Home » Software » PostGIS: Spatially enabled Relational Database Sytem » Affine Transformation Operations in PostGIS » Case Study: Fixing Bad TIGER Line data with R and PostGIS » Comparision with output from v.transform
Comparision with output from v.transform
First the output from R:
Looking at the residuals from the regression model used to map bad coordinates (x,y) to good coordinates (nx,ny):
## we made the linear model object 'l' above ## extract as dataframe ## residuals are computed for x and y separately r <- as.data.frame(resid(l)) ## compute vector product of the (x,y) residuals: c$resid <- sqrt(r$nx^2 + r$ny^2) print(c)
x y nx ny resid 1 -2078417 -14810.570 -2078314 -14838.378 46.617600 2 -2078743 -16057.955 -2078636 -16081.790 62.041274 3 -2077261 -16435.348 -2077170 -16463.156 40.905132 4 -2076709 -14405.369 -2076606 -14433.177 29.406399 5 -2074179 -15830.248 -2074084 -15901.558 33.111981 6 -2073850 -15707.435 -2073763 -15798.554 37.362736 7 -2073450 -13873.171 -2073359 -13920.712 21.623235 8 -2072359 -15204.613 -2072276 -15323.138 38.997678 9 -2072545 -14402.596 -2072450 -14513.219 32.918889 10 -2072189 -16022.434 -2072098 -16129.106 33.834074 11 -2071991 -16856.058 -2071928 -16942.976 6.277554 12 -2068407 -12999.396 -2068296 -13133.170 6.579285 13 -2069870 -12613.813 -2069764 -12731.848 2.357631 14 -2067635 -13188.253 -2067517 -13337.765 11.604519 15 -2066931 -13377.110 -2066809 -13518.753 22.719625 16 -2067411 -15084.692 -2067313 -15190.924 41.907273 17 -2066795 -18714.093 -2066741 -18846.019 14.541358 18 -2066384 -17080.538 -2066299 -17212.464 26.717495 19 -2068634 -19742.339 -2068580 -19835.464 27.483654 20 -2053326 -16930.710 -2053276 -17226.351 65.746074 21 -2051797 -17321.500 -2051899 -17579.762 227.516944 22 -2068307 2826.921 -2068066 2638.276 12.587853 23 -2067543 2648.205 -2067328 2449.631 37.729747 24 -2067126 4276.510 -2066904 4081.246 46.774630 25 -2066748 4170.604 -2066527 4001.816 59.509843 26 -2066068 2292.295 -2065860 2094.699 46.681553 27 -2065337 2107.872 -2065126 1900.397 43.386956 28 -2064606 1913.570 -2064378 1692.922 26.460389 29 -2064199 3558.561 -2063961 3356.401 47.742696 30 -2037464 6512.455 -2037076 5864.398 50.762994 31 -2036722 6825.682 -2036338 6199.227 22.699467 32 -2036876 6366.642 -2036498 5742.888 22.120176 33 -2040225 7150.180 -2039706 6575.029 161.631199 34 -2041064 7144.779 -2040732 6569.629 26.657582 35 -2044702 -15548.033 -2044564 -16024.903 23.844817 36 -2043992 -15723.521 -2043824 -16223.282 48.063840 37 -2043790 -14907.119 -2043611 -15383.990 34.844851 38 -2040616 -14820.445 -2040453 -15349.974 21.196233 39 -2039595 -15081.427 -2039485 -15588.263 47.263287
The Root-Mean-Square-Error (RMSE) for the fitted transform (in meters) is:
The output from v.transform on the same set of control points:
Transformation Matrix | xoff a b | | yoff d e | ------------------------------------------- 5301.399323 1.002469 0.009172 -28155.882288 -0.013530 0.997547 -------------------------------------------
full output including the residuals:
CHECK MAP RESIDUALS
Current Map New Map
POINT X coord Y coord | X coord Y coord | residuals
1. -2078417.36 -14810.57 | -2078314.07 -14838.38 | 46.81
2. -2078743.11 -16057.95 | -2078635.85 -16081.79 | 62.22
3. -2077261.34 -16435.35 | -2077169.97 -16463.16 | 41.05
4. -2076709.16 -14405.37 | -2076605.87 -14433.18 | 29.59
5. -2074178.76 -15830.25 | -2074083.67 -15901.56 | 33.21
6. -2073849.93 -15707.44 | -2073762.78 -15798.55 | 37.42
7. -2073449.80 -13873.17 | -2073358.68 -13920.71 | 21.62
8. -2072358.86 -15204.61 | -2072275.89 -15323.14 | 39.02
9. -2072544.55 -14402.60 | -2072449.73 -14513.22 | 32.97
10. -2072188.97 -16022.43 | -2072098.11 -16129.11 | 33.87
11. -2071991.43 -16856.06 | -2071928.22 -16942.98 | 6.27
12. -2068406.55 -12999.40 | -2068296.38 -13133.17 | 6.60
13. -2069870.19 -12613.81 | -2069763.96 -12731.85 | 2.33
14. -2067635.38 -13188.25 | -2067517.35 -13337.76 | 11.63
15. -2066931.10 -13377.11 | -2066809.13 -13518.75 | 22.74
16. -2067411.11 -15084.69 | -2067312.75 -15190.92 | 41.93
17. -2066795.16 -18714.09 | -2066740.84 -18846.02 | 14.64
18. -2066383.87 -17080.54 | -2066298.50 -17212.46 | 26.74
19. -2068634.37 -19742.34 | -2068580.05 -19835.46 | 27.53
20. -2053326.48 -16930.71 | -2053275.51 -17226.35 | 66.09
21. -2051797.30 -17321.50 | -2051899.25 -17579.76 | 227.91
22. -2068307.24 2826.92 | -2068065.64 2638.28 | 12.41
23. -2067542.73 2648.21 | -2067327.61 2449.63 | 37.44
24. -2067125.72 4276.51 | -2066903.98 4081.25 | 46.40
25. -2066748.43 4170.60 | -2066526.69 4001.82 | 59.12
26. -2066067.79 2292.29 | -2065860.31 2094.70 | 46.35
27. -2065336.69 2107.87 | -2065125.92 1900.40 | 43.07
28. -2064605.58 1913.57 | -2064378.35 1692.92 | 26.16
29. -2064199.15 3558.56 | -2063961.13 3356.40 | 47.43
30. -2037464.39 6512.45 | -2037075.56 5864.40 | 50.66
31. -2036721.82 6825.68 | -2036338.39 6199.23 | 22.54
32. -2036875.74 6366.64 | -2036497.71 5742.89 | 21.95
33. -2040224.67 7150.18 | -2039706.23 6575.03 | 161.54
34. -2041064.45 7144.78 | -2040732.32 6569.63 | 26.74
35. -2044701.68 -15548.03 | -2044564.34 -16024.90 | 23.64
36. -2043992.10 -15723.52 | -2043824.24 -16223.28 | 47.60
37. -2043789.90 -14907.12 | -2043610.60 -15383.99 | 34.35
38. -2040615.94 -14820.44 | -2040453.30 -15349.97 | 20.77
39. -2039594.70 -15081.43 | -2039485.02 -15588.26 | 47.85
Number of points: 39
Residual mean average: 57.082951
Software
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- PostGIS: Spatially enabled Relational Database Sytem
- PROJ: forward and reverse geographic projections
- GDAL and OGR: geodata conversion and re-projection tools
- R: advanced statistical package
- GRASS GIS: raster, vector, and imagery analysis
- Generic Mapping Tools: high quality map production
