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Real geospatial problems, graded automatically. Choose a track, a level and what you want to hand in.

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Spatial Analysis Clear all

Not started, Docks and bikes per borough, in one querySpatial analysisSQL
A point-in-polygon join with GROUP BY — and the outer join that keeps the boroughs with nothing to count.
PostGIS / SQLbeginnerDifficulty: beginner150 XP~25 min4 checksPostGIS / SQLbeginnerDifficulty: beginner15025m4
You hand in a spatial SQL query.
Datasets
London boroughs · Polygon · 33 features · EPSG:4326
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Join stations to boroughs with a spatial predicate
Aggregate per borough
Keep every borough, including the ones outside the scheme
ST_ContainsGROUP BYouter jointransportLondonAlso trains Spatial Analysis
Open problem
Not started, Normalise the map before you shade itCartographyGeoJSON
Turn a count into a rate: population per square kilometre for every country, with the area measured rather than looked up.
CartographybeginnerDifficulty: beginner140 XP~25 min6 checksCartographybeginnerDifficulty: beginner14025m6
You hand in a GeoJSON result.
Dataset
Countries · MultiPolygon · 241 features · EPSG:4326
What you’ll do
Measure every polygon's area in an equal-area projection
Compute a rate by dividing the count by the area
Return every country, ready to shade
choroplethnormalisationEqual EarthdensityglobalAlso trains Spatial Analysis
Open problem
Not started, Package one playground location per source featureData preparationDataset file
Turn mixed playground points and areas into a projected GeoPackage site directory.
Data ManagementbeginnerDifficulty: beginner130 XP~30 min6 checksData ManagementbeginnerDifficulty: beginner13030m6
You hand in a prepared dataset file.
Dataset
Sydney playground mixed-geometry source · Point · 8 features · EPSG:4326
What you’ll do
Convert mixed source geometry into a consistent site layer.
Reproject coordinates to the receiving metric CRS.
Package exact schema and layer name in GeoPackage.
data-preparationsydneyfileAlso trains Spatial Analysis
Open problem
Not started, Which airports sit inside a city?Spatial analysisGeoJSON
Point-in-polygon screening: the airports whose point falls inside a built-up footprint, for a noise-exposure programme.
Spatial AnalysisbeginnerDifficulty: beginner130 XP~20 min5 checksSpatial AnalysisbeginnerDifficulty: beginner13020m5
You hand in a GeoJSON result.
Datasets
World airports · Point · 893 features · EPSG:4326
Urban areas · Polygon · 2,143 features · EPSG:4326
What you’ll do
Test each point for containment in any polygon of a second layer
Return the qualifying points once each, with their attributes
point-in-polygonspatial joinscreeningaviationglobalAlso trains Data Management
Open problem
Not started, Which boroughs is the cycle-hire scheme actually in?Spatial analysisGeoJSON
Join 799 docking stations to 33 boroughs and rank them by docks per square kilometre.
Spatial AnalysisbeginnerDifficulty: beginner130 XP~25 min6 checksSpatial AnalysisbeginnerDifficulty: beginner13025m6
You hand in a GeoJSON result.
Datasets
London boroughs · Polygon · 33 features · EPSG:4326
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Spatially join docking stations to the borough that contains them
Sum docking points per borough without dropping the boroughs that have none
Measure borough area in a metric CRS and compute docks per km²
spatial joinaggregationCRStransportLondonAlso trains Data Management
Open problem
Not started, Which countries have no airport in the registry?Spatial analysisPython
A points-in-polygons count as a script the grader runs: one row per country, zeros kept, on a global registry that misses a lot of places.
PythonbeginnerDifficulty: beginner150 XP~30 min4 checksPythonbeginnerDifficulty: beginner15030m4
You hand in a Python script.
Datasets
Countries · MultiPolygon · 241 features · EPSG:4326
World airports · Point · 893 features · EPSG:4326
What you’ll do
Read both layers from /data and join points to polygons with a spatial predicate
Count per country without losing the countries that count zero
Assign the finished GeoDataFrame, one row per country, to result
GeoPandasspatial joinreproducibilityaviationglobalAlso trains Spatial Analysis
Open problem
Not started, Airports per state, as a script that runs againSpatial analysisPython
Spatially join a global airport layer to US states and hand back one row per state, zeros included — in a script the grader runs.
PythonintermediateDifficulty: intermediate190 XP~35 min5 checksPythonintermediateDifficulty: intermediate19035m5
You hand in a Python script.
Datasets
United States states · Polygon · 51 features · EPSG:4326
World airports · Point · 893 features · EPSG:4326
What you’ll do
Read both layers from /data and join airports to states with a spatial predicate
Count airports and major airports per state without losing a state that has none
Assign the finished GeoDataFrame, one row per state, to the variable named result
GeoPandasspatial joinreproducibilityaviationAlso trains Spatial Analysis
Open problem
Not started, At which zoom does the dock layer fit in a tile?Web deliveryAnswers
Count features per XYZ tile across zoom levels to set a vector-tile pipeline's minimum zoom and its per-tile limit honestly.
Web GISintermediateDifficulty: intermediate190 XP~30 min4 checksWeb GISintermediateDifficulty: intermediate19030m4
You hand in your answers as values.
Dataset
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Convert coordinates to tile indices at several zooms
Count features per tile and find the maximum
Choose a minimum zoom from the data rather than a default
vector tilesXYZtippecanoezoom levelsLondonAlso trains Spatial Analysis
Open problem
Not started, Build the global seismic reconnaissance watchlistDecision supportPython
Turn a fixed USGS event feed into a mapped, reproducible shallow strong-event watchlist for requesting impact products.
Spatial AnalysisintermediateDifficulty: intermediate240 XP~40 min5 checksSpatial AnalysisintermediateDifficulty: intermediate24040m5
You hand in a Python script.
Dataset
Global M5.5+ earthquakes, Jan–Mar 2025 · Point · 86 features · EPSG:4326
What you’ll do
Apply two inclusive, unit-aware filters to the frozen event snapshot
Carry stable event identifiers and response metadata into a point GeoDataFrame
Map event magnitude as a proportional point result without claiming it is impact
USGSearthquakesGeoPandasemergency screeningglobalAlso trains Python, Data Management, Cartography
Open problem
Not started, How big does Web Mercator make Greenland?CartographyAnswers
Put a number on the most famous distortion in cartography: the same countries measured on the ellipsoid and on the projection every web map uses.
CartographyintermediateDifficulty: intermediate210 XP~30 min4 checksCartographyintermediateDifficulty: intermediate21030m4
You hand in your answers as values.
Dataset
Countries · MultiPolygon · 241 features · EPSG:4326
What you’ll do
Measure area geodesically or in an equal-area projection
Project the same polygons to Web Mercator and take their planar area
Express the distortion as ratios a reader can repeat
Web Mercatorprojectionarea distortionEqual EarthglobalAlso trains Spatial Analysis
Open problem
Not started, How much of each borough lies below ten metres?Raster analysisGeoJSON
Threshold a DEM, count inside polygons, and turn pixels into square kilometres on a geographic grid — a flood-screening figure per borough.
Spatial AnalysisintermediateDifficulty: intermediate240 XP~40 min6 checksSpatial AnalysisintermediateDifficulty: intermediate24040m6
You hand in a GeoJSON result.
Datasets
Greater London elevation (Copernicus DEM GLO-30) · Raster · EPSG:4326
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Threshold a continuous raster
Count the thresholded pixels per polygon
Convert pixels to ground area on a geographic grid
DEMthresholdzonalpixel areafloodLondonAlso trains Remote Sensing
Open problem
Not started, How much of Inner London is a five-minute walk from a dock?Spatial analysisGeoJSON
Buffer, dissolve and clip: the covered share of each Inner London borough at a 400 m walk.
Spatial AnalysisintermediateDifficulty: intermediate240 XP~45 min5 checksSpatial AnalysisintermediateDifficulty: intermediate24045m5
You hand in a GeoJSON result.
Datasets
London boroughs · Polygon · 33 features · EPSG:4326
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Buffer 799 stations by 400 m in a metric CRS
Dissolve the buffers into one coverage geometry
Clip the coverage to each Inner London borough
bufferdissolveclipCRStransportLondonAlso trains Cartography
Open problem
Not started, The buffer that shrank on the way to the mapCartographyAnswers
Quantify what a 400 m buffer drawn in EPSG:3857 actually covers on the ground in London, and how much of the coverage report it loses.
CartographyintermediateDifficulty: intermediate200 XP~30 min4 checksCartographyintermediateDifficulty: intermediate20030m4
You hand in your answers as values.
