Vrije Universiteit Amsterdam
Amsterdam, The Netherlands
Area of Study
Geology, Information Sciences
Taught In English
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units4
Hours & Credits
The outcomes and learning objectives for this course are:
• Being able to define and implement a systematic approach to a spatial problem as PPDAC cycle, this course focuses on Problem, Plan and Data.
• Being able to select the relevant geographic data and techniques for source data to convert to basic data, dependent on the purpose of the question to answer.
• Being able to determine possible and appropriate data models based on characteristics of data, such as spatial unit and scale.
• The ability to understand the consequences of the choice of data, data models and pre-processing with respect to the (final) result.
• Being able to use techniques to collect and harvest data and transform it for analysis.
Geo Data (a.k.a. Geospatial data, spatial data or geographic information) is data referenced to a place recorded as a set of coordinates). Everything happens somewhere and often taking into account the location of an event or phenomena is important in understanding it. we use Geographic Information systems to capture, manipulate, analyse and visualize data. In this course you will get an insight into the ways in which the geo is obtained and learn in a structured way to assess the applicability of the geo data. You will use Open Data and Remote Sensing and assess, among other, its thematic and spatial quality. You will be able to identify the requirements of the geo data in order to solve a spatial problem. In addition, you will prepare your geo data so that it is suitable for the geographic analysis and visualization.
The following topics are covered:
• PPDAC framework (Problem, Plan, Data, Analysis, Conclusions);
• Spatial data models;
• Data sources;
• Introduction to python (collecting data via APIs);
• Data preprocessing;
• Earth Observation using Remote Sensing;
• Data quality theory.
The topics are introduced and discussed in lectures in the morning. In the afternoon, we will apply the theory in practical assignments. The
practical exercises can be used for the case study (as examples or direct input).
TYPE OF ASSESSMENT
Your skills will be assessed via a closed book exam with open and closed questions (individual, weighting 60%) and 40% of the grade via a case study (30% for the report of the case study and 10% for the poster and its presentation).
Courses and course hours of instruction are subject to change.
Some courses may require additional fees.