在全球化時代下，城市區域空間組織的形成，將促進都市間相互合作以提高整體城市區域的競爭力，台灣因應這股趨勢走向區域空間下整合各都市之方向，提出行政區域重劃政策，進行縣市合併之計畫，進而引發縣市合併後該投入何種空間發展策略，以求發揮縣市合併所創造的空間優勢的議題，同時，情境模擬被認為是以一種有組織的方式探索未來各種可能性之方法，通常透過數理模型進行定量的模擬，因此，利用模型預測城市區域未來發展及投入各種空間策略以測試影響情形實有必要，另一方面，大眾運輸導向發展(Transit-Oriented Development，簡稱TOD)被視為串連整體區域空間，創造地區再發展之重要規劃手段，過去相關研究已證實大眾運輸場站周邊交通與土地使用的整合有利於都市發展，然而，這類研究多以單一場站為實證案例，難以了解整個大眾運輸系統在TOD的概念下，對於區域空間系統性的影響。
爰此，本研究嘗試建構一套以城市區域為空間範圍的模型，以細胞自動機為基礎，模擬未來投入大眾運輸導向發展政策下之土地使用發展形態的改變。為提升模擬的精確度，本研究將細胞自動機結合馬可夫鏈與多準則評估建立模型，並藉由相關分析的數值來調整土地使用變遷影響因子之間的權重以達到模型校準的效果，使模型對於預測未來發展具有可信的解釋力。本研究進而設計出未考慮TOD的自然發展情境、以及依鐵路各車站周邊高強度之發展的TOD情境，投入模型模擬不同情境下未來發展的情形，模擬結果顯示，投入大眾運輸導向發展之成長策略後，在新台南市範圍內之各台鐵站周邊土地的住宅、商業使用都有顯著的增加，對於原先在自然發展情境下郊區的都市蔓延情形則略有減緩，這樣的發展形態使台南市在空間結構上更為緊密、集中，減少郊區破碎化的發展，對於縣市合併後未來整體的空間規劃是一個理想的發展方向。

In the age of globalization, the form of city-region will improve the cooperation of inter-city in order to enhance the competitiveness of the whole city-region. Taiwan is also facing the cooperation among cities under the scope of region. By administrative region rezoning policy, the cities and counties are going to be consolidated. And it bring out an issue of what kind of spatial development strategy should be incorporate into City-Region for evoking the advantages of whole place. Meanwhile, scenario simulation is thought to be an organized way to scan the possibilities of future, and usually through quantitative modeling by mathematical model. Therefore, it is necessary to predict the future development of City-Region and test varied spatial strategic by model. On the other hand, Transit-Oriented Development (TOD) was seen as the way of connecting the whole Region and can also create an opportunity for local redevelopment. Past studies have proof that the combination of transportation and land use near of Public transit station will be good for urban development, however, those studies usually used only a single station as study cases, and it hard to understand the impact of all of TOD stations to Region.
Therefore, this paper set City-Region as scope, used Cellular Automata to simulate the change of land use development pattern in future after incorporating TOD strategies. For increasing accuracy of simulation, this paper combined Markov Chain and Multi-Criteria Evaluation with Cellular Automata, and tested different weights of factors for model calibration which proved the ability of the model. Non-TOD scene and intensive grow near Rail-stations to create TOD scenes then incorporate into model for modeling the future development by different scenes. The results showed that after the incorporated of TOD strategies in Taiwan Railway scope of Tainan City, the area of residential and commercial both obviously expand a lot, compared with "Natural Growing Scene" it also slow urban sprawl on suburbs. These kinds of development formed Tainan City more compact and intensive, degrading the fragmentation development of Suburbs. It is an ideal way for future spatial planning after the consolidation of cities and counties.

