barcodefontsoft.com

Data completeness and spatial data analysis in .NET Get PDF 417 in .NET Data completeness and spatial data analysis

Data completeness and spatial data analysis using visual studio .net toreceive pdf-417 2d barcode with asp.net web,windows application What is GS1 DataBar Figure 4.7 Pattern of sites for the worked examples in tables 4.1 and 4.2 Table 4.1(a) Distances (. h. ) between sites in gure 4.7; data values recorded at the seven sample sites 0 0 1 2 3 4 5 6 Values 0. pdf417 for .NET 0 1 2.

0 0.0 2 1.5 2.

9 0.0 3 2.0 3.

7 1.0 0.0 4 2.

5 4.2 1.3 0.

8 0.0 5 4.0 5.

7 4.5 3.8 4.

6 0.0 75 6 3.7 5.

5 4.5 3.9 4.

7 0.5 0.0 78 7 3.

0 3.2 4.6 4.

8 5.5 4.3 3.

7 80. Table 4.1(b) Variance cov ariance matrix between the sample (1 7): . 5.00 2.799 2.

385 2.158 1. pdf417 2d barcode for .

NET 599 1.664 2.636 2.

799 5.00 4.093 3.

855 2.032 2.032 1.

992 2.385 4.093 5.

00 4.260 2.338 2.

292 1.914 2.158 3.

855 4.260 5.00 1.

992 1.953 1.664 1.

599 2.032 2.338 1.

992 5.00 4.524 2.

115 1.664 2.032 2.

292 1.953 4.524 5.

00 2.385 2.636 1.

992 1.914 1.664 2.

115 2.385 5.00.

case of the kriging predi ctions note that the severest downweighting in 1 to compensate for the effect of clustering is associated with sites 5 and 6 ( 0.998), 3 and 4 ( 0.524) and 2 and 3 ( 0.

365). Sites 4 and 5 both receive negative kriging weights that re ect the fact that 4 is hidden by 2 and 3 and 5 is hidden . Data quality: implications Table 4.1(c) Inverse of v ariance covariance matrix : 1. 0.347 0.148 0.

005 0.010 .NET pdf417 0.

010 0.013 0.127 0.

148 0.725 0.365 0.

180 0.011 0.013 0.

009 0.005 0.365 1.

002 0.523 0.084 0.

020 0.015 0.010 0.

180 0.523 0.777 0.

002 0.005 0.006 0.

010 0.011 0.084 0.

002 1.129 0.998 0.

033 0.013 0.013 0.

020 0.005 0.998 1.

175 0.134 0.127 0.

009 0.015 0.006 0.

033 0.134 0.324.

Table 4.1(d) Covariances (c) between sample points and prediction site o (cT ). 1 covariances 3.351 2 3.704 3 3.352 4 3.033 5 2.247 6 2.386 7 2.744 Table 4.1(e) Distances and covariances (c) to sample points and prediction site x 1 distance to x covarianc .NET PDF417 es 3.0 2.

744 2 2.5 3.032 3 2.

5 3.032 4 3.4 2.

533 5 2.9 2.799 6 2.

5 3.032 7 2.4 3.

093. Table 4.2(a) Weights associated with different interpolation methods for site o Site 1 2 3 4 5 6 7 Interp Visual Studio .NET pdf417 2d barcode olation a 0.143 0.

143 0.143 0.143 0.

143 0.143 0.143 60.

43 b 0.2 0.2 0.

2 0 0 0.2 0.2 59.

8 c 0.24 0.22 0.

16 0 0 0.16 0.22 59.

74 d 0.250 0.083 0.

083 0.083 0.125 0.

125 0.250 64.125 e 0.

17 0.23 0.17 0.

14 0.09 0.09 0.

11 56.1 f 0.19 0.

32 0.19 0.12 0.

05 0.05 0.08 52.

57 g 0.16 0.18 0.

16 0.15 0.11 0.

11 0.13 58.04.

Notes: a: Arithmetic mean . b: Dirichlet neighbours. c: Dirichlet neighbours weighted by length of shared common border.

d: Cell declustering (N S/E W axes used for quadrant borders). e: Inverse distance weighting (4.36: (i, j ) = .

h. 1 ). f: Inverse distance weighting (4.36: (i, j ) = .

h. 2 ). g: Negative exponen tial weighting (4.36: (i, j) = exp( 0.

2 . h. ).. Data completeness and spatial data analysis Table 4.2(b) Triangulation method applied to site o (i) Co-ordinates of sites 0 E W N S 35 43 1 16 49 2 44 55 3 53 50 6 55 12 7 18 17 (ii) Coef cient estimates and interpolated values Triangulation sites (1,2, Visual Studio .NET PDF 417 6) (1,2,7) Average a (E W) 0.182 0.

187 b (N S) 0.814 0.792 c 97.

816 96.850 Interpolated value 56.416 56.

249 56.332. 4.2(c) Weights associated with simple kriging and ordinary kriging (sites o and x). Simple kriging Site 1 2 3 PDF 417 for .NET 4 5 6 7 Sum site (o) 0.286 0.

387 0.119 0.004 0.

041 0.136 0.151 1.

04 site (x) 0.129 0.190 0.

244 0.107 0.024 0.

302 0.282 1.02 Ordinary kriging site (o) 0.

278 0.385 0.120 0.

013 0.048 0.133 0.

144 1.00 site (x) 0.125 0.

189 0.244 0.112 0.

028 0.301 0.278 1.

00. 4.2(d) Predictions and pr ediction errors for simple and ordinary kriging (sites o and x). Simple kriging (o) Predic pdf417 for .NET ted value Prediction error Lagrange multiplier (m) 57.523 1.

571 (x) 64.875 1.872 Ordinary kriging (o) 55.

25 1.574 0.10 (x) 63.

675 1.873 0.05.

Note: The predicted value for simple kriging is obtained from (4.37) for ordinary kriging (4.39).

The prediction error for simple kriging is obtained from (4.38) for ordinary kriging (4.42).

. Data quality: implications by 6. Although both sites 1 and 3 are at equal distances from the site to be predicted the weighting for site 1 is the larger since there are no other sites close to 1. The ordinary kriging error is only slightly larger than the simple kriging prediction error because the simple kriging weights are only slightly greater than 1.

0. To illustrate how prediction error is affected by the distribution of sample sites in relation to the site where the prediction is needed, a second site (x) has been evaluated. The distances (from site x to the sample sites) and the corresponding elements of the vector c are shown in table 4.

1(e). Note the increase in the prediction error at x relative to o (table 4.2(d)).

The closest neighbour of x is 2.4 units, compared to 1.5 for o, which has three neighbours less than or equal to 2.

0 units away. Apart from inverse distance weighting ( =2) where the weights decay very rapidly with increasing distance all the methods provide predictions that are larger than the prediction provided by ordinary kriging which should be considered the gold standard . Note this remark also applies to the distance weighting method using the exponential function (exp( 0.

2. h. )). The cell-declustering method (based on a north south/east west partition of the area) gives the largest value which differs most from the gold standard . Two triangulations are examined and the average of the two computed because there seems nothing to choose between them.

This table of results should not be taken as indicative of which methods come closest to approximating the gold standard since with other data sets other rank orderings might arise. The purpose is to show the range of predictions arising from different methods. Figure 4.

8 summarizes the approaches to missing-data estimation and spatial interpolation and prediction. 4.4.

3.
Copyright © barcodefontsoft.com . All rights reserved.