Dynamics,” International Journal of Geographical Information Science, Vol. 19, No. 2, 2005, pp. 99-102. doi:10.1080/13658810410001713362
[10] J. Wu and R. Hobbs, “Key Issues and Research Priorities in Landscape Ecology: An Idiosyncratic Synthesis,” Landscape Ecology, Vol. 17, No. 4, 2002, pp. 355-365. doi:10.1023/A:1020561630963
[11] E. G. Irwin, “New Directions for Urban Economic Models of Land Use Change: Incorporating Spatial Dynamics and Heterogeneity,” Journal of Regional Science, Vol. 50, No. 1, 2010, pp. 65-91. doi:10.1111/j.1467-9787.2009.00655.x
[12] J. R. Eastman, L. A. Solorzano and M. E. Van Fossen, “Transition Potential Modeling for Land-Cover Change,” In: D. J. Maguire, M. Batty and M. F. Goodchild, Eds., GIS, Spatial Analysis, and Modeling, ESRI Press, Redlands, 2005, pp. 357-385.
[13] J. R. Eastman, “Idrisi Kilimanjaro, Manual,” Clark Labs, Clark University, Worcester, 2003.
[14] R. Aspinall, “Modelling Land Use Change with Generalized Linear Models—A Multi-Model Analysis of Change between 1860 and 2000 in Gallatin Valley, Montana,” Journal of environmental management, Vol. 72, No. 1-2, 2004, pp. 91-103. doi:10.1016/j.jenvman.2004.02.009
[15] Sebastian-Lopez, R. Salvador-Civil, J. Gonzalo-Jimenez and J. Sanmiguel-Ayanz, “Integration of Socio-Economic and Environmental Variables for Modelling Long-Term Fire Danger in Southern Europe,” European Journal of Forest Research, Vol. 127, No. 2, 2008, pp. 149-163. doi:10.1007/s10342-007-0191-5
[16] J. Allen and K. Lu, “Modeling and Prediction of Future Urban Growth in the Charleston Region of South Carolina: A GIS-Based Integrated Approach,” Ecology and Society, Vol. 8, No. 2, 2003, p. 2.
[17] P. H. Verburg, T. C. M. De Nijs, J. Ritsema Van Eck, H. Visser and K. De Jong, “A Method to Analyse Neigh- bourhood Characteristics of land Use Patterns,” Computers, Environment and Urban Systems, Vol. 28, No. 6, 2004, pp. 667-690. doi:10.1016/j.compenvurbsys.2003.07.001
[18] Z. Hu and C. P. Lo, “Modeling Urban Growth in Atlanta Using Logistic Regression,” Computers, Environment and Urban Systems, Vol. 31, No. 6, 2007, pp. 667-688. doi:10.1016/j.compenvurbsys.2006.11.001
[19] B. Huang, L. Zhang and B. Wu, “Spatiotemporal Analysis of Rural-Urban Land Conversion,” International Jour- nal of Geographical Information Science, Vol. 23, No. 3, 2009, pp. 379-398. doi:10.1080/13658810802119685
[20] N. H. Augustin, M. A. Mugglestone and S. T. Buckland, “An Autologistic Model for the Spatial Distribution of Wildlife,” Journal of Applied Ecology, Vol. 33, No. 2, 1996, pp. 339-347. doi:10.2307/2404755
[21] K. P. Overmars, G. H. J. De Koning and A. Veldkamp, “Spatial Autocorrelation in Multi-Scale Land Use Models,” Ecological Modelling, Vol. 164, No. 2-3, 2003, pp. 257-270. doi:10.1016/S0304-3800(03)00070-X
[22] C. F. Dormann, “Assessing the Validity of Autologistic Regression,” Ecological Modelling, Vol. 207, No. 2-4, 2007, pp. 234-242. doi:10.1016/j.ecolmodel.2007.05.002
[23] Getis and D. A. Griffith, “Comparative Spatial Filtering in Regression Analysis,” Geographical Analysis, Vol. 34, No. 2, 2002, pp. 130-140.