1. 丁志堅（1997），運用馬可夫鏈模式度量土地利用變遷之研究，「台灣大學地理學系研究所碩士論文」。
2. 王一帆（2006），捷運沿線土地使用變遷之影響因素分析-台北捷運板南線之實證研究，「交通大學交通運輸研究所碩士論文」。
3. 內政部地政司(1995)，民國84年台灣地區國土利用現況調查報告書，「國土利用調查成果資訊網」，http://lui.nlsc.gov.tw/LUWeb/，(2011年5月15日)。
4. 內政部國土測繪中心（2008），國土利用分類說明，「國土利用調查成果資訊網」，http://lui.nlsc.gov.tw/LUWeb/，(2011年5月15日)。
5. 行政院主計處（2010），民國99年人力資源調查統計年報，「行政院主計處網站」，http://www.dgbas.gov.tw/mp.asp?mp=1，(2011年5月15日)。
6. 交通部臺灣鐵路管理局（2011），鐵路統計專有名詞-運務類，「交通部臺灣鐵路管理局網站」，http://www.railway.gov.tw/tw/CP.aspx?sn=7474，(2011年5月15日)。
7. 交通部臺灣鐵路管理局（2011），運務段/車站專區，「交通部臺灣鐵路管理局網站」，http://www.railway.gov.tw/tw/CP.aspx?sn=3734&n=6872，(2011年5月15日)。
8. 何春陽、史培軍、陳晉、潘耀忠、李曉兵、李京、李月臣、李景剛（2005），基於系統動力學模型和元胞自動機模型的土地利用情景模型研究，「中國科學D輯」，第35卷，第5期，第464-473頁。
9. 何錦杭（2006），一個以CA為基礎之永續城鄉土地劃設支援模式，「成功大學都市計劃研究所碩士論文」。
10. 何東波（2008），財稅及交易成本與土地使用變遷之互動影響分析，「國科會研究計畫」。
11. 李怡婷（2005），大眾運輸導向發展策略對捷運站區房地產價格之影響分析，「成功大學都市計劃研究所碩士論文」。
12. 李家儂、賴宗裕（2005），台灣地區大眾運輸導向發展之落實－借鏡美國的實施經驗，「都市交通季刊」，第20卷，第3期，第1-16頁。
13. 李家儂（2006），交通運輸與土地使用整合規劃之演變～大眾運輸導向發展的都市發展模式，「土地問題研究季刊」，第5卷，第3期，第70-83頁。
14. 李家儂（2008），都市交通與發展從混亂到共和的經驗，「土地問題研究季刊」，第7卷，第1期，第53-64頁。
15. 李家儂、賴宗裕（2009），交通運輸與土地使用連結下的都市模式演變－全球36個主要城市比較分析，「都市與計劃」，第36卷，第1期，第25-49頁。
16. 李森、鄧衛、陳敬敏（2007），TOD成效影響分析與評價策略研究，「道路交通與安全」，第7卷，第4期，第28-31頁。
17. 吳振發（2004），農地轉用方案之地景生態效益評估，「經社法制論叢」，第33期，第45-85頁。
18. 宋颺、王士君、馮章獻（2007），東北地區城市群組城市流強度研究，「東北師大學報（自然科學版）」，第39卷，第1期，第114-118頁。
19. 尚正永、毛廣雄（2007），國內外城市區域的研究進展，「世界地理研究」，第16卷，第2期，第10-16頁。
20. 林士弘（2002），結合宮格自動機與地理資訊系統在台北盆地土地使用變遷模擬之研究，「臺灣大學土木工程學研究所碩士論文」。
21. 林峰田、林佩憓（2005），結合宮格自動機與地理資訊系統在台北盆地土地使用變遷模擬之研究，「規劃學報」，第32期，第1-12頁。
22. 林楨家、施亭伃（2007），大眾運輸導向發展之建成環境對捷運運量之影響─臺北捷運系統之實證研究，「運輸計劃季刊」，第36卷，第4期，第451-476頁。
23. 林裕彬、朱宏杰、吳振發（2011）土地使用變遷模式回顧與比較，「國土資訊系統通訊」，第77期，第46-53頁。
24. 林嘉慧（2008），大眾運輸導向發展引導都市空間再造之策略研究，「中國文化大學建築及都市計畫研究所碩士論文」。
25. 胡怡鴦（2002），台北捷運營運前後發展核心之變化，「國立台北大學都市計畫學系碩士論文」。
26. 陳勝智（2001），以大眾運輸導向發展理念進行車站地區都市再發展之探討，「成功大學都市計畫研究所碩士論文」。
27. 張曜麟（2005），都市土地使用變遷之研究，「成功大學都市計劃研究所博士論文」。
28. 張華、張勃（2005），國際土地利用/覆蓋變化模型研究綜述，「自然資源學報」，第20卷，第3期，第422-431頁。
29. 張政亮、張瑞津（2006），運用馬可夫鏈模型與細胞自動機理論模擬植生復育之研究：以九九峰地區為例，「地理研究」第45期，第123-142頁。
30. 張笛箏（2007），捷運站區土地使用變遷之模擬分析-以台北捷運木柵線為例，「交通大學交通運輸研究所碩士論文」。
31. 張學孔、呂英志（2009），大眾運輸導向發展下運輸系統技術方案適用性之比較研究，「都市與計劃」，第22卷，第1期，第51-79頁。
32. 張智凱（2007），G-Norm: 自動化PubMed 助理以兩階段基因名稱正規化方法，「成功大學資訊工程學系碩士論文」。
33. 許澤仁（2009），智慧型成長管理政策對於都市發展影響之情境模擬研究-以嘉義市為例，「成功大學都市計劃研究所碩士論文」。
34. 馮邦彥、彭嵐（2009），廣東經濟空間結構演變及優化，「廣東商學院學報」，第24卷，第6期，第84-89頁。
35. 新台南市政府 （2011），臺南市議會第一屆第一次定期大會「施政總報告」草案，「新台南市政府網站」，http://www.tainan.gov.tw/tainan/default.asp ，(2011年5月15日)。
