Geospatial Python
7.5 ECTS creditsThis course holds significance due to the growing importance of geospatial data analysis and the utility of Python within GIS (Geographic Information Systems). With the increasing availability of geospatial data, professionals in fields such as environmental management, urban planning, and logistics require skills to analyze and interpret such data. Python, with its powerful libraries like GeoPandas and Shapely, provides an efficient toolkit for geospatial analysis. Through this course, participants acquire knowledge in Python programming related to spatial analysis with or without GIS integration, enabling them to automate tasks, create customized workflows, and solve complex geospatial problems without the need for proprietary software.
In this course, the following Python concepts are covered:
- Creating simple to advanced functions for processing geospatial data.
- Utilizing spatial Python libraries to understand, process, and analyze geometric elements.
- Performing basic data analysis (reading, converting, and extracting information).
- Managing and exploring spatial data from various sources, such as local storage, online databases, and API services.
- Using Python within GIS programs to customize and perform advanced spatial analysis.
Programming Environment:
The programming environment is an interactive one (Jupyter Notebooks). The Jupyter Lab environment can be accessed through both open cloud-based websites and local installations. The course includes lectures that discuss specific topics related to Python programming and exercises that correspond to the topic covered. Finally, a project assignment is conducted based on the knowledge acquired in the course.
Admission requirements: 60 ECTS credits of a technical or natural science education, including 7,5 ECTS credits in programming, upper secondary level Swedish 3 or Swedish as a Second Language 3, and English 6, or equivalent.
In this course, the following Python concepts are covered:
- Creating simple to advanced functions for processing geospatial data.
- Utilizing spatial Python libraries to understand, process, and analyze geometric elements.
- Performing basic data analysis (reading, converting, and extracting information).
- Managing and exploring spatial data from various sources, such as local storage, online databases, and API services.
- Using Python within GIS programs to customize and perform advanced spatial analysis.
Programming Environment:
The programming environment is an interactive one (Jupyter Notebooks). The Jupyter Lab environment can be accessed through both open cloud-based websites and local installations. The course includes lectures that discuss specific topics related to Python programming and exercises that correspond to the topic covered. Finally, a project assignment is conducted based on the knowledge acquired in the course.
Admission requirements: 60 ECTS credits of a technical or natural science education, including 7,5 ECTS credits in programming, upper secondary level Swedish 3 or Swedish as a Second Language 3, and English 6, or equivalent.
Progressive specialisation:
A1N (has only first‐cycle course/s as entry requirements)
Education level:
Master's level
Admission requirements
60 ECTS credits completed in a Science or Technology programme, 7.5 ECTS credits in Programming, and upper secondary level English 6, or equivalent
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.