*************************************************
* Geographically Weighted Regression *
* Release 3.0.1 *
* Dated: 06-vii-2003 *
* *
* Martin Charlton, Chris Brunsdon *
* Stewart Fotheringham *
* (c) University of Newcastle upon Tyne *
*************************************************
Program starts at: Tue May 27 17:11:11 2008
** Program limits:
** Maximum number of variables..... 52
** Maximum number of observations.. 80000
** Maximum number of fit locations. 80000
struttura
** Observed data file: C:\Documents and Settings\Msassi\Desktop
** Prediction location file: Estimation at sample point locations
** Result output file: C:\Documents and Settings\Msassi\Desktop
** Variables in the data file...
ID Lati Long Tyto Lnyo lnst
** Dependent (y) variable...... Tyto
** Easting (x-coord) variable.....Lati
** Northing (y-coord) variable.....Long
** No weight variable specified
** Independent variables in your model...
Lnyo lnst
** Kernel type: Adaptive
** Kernel shape: Bi-Square
** Bandwidth selection by AICc minimisation
** Use all regression points
** Calibration history requested
** Prediction report requested
** Output estimates to be written to .txt file
** Monte Carlo significance tests for spatial variation
** Casewise diagnostics to be printed
*** Analysis method ***
*** Geographically weighted multiple regression
** Cartesian coordinates: Euclidean Distance
***************************************************************
* *
* GEOGRAPHICALLY WEIGHTED GAUSSIAN REGRESSION *
* *
***************************************************************
Number of data cases read: 97
Observation points read...
Dependent mean= 0.0315051526
Number of observations, nobs= 97
Number of predictors, nvar= 2
Observation Easting extent: 9.56700039
Observation Northing extent: 10.8660002
*Finding bandwidth...
... using all regression points
This can take some time...
*Calibration will be based on 97 cases
*Adaptive kernel sample size limits: 10 97
*AICc minimisation begins...
Bandwidth AICc
36.884478565000 -420.621075018794
53.500000000000 -414.087237267044
26.615521556596 -418.105745507336
43.231042991596 -419.167387754438
32.962086029639 -419.758573652801
39.308650456235 -420.265616547112
35.386257938614 -420.243295302634
37.810429829849 -420.183957691860
36.312209210240 -420.262540498648
** Convergence after 9 function calls
** Convergence: Local Sample Size= 37
**********************************************************
* GLOBAL REGRESSION PARAMETERS *
**********************************************************
Diagnostic information...
Residual sum of squares...... 0.089675
Effective number of parameters.. 3.000000
Sigma...... 0.030887
Akaike Information Criterion.... -393.959681
Coefficient of Determination.... 0.353946
Adjusted r-square...... 0.333105
Parameter Estimate Std Err T
------
Intercept 0.169168625292 0.027912591464 6.060656547546
Lnyo -0.046154291727 0.007656559566 -6.028071880341
lnst 0.000816297822 0.001659573155 0.491872161627
**********************************************************
* GWR ESTIMATION *
**********************************************************
Fitting Geographically Weighted Regression Model...
Number of observations...... 97
Number of independent variables... 3
(Intercept is variable 1)
Number of nearest neighbours...... 37
Number of locations to fit model.. 97
Diagnostic information...