36. 鄒克萬、郭幸福、林漢彬（2005），一個以GIS 為基礎之城鄉發展區劃設方法，「規劃學報」，第32期，第45-85頁。
37. 廖怡雯（2002），運用馬可夫鏈模式於台中市土地利用變遷之研究，「逢甲大學土地管理所碩士論文」。
38. 蔡佩璇（2009），土地使用分區管制對都市土地使用變遷之影響，「成功大學都市計劃研究所碩士論文」。
39. 薛華培（2005），軌道交通與我國大城市的空間結構優化，「城市交通」，第3卷，第4期，第39-43頁。
40. 顏子揚（2006），捷運沿線土地使用變遷模擬模式之建構與應用，「交通大學交通運輸研究所碩士論文」。
41. 簡龍鳳、賴宗裕（2007），捷連場站毗鄰地區空間結構理論模型之建立，「運輸計劃季刊」，第97期，第43-51頁。
42. 舊台南縣政府、舊台南市政府（2009），臺南縣市合併改制直轄市改制計畫書，「臺南市議會資訊網」，http://www.tncc.gov.tw/default.asp，(2010年9月10日)。
43. 舊台南縣政府 （2005），台南縣整體交通路網計畫，「舊台南縣政府網站」，http://www9.tainan.gov.tw/cp/10133/tou_6.aspx，(2011年5月15日)。
44. 舊台南市政府 （2010），第十六屆第十次定期大會施政總報告-重大建設計畫，「舊台南市政府網站」，http://www2.tncg.gov.tw ，(2011年5月15日)。
45. 藺卿、羅格平、陳曦（2005），LUCC驅動力模型研究綜述，「地理科學進展」，第24卷，第5期，第79-87頁。
46. 顧嘉安（2010），以馬可夫鍊細胞自動機模型模擬極端洪水對都市土地利用型態之影響─以台北市為例，「成功大學都市計劃研究所碩士論文」。
47. Al-Ahmadi, K., See, L., Heppenstall, A. and Hogg, J. (2009). Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia, Ecological Complexity, 6(2):80-101.
48. Almeida, C. M., Monteiro, A. M. V., Câmara, G., Soares-Filho, B. S., Cerqueira, G. C., Pennachin, C. L. and Batty, M. (2005). GIS and remote sensing as tools for the simulation of urban land-use change, International Journal of Remote Sensing, 26(4):759 - 774.
49. Asgary, A., Klosterman, R. and Razani, A. (2007). Sustainable Urban Growth Management Using What-If?, International Journal of Environmental Research, 1(3):218-230.
50. Atkinson-Palombo, C. and Kuby, M. J. (2011). The geography of advance transit-oriented development in metropolitan Phoenix, Arizona, 2000-2007, Journal of Transport Geography, 19(2):189-199.
51. Bae, C.-H. C., Jun, M.-J. and Park, H. (2003). The impact of Seoul's subway Line 5 on residential property values, Transport Policy, 10(2):85-94.
52. Bailey, D., Goonetilleke, A. and Campbell, D. A. (2003). A new fuzzy multi-criteria evaluation method for group site selection in GIS, Journal of Multi-Criteria Decision Analysis, 12(6):337-347.
53. Barredo, J. I., Demicheli, L., Lavalle, C., Kasanko, M. and 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.
54. Barredo, J. I., Kasanko, M., McCormick, N. and Lavalle, C. (2003). Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata, Landscape and Urban Planning, 64(3):145-160.
55. Batty, M., Xie, Y. and Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata, Computers, Environment and Urban Systems, 23(3):205-233.
56. Calthorpe, P. (1993). The Next American Metropolis: Ecology, Community, and the American Dream: Princeton Architectural Press.
