本計劃因應台灣地區大量適婚年齡男女未嫁娶與少子化，所以許多人將寵物視為家庭的一份子，讓台灣寵物市場穩定成長，因此計劃打造一個以寵物為主題的行動平台，本平台將建立一個結合交友、社群與電子商務的綜合產業生態圈。 本計劃規劃的執行方式為藉由App的方式，讓會員免費使用交友與社團服務，建立以寵物為共同興趣的大型社群；再透過大數據與LBS機制（Location-Based Service基於位置的服務）進行寵物用品與食品相關的電子商務與廣告媒合。 本計劃預期效益為三年內產生8千萬元台幣營收與締造會員30萬人；針對個人方面，本計劃為對寵物有興趣的男女進行交友媒合，並為其建立實體與線上的交流社團；針對廠商方面，本計劃為大型寵物食品用品廠商建立行銷廣告通路，為小型寵物食品用品與文創商品廠商建立銷售通路。在商家方面，本計劃為寵物用品店、寵物醫院、寵物旅館、寵物美容院、寵物餐廳等等建立與客戶連絡與溝通管道。

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.