Datasets
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Derive the Web Mercator scale factor at London's latitude
Translate a projected buffer distance into its ground equivalent
Re-run the coverage analysis with the shrunken buffer and measure the shortfall
Web Mercatorscale factorprojectionbufferLondonAlso trains Spatial Analysis
Open problem
Not started, Where are the bikes, as a share of the docks?Spatial analysisPython
A scripted per-borough ratio from a live snapshot: bikes over docking points, every borough kept, so it can run again on the next snapshot.
PythonintermediateDifficulty: intermediate190 XP~35 min5 checksPythonintermediateDifficulty: intermediate19035m5
You hand in a Python script.
Datasets
London boroughs · Polygon · 33 features · EPSG:4326
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Read both layers from /data and assign stations to boroughs with a spatial predicate
Sum two columns per borough and compute their ratio, with zero where the denominator is zero
Assign the finished GeoDataFrame, one row per borough, to result
GeoPandasspatial joinratioreproducibilitytransportLondonAlso trains Spatial Analysis
Open problem
Not started, Where should a waterway survey start?Decision supportSQL
Screen large global urban footprints for mapped river contact while keeping scale limitations explicit.
Spatial AnalysisintermediateDifficulty: intermediate320 XP~50 min9 checksSpatial AnalysisintermediateDifficulty: intermediate32050m9
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 Management
Open problem
Not started, Where should the sewer service desk review closure lag?Decision supportPython
Turn a fixed 311 complaint cohort into a map of requests open longer than an analyst-defined day.
Spatial AnalysisintermediateDifficulty: intermediate320 XP~50 min5 checksSpatial AnalysisintermediateDifficulty: intermediate32050m5
You hand in a Python script.
Dataset
NYC 311 sewer requests, 1–7 June 2025 · Point · 408 features · EPSG:4326
What you’ll do
Calculate closure lag from timestamp fields without inventing values for open requests
Select the service manager's review queue
Map the concentration of selected requests while retaining per-request details
NYC 311urban servicestemporal GISheat mapGeoPandasAlso trains Python, Cartography, Data Management
Open problem
Not started, Which airfields should the response desk check?Decision supportSQL
Use a metre-based spatial join to identify civil airfields near strong, shallow earthquake epicentres, with the facilities drawn on the map.
PostGIS / SQLintermediateDifficulty: intermediate260 XP~45 min6 checksPostGIS / SQLintermediateDifficulty: intermediate26045m6
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, Cartography
Open problem
Not started, Which boroughs have the least tree cover?Raster analysisGeoJSON
Zonal statistics on a categorical raster: the share of tree-cover pixels inside each borough polygon, for a tree-planting programme's targeting.
Remote SensingintermediateDifficulty: intermediate230 XP~40 min7 checksRemote SensingintermediateDifficulty: intermediate23040m7
You hand in a GeoJSON result.
Datasets
Greater London land cover 2021 (ESA WorldCover 10 m) · Raster · EPSG:4326
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Compute zonal statistics of a categorical raster over polygons
Express the class as a share per zone
Return the polygons ready for a choropleth
zonal statisticsland coverraster–vectorWorldCoverLondonAlso trains Spatial Analysis
Open problem
Not started, Which capitals are more than an hour from a scheduled airport?Decision supportGeoJSON
Nearest-neighbour from 200 national capitals to 872 civil airports, geodesically, with a threshold that has to be applied honestly.
Spatial AnalysisintermediateDifficulty: intermediate230 XP~40 min5 checksSpatial AnalysisintermediateDifficulty: intermediate23040m5
You hand in a GeoJSON result.
Datasets
Populated places · Point · 1,251 features · EPSG:4326
World airports · Point · 893 features · EPSG:4326
What you’ll do
Filter the places to national capitals and the airports to civil scheduled-traffic fields
Find each capital's nearest qualifying airport with a geodesic distance
Keep the capitals beyond 75 km and report the airport and the distance
nearest neighbourgeodesichaversinelogisticsglobalAlso trains Data Management
Open problem
Not started, Which ports in the registry are the same port?Data qualityAnswers
Find probable duplicates in a facility registry by combining a name match with a distance rule — and separate them from the honest homonyms.
Data ManagementintermediateDifficulty: intermediate190 XP~30 min4 checksData ManagementintermediateDifficulty: intermediate19030m4
You hand in your answers as values.
Dataset
Ports · Point · 1,081 features · EPSG:4326
What you’ll do
Find repeated identifiers in a registry
Combine an attribute match with a spatial rule to separate duplicates from homonyms
Size the missing attribute that would resolve the rest
data qualitydeduplicationhaversineregistryglobalAlso trains Spatial Analysis
Open problem
Not started, Build an earthquake contact reconnaissance queueProjectDecision support3 steps
Screen strong shallow events, quantify airfield and settlement proximity, and hand over a traceable contact map.
Spatial AnalysisadvancedDifficulty: advanced550 XP~95 min12 checksSpatial AnalysisadvancedDifficulty: advanced55095m12
Worked in 3 steps, each checked before the next one opens.
Select the event queue · SQL
Measure contact gaps · Answers
Hand over the access map · Python
Datasets
Global M5.5+ earthquakes, Jan–Mar 2025 · Point · 86 features · EPSG:4326
World airports · Point · 893 features · EPSG:4326
Populated places · Point · 1,251 features · EPSG:4326
What you’ll do
Select a reproducible event queue in SQL
Measure two independent global proximity indicators
Deliver an auditable access map with an explicit contact-gap distinction
projectUSGSnearest neighbourlogisticsglobalAlso trains Python, Data Management, Cartography
Open project
Not started, Classify a map where most of the values are zeroCartographyAnswers
Quantile breaks collapse on a distribution dominated by zeros. Measure how, on the river-kilometres-per-country table, and say what a map maker should do instead.
CartographyadvancedDifficulty: advanced290 XP~45 min6 checksCartographyadvancedDifficulty: advanced29045m6
You hand in your answers as values.
Datasets
Countries · MultiPolygon · 241 features · EPSG:4326
Rivers and lake centrelines · LineString · 461 features · EPSG:4326
What you’ll do
Reproduce a derived per-country measure
Compute quantile and equal-interval breaks on a zero-heavy distribution
Quantify how each scheme distributes the countries
choroplethclassificationquantileszero-inflatedhydrologyAlso trains Python, Spatial Analysis
Open problem
Not started, Defend a borough greening shortlistProjectDecision support3 steps
Measure canopy and summer vegetation on their own grids, then add low-ground context and apply one transparent shortlist rule.
Remote SensingadvancedDifficulty: advanced620 XP~110 min13 checksRemote SensingadvancedDifficulty: advanced620110m13
Worked in 3 steps, each checked before the next one opens.
Audit the vegetation rasters · Answers
Deliver the borough evidence table · Dataset file
Add low-ground context and shortlist · GeoJSON
Datasets
Greater London land cover 2021 (ESA WorldCover 10 m) · Raster · EPSG:4326
Greater London, Sentinel-2 red and NIR, 11 July 2025 · Raster · EPSG:32630
Greater London elevation (Copernicus DEM GLO-30) · Raster · EPSG:4326
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Audit the two vegetation measures from observed raster pixels
Hand over a per-borough canopy and NDVI table as a GIS file
Add area-weighted low ground and an explicit shortlist flag
zonal statisticsNDVIland coverDEMmulti-criteriaLondonAlso trains Spatial Analysis, Data Management
Open project
Not started, Deliver a field-ready public asset registerProjectData preparation4 steps
Merge two open asset extracts into a quality-controlled GeoPackage for field inspection.
Data ManagementadvancedDifficulty: advanced600 XP~105 min17 checksData ManagementadvancedDifficulty: advanced600105m17
Worked in 4 steps, each checked before the next one opens.
Audit both incoming sources · Answers
Build the common asset model · Dataset file
Verify and round coordinates · GeoJSON
Package and verify the handover · Dataset file
Datasets
Melbourne toilet source · Point · 7 features · EPSG:4326
Melbourne drinking-water source · Point · 21 features · EPSG:4326
What you’ll do
Profile and reconcile two source layers without multiplying assets.
Normalise access without representing unknown status as public.
Deliver a reproducible, typed WGS 84 GeoPackage handover.
data-preparationprojectgeopackagemelbournehandoverAlso trains Spatial Analysis, Cartography
Open project
Not started, Deliver an access-safe public toilet layerData preparationDataset file
Classify access honestly and deliver a typed projected GeoPackage without null locations.
Data ManagementadvancedDifficulty: advanced365 XP~55 min6 checksData ManagementadvancedDifficulty: advanced36555m6
You hand in a prepared dataset file.
Dataset
Amsterdam toilet access staging · Point · 14 features · EPSG:4326
What you’ll do
Distinguish missing access from public access.
