都市空間結構可以被視為活動、人類和都市資源的綜合相互作用，其功能分派及資源分布產生內部的旅次需求，而藉由對人口流動、活動強度的分析，即可產生由下而上描繪居民日常的空間結構。由居民感知角度的微觀層面來掌握具有共同特徵的生活單元，透過多元大數據從時空維度揭示都市內的個體如何利用都市空間，將可加深對人群移動模式與都市空間結構間耦合關係的理解。近年來國外研究雖已建構其研究方法框架進行都市空間結構之解析，惟研究架構鮮少綜合多種大數據進行探討，而國內研究更仍處於初步階段。
本研究以臺北市為實證地區，提出一種結合不同大數據特性下的分析架構，以識別具多核心發展特性之都市空間結構。首先，透過Infomap網絡分析分析下透過公車刷卡資料探索在空間交互作用下的都市空間區劃，將具有緊密聯繫關係的地區視為一生活圈。再者，結合信令資料探索活動人口的強度變化，提取其時序性特徵並進行模糊集群分析，找尋具有相似功能屬性的地區進行空間結構的探索，最後則透過國土利用調查進行驗證。而綜合流動人口結構特徵及活動人口時序變化，瞭解以大數據為基礎解析之都市空間結構。
實證成果指出，本研究所建構之分析架構具一定的解釋能力。依據平日及假日流量結構關係，將臺北市劃分為9個生活圈並判別定位，根據流量、中心度及空間交互作用等特徵將其分為核心生活圈、次生活圈及衛星生活圈，並描繪自微觀角度檢視之區域空間層次關係。另外，透過活動人口變化趨勢將臺北市的平日及假日從住宅至商業使用分別區分出4種不同的都市土地功能分類，與國土利用調查交叉比對後在住宅和商業機能下均有高達7成的檢測水準。並在將都市功能分區對照至生活圈區劃後，得出基於大數據所檢視的臺北市都市空間結構。
本研究的重點在於體現了人類的旅次活動與都市空間結構的耦合關係，除了探討實證地區之都市空間結構外，並確立了一套基於大數據檢視地區空間結構的流程。在探索的過程中驗證基於使用者行為的空間區劃及定義具有準確性及其價值，所得出的結論對於後續通盤檢討計畫抑或是資源調派等政策實務運用，均可作為空間規劃之依歸。

Urban spatial structure can be regarded as an integrated interaction of activities, people, and urban resources, with the distribution of functions and resources generating an internal sub-demand for travel. In contrast, the analysis of population movements and activity intensity can produce a bottom-up depiction of the spatial structure of residents' daily lives. Therefore, this study aims to capture the typical characteristics of urban units from the micro level of residents' perceptions and to reveal how individuals use urban space within the city from a spatial and temporal perspective. Finally, the study proposes an urban spatial structure identification method based on big human movement data. The Infomap network analysis is carried out with the data of boarding and descending on the bus representing the regional connection, exploring the urban spatial division under the interaction of space. And use the signaling data to explore the intensity changes of the active population, extract its temporal characteristics and carry out fuzzy cluster analysis to find areas with similar population intensity changes to explore functional attributes.
According to the research results, Taipei City is divided into nine living circles, and their positioning is determined. Area positioning is judged according to the flow and structural relationship. According to flow, centrality, and spatial interaction characteristics, the city was divided into main living circles, sub-living circles, and satellite living circles. They depict the regional spatial hierarchical relationship viewed from a microscopic perspective. In addition, through the changing trend of the active population, Taipei City is divided into four different urban land function classifications into weekdays and holidays. After comparing the urban functional zoning with the living circle zoning, the Taipei City metropolitan area based on big data is obtained.

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