[24] J. Malczewski, “Ordered Weighted Averaging with Fuzzy Quantifiers: GIS-Based Multicriteria Evaluation for Land-Use Suitability Analysis,” International Journal of Applied Earth Observation and Geoinformation, Vol. 8, No. 4, 2006, pp. 270-277. doi:10.1016/j.jag.2006.01.003
[25] Gemitzi, V. A. Tsihrintzis, E. Voudrias, C. Petalas and G. Stravodimos, “Combining Geographic Information System, Multicriteria Evaluation Techniques and Fuzzy Logic in Siting MSW Landfills,” Environmental Geology, Vol. 51, No. 5, 2007, pp. 797-811. doi:10.1007/s00254-006-0359-1
[26] M. Zarghami and F. Szidarovszky, “Fuzzy Quantifiers in Sensitivity Analysis of OWA Operator,” Computers and Industrial Engineering, Vol. 54, No. 4, 2008, pp. 1006-1018. doi:10.1016/j.cie.2007.11.012
[27] Q. Yang, X. Li and X. Shi, “Cellular Automata for Simulating Land Use Changes Based on Support Vector Machines,” Computers and Geosciences, Vol. 34, No. 6, 2008, pp. 592-602. doi:10.1016/j.cageo.2007.08.003
[28] C. Huang, K. Song, S. Kim, J. R. G. Townshend and P. Davis, “Use of a Dark Object Concept and Support Vector Machines to Automate Forest Cover Change Analysis,” Remote Sensing of Environment, Vol. 112, No. 3, 2008, pp. 970-985. doi:10.1016/j.rse.2007.07.023
[29] T. Kuemmerle, P. Hostert, V. C. Radeloff, S. Van der Linden and K. Perzanowski, “Cross-Border Comparison of Post-Socialist Farmland Abandonment in the Carpathians,” Ecosystems, Vol. 11, No. 4, 2008, pp. 614-628. doi:10.1007/s10021-008-9146-z
[30] D. E. Goldberg, “Neighborhood Competition in an Old-Field Plant Community,” Ecology, Vol. 68, No. 5, 1987, pp. 1211-1223. doi:10.2307/1939205
[31] P. J. Burton, “Some Limitations Inherent to Static Indices of Plant Competition,” Canadian Journal of Forest Research, Vol. 23, No. 10, 1993, pp. 2141-2152. doi:10.1139/x93-267
[32] W. D'amato and K. J. Puettmann, “The Relative Dominance Hypothesis Explains Interaction Dynamics in Mixed Species Alnus Rubra/Pseudotsuga Menziesii Stands,” Journal of Ecology, Vol. 92, No. 3, 2004, pp. 450-463. doi:10.1111/j.0022-0477.2004.00888.x
[33] S. W. Simard and B. J. Zimonick, “Neighborhood Size Effects on Mortality, Growth and Crown Morphology of Paper Birch,” Forest Ecology and Management, Vol. 214, No. 1-3, 2005, pp. 251-265. doi:10.1016/j.foreco.2005.04.009
[34] C. He, N. Okada, Q. Zhang, P. Shi and J. Li, “Modelling Dynamic Urban Expansion Processes Incorporating a Potential Model with Cellular Automata,” Landscape and Urban Planning, Vol. 86, No. 1, 2008, pp. 79-91. doi:10.1016/j.landurbplan.2007.12.010
[35] S. J. Walsh, J. P. Messina, C. F. Mena, G. P. Malanson and P. H. Page, “Complexity Theory, Spatial Simulation Models, and Land Use Dynamics in the Northern Ecuadorian Amazon,” Geoforum, Vol. 39, No. 2, 2008, pp. 867-878. doi:10.1016/j.geoforum.2007.02.011
[36] Menard and D. J. Marceau, “Simulating the Impact of Forest Management Scenarios in an Agricultural Landscape of Southern Quebec, Canada, Using a Geographic Cellular Automata,” Landscape and Urban Planning, Vol. 79, No. 3-4, 2007, pp. 253-265. doi:10.1016/j.landurbplan.2006.02.016
[37] H. Couclelis, “From Cellular Automata to Urban Models: New Principles for Model Development and Implementation,” Environment and Planning B: Planning and Design, Vol. 24, No. 2, 1997, pp. 165-174. doi:10.1068/b240165
