交通與土地變遷之於社會經濟發展之研究—以桃園地區為例

運輸系統、土地使用與社會經濟發展之間存在一定程度上的交互作用關係，過往文獻中皆有研究以量化方式來探討其關聯性；然而，多針對單一運輸系統所對地方帶來的影響，相對較少以量化模型探討一地在一段時間整體的發展情形。因此本研究試圖探討近年來受到臺北都會區外溢影響的桃園地區，在高速發展的情況下探討影響當地發展的主要因素，以及桃園地區在運輸系統、土地使用、社會經濟發展變數之間的交互作用關係。本研究利用兩組計量模型—追蹤資料分析與聯立方程模型針對桃園市的兩大都心—桃園區與中壢區進行分析，試圖找出影響桃園、中壢兩區發展的主要因素。同時勾勒出桃園區、中壢區在運輸系統、土地使用與社會經濟發展上因子的交互作用關係，並利用聯立方程模型預測房價及社會增加率並以實際的資料進行驗證，以檢視模式的準確度。本研究以 2015 年至 2019 年的資料建構出桃園區、中壢區在運輸系統、土地使用與社會經濟發展變數的交互作用模型，並用以預測 2020 年上半年的發展，在預測方面準確度整體而言仍有一定水準，顯示模式能解釋、描繪出桃園、中壢兩區發展的軌跡，可做為相關單位在日後都市計畫或是運輸規劃上的參考。

關鍵字

運輸系統與土地使用互動關聯；聯立方程模型；追蹤資料分析；時間延滯效應

並列摘要

This study aims to explore the spatiotemporal interactions between transportation systems, land use, and socioeconomic development in the Taoyuan Area, the fast-growing area due to the urban sprawl of the Taipei Metropolitan Area in recent years. Two quantitative methods are adopted in the research: a panel data analysis to identify the key factors that may influence the development of the Taoyuan Area and a simultaneous equation model to investigate the spatiotemporal interaction between the variables associated with transportation systems, land use, and socioeconomic development while considering potential time-lagged effects. In consideration of data availability, this study focus on the two city centers in Taoyuan Area: Taoyuan District and Zhongli District. A panel data analysis model and a simultaneous equation model are constructed for each district to explain the development trajectory of each district and how it was influenced and defined by the interactive effects between identified significant variables. In addition, the prediction of house prices and social increase rates is conducted and compared with the real data to examine the prediction capability of the models. In this study, the data from 2015 to 2019 are used to construct the simultaneous equation model of the interactions between variables of the transportation system, land use, and socioeconomic development. The house price and social increase rate in the first half of 2020 are predicted, and the accuracy of the prediction is more than reasonable, suggesting that the model can be referred to authorities for urban and transportation planning.