This course introduces participants to the fundamentals of retrieving and analyzing social media geographic data (SMGI) with Python. Using real-world examples, such as searching and filtering Flickr data by keywords, tags, geotags, and date ranges, students learn how to investigate urban changes and transformations. Participants explore radius and bounding box techniques for targeted searches, uncover patterns of human behavior, and examine regional trends, particularly those tied to recent events like the COVID-19 pandemic. By the end of the course, students will have hands-on experience building Python scripts in Jupyter notebooks, integrating APIs, and interpreting location-based social media datasets to gain insights into evolving urban environments (Access this course on GitLab).
Intermediate, Advanced
Basic knowledge of Python, Basic knowledge of digital image processing and Geographic Information Systems
Geoinformatics
The future is urban, the data is smart by Andreas Rienow, Lars Tum