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Kharita Challenges

Problems

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

59 problems

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Track
Spatial Analysis36 problemsPostGIS / SQL13 problemsPython17 problemsRemote Sensing9 problemsCartography16 problemsWeb GIS8 problemsData Management36 problemsGIS Architecture6 problems
Level
Beginner12 problemsIntermediate23 problemsAdvanced22 problemsExpert2 problems
Hand in
SQL14 problemsPython14 problemsGeoJSON15 problemsAnswers20 problemsSchema1 problemDecisions2 problemsDataset file5 problems
Category
Data preparation7 problemsData quality7 problemsSpatial analysis14 problemsRaster analysis6 problemsCartography5 problemsDecision support11 problemsGeospatial systems3 problemsWeb delivery6 problems
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  1. Not started, Can the cycle parking feed report real capacity?Data preparationSQL

    Clean a Berlin cycle parking staging table for an operations map and capacity rollup.

    Data ManagementbeginnerDifficulty: beginner130 XP~25 min7 checks

    You hand in a spatial SQL query.

    Dataset

    • Berlin bicycle parking capacity staging · Point · 27 features · EPSG:4326

    What you’ll do

    • Resolve source precedence before casting.
    • Keep unknown capacity distinct from zero.
    • Return geometry for the operational map.
    data-preparationberlinsqlAlso trains PostGIS / SQL
    Open problem
  2. 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 checks

    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
  3. Not started, How many mapped rivers touch each country?Spatial analysisSQL

    ST_Intersects between lines and polygons with GROUP BY — and the outer join that keeps the countries with none.

    PostGIS / SQLbeginnerDifficulty: beginner150 XP~25 min4 checks

    You hand in a spatial SQL query.

    Datasets

    • Countries · MultiPolygon · 241 features · EPSG:4326
    • Rivers and lake centrelines · LineString · 461 features · EPSG:4326

    What you’ll do

    • Join lines to polygons with a spatial predicate
    • Aggregate per polygon
    • Keep every polygon, including those with no match
    ST_IntersectsGROUP BYouter joinhydrologyglobal
    Open problem
  4. Not started, How many tiles does an offline London need?Web deliveryAnswers

    From a bounding box to a tile count: size the offline basemap for a field app across zoom 10–15.

    Web GISbeginnerDifficulty: beginner140 XP~25 min3 checks

    You hand in your answers as values.

    Dataset

    • London boroughs · Polygon · 33 features · EPSG:4326

    What you’ll do

    • Take the bounding box of the borough layer
    • Convert box corners to tile coordinates at each zoom
    • Count tiles per zoom and total them into a storage estimate
    XYZ tilesWeb Mercatorofflinecapacity planningLondonAlso trains Cartography
    Open problem
  5. Not started, Make the pharmacy handover loadableData preparationPython

    Repair a field table so a Paris health directory can map its pharmacy locations reliably.

    Data ManagementbeginnerDifficulty: beginner125 XP~25 min6 checks

    You hand in a Python script.

    Dataset

    • Raw Paris pharmacy handover · Table · 21 rows · EPSG:4326

    What you’ll do

    • Parse and validate coordinates before constructing point geometry.
    • Preserve the first source record for each reference.
    • Deliver a typed, spatially plausible point layer.
    data-preparationcoordinatesquality-controlparisAlso trains Python
    Open problem
  6. 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 checks

    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
  7. 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 checks

    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
  8. Not started, What is Greater London made of?Raster analysisAnswers

    A class histogram on a 10 m land-cover raster: the shares a planning document quotes, and the reason a pixel count is not quite an area.

    Remote SensingbeginnerDifficulty: beginner140 XP~25 min5 checks

    You hand in your answers as values.

    Dataset

    • Greater London land cover 2021 (ESA WorldCover 10 m) · Raster · EPSG:4326

    What you’ll do

    • Read a categorical raster and count pixels per class
    • Express classes as shares of the valid pixels
    • State what the number is and is not
    land coverWorldCoverhistogramrasterLondonAlso trains Data Management
    Open problem
  9. Not started, Which airport codes will break the schedule join?Data qualityAnswers

    Audit a registry's identifiers before they become a join key: missing, malformed and duplicated codes, and positions that are only approximate.