[38] R. White and G. Engelen, “High-Resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems,” Computers, Environment and Urban Systems, Vol. 24, No. 5, 2000, pp. 383-400. doi:10.1016/S0198-9715(00)00012-0
[39] X. Liu, X. Li, L. Liu, J. He and B. Ai, “A Bottom-Up Approach to Discover Transition Rules of Cellular Automata Using Ant Intelligence,” International Journal of Geographical Information Science, Vol. 22, No. 11-12, 2008, pp. 1247-1269. doi:10.1080/13658810701757510
[40] J. V. Vliet, R. White and S. Dragicevic, “Modeling Urban Growth Using a Variable Grid Cellular Automaton,” Computers, Environment and Urban Systems, Vol. 33, No. 1, 2009, pp. 35-43. doi:10.1016/j.compenvurbsys.2008.06.006
[41] F. Del Frate, F. Pacifici, G. Schiavon and C. Solimini, “Use of Neural Networks for Automatic Classification from High-Resolution Images,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 4, 2007, pp. 800-809. doi:10.1109/TGRS.2007.892009
[42] M. J. Canty, “Boosting a Fast Neural Network for Supervised Land Cover Classification,” Computers and Geosciences, Vol. 35, No. 6, 2009, pp. 1280-1295. doi:10.1016/j.cageo.2008.07.004
[43] T. Kavzoglu, “Increasing the Accuracy of Neural Network Classification Using Refined Training Data,” Environmental Modelling Software, Vol. 24, No. 7, 2009, pp. 850. doi:10.1016/j.envsoft.2008.11.012
[44] JR. R. G. Pontius, W. Boersma, J. Castella, K. Clarke and T. Nijs, “Comparing the input, output, and Validation Maps for Several Models of Land Change,” Annals of Regional Science, Vol. 42, No. 1, 2008, pp. 11-37. doi:10.1007/s00168-007-0138-2
[45] T. Lakes, D. Muller and C. Kruger, “Cropland Change in Southern Romania: A Comparison of Logistic Regressions and Artificial Neural Networks,” Landscape Ecology, Vol. 24, No. 9, 2009, pp. 1195-1206. doi:10.1007/s10980-009-9404-2
[46] B. C. Pijanowski, A. Tayyebi, M. R. Delavar and M. J. Yazdanpanah, “Urban Expansion Simulation Using Geospatial Information System and Artificial Neural Networks,” International Journal of Environmental Research, Vol. 3, No. 4, 2009, pp. 493-502.
[47] B. C. Pijanowski, D. G. Brown, B. A. Shellito and G. A. Manik, “Using Neural Networks and GIS to Forecast Land Use Changes: A Land Transformation Model,” Computers, Environment and Urban Systems, Vol. 26, No. 6, 2002, pp. 553-575. doi:10.1016/S0198-9715(01)00015-1
[48] C. E. Rizkalla and R. K. Swihart, “Forecasting the Effects of Land-Use Change on Forest Rodents in Indiana,” Environmental Management, Vol. 44, No. 5, 2009, pp. 899-908.
[49] W. Liu and K. C. Seto, “Using the ART-MMAP neural Network to Model and Predict Urban Growth: A Spatiotemporal Data Mining Approach,” Environment and Planning B: Planning and Design, Vol. 35, No. 2, 2008, pp. 296-317. doi:10.1068/b3312
[50] X. Li and A. G. Yeh, “Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems,” Environment and Planning A, Vol. 33, No. 8, 2001, pp. 1445-1462. doi:10.1068/a33210
[51] Y. Mahajan and P. Venkatachalam, “Neural Network Based Cellular Automata Model for Dynamic Spatial Modeling in GIS,” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5592, Part 1, 2009, pp. 341-352.
