京都大学 / 0048 / 新制・課程博士 / 博士(教育学) / 甲第18733号 / 教博第171号 / 新制||教||152(附属図書館) / 31684 / 京都大学大学院教育学研究科教育科学専攻 / (主査)教授 楠見 孝, 教授 子安 增生, 准教授 齊藤 智 / 学位規則第4条第1項該当 / Doctor of Philosophy (Education) / Kyoto University / DGAM

步行者在都市環境之移動與空間選擇行為，是規劃者長久以來在進行都市空間規劃與分析之重要課題。其中，對步行者行為的了解，更是進行都市空間分析的重要課題。一般來說，步行者的活動行為與空間選擇，會受環境資訊與過去空間經驗等認知之影響，步行者在複雜的都市空間中活動時，如何在環境中進行空間選擇？在空間選擇時，步行者對空間之認知狀況，又會對其空間選擇產生什麼影響？則是一個有趣的課題。 然而，過去探討步行者空間選擇之相關研究，極少全面探討空間認知對個體空間選擇產生之影響，故本研究之目的，在探索步行者空間認知結果，如何影響其對空間選擇意願。本研究以台北市西門徒步區為例，將步行者之空間選擇分為活動點選擇、找路判斷與路徑選擇三部份，分別運用理論歸納、訪談與探索性因素分析(EFA)，建立步行者空間選擇影響因素後，再運用結構方程模式(SEM)，探討步行者對空間選擇影響因素及影響關係，將步行者“空間認知”對其空間選擇之影響關係進行呈現。 研究結果顯示，個體空間認知結果是影響個體空間選擇意願之主要影響因素，其對步行者活動點選擇之影響為間接影響，對步行者路徑選擇之影響則為直接影響，而個體空間認知結果包含了個體找路判斷認知結果與地區環境資訊認知結果兩部分。 在步行者找路判斷之影響因素方面，影響個體步行者找路判斷之因素，分別為地區環境資訊因素與個體空間熟悉因素；其中，地區環境資訊因素為主要影響因素，地區環境資訊因素並對個體找路判斷同時具有直接與間接之影響。 此外，在影響途徑方面，步行者於活動點選擇時，空間認知結果會透過影響地區環境對步行者之吸引，間接影響個體逛選偏好，而個體逛選偏好則對個體活動點空間選擇之意願具有直接影響。而在步行者路徑選擇時，空間認知結果會直接影響步行者之路徑選擇意願，而地區活動吸引亦會透過直接影響個體空間認知結果，間接影響步行者路徑選擇意願。 關鍵字：步行者、空間認知、空間選擇、找路 / Pedestrian movement has been analyzed by urban geographers and environmental psychologists from the mid-1960’. Particularly, spatial choice is an important part of the spatial movement to researchers. Researchers in the past studies have assumed that pedestrian spatial choice can be viewed as the result of utility-maximizing behavior which pedestrian have full environmental information. It was argued that this assumption may not reflect the real behavior. Because pedestrians do not behave with full information. The lacks of full information open the way for cognitive behavioral approach to understand the spatial decision-making process of pedestrians. The aim of this article is to explore how the spatial cognition affects an individual pedestrian’s space choice behavior. The empirical study is conducted in the Shi-Men pedestrian mall in Taipei city and the structural equation model (SEM) is used to obtain the relationship between individual spatial cognition and space choice behavior. The results are shown as follows: First, spatial cognition affects the pedestrian route choice directly but affects stops choice indirectly. The pedestrian spatial cognitive outcome directly affects the environmental attractiveness of stops, the environmental attractiveness of stops directly affects the individual shopping preference and the individual shopping preference directly affects the choice desire of stops when pedestrians choice stops in pedestrian mall. The environmental active attractiveness of stops directly affects the spatial cognition and the spatial cognitive outcome directly affects the desire of space on pedestrians route choice. Spatial cognition includes two parts: one is wayfinding cognition and the other is local environmental information cognition. Second, the factors of pedestrian wayfinding include the degree of difficulty of wayfinding, the local environmental information and the individual familiarity of space. Among these factors, the local environmental information is the main factor of pedestrian wayfinding. Pedestrian wayfinding is influenced by direct factor as well as indirect factor of the local environmental information. Keywords：Pedestrian, Spatial choice, Spatial cognition, Wayfinding.

