Spatial Data Management For GIS and Data Scientists
Written by Nikos Vaggalis   
Friday, 24 November 2023

Videos of the lectures taught in Fall 2023 at the University of Tennessee are now available as a YouTube playlist. They provide a complete overview of the concepts of GeoSpatial science using Google Earth Engine, PostgresSQL GIS , DuckDB, Python and SQL.

Taught on campus, but recorded for the rest of us to enjoy for free, by Dr. Qiusheng Wu, an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee. Dr. Qiusheng is also an Amazon Visiting Academic and a Google Developer Expert (GDE) for Earth Engine.

The target groups addressed by the course are GIScientists and geographers who want to learn about Data Science,
and the other way around, data scientists who want to work with geographical data; and of course students in that area.

Geographical data nowdays are everywhere. At its simplest form you'll be familiar with Google Maps, Mobile applications and social media metadata, while at the more advanced, there's the need to model objects that exist in the real world and are location aware. The software industry aside, lately there's many traditional business that started working with that kind of data.

In this course then you'll learn how to manage geospatial and big data using Google Earth Engine, PostgresSQL GIS , DuckDB, Python and SQL which you will use to query, analyze,
and manipulate spatial databases effectively. Take note that PostGIS, a geospatial extension to Postgres is the the most popular Postgres extension. Under that perspective, the course's value which explores various techniques for efficiently retrieving and managing spatial data, explodes multifold.

As such, students who successful complete the course should be able to:

  • Know the commonly used vector and raster formats
  • Understand the basics of Python (e. g. , variables, data types, functions, loops, modules)
  • Use practical tools for data science (e. g. , Jupyter notebook, Colab, Anaconda, VS Code)
  • Explain the Earth Engine data types (e. g. , Image, ImageCollection, FeatureCollection)
  • Visualize local vector and raster datasets interactively in a Jupyter environment
  • Visualize Earth Engine vector and raster datasets interactively in a Jupyter environment
  • Perform geospatial analysis with Earth Engine datasets
  • Export Earth Engine datasets
  • Create spatial databases with PostgreSQL and PostGIS
  • Store and query spatial data with PostGIS
  • Perform spatial analysis with PostGIS
  • Download spatial data from various sources efficiently

The tech stack used throughout the course is impressive too. Tools that are going to be used include:

  • ArcGIS Pro
  • QGIS
  • Miniconda
  • Google Colab
  • Visual Studio Code
  • PyCharm
  • Google Earth Engine
  • Geemap
  • PostgreSQL

The course is making use of that stack beginning very early on, as seen by the curriculum spanning 13 weeks:

Week 1: Course Introduction
Week 1: Spatial Data Models
Week 2: Installing Miniconda and geemap
Week 2: Introducing Visual Studio Code
Week 2: Setting Up Powershell for VS Code
Week 2: Introducing Git and GitHub
Week 3: Python Basics
Week 3: Getting Started with Geemap
Week 4: Using Earth Engine Image
Week 4: Filtering Image Collection
Week 4: Filtering Feature Collection
Week 5: Styling Feature Collection
Week 5: Earth Engine Data Catalog
Week 5: Visualizing Cloud Optimized GeoTIFF (COG)
Week 6: Visualizing STAC and Vector Data
Week 6: Downloading OpenStreetMap Data
Week 6: Visualizing Earth Engine Data
Week 7: Timeseries visualization and zonal statistics
Week 7: Parallel processing with the map function
Week 7: Earth Engine data reduction
Week 8: Creating Cloud-free Imagery with Earth Engine
Week 9: Downloading Earth Engine Images
Week 9: Downloading Earth Engine Image Collections
Week 9: Earth Engine Applications
Week 10: DuckDB for Geospatial
Week 10: Introduction to DuckDB (CLI, Python API, VS Code, DBeaver)
Week 10: DuckDB CLI and SQL Basics
Week 10: Introducing SQL Basics with DuckDB
Week 11: Intro to the DuckDB Python API
Week 11: Importing Spatial Data Into DuckDB
Week 11: Exporting Spatial Data From DuckDB
Week 12: Working with Geometries in DuckDB
Week 13: Analyzing Spatial Relationships with DuckDB
Week 13: Visualizing Geospatial Data in DuckDB with leafmap and lonboard

Of course 13 weeks was the duration on campus. The rest we can enjoy at a self pace. The videos are also accompanied by an online reference book in HTML format. 

Quality wise, Dr. Qiusheng Wu clearly explains the concepts and showcases the whole process of working with the tools that handle geodata. Which means that even if you are not familiar with Geo-science, the course is well worth attending regardless due to the tech stack employed, especially the PostgreSQL part. If on the other hand you already are a data scientist, then this is a must do.


More Information

Youtube playlist




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Last Updated ( Friday, 24 November 2023 )