This is a python labcourse about geo spatial data analysis used in mobility data analysis
After having participated in the course the students will be able to name the essential elements of a data analysis pipeline for mobility data and build a corresponding framework with open source software. In addition, the students are familiar with different possibilities and formats of data collection, aggregation and storage. They will be able to record mobility data and evaluate the collected data. In addition to classical statistical evaluation methods, they are familiar with other methods that are especially relevant for mobility data, such as hotspot analysis, spatial clustering, geo-fencing and simple machine learning methods for classifying modes of transport. By applying these methods, students can question the collected data critically using domain-specific indicators and generate corresponding visualizations.
Clone the repository into your jupyter enviroment and install the packages from requirements.txt
all required python packages are listed in requirements.txt
Give the example
And repeat
until finished
Adenaw, Lennart
Kreibich, Julian
Merkle, Lukas
Schmid, Florian
Schmid, Werner
Wittmann, Michael
Ziegler, David
This project is licensed under the LGPL License - see the LICENSE.md file for details