軟體創新服務的廣泛，由使用者端產生的大量資料，讓使用者生成內容(User-Generated Content, UGC) 類型資料劇增，例如：Google Map的地圖服務之興趣點資料Point Of Interest, POI)、Facebook和Instagram等社交軟體的打卡資料等，皆為UGC資料，POI具有樣本量大、涵蓋資訊細緻等優勢。都市的快速演變為都市規劃和管理帶來新的挑戰，在都市議題越漸複雜的情況下，新的數位工具與資訊技術發展將能用以發現和解決課題，並協助規劃者進行不同角度的研究。
土地混合使用為土地使用型態之一，既有國土利用調查已無法精確表達土地特性，本研究運用POI資料分析住宅區內部機能設施樣態，驗證都市為因應人類活動而有機生長，並呈現相較國土利用調查更為貼切實際土地使用之樣貌。
都市形態的形成與轉變都是都市有機屬性的特徵，也是都市有機體生命活動表現的形式。本研究以有機都市的概念為基礎，都市應隨著人類生活需求呈現有機狀生長。將每項人類生活需求之生活機能視為一個節點，運用POI可精準展現生活機能住宅使用之間的空間關係，以及對於都市功能分區的樣態影響。相較於現有無法精準展現空間特性的塊狀土地使用資料，POI 有更精細的空間點位和屬性分類，能精準展現空間特性，相同類型的POI可以位於不同的土地使用中，也可支持不同的功能。藉由POI探討住宅區的各種機能設施分布與屬性，突破以往僅侷限於使用分區的功能限制，確切掌握使用分區內的各項功能，以利於得知實際機能設施樣態。
研究設計主要分為三階段，第一階段為資料蒐集、處理和POI類型分類。第二階段為資料分析，此階段分為兩部分，第一部分為空間自相關分析，分別以全域型空間自相關Moran’s I和區域型空間自相關LISA，了解POI之間的空間相依性；第二部分為針對POI做路網分析和集群分析，建立住宅區與POI的空間關係；第三部分運用POI定量識別各單元功能區，探討基隆市之POI的樣態與特性。
運用POI取代國土利用調查快速且準確呈現住宅區機能設施樣態，POI不僅資料相較於國土利用調查取得更容易取得，資訊也相對細緻，主要成果為建立POI與住宅區的空間連結，結合區位特性等因素做綜合評估，了解POI機能設施的空間相依性和特性，藉此判別住宅區發展狀態與類型。後續建議可做POI的時間序列分析和住宅區居民偏好分析。隨時間變化的POI樣態做時間序列分析，辨識住宅區的發展歷程；以及結合社會經濟背景做偏好分析，了解人口或經濟等對於POI機能設施的發展影響，並判別各類型住宅區居住族群。

The widespread use of software innovation services has led to an increase in User-Generated Content (UGC), such as POI (Point of Interest) data from Google Maps and check-ins from social media platforms like Facebook and Instagram. POI data provides large sample sizes and detailed information, offering advantages over traditional land use surveys. The rapid evolution of cities presents new challenges for urban planning and management. As urban issues become more complex, new digital tools and technologies can help discover and solve problems from various perspectives.
This study takes Keelung City as the empirical area and uses POI data to analyze residential area functions and verify the organic growth of cities in response to human activity. POI data, compared to traditional 2D land use surveys, provides more accurate land use information and showcases the characteristics of urban growth. The research is divided into three stages: data collection and POI classification, spatial autocorrelation and network analysis, and identifying functional zones using POI. Through spatial analysis, the study reveals the interdependence of POIs and the spatial relationship within residential areas. It also explores POI characteristics to understand the functions of residential areas. The study applies the concept of an organic city, using 3D POI data to better represent the urban environment and its development.

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