A few months ago I attended the Spatial Data Science Conference in New York City, hosted by Carto.
Spatial Data Science is a field that deals with spatial data – that is, data points that have a geography associated with them. For example, a database of sales by ZIP code would be spatial data. In the planning world, spatial data tends to show up in conjunction with GIS (Geographic Information Systems), the most prevalent of which is ArcGIS (and its open-source equivalent, QGIS).
In college, I worked for a semester at the maps library at the University of Michigan. I like maps, and spatial data, so when I found out about this conference I was excited to see how spatial data was used in industry.
I will have to type up my notes from the conference in a different post, but I’m writing this post because I want to learn more about spatial data science. I’m ~manifesting~ this by writing about it, hoping that it will keep me accountable. Back in 2023, I wrote about GIS being one of the three technical skills I wanted to learn.
Here are some things that interest me about spatial data science, that I would like to learn. I don’t have a plan or goal associated with this yet, because I want to make sure that anything I commit to is realistic (which I haven’t done the greatest job of doing in the past…oops).