博碩士論文 103022005 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:7 、訪客IP:54.198.28.114
姓名 莊詠婷(Yung-Ting Chuang)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 都市三維結構變遷之分析-以臺灣臺北市為例
(Analyzing 3D Urban Development in Taipei City, Taiwan)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2017-12-31以後開放)
摘要(中) 都市人口持續上升、都市面積不斷擴張,為避免不當的開發使用造成土地資源浪費與都市問題,都市的規劃設計與永續發展逐漸受到重視,而一個完善的都市土地利用計畫應考量長期的土地利用變遷過程,預期未來可能的變遷作為參考之依據。分析不同土地利用類別之面積變化是土地利用變遷研究中最直接的量化分析方式,許多都市土地利用變遷研究應用衛星遙測影像判釋建立地表覆蓋的基礎資訊,針對不同時期都市土地利用在二維平面面積的改變分析都市發展情形,並無考慮都市發展在三維垂直結構,如樓高的變化發展。根據國土利用監測計畫的都市面積統計數據顯示,臺北市為臺灣首要的都會地區,西元1980至2010年其都市面積比從21.7%上升至41.3%,都市面積的成長有逐漸減緩的趨勢,顯示了在都市發展飽和的地區,都市二維平面面積的改變可能無法直接反應出地區的都市發展情形。
本研究選定臺北市作為研究範圍,蒐集了西元1969、1980、1991、2002、2007年共五個時期的3D建物模型,分別量化單元空間下的建物面積與建物樓高,以建物覆蓋率(Build Area Ratio,BAR)和整體建物容積(Generalized Building Capacity,GBC),作為都市二維與三維都市發展之量化指標,利用類神經網路建立都市土地利用變遷模式,進行都市空間結構的模式化,並以類神經網路之敏感度分析法,分析都市土地利用變遷與驅動因子之關聯性。分析結果顯示,臺北市的都市發展在建物面積與建物容積上皆呈現上升趨勢,西元1991年之前都市發展主要為二維空間之建物面積的增長,西元1991年之後都市三維空間之建物容積有明顯的上升情形,顯示臺北都市面積逐漸發展飽和,都市的發展由二維平面的擴張,轉為三維垂直容積的增加,高密度、高樓層的發展取代了橫向的都市土地面積的擴張,因此本研究建議在都市發展的分析,特別是發展飽和之地區(如臺北市),可由二維平面空間延伸探討三維空間結構的轉變。
摘要(英) Population growth and urban expansion lead urban sustainable development a worldwide issue in recent years. Previous studies have used satellite imagery to explore the land use change and analyzed urban growth by correlating the relationships between land uses and driving factors. Most of them investigate the change of urban development in the planar dimension (building area), while few have been focused on the development of urban structure in the third dimension (building height)—the vertical change of urban. According to the urban land monitoring data, the growth of urban area in Taipei City, the capital city of Taiwan, is becoming slow in the past two decades, and the urban area is considered fully saturated. Taipei City, the capital city of Taiwan, is selected as the study area. This study aims to develop an urban model for assessing the development process of urban development of Taipei City, in both 2D and 3D dimensions. The 3D building models of Taipei City in 1969, 1980, 1991, 2002 and 2007 are used to analyze the change of 2D and 3D urban development. In this study, the machine-learning algorithm—artificial neural networks (ANN) model is applied to assess complex and nonlinear 2D and 3D urban development processes, and the 2D and 3D urban development was quantified by two designed parameters, the build area ratio (BAR) and generalized building capacity (GBC).
Results show that the urban area (2D) represents a higher increasing rate before 1991; however, the building height (3D) represents an inverse trend against the urban area, which has a significant increase after 1991. This study considers that in an intensively developed urban, if the land available for constructing new buildings is becoming limited, the urban development could shift from 2D to 3D—the building height.