Preserve first-source precedence and drop null geometry.
Deliver a named GeoPackage in the receiving CRS.
data-preparationamsterdamfileAlso trains Spatial Analysis
Open problem
Not started, How much of each state is built up?Spatial analysisPython
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.
PythonadvancedDifficulty: advanced320 XP~55 min5 checksPythonadvancedDifficulty: advanced32055m5
You hand in a Python script.
Datasets
United States states · Polygon · 51 features · EPSG:4326
Urban areas · Polygon · 2,143 features · EPSG:4326
What you’ll do
Reproject both layers to an equal-area CRS
Clip the urban footprints to the states with an overlay
Sum clipped area per state and express it as a share of the state's area
GeoPandasoverlayequal-areaAlbersland useAlso trains Spatial Analysis, Data Management
Open problem
Not started, Mean NDVI per borough, across a projection boundaryRaster analysisGeoJSON
Zonal statistics where the raster is in UTM and the polygons are in WGS 84: reproject the right layer, compute the index per borough, and hand back a map.
Remote SensingadvancedDifficulty: advanced300 XP~50 min7 checksRemote SensingadvancedDifficulty: advanced30050m7
You hand in a GeoJSON result.
Datasets
Greater London, Sentinel-2 red and NIR, 11 July 2025 · Raster · EPSG:32630
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Recognise a CRS mismatch between a raster and a vector layer
Reproject the vector layer rather than resample the raster
Compute a per-zone mean of a derived index
NDVIzonal statisticsCRSUTMSentinel-2LondonAlso trains Spatial Analysis, Data Management
Open problem
Not started, Plan a cycle redistribution shiftProjectDecision support3 steps
Classify the dock snapshot, propose bounded bike moves, and size the resulting shift with its unserved stations visible.
Spatial AnalysisadvancedDifficulty: advanced600 XP~100 min11 checksSpatial AnalysisadvancedDifficulty: advanced600100m11
Worked in 3 steps, each checked before the next one opens.
Classify the snapshot · SQL
Map feasible transfers · GeoJSON
Size the preliminary shift · Answers
Dataset
Santander Cycles docking stations (live snapshot) · Point · 799 features · EPSG:4326
What you’ll do
Classify full and empty active stations in SQL
Build a bounded nearest-source transfer map
Size a shift and report unserved empties from the same rules
nearest neighbouroperationscapacityCRStransportLondonAlso trains Data Management, GIS Architecture
Open project
Not started, Screen capital access exceptionsDecision supportGeoJSON
Use airport and port proximity to flag capitals that need direct logistics verification.
Spatial AnalysisadvancedDifficulty: advanced580 XP~100 min6 checksSpatial AnalysisadvancedDifficulty: advanced580100m6
You hand in a GeoJSON result.
Datasets
Populated places · Point · 1,251 features · EPSG:4326
World airports · Point · 893 features · EPSG:4326
Ports · Point · 1,081 features · EPSG:4326
What you’ll do
Compute nearest-neighbour distances from capitals to two facility layers, geodesically
Apply an ordered rule set to derive a category
Return the exceptions with the distances that justify them
nearest neighbourgeodesicclassificationlogisticsglobalAlso trains Data Management
Open problem
Not started, Screen Newham’s greening gridProjectRaster analysis3 steps
Audit a real land-cover chip, deliver a measured grid file, and identify cells for field verification.
Remote SensingadvancedDifficulty: advanced600 XP~100 min10 checksRemote SensingadvancedDifficulty: advanced600100m10
Worked in 3 steps, each checked before the next one opens.
Audit the class chip · Answers
Hand over the measured grid · Dataset file
Map field-verification cells · GeoJSON
Datasets
Newham WorldCover 2021 class chip · Raster · EPSG:4326
London boroughs · Polygon · 33 features · EPSG:4326
What you’ll do
Audit class counts in the clipped raster
Prepare a traceable borough grid as an uploaded GIS file
Return only cells that meet a stated greening screen
NewhamWorldCoverurban greeningoffline rasterAlso trains Data Management, Spatial Analysis
Open project
Not started, Urban share of each state, in SQLSpatial analysisSQL
ST_Intersection for the pieces and ST_Area on geography for the metres — the overlay-and-measure pattern inside the database.
PostGIS / SQLadvancedDifficulty: advanced300 XP~45 min5 checksPostGIS / SQLadvancedDifficulty: advanced30045m5
You hand in a spatial SQL query.
