Table 2

Likelihood-based parameter estimates for the best-fitting models.

Error dataset
Component
Proportion
μX
μY
σX
σY
ρ
v

(a)
1
0.571
-12.1
-10.7
61.6
54.1
-0.05
1.6

2
0.253
-4.7
-350.0
75.9
550.0
0.18
6.5

3
0.176
352.8
-12.6
540.3
84.9
-0.03
16.7
(b)
1
0.560
-0.8
-14.2
39.4
75.9
0.06
1.8

2
0.440
372.1
-6.7
523.6
90.3
-0.10
5.9
(c)
1
0.519
4.9
-5.4
62.3
60.8
-0.10
1.8

2
0.292
13.6
-35.0
289.1
54.9
-0.14
2.4

3
0.189
14.9
-10.2
62.1
354.4
0.14
2.4
(d)
1
0.700
5.9
-4.3
47.0
100.7
0.06
1.8

2
0.300
29.3
-6.2
62.1
419.5
0.16
3.0

Models and the datasets to which they were fitted are: (a) the three-component t mixture model for the automated geocoding positional errors; (b) the two-component t mixture model for the automated geocoding positional errors aligned with axial direction of corresponding street segment; (c) the three-component t mixture model for the E911 positional errors; (d) the two-component t mixture model for the E911 positional errors aligned with axial direction of corresponding street segment. Means are denoted by μX and μY, standard deviations by σX and σY, correlation coefficient by ρ, and degrees of freedom by v. Units of measurement for means and standard deviations are meters.

Zimmerman et al. International Journal of Health Geographics 2007 6:1   doi:10.1186/1476-072X-6-1