Presentation adv gis 08 01-2014
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Transcript of Presentation adv gis 08 01-2014
Outline Introduction of A Study Area Interpolation Spatial Interpolation & Methods
- Spline- Inverse Distance Weighting(IDW)- Kriging
Methodology- Study site
Data Collection Process & Representation Tools Results Conclusion
Introduction of A Study Area
In a pilot Area of Caracas, Venezuela
Frequency range of 100 kHz to 6 GHz
Taking 35 samples per second during a 6 minutes , 206 measurements points
Data points spaced approximately 100m from each other is fixed over the 2.64km2 pilot area
Interpolation?
Interpolation is a method of constructing
new data points within the range of a discrete set of known data points.
Spatial Interpolation?
Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data: elevation, rainfall, chemical concentrations, noise levels, and so on.
Spatial Interpolation Methods
1.Spline
method estimates values using a mathematical function
that minimizes the total surface curvature, resulting in a
smooth surface that passes exactly through the sampled points
2.Inverse Distance Weighting(IDW)
• is based on the assumption that the nearby values contribute more to the interpolated values than distant observations.
3. Kriging
depends on spatial and statistical relationships to
calculate the surface.
MethodologyStudy Site.
The measurements were taken in a pilot zone of Caracas,
Venezuela(Figure 1)with an approximate area of 2.64km2,
which represents 0.609% of the total geographical area of
the Caracas. A total of 206 measurements points, were
selected over the pilot zone. This area is characterized by
a dynamic economic-business activity which is evident
given the presence of shopping centers, office buildings of
the national telephone operators and other
telecommunications companies. On the other hand, this
area also boasts a large number of hospitals and schools,
which is of interest to know the impact of electromagnetic fields.
Data Collection Process
Time considerations : Measurements were taken over a period of 30 days, from
February 15th to March 15th of 2010. Measurements were performed only on working days (from Mondays to Fridays).Each measurement was taken between 8:00 am and 5:00 pm.
Geographical considerations:
each measurement point geographical coordinates were taken using a GPS navigation unit.
Measurement considerations:
Data Process and Representation Tools
Two informatics tools
1: GvSIG 1.9
2: Past 2.02. GvSIG is a Geographic Information System (GIS)
Both free software tools distributed under the GNU/GPL license.
.Table 1. Results of the measures of ¯t applied to the interpolation methods.
MEASURES MAE MSE D (V/m)
Max Error Min Error
OF FIT (V/m) (V/m)2 (V/m) (V/m)
IDW 0.17 0.05 1.01 0.55 0.008
KRINGING 0.74 0.73 3.81 1.66 0.111
SPLINE 0.89 1.11 4.71 1.98 0.034
.
Averag
eM
ag
nit
ud
e(V
/m)
Estim
ate
d E
lectr
ic F
ield
Comparison of Spatial Interpolation Methods IDW, KIRGING and SPLINE for Estimation of Average Electric Fiels Magnitude
3 2.5
2 1.5
1
0.5 Perfect prediction
IDW Method
KRIGING Method
SPLINE Method
Upper acceptance limit
0 Lower acceptance limit
0 0.5 1 1.5 2 2.5 3 Measured Electric Field Average Magnitude (V/m)
Conclusion
This study has shown that IDW interpolation
method is most likely to produce the best
estimation of a continuous surface of the
average magnitude of electric field intensity.
The IDW method exactness was superior to
the one shown by the SPLINES and
KRIGING techniques.