57. Cervero, R. and Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design, Transportation Research Part D: Transport and Environment, 2(3):199-219.
58. Cervero, R., Murphy, S., Ferrell, C., Goguts, N., Tsai, Y. H., Arrington, G.B., Boroski, J., Smith-Heimer, J., Golem, R., Peninger, P., Nakajima, E., Chui, E., Dunphy, R., Myers, M., McKay, S. and Witenstein, N. (2004). Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects. Transit Cooperative Research Program (TCRP) Report 102, Washington, D.C.: Transportation Research Board.
59. Cheng, J. and Masser, I. (2004). Understanding spatial and temporal processes of urban growth: cellular automata modelling, Environment and Planning B: Planning and Design, 31(2):167-194.
60. City of Austin.(2006). TOD Guidebook, City of Austin.
61. City and County of Denver. (2006) Transit-Oriented Development Strategic Plan, Community Planning and Dvelopment, Denver, Colorado.
62. Clarke, K. C. and Gaydos, L. (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.
63. Clarke, K. C., Hoppen, S. and Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area, Environment and Planning B: Planning and Design, 24(2):247-261.
64. Couclelis, H. (1985). Cellular worlds: a framework for modeling micro - macro dynamics, Environment and Planning A, 17(5):585-596.
65. Davoudi, S. (2008). Conceptions of the city-region: a critical review, Proceedings of the ICE - Urban Design and Planning, 161(2):51-60.
66. Djenaliev, A. (2007). Multicriteria decision making and GIS for railroad planning in Kyrgyzstan. Europe, (May), 60.
67. Dock, F. and Swenson, C. (2003). Transit-Oriented Urban Design Impacts on Suburban Land Use and Transportation Planning, [10.3141/1831-21], Transportation Research Record: Journal of the Transportation Research Board, 1831(-1):184-192.
68. Eastman, J. R. (2006). IDRISI Andes guide to GIS and image processing, Clark Labs, Clark University, Worcester, MA.
69. Evans, T. P., Manire, A., de Castro, F., Brondizio, E. and McCracken, S. (2001). A dynamic model of household decision-making and parcel level landcover change in the eastern Amazon, Ecological Modelling, 143(1-2):95-113.
70. Fan, F., Wang, Y. and Wang, Z. (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images, Environmental Monitoring and Assessment, 137(1-3):127-147.
71. Fonstad, M. A. (2006). Cellular automata as analysis and synthesis engines at the geomorphology-ecology interface, Geomorphology, 77(3-4):217-234.
72. Fontaine, C. and Rounsevell, M. (2009). An agent-based approach to model future residential pressure on a regional landscape, Landscape Ecology, 24(9):1237-1254.
73. Fuller, D. O., Meijaard, E. M., Christy, L. and Jessup, T. C. (2010). Spatial assessment of threats to biodiversity within East Kalimantan, Indonesia, Applied Geography, 30(3):416-425.
74. Hall, P. (2009). Looking Backward, Looking Forward: The City Region of the Mid-21st Century, Regional Studies, 43(6):803-817.
75. Han, J., Hayashi, Y., Cao, X. and Imura, H. (2009). Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China, Landscape and Urban Planning, 91(3):133-141.
76. He, C., Okada, N., Zhang, Q., Shi, P. and Li, J. (2008). Modelling dynamic urban expansion processes incorporating a potential model with cellular automata, Landscape and Urban Planning, 86(1):79-91.
77. He, C., Okada, N., Zhang, Q., Shi, P. and Zhang, J. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China, Applied Geography, 26(3-4):323-345.
78. Iovine, G., D'Ambrosio, D. and Di Gregorio, S. (2005). Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects, Geomorphology, 66(1-4):287-303.
79. Irwin, E. G. and Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change, Agriculture, Ecosystems & Environment, 85(1-3):7-24.
80. Jankowski, P. (1995). Integrating geographical information systems and multiple criteria decision-making methods, International Journal of Geographical Information Systems, 9(3):251 - 273.
81. Jiang, H. and Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS, International Journal of Geographical Information Science, 14(2):173-184.
82. Kaimowitz, D. and Angelsen, A. (1998). Economic models of tropical deforestation: a review. Bogor, Indonesia: CIFOR.
83. Kamusoko, C., Aniya, M., Adi, B. and Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe – Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model, Applied Geography, 29(3):435-447.
84. Koomen, E., Stillwell, J., Bakema, A., & Scholten, H. J. (Eds.). (2007). Modelling land-use change :progress and applications. Dordrecht: Springer.