[52] B. C. Pijanowski, S. Pithadia, B. A. Shellito and K. Alexandridis, “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, Vol. 19, No. 2, 2005, pp. 97-215. doi:10.1080/13658810410001713416
[53] Q. Guan, L. Wang and K. C. Clarke, “An Artificial- Neural-Network-Based, Constrained CA Model for Si- mulating Urban Growth,” Cartography and Geographic Information Science, Vol. 32, No. 4, 2005, pp. 369-380. doi:10.1559/152304005775194746
[54] C. M. Almeida, J. M. Gleriani, E. F. Castejon and B. S. Soares-Filho, “Using Neural Networks and Cellular Automata for Modelling Intra-Urban Land-Use Dynamics,” International Journal of Geographical Information Science, Vol. 22, No. 9, 2008, pp. 943-963.
[55] J. C. Chan, K. Chan and A. G. Yeh, “Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms,” Photogrammetric Engineering and Remote Sensing, Vol. 67, No. 2, 2001, pp. 213-225.
[56] X. Li and C. Claramunt, “A Spatial Entropy-Based Decision Tree for Classification of Geographical Information,” Transactions in GIS, Vol. 10, No. 3, 2006, pp. 451-467. doi:10.1111/j.1467-9671.2006.01006.x
[57] X. Liu, X. Li, X. Shi, S. Wu and T. Liu, “Simulating Complex Urban Development Using Kernel-Based Non- Linear Cellular Automata,” Ecological Modelling, Vol. 211, No. 1-2, 2008, pp. 169-181. doi:10.1016/j.ecolmodel.2007.08.024
[58] X. Li and A. G. Yeh, “Analyzing Spatial Restructuring of Land Use Patterns in a Fast Growing Region Using Remote Sensing and GIS,” Landscape and Urban Planning, Vol. 69, No. 4, 2004, pp. 335-354. doi:10.1016/j.landurbplan.2003.10.033
[59] S. E. Sesnie, P. E. Gessler, B. Finegan and S. Thessler, “Integrating Landsat TM and SRTM-DEM Derived Variables with Decision Trees for Habitat Classification and Change Detection in Complex Neotropical Environments,” Remote Sensing of Environment, Vol. 112, No. 5, 2008, pp. 2145-2159. doi:10.1016/j.rse.2007.08.025
[60] J. R. Quinlan, “Probabilistic Decision Tress,” In: K. Yves and R. Michalski, Eds., Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, Vol. 3, 1983, pp. 140-152.
[61] R. I. McDonald and D. L. Urban, “Spatially Varying Rules of Landscape Change: Lessons from a Case Study,” Landscape and Urban Planning, Vol. 74, No. 1, 2006, pp. 7-20. doi:10.1016/j.landurbplan.2004.08.005
[62] J. Liu and W. W. Taylor, “Integrating Landscape Ecology into Natural Resource Management,” Cambridge University Press, Cambridge, 2002. doi:10.1017/CBO9780511613654
[63] D. G. Brown, S. Page, R. Riolo, M. Zellner and W. Rand, “Path Dependence and the Validation of Agent-Based Spatial Models of Land Use,” International Journal of Geographical Information Science, Vol. 19, No. 2, 2005, pp. 153-174. doi:10.1080/13658810410001713399
[64] J. Wang and G. Mountrakis, “Developing a Multi-Network Urbanization (MuNU) Model: A Case Study of Urban Growth in Denver, Colorado,” International Journal of Geographical Information Science, Vol. 25, No. 2, 2011, pp. 229-253. doi:10.1080/13658810903473213
[65] M. Raty and A. Kangas, “Localizing General Models with Classification and Regression Trees,” Scandinavian Journal of Forest Research, Vol. 23, No. 5, 2008, pp. 419-430. doi:10.1080/02827580802378826
[66] M. Herold, H. Couclelis and K. C. Clarke, “The Role of Spatial Metrics in the Analysis and Modeling of Urban Land Use Change,” Computers, Environment and Urban Systems, Vol. 29, No. 4, 2005, pp. 369-399. doi:10.1016/j.compenvurbsys.2003.12.001
[67] L. Porter-Bolland, E. A. Ellis and H. L. Gholz, “Land Use Dynamics and Landscape History in La Montan?a, Campeche, Mexico,” Landscape and Urban Planning, Vol. 82, No. 4, 2007, pp. 198-207.
