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研究生: 阮英芳
Nguyen Anh Phuong
論文名稱: 應用基於人工神經網絡的元胞自動機模型 調查旅遊業對胡志明市土地利用土地覆蓋變化的影響
An Artificial Neural Network-Based Cellular Automata Model to Investigate the Effects of the Tourism Industry on Land Use Land Cover Changes in Ho Chi Minh City
指導教授: 李子璋
Lee, Tzu-Chang
學位類別: 碩士
Master
系所名稱: 規劃與設計學院 - 都市計劃學系
Department of Urban Planning
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 87
中文關鍵詞: 旅遊發展土地利用和土地覆被變化ANN-CA地理標記照POI
外文關鍵詞: Tourism development, Land use land cover changes, ANN-CA, geotagged photos, POI
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  • 胡志明市旅遊業蓬勃發展。旅遊業的增長導致對交通和基礎設施用地的需求大幅增加。它顯著影響城市的土地利用和土地覆蓋。儘管對人類活動對土地利用變化的影響進行了廣泛的研究,但對旅遊發展與土地利用和土地覆蓋變化之間的相互作用的了解有限。受上述觀察的啟發,本研究調查了胡志明市旅遊業的增長以及旅遊業發展與土地利用土地覆被變化過程之間的關係。本研究分為兩個階段。在第一階段,通過使用 Flickr 應用程序編程接口 (API) 於 2010 年 1 月 1 日至 2019 年 12 月 31 日拍攝的 19,474 張地理標記照片,探索了胡志明市旅遊業的發展。在第二階段,本研究利用土地覆蓋圖和興趣點(POI)以及社會人口數據分析了胡志明市的土地利用和土地覆蓋變化。最後,應用基於人工神經網絡的元胞自動機模型(ANN-CA)來分析旅遊業對胡志明市土地利用和土地覆蓋變化的影響。結果表明,該市的旅遊模式在過去十年中發生了顯著變化。 2010-2011年,遊客分佈完全隨機。然而,2012-2019年,旅遊熱點在市中心出現並發展。 ANN-CA模型的結果表明,旅遊業的發展對胡志明市的土地利用變化有影響,例如減少研究區的農用地和草地。結果,城市的生態系統可能會受到破壞。本研究揭示了胡志明市旅遊業的發展以及土地利用變化與旅遊業之間的互動關係。本研究的結果可以在戰略規劃過程中幫助政策制定者、城市規劃者和目的地管理組織發展城市的旅遊業。

    The tourism industry in Ho Chi Minh City has flourished remarkably. The growth of tourism has led to a massive increase in the demand for land for transportation and infrastructure. It significantly impacts on land use land cover (LULC) of the city. Despite extensive research on the effects of human activities on land-use change, there is limited knowledge about the interactions between tourism development and LULC changes due to the lack of micro-level georeferenced datasets in both phenomena, and the difficulty of tracking tourism activities. Motivated by the above observation, this study investigates the growth of the tourism industry as well as the relationship between tourism development and the LULC changes in Ho Chi Minh City. There are two phases of this study. In the first phase, the development of the tourism industry in Ho Chi Minh City was explored by using 19,474 geotagged photos taken from 1/1/2010 to 12/31/2019 by using Flickr Application Programming Interface (API). In the second phase, the Artificial-neural-network based cellular automaton model (ANN-CA) was applied to analyze the impact of tourism development on LULC changes in Ho Chi Minh City by using LULC maps, Point of interest (POI), and social demographic data. The spatial-temporal analysis of tourist distribution revealed that cultural heritage located in the center of the city attracted more foreign tourists than other types of attractions. Tourist patterns in the city evolved significantly in the past decade. Between 2010 and 2011, tourists were scattered in the city. However, during the years 2012-2019, tourist hot spots appeared and developed in the city center. The results of the ANN-CA model indicated that tourism development affects LULC in Ho Chi Minh City. The expansion of the tourism industry led to the loss of agricultural lands and grasslands. Furthermore, tourism developments are mainly concentrated along the river. Consequently, the ecosystem of the city could be damaged. This study has shed light on the development of the tourism industry in Ho Chi Minh City as well as the interactive relationship between LULC changes and tourism. The results of this study could support policymakers, urban planners, and destination management organizations during the strategic planning process to develop the tom in the city.

    1. INTRODUCTION 1 1.1. Background and motivation 1 1.2. Research objectives 2 1.3. Structure of the thesis 2 2. LITERATURE REVIEW 4 2.1. Land use and land cover changes 4 2.2. Spatial-temporal analysis of tourism 5 2.3. Land use land cover changes and tourism development 9 2.4. Spatial scale issues in Cellular Automata (CA) model 12 3. METHODOLOGY 15 3.1. Study area and datasets 15 3.2. Method 28 4. SPATIAL-TEMPORAL EVOLUTION OF TOURISTS DISTRIBUTION 42 4.1. Tourist classification 42 4.2. Temporal characteristic of tourist distribution 43 4.3. Spatial characteristic of tourist distribution 47 5. LULC CHANGES MODEL 52 5.1. Land use changes detection 52 5.2. Data preprocessing 54 5.3. Model training 59 5.4. Model calibration 61 5.5. Model validation 67 5.6. Model simulation 69 6. CONCLUSIONS 74 6.1. Important results 74 6.2. Limitations 75 6.3. Future work 76 REFERENCES 77 APPENDIX 1 84 APPENDIX 2 85 APPENDIX 3 86

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