臺灣博碩士論文加值系統

本研究目的為透過遊客自主攝影法及影像分析、問卷調查，了解都市公園使用者景觀偏好之景觀元素與場所依戀高低程度，以了解使用者的實質層面與心理層面。因此本研究期望能了解使用者的景觀偏好之元素與場所依戀，以便提供政府在未來發展都市公園景觀規劃設計上的參考方針。因此，本研究透過現地採取立意抽樣蒐集遊客樣本資料，獲得127份有效問卷和1,270張影像資料，並透過Google Cloud Vision API(以下用Cloud Vision稱之)結合統計軟體SPSS21.0，並將Cloud Vision影像分析出標籤與問卷編碼至統計軟體中，且將影像標籤分析其次數，再將影像標籤歸納至景觀元素裡，最終確立41個不同標籤與11種不同景觀元素類別，分別為：地貌、植物材料、鋪面、基地結構物、建築物、水、戶外設施、車輛、自然環境、動物、藝術、活動、人、衣著、顏色。由研究結果得知，不同性質使用者之場所依戀程度與拍攝景觀偏好選擇有顯著差異以及場所依戀程度不同拍攝之景觀偏好選擇亦不相同，且本研究確認遊客自主攝影法與Cloud Vision用於探討景觀偏好之元素分析之可行性，期望可以補足過去鮮少透過此類方法進行景觀偏好研究之缺口，並且將其結果供未來都市公園設計上之元素參考。

This research uses visitor-employed photography, image analysis, and questionnaire survey to understand urban park users' preferred landscape elements and degrees of place attachment, thereby achieving a more comprehensive understanding of the users' actual experiences and their state of mind. It is hoped that understanding users' preferred landscape elements and place attachment may provide the government with reference data for future urban park development and landscape design. Therefore, this research adopted the on-site purposive sampling approach to collect visitor sample data, acquiring 127 valid questionnaire returns and 1,270 photo images. The data is paired with Google Cloud Vision API (hereon Cloud Vision) and statistic software SPSS21.0. The Cloud Vision imagery tags and questionnaire numbering sequence was imported into the statistic software to determine the counts based on imagery tagging. The image tags are then sorted into identified landscape elements. In all, this study established 41 unique tags and 11 different landscape elements, the latter of which includes: geological features, flora, pavings, base structures, buildings, water features, outdoor facilities, natural environment, fauna, art, activities. The research results found that different types of users have different degrees of place attachment and preferences for landscape elements. Their choices in scenic photography also differ based on their varying degrees of place attachment. This research also confirmed the viability of using visitor-employed photography and Cloud Vision to conduct element analysis on landscape preferences. It is hoped that this rarely used approach fills a gap in existing landscape preference research, and it can serve as a reference for future urban park planning and landscape element design.

摘要........................................... i
Abstract...................................... ii
誌謝........................................... iv
目錄........................................... v
表目錄......................................... vii
圖目錄......................................... ix
第一章 緒論..................................... 1
1.1 研究背景與動機............................... 1
1.2 研究目的.................................... 2
1.3 研究流程.................................... 3
第二章 文獻探討.................................. 4
2.1 景觀元素.................................... 4
2.1.1 景觀元素之內涵............................. 4
2.1.2 景觀元素於都市公園中之應用.................. 5
2.2 場所依戀.................................... 6
2.2.1 場所之定義................................ 6
2.2.2 場所依戀之定義............................ 6
2.2.3 場所依戀之構面............................. 7
2.2.4 場所依戀之相關研究與測量量表................ 8
2.2.5 小結..................................... 11
2.3景觀偏好..................................... 11
2.3.1 景觀偏好之定義............................. 11
2.3.2 影響景觀偏好之因素......................... 12
2.4 遊客自主攝影法............................... 14
2.4.1 遊客自主攝影法之定義....................... 14
2.4.2 遊客自主攝影法之特色....................... 14
2.4.3 遊客自主攝影法之相關研究.................... 15
第三章 研究方法.................................. 17
3.1 研究架構.................................... 17
3.2 研究設計.................................... 18
3.2.1 研究場域.................................. 18
3.2.2 研究對象與施測流程......................... 19
3.2.3 研究資料蒐集.............................. 20
3.2.4 資料分析方法.............................. 23
第四章　研究結果與討論........................... 25
4.1 受測者社經背景描述性分析...................... 25
4.2 受測者與場所依戀之關係....................... 29
4.3 個人屬性與場所依戀之關係...................... 31
4.4 場所依戀之集群分析........................... 44
4.5 受測者影像之描述性樣本分析.................... 49
4.5.1 Cloud Vision標籤描述性統計................. 49
4.5.2 受測者性質與Cloud Vision標籤描述性統計...... 57
第五章 結論與建議.................................86
5.1 研究結論.................................... 86
5.2 研究限制與未來研究建議....................... 88
參考文獻........................................ 90
附錄一.......................................... 96
附錄二.......................................... 98
Extended Abstract.............................. 119

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