Geospatial Data Science with Python: GeoPandas
What you'll learn
- How to analyze geospatial data using the python data science ecosystem
- Using Jupyter notebooks to provide complete documentation of your workflow and interactive code examples
- The basics of the python data science ecosystem: NumPy, Matplotlib, Pandas, etc.
- Geospatial extensions to the Python data science ecosystem: Fiona, Shapely, GDAL, and most importantly GeoPandas
- Perform common vector analysis tasks with GeoPandas
Requirements
- Basic understanding of GIS operations for data analysis (buffers, intersections, etc)
- Basic understanding of Python (You can get what you need from my course Survey of Python for GIS analysis)
Description
Learn why the Geospatial Data Science tools are becoming so popular in the Geospatial sector. The combination of Jupyter Notebooks with Python and GeoPanda's allows you to analyze vector data quickly, repeatably, and with full documentation of every step along the way so your entire analysis can be repeated at the touch of a button in a notebook format that can be shared with colleagues.
If you ever get asked to explain your analysis, either for a scientific paper, to defend your results in a court, or simply to share what you've done with others so they can follow your steps than you will be glad that you conducted your analysis in Jupyter notebooks with GeoPanda's rather than in a traditional desktop GIS system.
If you ever get frustrated with limitations in desktop GIS software, some of which is still 32 bit, single core software that uses decades old technology under the hood then you will appreciate the performance that can be achieved with this approach.
Who this course is for:
- GIS analysts who want to increase their understanding of data science
- Data scientists who want to increase their understanding of geospatial analysis
Instructor
- 4.5 Instructor Rating
- 8,106 Reviews
- 24,360 Students
- 20 Courses
I have been programming and working with database applications for over 30 years, and specializing in geospatial applications for over 20 years. I am a believer in the 80/20 pareto principle which suggests that you only need to understand 20% of a subject in order to do 80% of your work. My goal in all my courses is to teach at the level of that 20% sweet spot and to provide my students with the background and the tools they need to learn the rest of what they need on their own.