Problems
Real geospatial problems, graded automatically. Choose a track, a level and what you want to hand in.
- Not started, Protect the bus stop key during the loadData preparationSQLData ManagementintermediateDifficulty: intermediate210 XP~40 min7 checksData ManagementintermediateDifficulty: intermediate
Recover zero-padded stop identifiers before a transport timetable join.
You hand in a spatial SQL query.
Dataset
- Singapore bus stop code staging · Point · 31 features · EPSG:4326
What you’ll do
- Preserve identifier semantics through SQL casting.
- Resolve repeated source rows deterministically.
- Return a clean point layer for the timetable join.
data-preparationsingaporesqlAlso trains PostGIS / SQLOpen problem - Not started, Redirect the rider at an empty dockDecision supportSQLPostGIS / SQLintermediateDifficulty: intermediate250 XP~40 min5 checksPostGIS / SQLintermediateDifficulty: intermediate
For every station with no bikes, the nearest station that has one, and the walk in metres — with the KNN operator so it stays fast at 799 stations or 79,000.
You hand in a spatial SQL query.
Dataset
- Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
- Filter to stations with no bikes
- Find each one's nearest stocked station with an index-assisted nearest-neighbour search
- Report the distance in metres
KNNLATERALGiSTgeographyperformancetransportOpen problem - Not started, Where should a waterway survey start?Decision supportSQLSpatial AnalysisintermediateDifficulty: intermediate320 XP~50 min9 checksSpatial AnalysisintermediateDifficulty: intermediate
Screen large global urban footprints for mapped river contact while keeping scale limitations explicit.
You hand in a spatial SQL query.
Datasets
- Urban areas · Polygon · 2,143 features · EPSG:4326
- Rivers and lake centrelines · LineString · 461 features · EPSG:4326
What you’ll do
- Apply a spatial join to real river and urban geometry
- Keep zero-contact footprints in the inventory
- Distinguish map-scale contact classes for survey planning
Natural Earthurban waterPostGISspatial screeningcategorical mapglobalAlso trains PostGIS / SQL, Cartography, Data ManagementOpen problem - Not started, Which airfields should the response desk check?Decision supportSQLPostGIS / SQLintermediateDifficulty: intermediate260 XP~45 min6 checksPostGIS / SQLintermediateDifficulty: intermediate
Use a metre-based spatial join to identify civil airfields near strong, shallow earthquake epicentres, with the facilities drawn on the map.
You hand in a spatial SQL query.
Datasets
- Global M5.5+ earthquakes, Jan–Mar 2025 · Point · 86 features · EPSG:4326
- World airports · Point · 893 features · EPSG:4326
What you’ll do
- Filter events by magnitude and depth
- Join the selected events to civil airfields within 250 km in metres
- Return traceable, mapped airport points and measured distances
PostGISearthquake responseairportsgeographyglobalAlso trains Spatial Analysis, CartographyOpen problem - Not started, Which ports are within 25 km of a capital?Spatial analysisSQLPostGIS / SQLintermediateDifficulty: intermediate220 XP~35 min5 checksPostGIS / SQLintermediateDifficulty: intermediate
ST_DWithin on geography: a proximity join in true metres that the index can still answer.
You hand in a spatial SQL query.
Datasets
- Populated places · Point · 1,251 features · EPSG:4326
- Ports · Point · 1,081 features · EPSG:4326
What you’ll do
- Write a proximity join with ST_DWithin
- Measure in metres on SRID 4326 data by casting to geography
- Keep the predicate in a form the GiST index can serve
ST_DWithingeographyGiSTproximityglobalOpen problem