Last time we installed ArcGIS with minimum fuss and began exploring their mapmaking tools, including raster pixel tilings and searching for more pre-uploaded GIS items online.
However, there’s a fairly fundamental rule of data science:
The data you want usually isn’t in the place or shape you want it to be.
This is one reason why I believe data science careers are fairly safe from AI replacement. Machine learning is great at re-treading previously seen pathways, but the first 80% of the data process — ETL, including cleaning and migrations — is variable enough to require human input.
It could also be that the people researching AI algorithms aren’t interested in making themselves obsolete, but I doubt it; the drive to automate and simplify your own workflow is generally too high for such far-flung fears.
Sourcing Proper Data
Let’s explore ArcGIS’ native data creation & import settings before running some more visualizations.
For any meaningful analysis you’d like to do, the data probably isn’t already up on the ArcGIS cloud and neatly formatted for feature layering, though they do have some nice searchable layers already out there.
If you’re fortunate you’ve just grabbed a pre-cleaned CSV from some government-hosted API, or perhaps you had to painstakingly query a SQL server that hasn’t been updated since 1980.
CSV-compatible formats are by far the most common, so we’ll begin working under those assumptions. Even graph networks are JSON-compatible, which is what we’ll eventually upload.
Go and find some data of interest to you. For example, the Johns Hopkins University Center for Systems Science and Engineering maintains a beautifully ordered repository of domestic and international COVID-19 data: