Intro to the geonewb series

Trying to learn about geospatial analytics


Mark Isken


January 24, 2023

I’ve always loved maps and over the years have dabbled with various pieces of GIS software. As I’ve gotten more involved in ecological and environmental analytics projects, I realized I really needed to do some serious geospatial analysis learning with a focus on open source tools (mostly Python but also some R and QGIS).

I thought I’d create a series of blog posts to document and share the things I learn. Specifically, I want to write these posts from the perspective of someone who is pretty new to geospatial analysis - a “geonewb” perspective.

Geospatial analysis is becoming increasingly popular and accessible. Pretty much every spot on earth is imaged from satellites every 5 or so days and the data is freely available to anyone. Location matters in all kinds of business, policy and every day life decisions. Free software is available and ecosystems of geospatial practitioners and knowledge are coalescing around around these tools and data. There’s going to be a whole lot of geonewbs out there. Hopefully some of my posts will be helpful to them.

In part 1 of the geonewb series, I’ll be starting a multi-part set of posts based on the Getting Started Tutorial that’s part of the Detecting harmful algal bloom challenge run by the Driven Data folks. Since I’ve worked just a bit with geographic data and even less with image data, I’m using this challenge as a vehicle for quickly learning more about these topics. It’s a terrific tutorial and as I was working through it I would branch off to learn more about some of the underlying concepts and technologies. This led to a mess of Jupyter notebooks and now I’m trying to bring all of that together into a somewhat extended version of the original tutorial - which I’ll share through a series of posts and accompanying Jupyter notebooks.

There is an overwhelming amount of material available online realated to earth observation, geospatial analysis, GIS, and related topics. Here are two “awesome” lists to provide a starting point for numerous trips down rabbit holes.