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A nonparametric spatial scan statistic for continuous data

Overview of attention for article published in International Journal of Health Geographics, October 2015
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  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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1 X user
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1 Facebook page

Citations

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18 Dimensions

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28 Mendeley
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Title
A nonparametric spatial scan statistic for continuous data
Published in
International Journal of Health Geographics, October 2015
DOI 10.1186/s12942-015-0024-6
Pubmed ID
Authors

Inkyung Jung, Ho Jin Cho

Abstract

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 4%
Colombia 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 29%
Student > Master 4 14%
Student > Bachelor 3 11%
Lecturer 2 7%
Student > Doctoral Student 2 7%
Other 4 14%
Unknown 5 18%
Readers by discipline Count As %
Mathematics 6 21%
Social Sciences 3 11%
Design 2 7%
Agricultural and Biological Sciences 2 7%
Environmental Science 1 4%
Other 7 25%
Unknown 7 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2015.
All research outputs
#16,074,502
of 24,457,696 outputs
Outputs from International Journal of Health Geographics
#431
of 642 outputs
Outputs of similar age
#162,049
of 288,522 outputs
Outputs of similar age from International Journal of Health Geographics
#3
of 8 outputs
Altmetric has tracked 24,457,696 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 642 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 288,522 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.