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Journal articles on the topic 'Disjunctive kriging'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Yates, S. R., A. W. Warrick, and D. E. Myers. "Disjunctive Kriging: 2. Examples." Water Resources Research 22, no. 5 (May 1986): 623–30. http://dx.doi.org/10.1029/wr022i005p00623.

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2

Yates, S. R. "Disjunctive Kriging: 3. Cokriging." Water Resources Research 22, no. 10 (September 1986): 1371–76. http://dx.doi.org/10.1029/wr022i010p01371.

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3

Azawi, Hayat, and May Samir Saleh. "Review of the Kriging Technique Applications to Groundwater Quality." Journal of Engineering 27, no. 12 (December 1, 2021): 23–32. http://dx.doi.org/10.31026/j.eng.2021.12.03.

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Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.
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4

Armstrong, M., and G. Matheron. "Disjunctive kriging revisited: Part I." Mathematical Geology 18, no. 8 (November 1986): 711–28. http://dx.doi.org/10.1007/bf00899739.

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5

Armstrong, M., and G. Matheron. "Disjunctive kriging revisited: Part II." Mathematical Geology 18, no. 8 (November 1986): 729–42. http://dx.doi.org/10.1007/bf00899740.

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6

OLIVER, M. A., R. WEBSTER, and S. P. McGRATH. "DISJUNCTIVE KRIGING FOR ENVIRONMENTAL MANAGEMENT." Environmetrics 7, no. 3 (May 1996): 333–57. http://dx.doi.org/10.1002/(sici)1099-095x(199605)7:3<333::aid-env209>3.0.co;2-v.

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7

Wood, G., M. A. Oliver, and R. Webster. "Estimating soil salinity by disjunctive kriging." Soil Use and Management 6, no. 3 (September 1990): 97–104. http://dx.doi.org/10.1111/j.1475-2743.1990.tb00817.x.

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8

Puente, Carlos E., and Rafael L. Bras. "Disjunctive kriging, universal kriging, or no kriging: Small sample results with simulated fields." Mathematical Geology 18, no. 3 (April 1986): 287–305. http://dx.doi.org/10.1007/bf00898033.

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9

Hawkins, Douglas M., and Jacques Rivoirard. "Introduction to Disjunctive Kriging and Nonlinear Geostatistics." Journal of the American Statistical Association 91, no. 433 (March 1996): 435. http://dx.doi.org/10.2307/2291430.

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10

Emery, Xavier. "Disjunctive Kriging with Hard and Imprecise Data." Mathematical Geology 35, no. 6 (August 2003): 699–718. http://dx.doi.org/10.1023/b:matg.0000002985.94274.8c.

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11

Yates, S. R., A. W. Warrick, and D. E. Myers. "A disjunctive kriging program for two dimensions." Computers & Geosciences 12, no. 3 (January 1986): 281–313. http://dx.doi.org/10.1016/0098-3004(86)90037-3.

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12

Campbell, Katherine. "Introduction to Disjunctive Kriging and Non-Linear Geostatistics." Technometrics 38, no. 2 (May 1996): 187–88. http://dx.doi.org/10.1080/00401706.1996.10484475.

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13

Tilke, Clemens. "Introduction to Disjunctive Kriging and Non-Linear Geostatistics." Computational Statistics & Data Analysis 25, no. 2 (July 1997): 244. http://dx.doi.org/10.1016/s0167-9473(97)89656-9.

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14

Yates, S. R., and Marylynn V. Yates. "Disjunctive Kriging as an Approach to Management Decision Making." Soil Science Society of America Journal 52, no. 6 (November 1988): 1554–58. http://dx.doi.org/10.2136/sssaj1988.03615995005200060006x.

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15

Yates, S. R., A. W. Warrick, and D. E. Myers. "Disjunctive Kriging: 1. Overview of Estimation and Conditional Probability." Water Resources Research 22, no. 5 (May 1986): 615–21. http://dx.doi.org/10.1029/wr022i005p00615.

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16

von Steiger, B., R. Webster, R. Schulin, and R. Lehmann. "Mapping heavy metals in polluted soil by disjunctive kriging." Environmental Pollution 94, no. 2 (1996): 205–15. http://dx.doi.org/10.1016/s0269-7491(96)00060-7.

