TU Delft
Year
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NEDERLANDSENGLISH
Organization
2017/2018 Architecture Master Geomatics
GEO1008
Geo Datasets and Quality
ECTS: 5
Responsible Instructor
Name E-mail
Dr. H. Ledoux    H.Ledoux@tudelft.nl
Dr.ir. M.J.P.M. Lemmens    M.J.P.M.Lemmens@tudelft.nl
Instructor
Name E-mail
Dr.ir. M.J.P.M. Lemmens    M.J.P.M.Lemmens@tudelft.nl
Contact Hours / Week x/x/x/x
4 hours per week
Education Period
3
Start Education
3
Exam Period
3
4
Course Language
English
Course Contents
Geo datasets are essential for solving spatial-related problems. These problems may be the problems of individuals (what is the closest café with wifi?) but may also concern problems of governmental organisations related to managing and planning of our intensively used environment (what is the best location for a new railway track?). The availability, interoperability and quality of geo datasets are key issues in projects where geo-data is needed to solve spatial-related problems. In addition, datasets of different data producers are very often heterogeneous with respect to file format, conceptual model, level-of-detail, semantic content etc. This should also be taken into account when performing analyses with geo datasets.
In this course students will learn how to find, access, assess and, when needed, harmonise geo datasets to use the data for spatial-related problems. In addition, they learn how to integrate different geo datasets to produce new information. To be able to assess the outcomes of spatial analyses based on data integration, the main methods to assess and describe geodata quality are presented, including the main error propagation methods. In case of data about natural (or otherwise continuous) phenomena (e.g. noise, temperature, rainfall) the inherent fuzziness of this kind of data asks for other methods than in the case of ‘discrete’ objects, such as buildings.

The course has a theoretical part and a practical part. In the practical part students do exercises and assignments in order to get hands-on experiences on using geo datasets for solving spatial related problems and on assessing the outcomes. One or two guest lectures will be scheduled to illustrate the concerning issues with examples from the field.

1. The availability of geo data and geo data integration. This part covers: (1) overview of available geo datasets and their sources, data content (data specifications), encoding of geo data, accessibility (use conditions), main applications; (2) techniques for combining spatial data from heterogeneous sources; (3) semantic translations between different datasets (i.e. from national to INSPIRE)
2. Quality issues of spatial data. This part covers: (1) causes and consequences of error; (2) assessing the quality of data sets; (3) fuzzy geographical objects and fuzzy classification (4) finite representation of numbers in computers (5) error propagation (analytical and Monte Carlo simulation).
Study Goals
The main objectives of this course are that students 1) know how to find geo datasets for use in spatial analyses; 2) know how to analyse the content and quality of geo data sets in order to assess their fitness-for-use and 3) value the output of spatial analyses
At the end of the course students will be able to:
• describe the quality aspects of geo datasets
• evaluate the content of a specific geo dataset based on its data specifications
• assess geo data based on data models and data specifications
• understand the aims and process of geo data harmonisation
• compare data models of different geo datasets and create transformation rules between them as step in the integration process
• know the process steps needed to integrate spatial data from heterogeneous sources in order to solve spatial related problems
• know how to cope with the inherent fuzziness of geo datasets
• apply the main error propagation methods to determine the quality of results of spatial analysis, given both the quality of the input data and how well the applied model represents reality
Education Method
Lectures 28h; supervised practicals 28h; self-study 84h. The practicals are meant to gain experience in collecting and integration geo datasets, to use them in spatial analysis and to assess the quality of the outcomes.
Reader
Selected scientific and professional articles; several book chapters
Assessment
Practicals and assignments.

- final written exam (40%; minimum 5.5 required).
- assignments (60%; minimum 4.5 for each assignment required)

A total of 6.0 or above is necessary to successfully pass the course.

There is one resit for each assignment and for the final exam. If the minimum mark is not obtained, then the student has to redo the whole course the following year.
Period of Education
Third quarter
Course evaluation
For the course evaluations see: http://kwaliteitszorg.bk.tudelft.nl/