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首頁> 外文學(xué)位 >THE GEOGRAPHIC AND STATISTICAL ANALYSIS OF AIR QUALITY DATA IN THE UNITED STATES (OHIO).
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THE GEOGRAPHIC AND STATISTICAL ANALYSIS OF AIR QUALITY DATA IN THE UNITED STATES (OHIO).

機(jī)譯：美國（OHIO）空氣質(zhì)量數(shù)據(jù)的地理統(tǒng)計(jì)分析。

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This dissertation contains the development and the application of analytic procedures for examining and exploring some air quality data collected by the Environmental Protection Agency from 1974 through 1976. They are collected at monitoring stations most of which are in metropolitan areas. These data are irregularly distributed discrete point measurements. The techniques explored here may be useful in other disciplines with the same type of data.; The analysis is concentrated on two pollutants, suspended particulate and sulfur dioxide. There are two reasons for this restriction: (i) they are the most heavily monitored and (ii) they are of interest to the health field. The state of Ohio is utilized as an example in most of these analyses. This is because Ohio is the most thoroughly monitored state in the United States. A list of the limitations of these data is given.; Interpolation schemes are explored and a model is chosen which is a two-dimensional analogue of the moving average model in time series. The model is; (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI); where, e(,i) = the estimated value at a point i; x(,j) = a measured value at point j; d(,ij) = the distance from the data point to the point of estimation; d(,0) = the smoothing parameter. The choice of d(,0) has been explored in great detail. Cross-validation was used and several measures for the "best" d(,0) were examined. This led to the development of a much more efficient method for choosing a smoothing parameter, the concept of local variability as a function of disk radius. Each disk radius corresponds to a d(,0), so by minimizing the local variability function the most appropriate d(,0) can be chosen. Local variability functions were calculated for Ohio, New York and Florida. This analysis as opposed to cross-validation makes the task of modeling the entire United States a much smaller one. This model combined with cross-validation has been useful in detecting outliers in these data.; The evaluation of the moving average model led to comparing to Akima's method of bivariate linear interpolation. A cross-validatory comparison for adequacy of estimation was done. Also, contour maps using each method are drawn and compared. The local variability function analysis allows for comparison by cross-validation to not be a two-deep cross-validatory choice. Some drawbacks to comparing cross-validation estimates are pointed out. How different goals may prescribe different estimation techniques is discussed.; The potential for further research in this field is shown. Time, which may be important in these analyses, has not been included because of data availability limitations. Using a time parameter similar to d(,0), the current distance parameter, has been suggested. Simulations may also be useful in evaluating the moving average model. The distributional theory of the local variability theory function is yet to be explored.
機(jī)譯：這篇論文包含了分析程序的發(fā)展和應(yīng)用，這些程序用于檢查和探索環(huán)境保護(hù)局從1974年到1976年收集的一些空氣質(zhì)量數(shù)據(jù)。這些數(shù)據(jù)是在大都市地區(qū)的監(jiān)測站收集的。這些數(shù)據(jù)是不規(guī)則分布的離散點(diǎn)測量值。這里探討的技術(shù)可能在其他具有相同數(shù)據(jù)類型的學(xué)科中有用。分析集中在兩種污染物上，即懸浮顆粒物和二氧化硫。造成這種限制的原因有兩個(gè)：（i）他們受到最嚴(yán)格的監(jiān)控；（ii）健康領(lǐng)域?qū)Υ撕芨信d趣。在大多數(shù)這些分析中，以俄亥俄州為例。這是因?yàn)槎砗ザ碇菔敲绹茏顝氐妆O(jiān)視的州。列出了這些數(shù)據(jù)的局限性。探索插值方案并選擇一個(gè)模型，該模型是時(shí)間序列中移動(dòng)平均模型的二維模擬。該模型是; （省略了圖表，表格或圖形...請參見DAI）;其中，e（，i）=在點(diǎn)i處的估計(jì)值； x（，j）=在點(diǎn)j處的測量值； d（，ij）=從數(shù)據(jù)點(diǎn)到估計(jì)點(diǎn)的距離； d（，0）=平滑參數(shù)。 d（，0）的選擇已被詳細(xì)探討。使用交叉驗(yàn)證，并檢查了“最佳” d（，0）的幾種度量。這導(dǎo)致開發(fā)出一種更加有效的方法來選擇平滑參數(shù)，即隨磁盤半徑變化的局部變化性概念。每個(gè)圓盤半徑對應(yīng)一個(gè)d（，0），因此通過最小化局部變異函數(shù)，可以選擇最合適的d（，0）。計(jì)算了俄亥俄州，紐約和佛羅里達(dá)的局部變異函數(shù)。與交叉驗(yàn)證相反，這種分析使對整個(gè)美國建模的任務(wù)要小得多。該模型與交叉驗(yàn)證相結(jié)合，可用于檢測這些數(shù)據(jù)中的異常值。通過移動(dòng)平均模型的評估，可以將其與Akima的雙變量線性插值方法進(jìn)行比較。進(jìn)行了交叉驗(yàn)證比較，以評估估計(jì)是否足夠。而且，繪制并比較了使用每種方法的輪廓圖。局部變異函數(shù)分析允許通過交叉驗(yàn)證進(jìn)行比較，而不是兩個(gè)深度的交叉驗(yàn)證選擇。指出了比較交叉驗(yàn)證估計(jì)的一些缺點(diǎn)。討論了不同的目標(biāo)如何規(guī)定不同的估算技術(shù)。顯示了在該領(lǐng)域進(jìn)一步研究的潛力。由于數(shù)據(jù)可用性的限制，未包括在這些分析中可能很重要的時(shí)間。建議使用類似于當(dāng)前距離參數(shù)d（，0）的時(shí)間參數(shù)。仿真在評估移動(dòng)平均模型時(shí)也可能有用。局部變異性理論函數(shù)的分布理論尚待探索。

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