不動產價格的影響因素可按影響範圍區分為三大類，分別為影響整體不動產市場的「總體環境因素」，對一定範圍內不動產產生價格影響的「區域環境因素」，及對於單一不動產價格有所影響的「房屋個體因素」。其中，區域環境因素為影響個別不動產價格之首要因素，不動產之價格會受到所屬區域之政治、經濟、自然、社會等因素影響，「公共建設因素」為重要之區域環境之一，包含公共設施水準及其配置狀態。影響個別不動產價格之次要因素為「房屋個體因素」，可再次細分為三大影響因素如下：房屋本身所具有的特徵因素，即建築物之內部結構；房屋的建築方式，住宅類型等與全棟房屋有關的因素；與房屋鄰近地區環境有關的因素。而集合式與獨立式住宅因分屬不同房屋類型，即上述房屋價格形成因素中「房屋之建築方式」。實際交易上，獨立式住宅多半以「整棟建物」作為交易計算單位，對於坐落之基地權利持分通常為全部，而集合式住宅係以「樓層」、「戶」作為交易之計算單位，所有之基地持分與其他住戶共同持有，基於上述差異，過去研究多將建築方式視為影響房屋價格的條件之一，並據此分類次市場，因此較少有研究同時探討二者在空間分布上所具有的區位差異，及購屋者對於環境的偏好是否有所不同。且過去文獻多半以使用傳統迴歸模型為主要分析方法。但傳統迴歸分析所使用最小平方法迴歸模型，經常會產生殘差項存在有空間自相關的問題，及空間本身所存在之空間異質性偏誤，即空間不穩定性。因此 本文以台中市都會區內之住家使用房屋為樣本，依特徵價格理論將獨立式住宅與集合式住宅視為差異化商品，其內外特徵納入變數，使用GeoDa軟體進行空間自相關分析，並使用ArcGIS軟體中的地理加權迴歸模組（GWR）進行迴歸分析，藉以探討不同類型房屋所偏好之外部特徵，瞭解不同空間環境對房屋價格之影響及台中市都會區空間發展型態，並驗證其於規劃建設產生的空間不穩定性。 研究結果顯示，台中市建立之重大市政建設及土地開發計畫會影響集合式住宅與獨立式住宅之地價熱點分布，其共同之房價熱點均座落於高地價市地重劃區及重大市政建設分布位置，而獨立式住宅之房價熱點，進一步分布於與高地價市重劃區鄰近之市地重劃區；在購屋者對周圍設施偏好方面，集合式住宅購屋者對於國中小學、大學、重大市政建設、市場、公園均有顯著偏好，惟獨立式住宅購屋者對於大學、重大市政建設、公園有顯著偏好，對於國中小學、市場有不偏好情形，顯示不同類型住宅對於公共設施之偏好不完全相同；集合式住宅與獨立式住宅之房屋特徵屬性呈現空間不穩定性，分析結果顯示，上述二種住宅類型，對於本研究所有公共設施距離特徵屬性均呈現空間不穩定、非均質性的結果，顯示不同類型住宅均會與彼此具有相依性，並形成各區域間的異質性。 / Locational characteristics are the determinants of house prices. While former research have examined the effects of proximity to resources and facilities have on residential property values, and the change of the importance as located regions or submarkets vary, the effects of different types of houses are rarely compared due to their dissimilarity in ways of building and ownership. Do house price effects of the same facility alter when properties are situated in different submarkets? Further, the issues of spatial non-stationarity are usually overlooked by previous studies. By using transaction data of two common types of residential houses in Taichung City, we found house price hot spots of both detached houses and condos in regions with major constructions and development plans. Apart from the mutual hot spots found in high land price redevelopment zones, we also discovery hot spots of detached houses in areas in proximity to these redevelopment zones. As for desirable facilities for home buyers, neighborhood schools, universities, major constructions, local markets and parks were found to have an notable price impact on condos, whereas only universities, major constructions and parks in vicinity of in detached houses can we found significant price effects, suggesting the differences in the preference of consumers in distinct regions. Also, spatial dependence and heterogeneity are verified in both types of houses, making the entire market area spatial non-stationary.

近年來由於溫室效應加劇以及氣候變遷加劇，因此符合綠色運輸特性的公共自行車系統，成為各國交通部門發展綠運輸政策時的目標之一，同時，大數據分析亦是目前受到高度關注的熱門議題。而本研究首先使用臺北市微笑單車租借大數據探討在不同時間點下民眾日常使用微笑單車之旅運行為，分析不同站點間的旅次特性。再運用社群網絡分析，以站點之間旅次連結多寡作為權重，探討站點間之緊密程度，以及不同時間點下微笑單車租借量之熱點分布情形，並將其視覺化呈現。 後續透過文獻分析，擷取影響公共自行車使用量之因素後，本研究嘗試運用一般線性迴歸模型與地理加權迴歸進行模型建立，並探討各影響因素對於旅運需求之影響情形。實證結果顯示，地理加權迴歸模型可以解決一般線性迴歸所產生空間自相關問題，使得模型解釋能力獲得改善。本研究並使用地理加權迴歸進行使用需求分析以及預測，對未來公共自行車營運以及站點擴張提出結論以及建議，期能提升公共自行車系統之使用量。 / Due to the climate change and aggravation of the greenhouse effect in recent years, the public bicycle system with the feature of low-carbon emission has raised more and more attention internationally, and has become one of the targets in developing green transportation policies of transportation departments of governments around the world. Meanwhile Big Data analysis issues, on the other hand, are currently a sought-after topic which has caused great concern as well. In this study, we utilize the rental data of the YouBike system in Taipei to discuss the public usage of YouBike tour at different periods. With the use of social network analysis, we discuss the relationships between different bicycle stops based on applying the number of travels between different sites as the weight. Eventually, the hotspot analysis will be carried out by operating the GIS system. In this way, we are able to discuss the hotspot distribution of YouBike rentals in different time and then visualize the result. After that this study pick up the variables which will effect the YouBike usage by reference review. This research try to built models by utilizing the Least Squares Method and Geographically Weighted Regression. Then we will have a discussion with the result of the two models. The result shows that Geographically Weighted Regression can resolve the spatial autocorrelation problem which happened in the Least Squares Method and to gain a better result. With the analysis and prediction of public bicycle system from Geographically Weighted Regression, we hope to raise the usage of public bicycle system by concluding as well as making recommendations for the future operation of public bicycle and the expansion of bicycle stops.