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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/69910

    Title: 台南地區都市熱島強度全年變動之研究
    Other Titles: Annual Variation of Urban Heat Island Intensity in the Tainan Region
    Authors: 孫振義
    Sun, Chen-Yi
    Contributors: 地政系
    Keywords: 都市熱島;都市化程度;地理資訊系統;汽車移動法
    Urban heat island;Level of urbanization;GIS;Moving automobile investigation
    Date: 2006.03
    Issue Date: 2014-09-15 14:45:11 (UTC+8)
    Abstract: 隨著都市化的發展趨勢,「都市熱島效應」日益嚴重,進而導致都市氣候環境品質惡化與都市高溫化、高空調耗電量的現象;本研究透過實測數據與氣候理論之整合,探索影響都市熱島之關鍵要素,以作為未來都市開發、土地使用規劃或國土資源政策之參考。本研究以台南縣市共計十四個鄉鎮市為實測範圍,以汽車移動法所得之數據配合各項基礎資料進行比對分析,得到以下結論:(1)由於氣溫低的天候條件下都市熱島效應會更顯著,因此本研究秋季所測得之最大熱島強度(ΔT)為3.79℃,較夏季測得之最大熱島強度(ΔT)2.85℃高出許多;且各季節平均都市熱島強度(ΔT')亦以秋季2.22℃最高,其次為冬季2.13℃、春季2.01℃與夏季1.47℃。(2)人口數低於三萬人且平均人口密度低(3~4人/公頃)的城鎮(如:六甲鄉、官田鄉),亦會產生顯著的都市熱島強度。(3)代表都市規模之人口對數以及代表都市開發程度的人口密度、非農業人口比例等因素,對於都市熱島強度存在著顯著的相關性。透過SPSS統計軟體進行複回歸分析結果,以人口對數、氣溫、農業用地比例、非農業人口比例為預測變數,共得「簡單式」及「精細式」二式以供作為未來預測參考之用。
    Urban heat island (UHI) resulting from urbanization has led to the deterioration of the urban environment, rising urban warming and higher air-conditioning energy consumption. To devise strategies that can be executed during the urban planning to alleviate UHI problem, this study integrated theory with empirical measurement data to explore the key influences on the UHI effect. This survey measured the air temperature of 14 settlements in the Tainan region by automobile traverse at cloudless midnight, once of month, during 2002~2003. The following conclusions can be drawn: (1) Urban heat island intensity (UHIs) is greater during cool weather: the maximum UHIs in winter was 3.79℃, approximately 1.3 times that in summer (2.85℃). The average UHIs of the four seasons were 2.01, 1.47, 2.22 and 2.13, respectively. (2) The UHIs are also significant even in small towns with low populations (below 30,000) and sparse population distribution (3~4 persons per hectare). (3) A significant correlation exists between UHIs and urbanization indexes (population, intensity and non-farming population). Based on Multiple Regression Analysis, Log (population), temperature, percentage of agricultural land and percentage of non-agricultural population are selected as the predictive variable, and two equations (the simple and precise equations) are derived to forecast UHIs in the future.
    Relation: 都市與計劃, Vol.33, No.1, pp.51-68
    Data Type: article
    Appears in Collections:[地政學系] 期刊論文

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