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17

Raspa, G., F. Salvi, and G. Torri. "Probability mapping of indoor radon-prone areas using disjunctive kriging." Radiation Protection Dosimetry 138, no. 1 (September 29, 2009): 3–19. http://dx.doi.org/10.1093/rpd/ncp180.

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18

Samui, P., and T. G. Sitharam. "Spatial variability of SPT data using ordinary and disjunctive kriging." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 4, no. 1 (March 2010): 22–31. http://dx.doi.org/10.1080/17499510902792209.

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19

WEBSTER, R. "Local disjunctive kriging of soil properties with change of support." Journal of Soil Science 42, no. 2 (June 1991): 301–18. http://dx.doi.org/10.1111/j.1365-2389.1991.tb00411.x.

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20

Emery, Xavier. "A disjunctive kriging program for assessing point-support conditional distributions." Computers & Geosciences 32, no. 7 (August 2006): 965–83. http://dx.doi.org/10.1016/j.cageo.2005.10.011.

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21

Carr, James R., Eddy D. Deng, and Charles E. Glass. "An application of disjunctive kriging for earthquake ground motion estimation." Mathematical Geology 18, no. 2 (February 1986): 197–213. http://dx.doi.org/10.1007/bf00898283.

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22

Thakur, Mainak, Biswajit Samanta, and Debashish Chakravarty. "A non-stationary spatial approach to disjunctive kriging in reserve estimation." Spatial Statistics 17 (August 2016): 131–60. http://dx.doi.org/10.1016/j.spasta.2016.06.001.

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23

Gaus, I., D. G. Kinniburgh, J. C. Talbot, and R. Webster. "Geostatistical analysis of arsenic concentration in groundwater in Bangladesh using disjunctive kriging." Environmental Geology 44, no. 8 (November 1, 2003): 939–48. http://dx.doi.org/10.1007/s00254-003-0837-7.

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24

Murthy, P. S. N. "Comparison of ordinary and disjunctive kriging in an Indian iron ore deposit." Mathematical Geology 21, no. 4 (May 1989): 443–61. http://dx.doi.org/10.1007/bf00897328.

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25

Lark, R. M., and R. B. Ferguson. "Mapping risk of soil nutrient deficiency or excess by disjunctive and indicator kriging." Geoderma 118, no. 1-2 (January 2004): 39–53. http://dx.doi.org/10.1016/s0016-7061(03)00168-x.

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26

Eldeiry, Ahmed A., and Luis A. Garcia. "Using Disjunctive Kriging as a Quantitative Approach to Manage Soil Salinity and Crop Yield." Journal of Irrigation and Drainage Engineering 138, no. 3 (March 2012): 211–24. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0000392.

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27

Hani, A., E. Pazira, M. Manshouri, S. Babaie Kafaky, and M. Ghahroudi Tali. "Spatial distribution and mapping of risk elements pollution in agricultural soils of southern Tehran, Iran." Plant, Soil and Environment 56, No. 6 (June 3, 2010): 288–96. http://dx.doi.org/10.17221/16/2010-pse.

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In order to evaluate risk elements and their spatial distribution in agricultural fields south of Tehran, statistics, geostatistics and geographic information system (GIS) were used. The content of Hg, As, Pb, Mo, and Be were determined in 106 samples. The results showed that primary inputs of As and Hg were due to anthropogenic sources, while Pb, Mo and Be were associated with pedogenic and anthropogenic factors. Ordinary kriging was carried out to map the spatial patterns of risk elements and disjunctive kriging was used to quantify the probability of risk elements concentrations higher than their guide value. The results show that As, Hg, Mo and Be exhibit pollution risk in the study area. The high pollution sources evaluated were related with usage of urban and industrial wastewater for agricultural practice. The results of this study are helpful for risk assessment of environmental pollution for decision making for vegetable production and ecosystem improving.
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28

WEBSTER, R., and M. A. OLIVER. "Optimal interpolation and isarithmic mapping of soil properties. VI. Disjunctive kriging and mapping the conditional porbability." Journal of Soil Science 40, no. 3 (September 1989): 497–512. http://dx.doi.org/10.1111/j.1365-2389.1989.tb01292.x.

