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標題: 臺灣農地價格區位分析
Locality Analysis on Farm Land Prices in Taiwan
作者: 黃友宣
Huang, Yo-shuein
Contributors: 林正錺
土壤環境科學系所
關鍵字: 區位;農地價格;地理資訊系統
location;farmland
日期: 2012
Issue Date: 2013-11-07 11:39:15 (UTC+8)
Publisher: 土壤環境科學系所
摘要: 台灣在近年來社會和產業轉型的過程中,由於土地大量的需求,農地逐漸成為土地開發目標中重要的一環。農地價格除了對土地和都市開發會造成影響外,同時也對台灣農業的發展。瞭解農地價格對於國家農業政策的執行,農業的運作以及土地的利用開發都是相當重要的事情。早年農地的交易都是通過私人交易或是依靠當地的房產仲介進行交易,農委會為推行農業政策和透明化農地價格建立了台灣農地銀行,讓農民能透過此平台進行農地的交易,也能紀錄和瞭解當前各地農地的價格。
本研究蒐集自農委會之台灣農地銀行資訊系統交易資料。第一階段分析農地價格的基本特性以及自我空間相關性,找出不同價位的農地之間在分布上是否互有關聯。第二階段以地理資訊系統技術與統計功能,分析農地買賣、交易資料間的相關性。最後分別從社經發展和農業發展的觀點選出可能會影響土地價格的區位條件。將台灣農地分為全台灣、特定縣市及鄉鎮不同的分析範圍,逐層找出會影響台灣農地價格的區位條件。
台灣出售農地價位在空間分布上的自我相關性極低,顯示出台灣農地價格未見區域性的關聯。在區位特性分析上,區域農地平均價格與區域條件屬性平均值對應分析中,社經條件包含都市化程度、不動

產價格、人口密度、平均可支配所得以及各縣市都更案件數量等資料的平均相關係數達到0.66,尤其又以都市化程度對於各縣市農地價格有最高的相關性,達到0.77。可見台灣現在土地市場受到都市化程度的價格影響非常深遠。而在農業區位條件中,台灣農地買賣價格和農業區位條件如農地密度以及農業生產產值之間均顯示較低相關,平均的相關係數低於0.4。在定點農地各別價格與定點條件屬性對應分析中,以社經條件而言,不同的縣市受到各縣市的縣市發展程度以及產業不同,因此沒有可以通用在全部縣市的單一農地判斷準則。
在所分析的特定縣市中,最接近都市中心和都市計劃區的農地會有較高的價格,但各縣市中受到都市中心以及都市計劃中心影響範圍的距離會根據縣市發展而有所不同。在都市化程度較高的縣市,農地受到重大商業建築的影響也越大,相關係數達到0.99。在農業條件中,生產力等級以及重要性等級皆無法對農地價格以及租價產生影響,顯示在單一農地方面,農業條件同樣無法做為農地價格判斷的標準。
Because of more land demand in the progress of social and industrial transformation in recent years, agricultural land is becoming an important issue of the land use in Taiwan. Price of agricultural land has great impact not only on land and urban development, but also to the development of agriculture. It is important of learning the agricultural land price as a way of knowing agricultural policy, agricultural operations, and land use policy. Most of agricultural land transactions are done in private way or by real estate brokers. In order to mandating agricultural policy and transparence land prices, Council of Agriculture has been setting up a website, called Bank of Taiwan Agricultural Land, as a platform for farmers to sell, buy and lease their farm lands.
In this study, data from the website were collected and analyzed. The first step of the analysis is to find characteristics of the agricultural land prices, as well as the spatial correlation associated with each other data point. The second phase is to use soft-wares of geographic information system and statistical functions in analyzing the correlation between the agricultural land sale and purchase transaction data. Finally, land-location conditions from the view points of socio-economic and agricultural environments that may affect the prices were correlated. The analysis was separated into regional average price to average factor and specific point data to point condition.
The prices distribution in whole area has little regional association. The analysis of regional characteristics showing that the averaged prices and conditions, especially the socio-economic conditions include the degree of urbanization, real estate prices, population density, average income, as well as counties with more data points, might resulted in correlation coefficients up to 0.66. In particular, the degree of urbanization has the highest correlation coefficient of 0.77. On the other hand, agricultural land price and agricultural conditions such as the density of agricultural land and agricultural production showed lower correlations. The average correlation coefficient was around 0.4. No common correlation was found between the individual price and its corresponding condition in different counties, due to their different development progresses.
In the analysis of specific counties, the nearest urban center and urban planning area for the agricultural lands have higher price. The effective ranges differed with the development in each county. In addition, major commercial area made greater impact on agricultural land prices in counties which have higher degree of urbanization. The correlation coefficient could be as high as 0.99. On the contrary, agricultural conditions, the land productivity and the hierarchy of land importance, have no significant impact on the land prices, showing that agricultural land has no single factor to determine its price.
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