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第21478号 / 文博第783号 / 新制||文||668(附属図書館) / 京都大学大学院文学研究科行動文化学専攻 / (主査)教授 蘆田 宏, 教授 板倉 昭二, 准教授 黒島 妃香 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DGAM

空間認知(Spatial Cognition)指出人所理解的空間複雜度，也就是人與環境互動的過程中，經由記憶與感官經驗，透過內化與重建產生物體在空間的關係認知。認知圖(Cognitive Map)是最常被使用在評估空間認知。分析學生所畫的認知圖有助於老師們瞭解學生的空間認知能力，進而擬定適當的地理教學設計。我們視空間認知發展的學習歷程檔案是由這些認知圖所構成。隨著數位學習科技的進步，我們可以透過探勘認知圖的方式，探討空間認知發展的學習歷程檔案。因此，我們藉由透過圖像的空間資料探勘，分析學生空間認知發展的學習歷程。 空間資料探勘(Spatial Data Mining)主要是從空間資料庫或圖像資料庫中找出有趣且有意義的樣式。在論文中，我們介紹一種空間樣式(Spatial Co-orientation Pattern)探勘以提供空間認知發展學習歷程的分析。Spatial Co-orientation Pattern是指圖像資料庫中，具有共同相對方向關係的物體(Object)常一起出現。例如，我們可以從圖像資料庫中發現物體P常出現在物體Q的左邊，我們利用二維字串(2D String)來表示物體分佈在圖像中的空間方向關係。我們透過Pattern-growth的方法探勘此種空間樣式，藉由實驗結果呈現Pattern-growth的方法與過去Apriori-based的方法[14]之優缺點。 我們延伸Spatial Co-orientation Pattern的概念至時空資料庫(Spatio-temporal Database)，提出從時空資料庫中，探勘Temporal Co-orientation Pattern。Temporal Co-orientation Pattern是指Spatial Co-orientation Pattern隨著時間的變化。論文中，我們提出兩種此類樣式，即是Coarse Temporal Co-orientation Pattern與Fine Temporal Co-orientation Pattern。針對此兩種樣式，我們提出三階段(three-stage)演算法，透過實驗分析演算法的效率。 / Spatial cognition means how human interpret spatial complexity. Cognitive maps are mostly used to test the spatial cognition. Analyzing cognitive maps drawn by students is helpful for teachers to understand students’ spatial cognitive ability and to draft geography teaching plans. Cognitive maps constitute the portfolios of spatial cognitive development. With the advance of e-learning technology, we can analyze portfolios of spatial cognitive development by spatial data mining of cognitive images. Therefore, we can analyze portfolios of spatial cognitive development by spatial data mining of images. Spatial data mining is an important task to discover interesting and meaningful patterns from spatial or image databases. In this thesis, we investigate the spatial co-orientation patterns for analyzing portfolios of spatial cognitive development. Spatial co-orientation patterns refer to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. For example, an object P is frequently left to an object Q among images. We utilize the data structure, 2D string, to represent the spatial orientation of objects. We propose the pattern-growth approach for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantages and disadvantages between pattern-growth approach and Apriori-based approach proposed by Huang [14]. Moreover, we extend the concept of spatial co-orientation pattern to that of temporal patterns. Temporal co-orientation patterns refer to the change of spatial co-orientation patterns over time. Two temporal patterns, the coarse temporal co-orientation patterns and fine temporal co-orientation patterns are introduced to be extracted from spatio-temporal databases. We propose the three-stage algorithms, CTPMiner and FTPMiner, for mining coarse and fine temporal co-orientation patterns, respectively. An experimental evaluation with synthetic datasets shows the performance of these algorithms.