Principles of Spatial Data Quality

Introduction


The module starts with a general overview of the main elements of SDQ, along with a summary of the basic mathematical concepts related to it. Most of the module is devoted to quantitative, probabilistic and statistical aspects of SDQ. Finally the concept of uncertainty and the application of fuzzy set theory to uncertainty modelling are covered.

 


Learning outcomes


By the end of the course, students have a good understanding of different SDQ issues and can use different tools and methods for the analysis and evaluation of SDQ. In particular, the students are expected to be able to:


  • Explain the main concepts, terms and elements of SDQ;
  • Explain and perform exploratory spatial data analysis methods;
  • Explain and perform statistical tests and determine confidence intervals;
  • Apply least-square adjustment and regression methods;
  • Explain the sources of uncertainty in spatial data and the ways to represent them;
  • Apply analytical and numerical methods to compute derivatives of functions;
  • Apply the core statistical concepts to attribute and positional uncertainty in the context of spatial data quality;

 

 


Content


  • Elements of SDQ (error, accuracy and precision, attribute accuracy, temporal accuracy,
  • lineage, completeness, logical consistency).
  • Basic concepts in calculus, probability and statistics related to SDQ;
  • Exploratory spatial data analysis methods;
  • Statistical testing and confidence intervals determination;
  • Least squares adjustment and regression methods;
  • Uncertainty in spatial and non-spatial data;
  • Application of fuzzy set theory in the presentation of uncertainty;
  • Attribute uncertainty (including thematic uncertainty, scale issues and MAUP);

 

 


Allocated time per teaching and learning method

 

Teaching / learning method

Allocated Hours

Lectures

36

Supervised practicals

30

Unsupervised practicals

14

Individual assignment

30

Group assignment

0

Self-study

30

Examination

4

Excursion

0



Examination


There will be a midterm and a final examination. In addition, the results of the assignments given during the practical part of the module will be evaluated. The assignments are related to statistical testing, least squares adjustment and regression.

 


Final Grade Determination


The assignments evaluation forms 30% and the closed-book written exams form 70% of the final rade.

Components

Weight

Assignments
 Evaluation

30 %

Midterm Examination

30 %

Final Examination

40 %

 

 


Prerequisites


Good basics in mathematics and programming, and the core modules.

 

Reference


http://onlinestatbook.com/
Bivand, R. S., & Pebesma, E. J. Gomez-Rubio, V. (2008) Applied Spatial Data Analysis with R., Springer Science+Business Media, LLC.

Date:
2018/06/02
review:
1046
K.N.Toosi University
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