| Title |
Geostatistical characterization of the soil of Aguascalientes, México, by using spatial estimation techniques
|
|---|---|
| Published in |
SpringerPlus, June 2016
|
| DOI | 10.1186/s40064-016-2593-7 |
| Pubmed ID | |
| Authors |
Ricardo Magdaleno-Márquez, María de la Luz Pérez-Rea, Víctor M. Castaño |
| Abstract |
Four spatial estimation techniques available in commercial computational packages are evaluated and compared, namely: regularized splines interpolation, tension splines interpolation, inverse distance weighted interpolation, and ordinary Kriging estimation, in order to establish the best representation for the shallow stratigraphic configuration in the city of Aguascalientes, in Central Mexico. Data from 478 sample points along with the software ArcGIS (Environmental Systems Research Institute, Inc. (ESRI), ArcGIS, ver. 9.3, Redlands, California 2008) to calculate the spatial estimates. Each technique was evaluated based on the root mean square error, calculated from a validation between the generated estimates and measured data from 64 sample points which were not used in the spatial estimation process. The present study shows that, for the estimation of the hard-soil layer, ordinary Kriging offered the best performance among the evaluated techniques. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 43% |
| Professor | 1 | 14% |
| Researcher | 1 | 14% |
| Student > Master | 1 | 14% |
| Unknown | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 43% |
| Agricultural and Biological Sciences | 1 | 14% |
| Nursing and Health Professions | 1 | 14% |
| Unknown | 2 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 05 July 2016.
All research outputs
#15,379,760
of 22,880,230 outputs
Outputs from SpringerPlus
#935
of 1,851 outputs
Outputs of similar age
#223,006
of 352,012 outputs
Outputs of similar age from SpringerPlus
#123
of 228 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,851 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 35th percentile – i.e., 35% 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 352,012 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 228 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.