Advanced Geospatial Data Analytics in Python

In this course, globally recognized expert Milan Janosov explores advanced geospatial data science using Python, equipping you with the tools to solve real-world problems across various sectors. Discover how to manipulate, model, and visualize geospatial data. Learn about the intricacies of spatial indexing and how to utilize geocoding to transform textual addresses into spatial coordinates. Dive into more complex skills and build your understanding of spatial temporal data, so you can track changes over time. Gain hands-on practice through examples and exercises, including a robust platform to build effective geospatial analytical pipelines. When you complete this course, you will have a solid foundation and enhanced expertise in Python-based geospatial data science, and you’ll be ready to tackle advanced spatial models and analyses in any professional or research setting.

Learning objectives
Distinguish between vector and raster data formats, and select the appropriate one for different geospatial tasks.
Transform data between vector and raster formats using Python libraries such as GeoPandas and Rasterio.
Implement and visualize spatial indexing using the H3 framework for efficient data aggregation and analysis.
Test the H3 methodology on real-world data and visualizations.
Overview, acquire, preprocess and visualize spatio-temporal dataset.
Change detection on spatio-temporal raster and the NetCDF format.
Apply geocoding and reverse geocoding techniques for both individual and batch address-to-coordinate conversions.
Review advanced map projections with PyProj.