85. Lahti, J. (2008). Modelling Urban Growth Using Cellular Automata: A case study of Sydney, Australia, Transport.
86. Lambin, E. F., Rounsevell, M. D. A. and 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.
87. Li, X., Yang, Q. and Liu, X. (2008). Discovering and evaluating urban signatures for simulating compact development using cellular automata, Landscape and Urban Planning, 86(2):177-186.
88. Li, X. and 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.
89. Li, X. and 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.
90. Liu, X., Li, X., Shi, X., Wu, S. and Liu, T. (2008). Simulating complex urban development using kernel-based non-linear cellular automata, Ecological Modelling, 211(1-2):169-181.
91. Loo, B. P. Y., Chen, C. and Chan, E. T. H. (2010). Rail-based transit-oriented development: Lessons from New York City and Hong Kong, Landscape and Urban Planning, 97(3):202-212.
92. Luo, X. and Shen, J. (2008). Why city-region planning does not work well in China: The case of Suzhou–Wuxi–Changzhou, Cities, 25(4):207-217.
93. Ma, L. J. C. (2005). Urban administrative restructuring, changing scale relations and local economic development in China, Political Geography, 24(4):477-497.
94. Mahiny, A. S. and Gholamalifard, M. (2007). Dynamic Spatial Modeling of Urban Growth through Cellular Automata in a GIS Environment, International Journal of Environmental Research, 1(3):272-279.
95. Marshall, E. and Randhir, T. O. (2008). Spatial modeling of land cover change and watershed response using Markovian cellular automata and simulation, Water Resour. Res., 44(4):W04423.
96. Mondal, P. and Southworth, J. (2010). Evaluation of conservation interventions using a cellular automata-Markov model, Forest Ecology and Management, 260(10):1716-1725.
97. Neuman, M. and Hull, A. (2009). The Futures of the City Region, Regional Studies, 43(6):777-787.
98. Newman, P. W. G. and Kenworthy, J. R. (1996). The land use--transport connection : An overview, Land Use Policy, 13(1):1-22.
99. Noth, M., Borning, A. and Waddell, P. (2003). An extensible, modular architecture for simulating urban development, transportation, and environmental impacts, Computers, Environment and Urban Systems, 27(2):181-203.
100. Office of the Deputy Prime Minister (ODPM). (2005) Planning Glossary 2005. A Public Guide to Land Use Planning Terms and Phrases, London.
101. Pain, K. (2008). Examining ‘Core–Periphery’ Relationships in a Global City-Region: The Case of London and South East England, Regional Studies, 42(8):1161-1172.
102. Parker, T., McKeever, M., Arrington, G. B. and Smith-Heimer, J. (2002). Statewide Transit-Oriented Development Study: Factors for Success in California, Business Transportation and Housing Agency, California Department of Transportation, Sacramento, CA.
103. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J. and 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.
104. Parr, J. (2005). Perspectives on the city-region, Regional Studies, 39(5):555-566.
105. Peterson, L., Bergen, K., Brown, D., Vashchuk, L. and Blam, Y. (2009). Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region, Forest Ecology and Management, 257(3):911-922.
106. Pontius, G. (2000). Quantification Error Versus Location Error in Comparison of Categorical Maps, Photogrammetric Engineering and Remote Sensing, 66(8):1011-1016.
107. Pontius, G. and Malanson, J. (2005). Comparison of the structure and accuracy of two land change models, International Journal of Geographical Information Science, 19(2):243-265.
108. Renne, J. L. and Wells, J. S. (2005). Transit-Oriented Development: Developing a Strategy to Measure Success, National Cooperative Highway Research Project 20-65 (5), Washington, D. C.: Transportation Research Board.
109. Rees, W. G. (2008). Comparing the spatial content of thematic maps, Int. J. Remote Sens., 29(13):3833-3844.
110. Rodríguez-Pose, A. (2008). The Rise of the “City-region” Concept and its Development Policy Implications, European Planning Studies, 16(8):1025-1046.
111. Sang, L., Zhang, C., Yang, J., Zhu, D. and Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA-Markov model, Mathematical and Computer Modelling, 54(3-4):938-943.
112. Santé, I., García, A. M., Miranda, D. and Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis, Landscape and Urban Planning, 96(2):108-122.
113. Saysel, A. K., Barlas, Y. and Yenigün, O. (2002). Environmental sustainability in an agricultural development project: a system dynamics approach, Journal of Environmental Management, 64(3):247-260.