關鍵字(中) ★ 都市土地
★ 建物樓高
★ 3D 建物模型
★ 類神經網路模式
★ 臺北市
關鍵字(英) ★ Urban area
★ building height
★ 3D building model
★ artificial neural network(ANN)
★ Taipei City
論文目次 第1章 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 4
1-3 研究架構 4
第2章 文獻回顧 5
2-1 都市土地利用變遷 5
2-2 都市土地利用相關研究 8
2-3 都市土地利用變遷影響因子 10
2-4 都市土地利用變遷模式 14
2-5 類神經網路模式應用於都市土地利用變遷 19
第3章 研究範圍與研究資料 24
3-1 研究範圍 24
3-1-1 空間範圍 24
3-1-2 時間範圍 24
3-1-3 研究區域介紹 25
3-2 研究資料 29
3-2-1 3D建物模型 29
3-2-2 都市驅動因子 30
3-2-3 驗證資料 33
第4章 研究方法 36
4-1 資料前處理 37
4-1-1 都市空間結構之評估指標 37
4-1-2 都市驅動因子之資料處理 38
4-1-3 模式驗證資料建置 46
4-2 倒傳遞類神經網路模式之模擬與預測 53
4-2-1 倒傳遞神經網路之訓練資料前處理 55
4-2-2 倒傳遞神經網路之參數設定 56
4-2-3 敏感度分析 59
4-3 模擬預測結果之評估 59
第5章 成果與討論 61
5-1 都市二維平面與三維空間結構變化 61
5-2 建物面積與建物容積之分級分析結果 68
5-3 都市土地變遷模式建構與驗證 77
5-3-1 都市二維模式 77
5-3-2 都市三維模式 83
5-3-3 都市驅動因子重要性評估 89
5-4 都市土地變遷模式驗證 92
5-4-1 都市二維模擬之模式驗證 92
5-4-2 都市三維模擬之模式驗證 94
第6章 結論與建議 98
6-1 結論 98
6-2 建議 99
參考文獻 100
參考文獻 中文部份
內政部營建署,2014。國土利用監測計畫:2001-2013年歷年成果彙編。
王思樺、張力方,2009。都市周邊土地使用與地表覆蓋變遷:驅動力與環境變遷議題。都市與計劃,36(4),361-285。
王翠華,2007。基隆河中上游流域聚落變遷型態之分析。國立臺灣大學理學院地理環境資源學系碩士論文。
何祥龍,2014。應用多目標最佳化模式於都市土地發展之研究。國立臺北大學不動產與城鄉環境學系碩士論文。
吳振發,2006。土地利用變遷與景觀生態評估方法之建立。國立臺北大學都市計劃研究所博士論文。
吳振發,2011。臺灣鄉村景觀變遷模擬之CLUE-s 模式最佳參數試驗。地理學報,62,103-125。
吳振發、林裕彬,2006。汐止市土地利用時空間變遷模式。都市與計劃,33(3),231-259。
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。國立臺灣大學土木工程學研究所碩士研究論文。
林峰田,吳秋慧,顧嘉安、曾琬瑜,2011。台北都會區土地使用變遷模型之研究-以淡水及新莊為例。國土資訊系統通訊季刊,77,62-69。
林峰田,林士弘,李萬凱,孫志鴻,林建元、李培芬,2002。宮格自動機於土地利用變遷模擬之結合機制。中華地理資訊學會學術研討會學術論文集。
林祥偉,2003。地理資訊系統與人工智慧整合之研究。國立臺灣大學地理環境資源學研究所博士論文。
林裕彬,朱宏杰、吳振發,2011。土地使用變遷模式回顧與比較。國土資訊系統通訊季刊,77,46-53。
洪于婷、鄒克萬,2006。地方永續發展空間結構變遷之分析。都市與計劃,33(4),321-344。
徐國城,2010。台北都會區空間發展型態變遷趨勢與原因之研究。國立政治大學地政學系博士論文。
徐國城,賴宗裕、詹士樑,2010。台北都會區空間蔓延與緊密發展型態趨勢之研究。都市與計劃,37(3),281-303。
張右峻,1999。利用類神經網路探討土地利用型態與環境變遷之研究。逢甲大學土地管理學系碩士論文。
張曜麟,2005。都市土地使用變遷之研究。國立成功大學都市計畫研究所博士論文。
陳俊榮,2005。都市擴張預測方法之建立:類神經網路工具為例。朝陽科技大學環境工程與管理系碩士論文。
曾露儀,2013。以網格模式探討臺北盆地淡水河系右岸之都市發展進程。國立師範大學地理學系碩士論文。
黃書禮、蔡靜如,2000。台北盆地土地利用變遷趨勢之研究。都市與計劃,27(1),1-22。
溫在弘,2007。GIS 應用於公共工程設施之公平性研討:以台灣高鐵/台鐵運輸網路為例。國土資訊通訊季刊 ,64,83-91。
葉怡成,2003。類神經網路模式應用與實作。儒林書局。
鄒克萬、張曜麟,2000。一個機率性土地發展分析模式。台灣土地研究(原台灣土地科學報),1,51-66。
鄒克萬、張曜麟,2004。都市土地使用變遷空間動態之研究 ,35,35-51。
廖怡雯,2003。運用馬可夫鏈模式於台中市土地利用變遷之研究。私立逢甲大學土地管理學系碩士論文。
中央研究院GIS專題中心的地址轉換座標工具之批次地址定位處理,2016。http://gissrv4.sinica.edu.tw/gis/tools/geocoding.aspx。
水利署地理資訊倉儲中心,2016。http://gic.wra.gov.tw/gic/API/Google/Index.aspx。
永慶房仲網,2016。https://evertrust.yungching.com.tw/。
交通部公路總局,2016。http://www.thb.gov.tw/sites/ch/modules/download/download_list?node=66bd0e89-dcdd-403d-8a6b-58c3ef70ff93&c=1ffd8655-5305-46f6-b076-48c60e8d117d。
行政院農業委員會資料開放平台,2016。http://data.coa.gov.tw/Query/OpenData.aspx。
臺北市政府民政局,2016。http://ca.gov.taipei/ct.asp?xitem=1503254&CtNode=41896&mp=102001。
臺北市政府資料開放平台,2016。http://data.taipei.gov.tw/opendata;jsessionid=D459CA0A12C915DFEFF21F88FDA42AE3。
臺北市統計資料查詢系統,2016。http://210.69.61.217/pxweb2007-tp/dialog/statfile9.asp。
臺北捷運公司,2016。http://www.metro.taipei/ct.asp?xItem=78479152&CtNode=70089&mp=122035。


英文部份
Agarwal, C., Green, G. M., Evans, T. P. and Schweik, C. M., 2002. A Review and Assessment of Land-Ued Change Models:Dynamics of Space, Time, and Human Choice.