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29

Hongyi, Li, Wu Cifang, Li Fanghao, and Shi Zhou. "Three-Dimensional Variation of Electrical Conductivity in a Paddy Rice Soil Based on the Disjunctive Kriging Method." Research Journal of Applied Sciences, Engineering and Technology 6, no. 8 (July 10, 2013): 1486–89. http://dx.doi.org/10.19026/rjaset.6.3977.

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30

Petitgas, Pierre. "Use of a disjunctive kriging to model areas of high pelagic fish density in acoustic fisheries surveys." Aquatic Living Resources 6, no. 3 (July 1993): 201–9. http://dx.doi.org/10.1051/alr:1993021.

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31

Mendes, Maria Paula, and Luís Ribeiro. "Nitrate probability mapping in the northern aquifer alluvial system of the river Tagus (Portugal) using Disjunctive Kriging." Science of The Total Environment 408, no. 5 (February 2010): 1021–34. http://dx.doi.org/10.1016/j.scitotenv.2009.10.069.

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32

Barca, E., and G. Passarella. "Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation." Environmental Monitoring and Assessment 137, no. 1-3 (June 13, 2007): 261–73. http://dx.doi.org/10.1007/s10661-007-9758-3.

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33

Jawak, S. D., and A. J. Luis. "Synergetic merging of Cartosat-1 and RAMP to generate improved digital elevation model of Schirmacher oasis, east Antarctica." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 517–24. http://dx.doi.org/10.5194/isprsarchives-xl-8-517-2014.

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Available digital elevation models (DEMs) of Antarctic region generated by using radar altimetry and the Antarctic digital database (ADD) indicate elevation variations of up to hundreds of meters, which necessitates the generation of local DEM and its validation by using ground reference. An enhanced digital elevation model (eDEM) of the Schirmacher oasis region, east Antarctica, is generated synergistically by using Cartosat-1 stereo pair-derived photogrammetric DEM (CartoDEM)-based point elevation dataset and multitemporal radarsat Antarctic mapping project version 2 (RAMPv2) DEM-based point elevation dataset. In this study, we analyzed suite of interpolation techniques for constructing a DEM from RAMPv2 and CartoDEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improves the elevation accuracy of DEM from synergistically fused RAMPv2 and CartoDEM point elevation datasets. RAMPv2 points and CartoDEM points were used as primary data for various interpolation techniques such as ordinary kriging (OK), simple kriging (SK), universal kriging (UK), disjunctive kriging (DK) techniques, inverse distance weighted (IDW), global polynomial (GP) with power 1 and 2, local polynomial (LP) and radial basis functions (RBF). Cokriging of 2 variables with second dataset was used for ordinary cokriging (OCoK), simple cokriging (SCoK), universal cokriging (UCoK) and disjunctive cokriging (DCoK). The IDW, GP, LP, RBF, and kriging methods were applied to one variable, while Cokriging experiments were employed on two variables. The experiment of dataset and its combination produced two types of point elevation map categorized as (1) one variable (RAMPv2 Point maps and CartoDEM Point maps) and (2) two variables (RAMPv2 Point maps + CartoDEM Point maps). Interpolated surfaces were evaluated with the help of differential global positioning system (DGPS) points collected from study area during the Indian Scientific Expedition to Antarctic (ISEA). Accuracy assessment of the RAMPv2 DEM, CartoDEM, and combined eDEM (RAMPv2 + CartoDEM) by using DGPS as ground reference data shows that eDEM achieves much better accuracy (average elevation error 8.44 m) than that of existing DEM constructed by using only CartoDEM (13.57 m) or RAMPv2 (41.44 m) alone. The newly constructed eDEM achieves a vertical accuracy of about 7 times better than RAMPv2 DEM and 1.5 times better than CartoDEM. After using accurate DGPS data for accuracy assessment, the approximation to the actual surface of the eDEM extracted here is much more accurate with least mean root mean square error (RMSE) of 9.22 m than that constructed by using only CartoDEM (RMSE = 14.15 m) point elevation data and RAMPv2 (RMSE = 69.48 m) point elevation data. Our results indicate that, the overall trend of accuracy for the interpolation methods for generating continuous elevation surface from CartoDEM + RAMPv2 point elevation data, based on RMSE, is as follows: GP1 > IDW > GP2 > OK > LP2 > DK > LP1 > RBF > SK > UK. In case of cokriging interpolation methods, OCoK yields more accurate eDEM with the least RMSE of 8.16 m, which can be utilized to generate a highly accurate DEM of the research area.. Based on this work, it is inferred that GP2 and OCok interpolation methods and synergistic use of RAMPv2 and CartoDEM-based point elevation datasets lead to a highly accurate DEM of the study region. This research experiment demonstrates the stability (w.r.t multi-temporal datasets), performance (w.r.t best interpolation technique) and consistency (w.r.t all the experimented interpolation techniques) of synergistically fused eDEM. On the basis of average elevation difference and RMSE mentioned in present research, the newly constructed eDEM may serve as a benchmark for future elevation models such as from the ICESAT-II mission to spatially monitor ice sheet elevation.
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34

