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| Data refinement method for sampling sites of agricultural soil heavy metals: A case study in Shunyi district, Beijing， China |
| Received:March 21, 2020 |
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| KeyWord:sampling sites;data refinement;even variation index;spatial distribution;deviation index |
| Author Name | Affiliation | E-mail | | TANG Gui-biao | College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China | | | ZHU Qing-wei | College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China | | | DONG Shi-wei | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | dongsw@nercita.org.cn | | GAO Bing-bo | College of Land Science and Technology, China Agricultural University, Beijing 100193, China | | | PAN Yu-chun | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | | | WANG Yi-rong | College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China | | | GAO Yun-bing | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | |
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| Abstract: |
| The spatial distribution of sampling sites is the key factor for the detection, evaluation, and mining analysis of sampling data. By selecting a case study in Shunyi district, Beijing, a data refinement method for the sampling sites of agricultural soil heavy metals was developed in this study. First, the even variation indices and even factor discrete graphs of sampling sites were constructed to detect their data uniformity. The sampling sites were then divided into even, aggregate, and sparse sampling sites, and their corresponding amounts were determined. Second, aggregate sampling sites were deleted and sparse sampling sites were densified based on the historical sites of the study area. Third, the data refinement effect was evaluated based on the even variation index in the geographical space, deviation index in the feature space, and spatial interpolation error. The results showed that the even variation index of sampling sites in the study area was 0.429, with aggregate and sparse sampling sites; the deviation index in the feature space and spatial interpolation error were 0.327 and 6.538, respectively. After redundant data refinement, aggregate sampling sites and sparse sampling sites were not found by uniformity detection. The even variation index and interpolation error were reduced to 0.406 and 6.357, respectively; the deviation index slightly decreased. This study suggests that the method can provide theoretical guidance for improving the uniformity and representativeness of sampling sites. It can support soil pollution prevention and control action plans, soil pollution situation detailed investigation, etc., providing some basic conditions for further accurate study of soil spatial information changes. |
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