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The course starts with a review of observation errors and their modelling, confidence level and error probability of statistical hypotheses (significance level, power of test, type I and type II errors), error propagation law and network design by pre-analysis, including simple pre-analysis of survey observations. Then it covers the formulation and derivation of different cases of least squares adjustments, such as parametric, conditional and combined (or general) mathematical models; the adjustment of control networks (1D, 2D, 3D) and other geomatics measurements, including data series; the adjustment with weighted parameters; the measures of quality, elimination of blunders by global and local tests, elimination of nuisance parameters, and sequential adjustments; problem of reliability and sensitivity; and the problem of datum, including datum transformations. The course ends with an introduction to filtering and prediction.
Students may receive approval to register for this course with completion of 2-year Geomatics diploma, and meeting all other prerequisites. To receive approval, please email your official academic transcript(s) and BCIT Student ID to email@example.com. If you are new to BCIT, create an ID at https://secure.bcit.ca/sis/reg/. **IMPORTANT: The mid-term and the final exams must be written at an approved Exam Centre only. Details will be emailed to you after registering for the course.
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Upon successful completion of the course, the student will be able to:
Review observation errors and their modelling, including error (random and systematic) propagations in survey measurements.
Evaluate survey measurements and the adjusted quantities using the concepts of confidence level and error probability of statistical hypotheses (significance level, power of test, type I and type II errors).
Design survey network by pre-analysis.
Apply matrix theory approach to data analysis and least squares estimation.
Formulate least squares adjustment problems (parametric, conditional, combined), including the functional and stochastic models.
Solve 1D-, 2D- and 3D- network problems relating to levelling, traverse, triangulation, trilateration, and GNSS surveys, using different least squares adjustment methods and constraints.
Assess quality of adjustment solutions using the concepts of global and local statistical tests, covariance matrices, and error ellipses.
Explain reliability and sensitivity of survey networks.
Formulate nuisance parameters elimination model and sequential adjustments model.
Analyze datum problems in geomatics, including free network adjustments and datum transformations.
Relate simple filtering and prediction methods with the standard least squares adjustment methods.
Effective as of Winter 2018
GEOM 6140 is offered as a part of the following programs:
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