Residual sum of squares...... 0.047495
Effective number of parameters.. 17.359380
Sigma...... 0.024421
Akaike Information Criterion.... -418.170212
Coefficient of Determination.... 0.657828
Adjusted r-square...... 0.582296
**********************************************************
* CASEWISE DIAGNOSTICS *
**********************************************************
Obs Observed Predicted Residual Std Resid R-Square Influence Cook's D
------
1 0.03700 0.05990 -0.02290 -0.498655 0.559325 0.194587 0.003461
2 -0.02300 0.00222 -0.02522 -0.519769 0.735506 0.100867 0.001746
3 -0.01500 -0.02266 0.00766 0.196866 0.789139 0.422693 0.001635
4 -0.02200 -0.00176 -0.02024 -0.458207 0.687880 0.255253 0.004145
5 0.00400 0.03332 -0.02932 -0.593180 0.776896 0.067217 0.001461
6 0.00900 0.01140 -0.00240 -0.049285 0.734396 0.092038 0.000014
7 0.10100 0.08246 0.01854 0.396112 0.764878 0.163590 0.001768
8 0.04700 0.03514 0.01186 0.263193 0.722189 0.224850 0.001158
9 0.05400 0.05847 -0.00447 -0.093908 0.825761 0.136308 0.000080
10 0.08900 0.09934 -0.01034 -0.243569 0.768757 0.311392 0.001545
11 0.01400 0.00418 0.00982 0.204760 0.817580 0.121774 0.000335
12 -0.04600 -0.00064 -0.04536 -0.913612 0.744202 0.059022 0.003016
13 0.04800 0.04877 -0.00077 -0.015701 0.726128 0.076315 0.000001
14 0.08200 0.07363 0.00837 0.192816 0.859264 0.280320 0.000834
15 -0.00200 -0.00162 -0.00038 -0.008581 0.827679 0.241266 0.000001
16 0.01100 0.05047 -0.03947 -0.834800 0.687227 0.146600 0.006896
17 0.04800 0.06193 -0.01393 -0.296167 0.581943 0.155867 0.000933
18 0.05800 0.07539 -0.01739 -0.363671 0.781012 0.127376 0.001112
19 0.05400 0.06295 -0.00895 -0.182068 0.765475 0.078164 0.000162
20 0.04800 0.05347 -0.00547 -0.115264 0.626734 0.140221 0.000125
21 0.07500 0.06369 0.01131 0.232574 0.672813 0.096693 0.000334
22 -0.02900 0.02051 -0.04951 -1.009137 0.795502 0.081216 0.005186
23 0.02100 0.00706 0.01394 0.298251 0.753951 0.166498 0.001024
24 0.01400 0.05752 -0.04352 -0.911771 0.677162 0.130028 0.007158
25 -0.01000 -0.01551 0.00551 0.124329 0.891570 0.249864 0.000297
26 0.08300 0.07192 0.01108 0.243252 0.668257 0.207943 0.000895
27 -0.00400 0.01020 -0.01420 -0.301525 0.731917 0.153588 0.000950
28 0.05800 0.05796 0.00004 0.001008 0.592256 0.484126 0.000000
29 0.05200 0.03976 0.01224 0.248337 0.731316 0.073137 0.000280
30 0.01500 0.03036 -0.01536 -0.317147 0.701011 0.103989 0.000672
31 0.02400 0.03954 -0.01554 -0.316734 0.769186 0.081341 0.000512
32 0.07200 0.06725 0.00475 0.109093 0.732292 0.275334 0.000260
33 0.04700 0.02024 0.02676 0.547664 0.770026 0.088461 0.001677
34 0.01300 0.01318 -0.00018 -0.003773 0.747083 0.119935 0.000000
35 -0.01100 0.03483 -0.04583 -1.147098 0.838780 0.390480 0.048560
36 0.01500 -0.00794 0.02294 0.484755 0.719256 0.145112 0.002298
37 0.11000 0.07890 0.03110 0.650026 0.780544 0.125889 0.003505
38 -0.04300 -0.04054 -0.00246 -0.066548 0.805335 0.479547 0.000235
39 0.08000 0.05623 0.02377 0.486064 0.770714 0.087240 0.001301
40 0.02600 0.01373 0.01227 0.256593 0.751901 0.126898 0.000551
41 0.06600 0.04893 0.01707 0.350979 0.677644 0.096946 0.000762
42 0.05200 0.04012 0.01188 0.278313 0.776914 0.304749 0.001956
43 0.03800 0.02134 0.01666 0.338317 0.723426 0.073951 0.000527
44 -0.00800 -0.01789 0.00989 0.219142 0.803534 0.222334 0.000791
45 -0.00800 0.00683 -0.01483 -0.309967 0.732076 0.125723 0.000796
46 -0.00300 0.02258 -0.02558 -0.531331 0.772342 0.114903 0.002111
