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Scatter Plot

Cheng WU


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    <dct:title>Scatter Plot</dct:title>
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    <dcat:keyword>Deming Regression</dcat:keyword>
    <dcat:keyword>York Regression</dcat:keyword>
    <dcat:keyword>Weighted orthogonal distance regression</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-05-01</dct:issued>
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    <dct:description>&lt;p&gt;Version 20190501&lt;/p&gt; &lt;p&gt;Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software had been extensively used, but they remain unable to fulfill a number of specified&amp;nbsp;research purposes in atmospheric science. That becomes the motivation of Scatter Plot development. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, and is more realistic for atmospheric applications.&amp;nbsp; Scatter Plot is Igor based, and packed with a variety of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z-axis, data filtering and grouping on different time scales (year, season, month, hour, day of week, etc). &amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;For more details regarding the evaluation and application of Scatter Plot, please refer to&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Wu, C.&lt;/strong&gt;&amp;nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&amp;nbsp;&lt;strong&gt;Atmos. Meas. Tech.&lt;/strong&gt;, 11, 1233-1250,&amp;nbsp;&lt;a href="https://doi.org/10.5194/amt-11-1233-2018"&gt;doi:10.5194/amt-11-1233-2018&lt;/a&gt;, 2018.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Please cite this paper if Scatter Plot is used in your publication.&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;The latest version of the program can be found on my website:&lt;/p&gt; &lt;p&gt;&lt;a href="https://sites.google.com/site/wuchengust/"&gt;https://sites.google.com/site/wuchengust/&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&lt;a href="https://doi.org/10.5281/zenodo.832416"&gt;https://doi.org/10.5281/zenodo.832416&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;Adoption in research publications:&lt;br&gt; &lt;br&gt; Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: 10.5194/acp-19-8569-2019, 2019.&lt;br&gt; &lt;br&gt; Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: 10.1016/j.atmosenv.2017.07.009, 2017.&lt;br&gt; &lt;br&gt; Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: Implications for inter-protocol data conversion, Atmos. Meas. Tech. doi: 10.5194/amt-9-4547-2016, 2016.&lt;br&gt; &lt;br&gt; Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, 2016.&lt;a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2016.08.054"&gt;http://dx.doi.org/10.1016/j.atmosenv.2016.08.054&lt;/a&gt;&lt;br&gt; &lt;br&gt; &lt;br&gt; Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015.&amp;nbsp;&lt;a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2015.03.010"&gt;http://dx.doi.org/10.1016/j.atmosenv.2015.03.010&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;=======================================================================================================&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。 虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。 本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。&lt;/p&gt; &lt;p&gt;有关Scatter Plot的评估和应用的更多细节,请参阅(&lt;strong&gt;如果你在文章中用到了本软件,请引用以下文章&lt;/strong&gt;)&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Wu, C.&lt;/strong&gt;&amp;nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&amp;nbsp;&lt;strong&gt;Atmos. Meas. Tech.&lt;/strong&gt;, 11, 1233-1250,&amp;nbsp;&lt;a href="https://doi.org/10.5194/amt-11-1233-2018"&gt;doi:10.5194/amt-11-1233-2018&lt;/a&gt;, 2018.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;关于程序的最新信息可以在我的网站上找到:&lt;/p&gt; &lt;p&gt;&lt;a href="https://sites.google.com/site/wuchengust/"&gt;https://sites.google.com/site/wuchengust/&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&lt;a href="https://doi.org/10.5281/zenodo.832416"&gt;https://doi.org/10.5281/zenodo.832416&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
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