In this course students are trained to become users of spatial data analysis techniques.
Students will gain a broad knowledge of the diversity of current approaches, which methods
are at hand and examples of applications using spatial data analysis in different fields (e.g.,
economic geography, epidemiology and urban safety).The learning outcomes of the course are:
- identify the appropriate approaches/techniques in spatial data analysis
- use relevant knowledge to solve spatial-related problems using real-life data sets and
spatial statistical tools, including visualization, interpolation, pattern identification
and modeling (spatial regression analysis)
- develop both technical and social skills by working in pairs to solve real-life problems
using different statistical software
- to analyze results of practical exercises and be able to point out challenges and
advantages with those tested techniques
- develop, interpret and critically reflect upon results of a case study using one (or
more) spatial data analysis technique(s) learned during the course.
- be able to use their new skills in spatial data techniques and communicating them to
an audience (written, graphically and orally).
- recognize and express the value of incorporating the spatial dimension of phenomena
and processes in social sciences