Carr, James R., and Eddy D. Deng. "Comparison of two techniques for applying disjunctive kriging: The Gaussian anamorphosis model versus the direct statistical inference of the bivariate distributions." Mathematical Geology 19, no. 1 (January 1987): 57–68. http://dx.doi.org/10.1007/bf01275434.

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35

Daya, Ali Akbar. "Application of disjunctive kriging for estimating economic grade distribution in an iron ore deposit: A case study of the Choghart North Anomaly, Iran." Journal of the Geological Society of India 83, no. 5 (May 2014): 567–76. http://dx.doi.org/10.1007/s12594-014-0085-3.

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36

Nugroho, Hary, Dewi Kania Sari, and Rika Hernawati. "Pemodelan Permukaan Digital Data Magnetik Survei Geofisika Udara menggunakan Metode Geostatistika untuk Ekplorasi Mineral (Daerah Studi: Wilayah Komopa, Papua)." REKA GEOMATIKA 2017, no. 2 (April 2, 2018). http://dx.doi.org/10.26760/jrg.v2017i2.1767.

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ABSTRAKDalam interpretasi data, data hasil survei geofisika udara umumnya perlu diubah menjadi model permukaan digital atau digital terrain model (DTM). Hal ini sebagai langkah untuk memudahkan dalam memahami kondisi data secara keseluruhan. Untuk membuat DTM banyak metode yang dapat diterapkan. Salah satu di antaranya adalah dengan metode Geostatistika Kriging. Penerapan metode Geostatistika Kriging dapat menggunakan berbagai macam teknik di antaranya adalah teknik Simple Kriging dan Disjunctive Kriging. Dalam penelitian ini dilakukan pengolahan DTM untuk data magnetik dengan menggunakan kedua teknik ini dengan aproksimasi Gaussian Kernel dan Density Skew. Wilayah studi pada penelitian ini adalah wilayah Komopa, Kabupaten Painai, Provinsi Papua yang merupakan wilayah Kontrak Karya PT. Freeport Indonesia. Adapun data yang digunakan adalah data hasil survei geofisika udara yang dilakukan pada periode 1983-1984. Hasil pemodelan yang diperoleh dari kedua teknik tersebut selanjutnya dibandingkan dan diperoleh hasil bahwa teknik Disjunctive Kriging dengan aproksimasi Density Skew lebih baik daripada teknik Simple Kriging dengan aproksimasi Gaussian Kernels maupun Density Skew.Kata kunci: survei geofisika udara, magnetik, DTM, geostatistika, krigingABSTRACTIn data interpretation, airborne geophysical survey results generally need to be transformed into a digital terrain model (DTM). This is an effort to facilitate in understanding the condition of the whole of data. To make the DTM, many methods can be applied. One of them is Kriging geostatistical method. Application of Kriging geostatistical method can use various techniques such as Simple Kriging and Disjunctive Kriging technique. In this research DTM processing for magnetic data has been performed by using both of these techniques with Gaussian Kernel and Density Skew approximation. The study area in this study is the area of Komopa, Painai District, Papua Province which is the area of Work Contract of PT. Freeport Indonesia. The data used is the data of airborne geophysical survey conducted in the period 1983-1984. The modelling results from the two techniques were then compared and the results showed that the Disjunctive Kriging technique with Density Skew approximation is better than Simple Kriging techique with Gaussian Kernels and Density Skew approximation.Keywords: airborne geophysical survey, magnetic, DTM, geostatistics, kriging
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37

Setiyoko, Andie, and Anil Kumar. "COMPARISON ANALYSIS OF INTERPOLATION TECHNIQUES FOR DEM GENERATION USING CARTOSAT-1 STEREO DATA." International Journal of Remote Sensing and Earth Sciences (IJReSES) 9, no. 2 (April 11, 2014). http://dx.doi.org/10.30536/j.ijreses.2012.v9.a1829.