[68] J. S. Deng, K. Wang, Y, Hong and J. G. Qi, “Spatio- Temporal Dynamics and Evolution of Land Use Change and Landscape Pattern in Response to Rapid Urbanization,” Landscape and Urban Planning, Vol. 92, No. 3-4, 2009, pp. 187-198. doi:10.1016/j.landurbplan.2009.05.001
[69] K. McGarigal, S. Tagil and S. A. Cushman, “Surface Metrics: An Alternative to Patch Metrics for the Quantification of Landscape Structure,” Landscape Ecology, Vol. 24, No. 3, 2009, pp. 433-450. doi:10.1007/s10980-009-9327-y
[70] D. Triantakonstantis and S. Barr, “A Spatial Structural and Statistical Approach to Building Classification of Residential Function for City-Scale Impact Assessment Studies,” Lecture Notes in Computer Science, Vol. 5592, 2009, pp. 221-236. doi:10.1007/978-3-642-02454-2_16
[71] Lagarias, “Fractal Analysis of the Urbanization at the Outskirts of the City: Models, Measurement and Explanation,” CyberGeo: European Journal of Geography, 2007, pp. 1-16.
[72] S. Barr and M. Barnsley, “A Region-Based, Graph-Theoretic Data Model for the Inference of Second-Order Thematic Information from Remotely-Sensed Images,” International Journal of Geographical Information Science, Vol. 11, No. 6, 1997, pp. 555-576. doi:10.1080/136588197242194
[73] K. Wu, N. Nunan, J. W. Crawford, I. M. Young and K. Ritz, “An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure,” Soil Science Society of America Journal, Vol. 68, No. 2. 2004, pp. 346-351. doi:10.2136/sssaj2004.0346
[74] J. Gong, Y. Liu and B. Xia, “Spatial Heterogeneity of Urban Land-Cover Landscape in Guangzhou from 1990 to 2005,” Journal of Geographical Sciences, Vol. 19, No. 2, 2009, pp. 213-224. doi:10.1007/s11442-009-0213-y
[75] T. G. Wade, J. D. Wickham, N. Zacarelli and K. H. Riitters, “A Multi-Scale Method of Mapping Urban Influence,” Environmental Modelling & Software, Vol. 24, No. 10, 2009, pp. 1252-1256. doi:10.1016/j.envsoft.2009.03.006
[76] M. Story and R. G. Congalton, “Accuracy Assessment: A User’s Perspective,” Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 3, 1986, pp. 397-399.
[77] R. G. Congalton, “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data,” Remote Sensing of Environment, Vol. 37, No. 1, 1991, pp. 35-46. doi:10.1016/0034-4257(91)90048-B
[78] J. Cohen, “A Coefficient of Agreement for Nominal Scales,” Educational and Psychological Measurement, Vol. 20, No. 1, 1960, pp. 37–46. doi:10.1177/001316446002000104
[79] R. Costanza, “Model Goodness of Fit: A Multiple Resolution Procedure,” Ecological Modelling, Vol. 47, No. 3-4, 1989, pp. 199-215. doi:10.1016/0304-3800(89)90001-X

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.