This course deals with the main concepts of Spatial Data Infrastructures (SDI), its components and purposes. In addition to this, several SDI- aspects will be studied and analyzed, such as: spatial datasets, access to spatial datasets, SDI-technology, SDI-policy, SDI-use, standardization and juridical issues. Anyone who works with setting up or managing a proper SDI for an area or is involved in any GIS project needs to be familiar to these kind of issues.
The overall aim of the course is to be acquainted with Spatial Data Infrastructures.
Digital spatial data is an essential information source within the management process of complex spatial relationships. This spatial information is structured and stored in Spatial Datasets. Nowadays, these datasets are used within an increasing amount of research areas; Spatial datasets form one of the components of a Spatial Data Infrastructure. However, to use spatial datasets effectively in analyses, sufficient background knowledge of these datasets and their characteristics are required. By means of different aspects, such as data quality, data format, metadata, these characteristics determine the suitability of a dataset for the purpose at hand.
After the course, students are expected to be able to:
This course on ‘spatial data infrastructures’ is partially based on a so-called problem-based learning environment in which students are confronted with a series of so-called cases. During this course a new case is presented at the beginning of each week. A case is presented as a description of a set of apparently unrelated phenomena or events, which need to be clarified in a meaningful context. In tutorial groups comprising approx. 4 students start to discuss cases in terms of concepts, underlying processes, principles or mechanisms in order to discover an underlying implicit problem. The group follows a systematic working procedure to analyze the problem.
The ideas how to solve case-related problems come from several sources: