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Scatter Plot

Cheng WU


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    "description": "<p>Version 20190501</p>\n\n<p>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&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.&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). &nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p>For more details regarding the evaluation and application of Scatter Plot, please refer to&nbsp;</p>\n\n<p><strong>Wu, C.</strong>&nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&nbsp;<strong>Atmos. Meas. Tech.</strong>, 11, 1233-1250,&nbsp;<a href=\"https://doi.org/10.5194/amt-11-1233-2018\">doi:10.5194/amt-11-1233-2018</a>, 2018.</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Please cite this paper if Scatter Plot is used in your publication.</strong></p>\n\n<p>&nbsp;</p>\n\n<p>The latest version of the program can be found on my website:</p>\n\n<p><a href=\"https://sites.google.com/site/wuchengust/\">https://sites.google.com/site/wuchengust/</a></p>\n\n<p><a href=\"https://doi.org/10.5281/zenodo.832416\">https://doi.org/10.5281/zenodo.832416</a></p>\n\n<p>&nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p>Adoption in research publications:<br>\n<br>\nJi, 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.<br>\n<br>\nWang, 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.<br>\n<br>\nWu, 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.<br>\n<br>\nZhou, 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.<a href=\"https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2016.08.054\">http://dx.doi.org/10.1016/j.atmosenv.2016.08.054</a><br>\n<br>\n<br>\nQiao, 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.&nbsp;<a href=\"https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2015.03.010\">http://dx.doi.org/10.1016/j.atmosenv.2015.03.010</a></p>\n\n<p>&nbsp;</p>\n\n<p>=======================================================================================================</p>\n\n<p>&nbsp;</p>\n\n<p>Scatter Plot\u662f\u4e00\u4e2a\u65b9\u4fbf\u7684\u5de5\u5177,\u53ef\u4ee5\u6700\u5927\u9650\u5ea6\u5730\u63d0\u9ad8\u5927\u6c14\u79d1\u5b66\u4e2d\u6570\u636e\u53ef\u89c6\u5316\u7684\u6548\u7387\u3002 \u867d\u7136\u6709\u8bb8\u591a\u73b0\u6709\u7684\u901a\u7528\u6570\u636e\u53ef\u89c6\u5316\u8f6f\u4ef6,\u4f46\u4e0d\u80fd\u6ee1\u8db3\u8bb8\u591a\u5927\u6c14\u79d1\u5b66\u7279\u5b9a\u7684\u7814\u7a76\u76ee\u7684,\u6240\u4ee5\u6211\u5f00\u53d1\u81ea\u5df1\u7684\u7a0b\u5e8f\u3002 \u672c\u7a0b\u5e8f\u5305\u62ecWODR, Deming\u548cYork\u7b97\u6cd5\u8fdb\u884c\u7ebf\u6027\u56de\u5f52,\u8fd9\u4e09\u79cd\u7b97\u6cd5\u8003\u8651\u4e86X\u548cY\u90fd\u5305\u542b\u4e0d\u786e\u5b9a\u6027(\u89c2\u6d4b\u8bef\u5dee),\u5bf9\u5927\u6c14\u7684\u5e94\u7528\u800c\u8a00\u66f4\u52a0\u5ba2\u89c2\u5730\u53cd\u6620\u771f\u5b9e\u60c5\u51b5\u3002\u5b83\u662f\u57fa\u4e8eIgor\u7684,\u5e76\u4e14\u5305\u542b\u5927\u91cf\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u56fe\u5f62\u7ed8\u56fe\u7684\u6709\u7528\u529f\u80fd,\u5305\u62ec\u6279\u91cf\u7ed8\u56fe,\u901a\u8fc7\u56fe\u5f62\u754c\u9762\u5b9e\u73b0\u6570\u636e\u63a9\u853d,Z\u8f74\u7684\u989c\u8272\u7f16\u7801,\u6839\u636e\u6570\u636e\u6216\u5b57\u7b26\u4e32\u8fdb\u884c\u8fc7\u6ee4\u548c\u5206\u7ec4\u3002</p>\n\n<p>\u6709\u5173Scatter Plot\u7684\u8bc4\u4f30\u548c\u5e94\u7528\u7684\u66f4\u591a\u7ec6\u8282,\u8bf7\u53c2\u9605(<strong>\u5982\u679c\u4f60\u5728\u6587\u7ae0\u4e2d\u7528\u5230\u4e86\u672c\u8f6f\u4ef6,\u8bf7\u5f15\u7528\u4ee5\u4e0b\u6587\u7ae0</strong>)</p>\n\n<p><strong>Wu, C.</strong>&nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&nbsp;<strong>Atmos. Meas. Tech.</strong>, 11, 1233-1250,&nbsp;<a href=\"https://doi.org/10.5194/amt-11-1233-2018\">doi:10.5194/amt-11-1233-2018</a>, 2018.</p>\n\n<p>&nbsp;</p>\n\n<p>\u5173\u4e8e\u7a0b\u5e8f\u7684\u6700\u65b0\u4fe1\u606f\u53ef\u4ee5\u5728\u6211\u7684\u7f51\u7ad9\u4e0a\u627e\u5230:</p>\n\n<p><a href=\"https://sites.google.com/site/wuchengust/\">https://sites.google.com/site/wuchengust/</a></p>\n\n<p><a href=\"https://doi.org/10.5281/zenodo.832416\">https://doi.org/10.5281/zenodo.832416</a></p>\n\n<p>&nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p>&nbsp;</p>", 
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        "affiliation": "Jinan University", 
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