47 -0.00500 -0.00558 0.00058 0.013516 0.843173 0.295928 0.000004
48 0.02200 0.03204 -0.01004 -0.207482 0.814878 0.105881 0.000294
49 0.06400 0.05770 0.00630 0.129416 0.712794 0.093923 0.000100
50 0.06200 0.05701 0.00499 0.100865 0.659894 0.064129 0.000040
51 0.01600 -0.00340 0.01940 0.413053 0.754511 0.158012 0.001844
52 0.06900 0.05743 0.01157 0.242446 0.761246 0.130669 0.000509
53 0.08900 0.06033 0.02867 0.660808 0.751462 0.281445 0.009853
54 -0.02000 -0.00060 -0.01940 -0.412692 0.742678 0.156212 0.001816
55 0.05800 0.04291 0.01509 0.311639 0.760519 0.105372 0.000659
56 0.03700 0.03615 0.00085 0.017516 0.844477 0.092832 0.000002
57 -0.03900 -0.02170 -0.01730 -0.369731 0.759964 0.164397 0.001549
58 0.00400 -0.00410 0.00810 0.183046 0.698430 0.252731 0.000653
59 0.00400 0.02014 -0.01614 -0.350011 0.795244 0.187708 0.001631
60 0.03500 0.03453 0.00047 0.009441 0.797849 0.058444 0.000000
61 0.05600 0.03970 0.01630 0.353674 0.848724 0.189157 0.001681
62 0.00500 0.02238 -0.01738 -0.430845 0.730387 0.378794 0.006520
63 0.07600 0.00982 0.06618 1.387102 0.717420 0.130955 0.016702
64 0.00600 0.02250 -0.01650 -0.344345 0.826129 0.123725 0.000964
65 0.04700 0.05190 -0.00490 -0.107732 0.736379 0.211413 0.000179
66 0.07900 0.04652 0.03248 0.672713 0.716444 0.109911 0.003219
67 0.07500 0.06365 0.01135 0.241241 0.601922 0.154207 0.000611
68 0.05900 0.08001 -0.02101 -0.500458 0.754734 0.327030 0.007011
69 0.05900 0.05607 0.00293 0.059813 0.659209 0.086729 0.000020
70 0.04400 0.04764 -0.00364 -0.075292 0.801344 0.106907 0.000039
71 0.06400 0.05815 0.00585 0.125258 0.729999 0.168229 0.000183
72 0.09000 0.06693 0.02307 0.559684 0.791439 0.351141 0.009765
73 0.03400 0.02044 0.01356 0.279626 0.714721 0.102384 0.000514
74 0.04100 0.04675 -0.00575 -0.138510 0.725180 0.342734 0.000576
75 0.07500 0.06419 0.01081 0.221536 0.761205 0.090849 0.000283
76 0.02800 0.00433 0.02367 0.482206 0.727173 0.079770 0.001161
77 -0.03500 0.01889 -0.05389 -1.109591 0.666398 0.099515 0.007838
78 0.07500 0.05445 0.02055 0.519667 0.626290 0.403053 0.010504
79 0.00300 0.02181 -0.01881 -0.438257 0.841933 0.296501 0.004663
80 0.04100 0.04865 -0.00765 -0.157566 0.691470 0.099662 0.000158
81 0.01900 0.04296 -0.02396 -0.485159 0.783279 0.069153 0.001007
82 0.00500 0.04744 -0.04244 -1.112461 0.705357 0.444429 0.057029
83 -0.02100 0.01139 -0.03239 -0.722786 0.739084 0.233366 0.009161
84 0.02400 0.05440 -0.03040 -0.629046 0.603329 0.108346 0.002770
85 0.05800 0.06214 -0.00414 -0.087615 0.868847 0.147746 0.000077
86 0.03600 0.04952 -0.01352 -0.280200 0.801877 0.111056 0.000565
87 -0.03000 -0.01099 -0.01901 -0.521429 0.841518 0.492735 0.015214
88 -0.00500 0.02603 -0.03103 -0.670960 0.835114 0.183424 0.005825
89 0.07300 0.06061 0.01239 0.378767 0.748365 0.591530 0.011968
90 0.00700 -0.00770 0.01470 0.309450 0.747979 0.138140 0.000884
91 0.08200 0.04853 0.03347 0.684941 0.795307 0.088435 0.002622
92 -0.03900 0.01247 -0.05147 -1.067594 0.748507 0.112723 0.008341
93 -0.03700 -0.00921 -0.02779 -0.564007 0.739981 0.073374 0.001451
94 0.01800 0.03530 -0.01730 -0.357734 0.848693 0.106991 0.000883
95 0.08100 0.07215 0.00885 0.196323 0.672464 0.223620 0.000640
96 0.05300 0.04604 0.00696 0.148174 0.789875 0.157184 0.000236
97 0.07300 0.03317 0.03983 0.809212 0.656848 0.075138 0.003065
** Results written to .txt file
Predictions from this model...
