Sample Unit Size Effects of <i>Oncomelania hupensis snail</i> on Its Spatial Statistics in the Marshland <i>schistosomiasis</i> Epidemic Area in China

Liu Qing; Zhao An; Ma Yukuan; Li Cui; Zhang Wenxin

doi:doi:10.11648/j.sd.20160403.12

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Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China

Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin

Published in Science Discovery (Volume 4, Issue 3)

Received: 1 July 2016 Accepted: Published: 5 July 2016

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Abstract

Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.

Published in	Science Discovery (Volume 4, Issue 3)
DOI	10.11648/j.sd.20160403.12
Page(s)	165-172
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Oncomelania snail, Sample Unit Size Effects, Spatial Variance, Geo-statistics

References

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APA Style

Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. (2016). Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Science Discovery, 4(3), 165-172. https://doi.org/10.11648/j.sd.20160403.12

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ACS Style

Liu Qing; Zhao An; Ma Yukuan; Li Cui; Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci. Discov. 2016, 4(3), 165-172. doi: 10.11648/j.sd.20160403.12

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AMA Style

Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci Discov. 2016;4(3):165-172. doi: 10.11648/j.sd.20160403.12

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@article{10.11648/j.sd.20160403.12,
  author = {Liu Qing and Zhao An and Ma Yukuan and Li Cui and Zhang Wenxin},
  title = {Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China},
  journal = {Science Discovery},
  volume = {4},
  number = {3},
  pages = {165-172},
  doi = {10.11648/j.sd.20160403.12},
  url = {https://doi.org/10.11648/j.sd.20160403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160403.12},
  abstract = {Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.},
 year = {2016}
}

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TY - JOUR
T1 - Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China
AU - Liu Qing
AU - Zhao An
AU - Ma Yukuan
AU - Li Cui
AU - Zhang Wenxin
Y1 - 2016/07/05
PY - 2016
N1 - https://doi.org/10.11648/j.sd.20160403.12
DO - 10.11648/j.sd.20160403.12
T2 - Science Discovery
JF - Science Discovery
JO - Science Discovery
SP - 165
EP - 172
PB - Science Publishing Group
SN - 2331-0650
UR - https://doi.org/10.11648/j.sd.20160403.12
AB - Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.
VL - 4
IS - 3
ER -

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Author Information

Liu Qing

School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China
Zhao An

School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China; Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang City, China
Ma Yukuan

School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China
Li Cui

School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China
Zhang Wenxin

School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

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APA Style

Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. (2016). Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Science Discovery, 4(3), 165-172. https://doi.org/10.11648/j.sd.20160403.12

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ACS Style

Liu Qing; Zhao An; Ma Yukuan; Li Cui; Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci. Discov. 2016, 4(3), 165-172. doi: 10.11648/j.sd.20160403.12

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AMA Style

Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci Discov. 2016;4(3):165-172. doi: 10.11648/j.sd.20160403.12

Copy | Download

@article{10.11648/j.sd.20160403.12,
  author = {Liu Qing and Zhao An and Ma Yukuan and Li Cui and Zhang Wenxin},
  title = {Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China},
  journal = {Science Discovery},
  volume = {4},
  number = {3},
  pages = {165-172},
  doi = {10.11648/j.sd.20160403.12},
  url = {https://doi.org/10.11648/j.sd.20160403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160403.12},
  abstract = {Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.},
 year = {2016}
}

Copy | Download