114. Silva, E. A. and Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal, Computers, Environment and Urban Systems, 26(6):525-552.
115. Stevens, D., Dragicevic, S. and Rothley, K. (2007). iCity: A GIS-CA modelling tool for urban planning and decision making, Environmental Modelling & Software, 22(6):761-773.
116. Sung, H. and Oh, J.-T. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea, Cities, 28(1):70-82.
117. Takeyama, M. and Couclelis, H. (1997). Map dynamics: integrating cellular automata and GIS through Geo-Algebra, International Journal of Geographical Information Science, 11(1):73-91.
118. Tewdwr-Jones, M. and McNeill, D. (2000). The Politics of City-Region Planning and Governance. Reconciling the National, Regional and Urban in the Competing Voices of Institutional Restructuring, European Urban and Regional Studies, 7(2):119-134.
119. Valbuena, D., Verburg, P. H. and Bregt, A. K. (2008). A method to define a typology for agent-based analysis in regional land-use research, Agriculture, Ecosystems & Environment, 128(1-2):27-36.
120. Veldkamp, A. and Fresco, L. O. (1996). CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica, Ecological Modelling, 91(1-3):231-248.
121. Veldkamp, A. and Lambin, E. F. (2001). Predicting land-use change, Agriculture, Ecosystems & Environment, 85(1-3):1-6.
122. Verburg, P., Nijs, T. C. M. d., Eck, J. R. R. v., Visser, H. and Jong, K. d. (2004a). A method to analyse neighbourhood characteristics of land use patterns, Computers, Environment and Urban Systems, 28(6):667-690.
123. Verburg, P. H., de Koning, G. H. J., Kok, K., Veldkamp, A. and Bouma, J. (1999). A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use, Ecological Modelling, 116(1):45-61.
124. Verburg, P. H., Schot, P. P., Dijst, M. J. and Veldkamp, A. (2004b). Land use change modelling: current practice and research priorities, GeoJournal, 61(4):309-324.
125. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V. and Mastura, S. S. A. (2002). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model, Environmental Management, 30(3):391-405.
126. Vermeijden, B. (2001). Dutch urban renewal, transformation of the policy discourse 1960–2000, Journal of Housing and the Built Environment, 16(2):203-232.
127. Vogel, R. K., Savitch, H. V., Xu, J., Yeh, A. G. O., Wu, W., Sancton, A., Kantor, P., Newman, P., Tsukamoto, T., Cheung, P. T. Y., Shen, J., Wu, F. and Zhang, F. (2010). Governing global city regions in China and the West, Progress in Planning, 73(1):1-75.
128. Waddell, P. (2002). Modeling Urban Development for Land Use, Transportation, and Environmental Planning, Journal of the American Planning Association, 68(3):297-314.
129. Waddell, P., Ulfarsson, G. F., Franklin, J. P. and Lobb, J. (2007). Incorporating land use in metropolitan transportation planning, Transportation Research Part A: Policy and Practice, 41(5):382-410.
130. Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling, Journal of Environmental Management, 64(3):273-284.
131. White, R. and Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems, Computers, Environment and Urban Systems, 24(5):383-400.
132. White, R., Engelen, G. and Uljee, I. (1997). The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics, Environment and Planning B: Planning and Design, 24(3):323-343.
133. Wolfram, S. (1984). Cellular automata as models of complexity, Nature, 311(5985):419-424.
134. Wood, L. and Dragicevic, S. (2007). GIS-Based Multicriteria Evaluation and Fuzzy Sets to Identify Priority Sites for Marine Protection, Biodiversity and Conservation, 16(9):2539-2558.
135. Wu, F. (1998). SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules, International Journal of Geographical Information Science, 12(1):63-82.
136. Wu, F. (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions, International Journal of Geographical Information Science, 16(8):795-818.
137. Wu, F. and 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(1):103-126.
138. Yeh, A. G.-O. and 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(5):733-753.
139. Zhang, J. and Wu, F. (2006). China's changing economic governance: Administrative annexation and the reorganization of local governments in the Yangtze River Delta, Regional Studies, 40(1):3-21.
140. Zhang, Q., Ban, Y., Liu, J. and Hu, Y. (2011a). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China, Computers, Environment and Urban Systems, 35(2):126-139.
141. Zhang, R., Tang, C., Ma, S., Yuan, H., Gao, L. and Fan, W. (2011b). Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain, China, Mathematical and Computer Modelling, 54(3-4):924-930.