隨著環保意識的提升和智慧城市概念的普及，公共自行車系統（Public Bicycle System, PBS）成為都市交通的重要組成部分。然而，過往對於公共自行車系統與都市興趣點兩者空間分群區塊的意涵多以文字敘述和主觀經驗為主，缺乏客觀的量化支持。這限制了對於都市空間結構和活動模式的深入理解，進而影響科學的都市規劃與管理。
本研究旨在探討公共自行車旅次與都市興趣點（POI）之間的空間分群相似程度，並以量化的方法揭示這些分群背後的實質空間意涵。透過資料探勘技術探索公共自行車旅次數據，結合具象指標興趣點數據進行深度挖掘，以及運用社會網絡分析、社群偵測及集群分析等方法進行空間分群。最後以調整蘭德指數（Adjusted Rand Index, ARI）量化公共自行車旅次與興趣點分群的相似度，揭示兩者之間的空間關係，來分析揭示空間分群背後的意涵。另外，針對不同分群數的圖層，本研究採用了合併集群（Cluster Merging）方法，透過調整分群數以提高調整蘭德指數的精確性，減少分群邊界的模糊性，進而揭示興趣點與公共自行車使用模式之間更清晰的關係。
同時，本研究為深入探討臺北市不同區域尺度下的空間分佈模式，範圍將涵蓋全市都會區的城市尺度和15分鐘鄰里生活圈尺度進行分析，此分析尺度類同於《新都市主義憲章》(Charter of the New Urbanism)中提到的概念。本研究鄰里生活圈選定市區外圍的內湖區和市中心的大安區，用以揭示群內互動上的差異。 研究結果數據指出:大安區作為市中心，興趣點如銀行、車站、健身房等的ARI數值突出，反映出該區作為交通樞紐與金融文化設施密集的角色；相對地，內湖區為新興商業區與住宅區，健身房、學校和咖啡廳等的ARI數值較突出，顯示其區域重視健康與便利生活。以上這些發現有助於理解不同區域的都市空間結構與活動特性，對未來的都市規劃和交通管理提供了重要參考。
本研究最終透過量化分析公共自行車旅次與都市興趣點兩者間的分群差異，來揭示背後的空間意涵，填補過去研究中缺乏量化支持的缺口。研究結果不僅可幫助政府與都市規劃者準確地理解公共自行車系統與都市活動空間關係，制定更有效的規劃及策略，推動智慧城市建設，也為後續的都市空間結構與活動模式研究提供了科學的依據，具重要的學術與實踐價值。
本研究的突破點分成三個部分，首先，透過調整蘭德指數量化空間關係，提供了客觀的數據支持，克服過去主觀經驗的局限性；第二，結合動態的公共自行車旅次資料以及具象的興趣點指標解釋公共自行車分布的空間意涵，同時以不同尺度來觀察驗證，提供了對於都市活動模式的不同視角；最後測試了調整蘭德指數進行空間分群分析及合併集群方法的適切性，提升了研究的精確性與可靠性，為後續的都市規劃提供了更具科學性的依據。

With growing environmental awareness and the rise of smart cities, Public Bicycle Systems (PBS) have become integral to urban transport. However, prior studies on the spatial clustering of PBS and urban Points of Interest (POI) have largely relied on qualitative descriptions, lacking quantitative analysis, which limits the understanding of urban spatial structures and activity patterns.

This study quantifies the spatial clustering similarity between PBS trips and POIs in Taipei, employing data mining, social network analysis, and cluster analysis. The Adjusted Rand Index (ARI) is used to measure the similarity between clusters, offering insights into their spatial relationships. The study also utilizes Cluster Merging to refine cluster accuracy, reducing boundary ambiguities.

The research examines spatial patterns at both the citywide scale and the 15-minute neighborhood level, comparing the city center (Daan District) and a peripheral area (Neihu District). Findings reveal distinct POI clustering characteristics, highlighting the differing urban structures and activity patterns.

This study addresses the gap in quantitative analysis within previous research, providing a scientific foundation for urban planning and smart city strategies. Key contributions include the objective quantification of spatial relationships, the integration of dynamic PBS and POI data, and the validation of clustering methodologies, enhancing the precision and reliability of urban spatial analysis.

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