Azizi, A., Malakmohamadi, B. and Jafari, H. R., 2016. Land use and land cover spatiotemporal dynamic pattern and predicting changes using integrated CA-Markov model. Global Journal of Environmental Science and Management, 2(3), 223-234.
Bagan, H., Wang, Q. and Watanabe, M., 2005. Land cover classification from MODIS EVI times-series data using SOM neural network. International Journal of Remote Sensing, 26(22), 4999-5012.
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, 145-160.
Batty, M. and Torrens, P. M., 2001. Modeling Complexity:The Limits to Prediction. Working Paper 36.
Berry, M. W., Flamm, R. O., Hazen, B. and Macintyre, R. L., 1996. The Land-Use Change Analysis System (LUCAS) for Evaluating Landscape Management Decisions. IEEE Computational Science and Engineering, 3(1), 24-35.
Bishnoi, S., Gaikwad, V. and Asegaonkar, S., 2011. Hopfield Neural Network for Change Detection in Multiemporal Image. International Conference on Recent Trends in Information Technology and Computer Science, 6-11.
Bishop, C. M., 1995. Neural Networks for Pattern Recognition Oxford University Press, New York.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. E., 1996. Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281-298.
Castella, J.-C., Kam, S. P., Quang, D. D., Verburg, P. H. and Hoanh, C. T., 2007. Combining top-down and bottom-up modelling approaches of land use/cover change to support public policies: Application to sustainable management of natural resources in northern Vietnam. Land Use Policy, 24, 531-545.
Chaudhuri, G. and Clarke, K. C., 2013. The SLEUTH land use change model: A review. The International Journal of Environmental Resources Research, 1(1), 88-104.
Cohen, B., 2006. Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society, 28, 63-80.
Dietzel, C. and Clarke, K., 2006. The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Computers, Environment and Urban Systems, 30, 78-101.
Dreiseitl, S. and Ohno-Machadob, L., 2002. Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35, 352-359.
Forman, R. T. T., 1995. Land mosaics : the ecology of landscapes and regions. Cambridge New York : Cambridge University Press,
Fotheringham, A. S. and Brunsdon, C., 1999. Local Forms of Spatial Analysis. Geographical Analysis, 31(4), 341-358.
Frenkel, A. and Ashkenazi, M., 2008. Measuring Urban Sprawl; How Can We Deal With It? Environment and Planning B, 35, 1-24.
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T. and Hokao, K., 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222, 3761-3772.
Guan, Q., Wang, L. and Clarke, K. C., 2005. An Artificial-Neural-Network-based, Constrained CA Model for Simulating Urban Growth. Cartography and Geographic Information Science, 32(4), 369-380.
Harris, C. D. and Ullman, E. L., 1945. The Nature of Cities. The ANNALS of the American Academy of Political and Social Science, 242(1), 7-17.
Herold, M., Goldstein, N. C. and Clarke, K. C., 2003. The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, 286-302.
Hoyt, H., 1939. The structure and growth of residential neighborhoods in American cities Washington, Federal Housing Administration.
Ito, Y. and Omatu, S., 1999. Extended LVQ Neural Network Approach to Land Cover Mapping. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 313-317.
Ji, C., 2000. Land-Use Classification of Remotely Sensed Data Using Kohonen Self-organizing Feature Map Neural Networks. Photogrammetric Engineering & Remote Sensing, 66(12), 1451-1460.
