290095 VU Big Data and Exploratory Spatial Data Analysis (2021W)

GIS, GPS, crowdsourced, and sensor data are massive and complex geodatasets that are stretching the limits of GIS packages, thus a real challenge is to develop innovative ways to deal with all the available data and datasets (i.e. Big Spatial Data). Nowadays, geodata analysts should be able to cure and manage both structured and unstructured data while at the same accomplish their tasks timely using computing architectures adequate for datasets of millions of spatiotemporal observations. Furthermore, demands on data exploration go beyond basic statistical measures, charts, and EDA techniques to spatial statistical methods that look at the global or local patterns of univariate-bivariate-multivariate geodata and various forms of cartographic visualization. An additional challenge is that although a significant percentage of big data can be characterized as big spatial data, at times the “spatial information” needs to be processed or even engineered. In this course, the students learn the fundamentals of big spatial data and exploratory spatial data analysis, and will also deepen their knowledge in various application domains such as health, natural disasters, crime analysis, agriculture, human mobility, or geodemographics.

The course has a fairly equal amount of both lectures and practical work. The practical work is either tutorials guided by the lecturer or exercises of multiple tasks to be solved/answered given sufficient support material. Most of the times learning of theory and methods precedes their application. There is also a rotational pattern in which topics/concepts of older lessons are re-used and tested within newer lessons. Game-based learning and other interactive learning approaches are regular activities during the lectures.

Entry requirements
Knowledge of basic Python scripting is a prerequisite for this course. Knowledge of descriptive statistics or spatial statistics would be advantageous but is not required.