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Digital Elevation Model (DEM) can be generated using several techniques such as photogrammetric technique, interferometry, Lidar, etc. In photogrammetric technique, a DEM generation using stereo images, accuracy of generated DEM is also dependent on interpolation techniques. The process of interpolation is conducted to generate DEM as a continuous data from the point map that contained height information as a discrete data. In this research, point map was extracted from Cartosat-1 stereo image and from geodetic single frequency GPS in differential mode. Different interpolation techniques were applied on these data sets with different combination within these data sets. In this study, analysis of DEM interpolation was conducted with deterministic interpolators such as inverse distance weighted (IDW), global polynomial, local polynomial, and radial basis functions (RBF); and probabilistic interpolators such as simple kriging, ordinary kriging, universal kriging, indicator kriging, probabilistic kriging, disjunctive kriging, and cokriging. The accuracy of generated DEMs through different interpolation techniques were evaluated with ground point data collected from geodetic single frequency GPS in differential mode. Based on the analysis, the range error of DEMs generated was between 1.29 m to 2.96 m. Interpolation method with the least error was ordinary kriging using point map data and GPS points, while the highest error was obtained from global polynomial method.
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38

Pang, Xinrui, Miao Ge, Congxia Wang, and Jinwei He. "Effects of geographical factors on reference values of the thyroid stimulating hormone in healthy adults in China and its clinical significance." Geospatial Health 17, no. 1 (May 6, 2022). http://dx.doi.org/10.4081/gh.2022.1030.

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The thyroid stimulating hormone (TSH) plays an important regulatory role in maintaining normal function of the thyroid gland. The purpose of this study was to explore the geographical, spatial distribution of TSH normal values in healthy Chinese adults to be used for the formulation of a standard reference. TSH values of 9321 healthy adults from 120 cities in China were collected together with 24 topographic, climatic and soil variables and used for the determination of spatial, significant relationships between TSH and these geographical factors by correlation analysis. Eleven significant factors were extracted and subjected to ridge regression and construction of vector machine models. The predicted values were tested for normality, with the disjunctive Kriging interpolation method used for geographical distribution. The values found showed a spatial pattern of higher values in the North and west but lower in the South and east We concluded that ridge regression models are useful for this kind of investigations and that certain geographical factors determine the level of TSH in healthy adults in a large expanse of land where topography, climate and soil indices vary.
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39

Yang, Wenjie, Miao Ge, Yabo Wang, Xinrui Pang, and Congxia Wang. "Spatial distribution differences of 25-hydroxyvitamin D in healthy elderly people under the influence of geographical environmental factors." Scientific Reports 12, no. 1 (July 27, 2022). http://dx.doi.org/10.1038/s41598-022-17198-9.

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AbstractThe main targets of this were to screen the factors that may influence the distribution of 25-hydroxyvitamin D[25(OH)D] reference value in healthy elderly people in China, and further explored the geographical distribution differences of 25(OH)D reference value in China. In this study, we collected the 25(OH)D of 25,470 healthy elderly from 58 cities in China to analyze the correlation between 25(OH)D and 22 geography secondary indexes through spearman regression analysis. Six indexes with significant correlation were extracted, and a ridge regression model was built, and the country’s urban healthy elderly’25(OH)D reference value was predicted. By using the disjunctive Kriging method, we obtained the geographical distribution of 25(OH)D reference values for healthy elderly people in China. The reference value of 25(OH)D for healthy elderly in China was significantly correlated with the 6 secondary indexes, namely, latitude (°), annual temperature range (°C), annual sunshine hours (h), annual mean temperature (°C), annual mean relative humidity (%), and annual precipitation (mm). The geographical distribution of 25(OH)D values of healthy elderly in China showed a trend of being higher in South China and lower in North China, and higher in coastal areas and lower in inland areas. This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of 25(OH)D index. A ridge regression model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the treatment factors of the vitamin D deficiency and prognostic factors of the COVID-19 using 25(OH)D reference value in different regions.
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