Global open data, scripted and audited
Natural Earth as a working dataset rather than a backdrop: geodesic distance across a planet, clip-and-credit overlays in an equal-area projection, and the audit that decides whether a reference file can be trusted at all.
Python
What you will be able to do
Work with worldwide vector data in Python without a single projected CRS to hide behind: audit a gazetteer against its own geometry, measure nearest-neighbour distances geodesically, and write clip-and-aggregate scripts in an equal-area projection that a reviewer can re-run.
The sequence
Each step assumes the one before it.
- Up nextWhen the attributes and the geometry disagreeUp nextA reference gazetteer carries a latitude and a longitude column beside its geometry. Audit how far apart they are, and what else does not add up.intermediateDifficulty: intermediate200 XP~35 minintermediateDifficulty: intermediate
- 2Which capitals are more than an hour from a scheduled airport?Nearest-neighbour from 200 national capitals to 872 civil airports, geodesically, with a threshold that has to be applied honestly.intermediateDifficulty: intermediate230 XP~40 minintermediateDifficulty: intermediate
- 3Airports per state, as a script that runs againSpatially join a global airport layer to US states and hand back one row per state, zeros included — in a script the grader runs.intermediateDifficulty: intermediate190 XP~35 minintermediateDifficulty: intermediate
- 4How much of each state is built up?Overlay the urban footprints on the states in an equal-area projection and credit each side of a state line only what lies on it.advancedDifficulty: advanced320 XP~55 minadvancedDifficulty: advanced
Where it leads
Python is examined in the Open-Data Analyst — Associate, which issues the Kharita Certified Open-Data Analyst (Associate) (KODA-A1). The problems here are the practice; the assessment is the same kind of problem, under time.