Obs Y(i) Yhat(i) Res(i) X(i) Y(i)
1 0.037 0.060 -0.023 37.300 13.600 F
2 -0.023 0.002 -0.025 44.917 8.617 F
3 -0.015 -0.023 0.008 43.617 13.517 F
4 -0.022 -0.002 -0.020 43.467 11.883 F
5 0.004 0.033 -0.029 42.850 13.567 F
6 0.009 0.011 -0.002 44.883 8.200 F
7 0.101 0.082 0.019 40.917 14.783 F
8 0.047 0.035 0.012 41.117 16.883 F
9 0.054 0.058 -0.004 46.133 12.217 F
10 0.089 0.099 -0.010 41.133 14.767 F
11 0.014 0.004 0.010 45.700 9.667 F
12 -0.046 -0.001 -0.045 45.567 8.050 F
13 0.048 0.049 -0.001 44.500 11.350 F
14 0.082 0.074 0.008 46.500 11.333 F
15 -0.002 -0.002 0.000 44.533 10.200 F
16 0.011 0.050 -0.039 40.650 17.933 F
17 0.048 0.062 -0.014 37.483 14.067 F
18 0.058 0.075 -0.017 41.567 14.650 F
19 0.054 0.063 -0.009 41.067 14.317 F
20 0.048 0.053 -0.005 37.500 15.083 F
21 0.075 0.064 0.011 38.900 16.583 F
22 -0.029 0.021 -0.050 42.350 14.167 F
23 0.021 0.007 0.014 45.800 9.083 F
24 0.014 0.058 -0.044 39.283 16.250 F
25 -0.010 -0.016 0.006 45.133 10.033 F
26 0.083 0.072 0.011 39.083 17.133 F
27 -0.004 0.010 -0.014 44.400 7.550 F
28 0.058 0.058 0.000 37.567 14.267 F
29 0.052 0.040 0.012 44.833 11.633 F
30 0.015 0.030 -0.015 43.767 11.250 F
31 0.024 0.040 -0.016 41.467 15.550 F
32 0.072 0.067 0.005 44.217 12.050 F
33 0.047 0.020 0.027 41.633 13.367 F
34 0.013 0.013 0.000 44.417 8.917 F
35 -0.011 0.035 -0.046 45.950 13.633 F
36 0.015 -0.008 0.023 43.883 8.017 F
37 0.110 0.079 0.031 41.600 14.233 F
38 -0.043 -0.041 -0.002 44.117 9.833 F
39 0.080 0.056 0.024 42.350 13.400 F
40 0.026 0.014 0.012 41.467 12.883 F
41 0.066 0.049 0.017 40.350 18.183 F
42 0.052 0.040 0.012 45.850 9.383 F
43 0.038 0.021 0.017 43.550 10.317 F
44 -0.008 -0.018 0.010 45.317 9.500 F
45 -0.008 0.007 -0.015 43.850 10.517 F
46 -0.003 0.023 -0.026 43.300 13.450 F
47 -0.005 -0.006 0.001 45.150 10.783 F
48 0.022 0.032 -0.010 44.333 10.083 F
49 0.064 0.058 0.006 40.683 16.600 F
50 0.062 0.057 0.005 38.183 15.567 F
51 0.016 -0.003 0.019 45.467 9.183 F
52 0.069 0.057 0.012 44.650 10.917 F
53 0.089 0.060 0.029 40.350 14.250 F
54 -0.020 -0.001 -0.019 45.450 8.633 F
55 0.058 0.043 0.015 45.400 11.883 F
56 0.037 0.036 0.001 44.800 10.350 F
57 -0.039 -0.022 -0.017 45.183 9.167 F
58 0.004 -0.004 0.008 43.117 12.400 F
59 0.004 0.020 -0.016 43.900 12.917 F
60 0.035 0.035 0.000 42.450 14.217 F
61 0.056 0.040 0.016 45.050 9.683 F
62 0.005 0.022 -0.017 43.717 10.400 F
63 0.076 0.010 0.066 43.933 10.917 F
64 0.006 0.022 -0.016 45.950 12.650 F
65 0.047 0.052 -0.005 40.633 15.817 F
66 0.079 0.047 0.032 43.883 11.100 F
67 0.075 0.064 0.011 36.933 14.733 F
68 0.059 0.080 -0.021 44.417 12.200 F
69 0.059 0.056 0.003 38.100 15.650 F
70 0.044 0.048 -0.004 44.700 10.633 F
71 0.064 0.058 0.006 42.367 12.867 F
72 0.090 0.067 0.023 44.067 12.567 F
73 0.034 0.020 0.014 41.900 12.483 F
74 0.041 0.047 -0.006 45.067 11.800 F
75 0.075 0.064 0.011 40.683 14.767 F
76 0.028 0.004 0.024 44.317 8.467 F
77 -0.035 0.019 -0.054 43.317 11.300 F
78 0.075 0.054 0.021 37.067 15.283 F
79 0.003 0.022 -0.019 46.183 9.883 F
80 0.041 0.049 -0.008 40.467 17.233 F
81 0.019 0.043 -0.024 42.667 13.717 F
82 0.005 0.047 -0.042 42.567 12.667 F
83 -0.021 0.011 -0.032 45.067 7.700 F
84 0.024 0.054 -0.030 38.017 12.533 F
85 0.058 0.062 -0.004 46.067 11.133 F
86 0.036 0.050 -0.014 45.667 12.250 F
87 -0.030 -0.011 -0.019 45.650 13.767 F
88 -0.005 0.026 -0.031 46.067 13.233 F
89 0.073 0.061 0.012 45.733 7.317 F
90 0.007 -0.008 0.015 45.833 8.817 F
91 0.082 0.049 0.033 45.433 12.350 F
92 -0.039 0.012 -0.051 45.933 8.550 F
93 -0.037 -0.009 -0.028 45.333 8.417 F
94 0.018 0.035 -0.017 45.450 11.000 F
95 0.081 0.072 0.009 38.667 16.010 F
96 0.053 0.046 0.007 45.550 11.550 F
97 0.073 0.033 0.040 42.550 12.117 F
**********************************************************
* ANOVA *
**********************************************************
Source SS DF MS F
OLS Residuals 0.1 3.00
GWR Improvement 0.0 14.36 0.0029
GWR Residuals 0.0 79.64 0.0006 4.9256
**********************************************************
* PARAMETER 5-NUMBER SUMMARIES *
**********************************************************
Label Minimum Lwr Quartile Median Upr Quartile Maximum
------
Intrcept 0.052657 0.164773 0.268217 0.307345 0.337338
Lnyo -0.095874 -0.084045 -0.072616 -0.046491 -0.003090
lnst -0.009281 -0.005868 -0.002002 -0.000266 0.003030
<------LOWER ------<------UPPER ------>
Label Far Out Outer Fence Outside Inner Fence Inner Fence Outside Outer Fence Far Out
------
Intrcept 0 -0.262944 0 -0.049086 0.521203 0 0.735062 0
Lnyo 0 -0.196708 0 -0.140377 0.009841 0 0.066172 0
lnst 0 -0.022676 0 -0.014272 0.008138 0 0.016541 0
*************************************************
* *
* Test for spatial variability of parameters *
* *
*************************************************
Tests based on the Monte Carlo significance test
procedure due to Hope [1968,JRSB,30(3),582-598]
Parameter P-value
------
Intercept 0.07000 n/s
Lnyo 0.00000 ***
lnst 0.41000 n/s
*** = significant at .1% level
** = significant at 1% level
* = significant at 5% level
Program terminates normally at: Tue May 27 17:11:11 2008