Topocad 23

Term

Description

K-Value

Enter checkability value for the plane net, i.e. the number of over-determinations divided by the number of observations. If you have measured the exact number of observations required to get the coordinates for the points, the K-value is 0, but HMK recommends 0.5 and higher for the backbone net. The normal values for polygon nets are 0.1-0.2.

No. over-determ.

Number of over-determinations in plane or height

Standard mean error

Size of net's standard mean error

Appd threshold fr. HMK

The threshold for the standard mean error that HMK has set up for the backbone net to be regarded as approved.

Scale factor

Calculated scale factor in plane for free scale. If this is not used the value 1.000000 is shown

Iterations

For plane adjustment a calculation is made of how much you need to adjust the approximate values of the point coordinates in order for the improved observations to correspond with them. If you have major errors in the net, the approximate values will be unsatisfactory and the results will not be correct. You then use the calculated coordinates as approximate values and readjust. The procedure continues until the observations agree with the points, and the number of calculations are specified as the number of iterations. 1-3 are normal values here, and the program has a maximum limit of 20 iterations to enable it to carry out an adjustment. This is due to the fact that if the observations are unsatisfactory enough, you will get values that are progressively worse for each calculation and thereby never arrive at a result.

Sigma levels

The number of observations that are within the various sigma levels are specified here. From a statistical perspective, 68% of the observations should be below level one, 95% below level two and 99.8% below level three. Observations with sigma levels above three are classed as gross errors, but also the levels between two and three should be checked in accordance with HMK.

Term

Description

PointID

Name of point.

X, Y, Z coordinate

Specified known coordinates for the point.

Mean error X, Y, Z

Calculated mean error for the point including centring error.

Centr. incorrect X, Y, Z

Specified centring error for the point in question.

Ellipse a

Error ellipse's large axis, i.e. the point's largest mean error in any direction.

Ellipse b

Error ellipse's small axis, i.e. the point's smallest mean error in any direction.

Ellipse bearing

The bearing for the error ellipse's large axis.

Term

Description

From Point

Specifies from which point you have measured. Normal station point

To point

The point to which the measurement runs.

Survey type

Shows length, horizontal angle, bearing or horizontal angle.

Survey value

For the actual observation, note that lengths, angles, bearings, and heights are separated, and that lengths are reported as horizontal. The direction series is reduced to zero for the backsight

Correction

The total correction for atmosphere, projection, and ellipsoid (height).

Improvement

How much the observation must be adjusted in order for it to tally with the calculated and known points. The greater the value, the worse the result. These values are used primarily to search for gross errors.

Aposteriori mean error

The calculated mean error for the measurement from the adjustment. If this error is greater than the apriori mean error for the measurement, your measurements are worse than what the instrument is capable of measuring.

Apriori mean error

This mean error is measured in the factory and describes the theoretical accuracy for angle, length, and height of the instrument. The mean error for heights varies depending on how long the length is.

Sigma (level)

Standardized mean error (1=the error is at level with the instrument's performance, 2 = twice as large error as the instrument's performance etc...). HMK specifies 3 as threshold in order for the observation to be classified as a gross error.

Smallest det. error

The smallest detectable error in the observation (inner reliability), i.e. the error that gives a sigma level of exactly 3.

Largest influence

Errors that are smaller than the smallest detectable errors cannot be eliminated. Here the maximum influence this error has on the coordinates for the points it is measured between is specified. Note that this value only applies to this observation's influence

Relative redundancy

Relative redundancy - how much the error that remains with the observation in the form of the improvement, (e.g. the value 0.43 means 43% of the error). If the error we measure is 35mm, this error will be spread out over the other observations and affect them. If we then have a K-Value of 0.43, the improvement will only be 15mm, i.e. the greatest share of the error remains, distributed over the other observations, and affects the results. This value is also called individual K-Value

Weight factor

The total calculated weight factor, which is calculated through 1/s², i.e. A calculated apriori mean error square". For a mean error of 1 milligon the weight factor will be 1,000,000. If we have then specified a weight constant other than 1 for the observation, this will also be calculated here.

Bearing

Approximate bearing for the measurement (comparative figure).

Length

Approximate length between from and to point (comparative figure).