Datasets
United States states · Polygon · 51 features · EPSG:4326
Urban areas · Polygon · 2,143 features · EPSG:4326
What you’ll do
Overlay polygons with ST_Intersection
Measure the pieces on geography rather than in degrees
Aggregate per state, keeping the states with nothing
ST_IntersectiongeographyoverlayGROUP BYland useAlso trains Spatial Analysis
Open problem
Not started, Which big cities does the gazetteer not know?Data qualityGeoJSON
Test one layer's completeness against another: large urban footprints with no populated place inside them at all.
Data ManagementadvancedDifficulty: advanced300 XP~45 min5 checksData ManagementadvancedDifficulty: advanced30045m5
You hand in a GeoJSON result.
Datasets
Urban areas · Polygon · 2,143 features · EPSG:4326
Populated places · Point · 1,251 features · EPSG:4326
What you’ll do
Measure footprint area in an equal-area projection and apply a size floor
Test each footprint for containment of any point from a second layer
Return the uncovered footprints with the area that says how much they matter
completenessspatial joinequal-areagazetteerglobalAlso trains Spatial Analysis
Open problem
Not started, Which metros straddle an international border?Spatial analysisGeoJSON
Overlay 2,143 urban footprints on 241 countries and keep the ones with real built-up area on both sides of a line.
Spatial AnalysisadvancedDifficulty: advanced320 XP~50 min5 checksSpatial AnalysisadvancedDifficulty: advanced32050m5
You hand in a GeoJSON result.
Datasets
Urban areas · Polygon · 2,143 features · EPSG:4326
Countries · MultiPolygon · 241 features · EPSG:4326
What you’ll do
Overlay a footprint layer on a boundary layer
Measure the pieces in an equal-area projection and apply a minimum
Aggregate pieces back to the footprint and keep the multi-country ones
overlayequal-areaaggregationbordersglobalAlso trains Data Management
Open problem
Not started, Which neighborhoods merit a service follow-up?Decision supportSQL
Join geocoded service requests to neighborhood polygons and map a review share only where the sample supports it.
PostGIS / SQLadvancedDifficulty: advanced390 XP~65 min8 checksPostGIS / SQLadvancedDifficulty: advanced39065m8
You hand in a spatial SQL query.
Datasets
2020 Neighborhood Tabulation Areas · MultiPolygon · 262 features · EPSG:4326
NYC 311 sewer requests, 1–7 June 2025 · Point · 408 features · EPSG:4326
What you’ll do
Join incident locations to neighborhoods with a boundary-inclusive predicate
Compute elapsed-time review share with a clear denominator
Suppress very small denominator areas before mapping percentages
NYC 311PostGISspatial joinservice operationsgraduated mapAlso trains Spatial Analysis, Cartography, Data Management
Open problem
Not started, River kilometres per country, measured on the ellipsoidSpatial analysisPython
Clip the river centrelines to the country polygons and measure each piece geodesically — no projection is right for a layer that spans the planet.
PythonexpertDifficulty: expert400 XP~70 min6 checksPythonexpertDifficulty: expert40070m6
You hand in a Python script.
Datasets
Countries · MultiPolygon · 241 features · EPSG:4326
Rivers and lake centrelines · LineString · 461 features · EPSG:4326
What you’ll do
Find every river–country pair that intersects
Cut each river to the country polygon
Measure the pieces geodesically and sum per country
GeoPandaspyprojgeodesicoverlayhydrologyglobalAlso trains Spatial Analysis, Data Management
Open problem
Not started, Screen rivers for a shared-water annexSpatial analysisPython
Measure mapped river lengths by country and screen the rivers whose courses are meaningfully shared.
PythonexpertDifficulty: expert650 XP~120 min7 checksPythonexpertDifficulty: expert650120m7
You hand in a Python script.
Datasets
Rivers and lake centrelines · LineString · 461 features · EPSG:4326
Countries · MultiPolygon · 241 features · EPSG:4326
What you’ll do
Intersect every river with every country it touches and measure each piece geodesically
Aggregate the pieces per river into a count of meaningful holders and a dominance share
Assign the shared rivers, ranked by how evenly they are shared, to result
GeoPandaspyprojgeodesicoverlayhydrologyglobalAlso trains Spatial Analysis, Data Management
Open problem

That’s all 36 problems.

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