Lee, S. and Lathrop, R. G., 2006. Subpixel Analysis of Landsat ETM+ Using Self-Organizing Map (SOM) Neural Networks for Urban Land Cover Characterization. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 44(6), 1642-1654.
Li, X. and Yen, A. G.-O., 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. Internationa l Journal of Geographical Information Science, 16(4), 323-343.
Lin, Y.-P., Chu, H.-J., Wu, C.-F. and Verburg, P. H., 2010. Predictive ability of logistic regression, autologistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25(1), 65-87.
Liu, H. and Shao, Y., 1998. An improved Learning Vector Quantization Neural Network for Land Cover Classification with Multi-temporal Radarsar Images. IEEE Computational Science and Engineering, 1787-1789.
Liu, W. and 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: lanning and Design, 35(2), 296-317.
Liu, X. and Jr, R. G. L., 2002. Urban change detection based on an articial neural network. Internationa l Journa l of Remote Sensing, 23(12), 2513-2518.
Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweed, V. and Gwynne, M., 1996. Urban Air Pollution in Megacities of the World. Atmospheric Enuironmrnr 30(5), 681-686.
McKinney, M. L., 2008. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst, 11.
Mehrotra, A., Singh, K. K. and Kirat Pal, M. J. N., 2013. Change Detection from Satellite Images Using PNN. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 115, 333-340.
Miller, D. M., Kaminsky, E. J. and Rana, S., 1995. Neural network classification of remote sensing data. Computers & Geosciences, 21(3), 377-386.
OECD, 2012. Compact city Policies:A Comparative Assessment.
Park, R. E., Burgess, E. W. and McKenzie, R. D., 1925. The city. The University of Chicaco Press.
Payal, K. J., 2011. A Review Study On Urban Planning & Artificial Intelligence. International Journal of Soft Computing and Engineering, 1(5), 101-104.
Peiser, R., 2001. Decomposing Urban Sprawl. The Town Planning Review, 72(3), 275-298.
Pijanowski, B. C. and Shellito, B. A., 2001. Using GIS, artificial neural networks and remote sensing to model urban change in the Minneapolis-St. Paul and Detroit metropolitan areas. American Society of Photogrammetry and Remote Sensing Meeting.
Pijanowski, B. C., Brownb, D. G., Shellitoc, B. A. and Manikd, 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.
Pijanowski, B. C., Pithadia, S., Shellito, B. A. and 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.
Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D. and Plourde, J., 2014. A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment. Environmental Modelling & Software, 51, 250-258.
Rygielski, C., Wang, J.-C. and Yen, D. C., 2002. Data mining techniques for customer relationship management. Technology in Society, 24, 483-502.
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, 938-943.
Tatem, A. J., Lewis, H. G., Atkinson, P. M. and Nixon, M. S., 2003. Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network. International Journal of Geographical Information Science, 17(7), 647-672.
Turner, B. L., Skole, D., Sanderson, S., Fischer, G., Fresco, L. and Leemans, R., 1995. Land -Use and Land-Cover Change Science/Research Plan. 35(7),
UN, 2015. World Urbanization Prospects: The 2014 Revision.
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, 231-248.
Veldkamp, A. and Lambin, E. F., 1996. CLUE:a conceptual model to study the Conversion of Land Use and its Effects. Agriculture Ecosystems and Environment, 85, 1-6.
Veldkamp, A. and Lambin, E. F., 2001. Predicting land-use change. Agriculture Ecosystems and Environment, 85, 1-6.
Verburg, P. H. and Veldkamp, A., 2004. Projecting land use transitions at forest fringes in the Philippines at two spatial scales. Landscape Ecology, 19, 77-98.
Verburg, P. H., Koning, G. H. J. d., 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, 45-61.
Verburg, P. H., Schot, P. P., Dijst, M. J. and Veldkamp, A., 2004. Land use change modelling: current practice and research priorities. GeoJournal, 61, 309-324.
Verburg, P. H., Soephboer, 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.
Webstera, F. V., Blya, P. H., Johnstona, R. H., Pauleya, N. and Dasguptaa, M., 1986. Changing Patterns of Urban Travel. Transport Reviews, 6(2),
White, R. and Engelen, G., 1993. Cellular automata and fractal urban form : a cellular modelling approach to the evolution of urban land-use patterns. 25, 1175-1199.
Zhou, W. and Li, Q., 2013. Complexity and Dynamic Modeling of Urban System. International Journal of Machine Learning and Computing, 3(5), 440-444.
United States Geological Survey, 2016. http://earthexplorer.usgs.gov/.
指導教授 姜壽浩 陳繼藩(Shou-Hao Chiang Chi-Farn Chen) 審核日期 2016-7-12
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明