    Data ManagementbeginnerDifficulty: beginner130 XP~25 min5 checks

    You hand in your answers as values.

    Dataset

    • World airports · Point · 893 features · EPSG:4326

    What you’ll do

    • Find nulls, duplicates and malformed values in an identifier column
    • Distinguish a key defect from a position defect
    • Report counts the integration can be sized against
    data qualityidentifiersjoin keysaviationAlso trains Python
    Open problem
  10. 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 checks

    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
  11. 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 checks

    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
  12. 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 checks

    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
  13. Not started, Accept the rasters, or send them backData qualityAnswers

    The checks a data manager runs on delivered rasters before anyone analyses them: extent, nodata, value range, and whether the figures are plausible for the place.

    Data ManagementintermediateDifficulty: intermediate200 XP~35 min6 checks

    You hand in your answers as values.

    Datasets

    • Greater London elevation (Copernicus DEM GLO-30) · Raster · EPSG:4326
    • 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

    What you’ll do

    • Read raster metadata and full-resolution statistics
    • Distinguish nodata from valid extremes
    • Record facts a later dispute can start from
    raster QAnodatametadataacceptanceLondonAlso trains Remote Sensing
    Open problem
  14. 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 checks

    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
  15. Not started, At which zoom does every capital get its own tile?Web deliveryAnswers

    Count capitals per XYZ tile across zooms to decide where a label layer switches from clustering to one label per city.

    Web GISintermediateDifficulty: intermediate180 XP~25 min4 checks

    You hand in your answers as values.

    Dataset

    • Populated places · Point · 1,251 features · EPSG:4326

    What you’ll do

    • Convert points to tile indices at several zooms
    • Find the busiest tile per zoom
    • Choose a switch-over zoom from the data
    XYZ tileslabelsclusteringzoom levelsglobalAlso trains Cartography
    Open problem
  16. 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 checks

    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
  17. 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 checks

    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
  18. 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 checks

    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
  19. Not started, How green was London on 11 July 2025?Raster analysisAnswers

    Compute NDVI from a two-band Sentinel-2 clip, handle nodata and integer types properly, and report the distribution a monitoring programme quotes.

    Remote SensingintermediateDifficulty: intermediate220 XP~35 min4 checks

    You hand in your answers as values.

    Dataset

    • Greater London, Sentinel-2 red and NIR, 11 July 2025 · Raster · EPSG:32630

    What you’ll do

    • Compute a band ratio index with correct types
    • Exclude nodata from every statistic
    • Summarise the index as the programme's three figures
    NDVISentinel-2band mathnodataLondon
    Open problem
  20. 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 checks

    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
  21. 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 checks

    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
  22. Not started, Protect the bus stop key during the loadData preparationSQL

    Recover zero-padded stop identifiers before a transport timetable join.

    Data ManagementintermediateDifficulty: intermediate210 XP~40 min7 checks

    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 / SQL
    Open problem
  23. Not started, Redirect the rider at an empty dockDecision supportSQL

    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.

    PostGIS / SQLintermediateDifficulty: intermediate250 XP~40 min5 checks

    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
    KNNLATERALGiSTgeographyperformancetransport
    Open problem
  24. 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 checks

    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
  25. Not started, When the attributes and the geometry disagreeData qualityAnswers

    A 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.

    Data ManagementintermediateDifficulty: intermediate200 XP~35 min5 checks

    You hand in your answers as values.

    Dataset

    • Populated places · Point · 1,251 features · EPSG:4326

    What you’ll do

    • Compare each feature's geometry to the coordinate attributes it carries
    • Count the disagreements at two thresholds and identify the worst
    • Find keys the downstream spreadsheet cannot distinguish
    data qualitygazetteerhaversineduplicatesglobalAlso trains Python
    Open problem
  26. 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 checks

    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
  27. 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 checks

    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
  28. 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 checks

    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
  29. 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 checks

    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
  30. 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 checks

    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
  31. 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 checks

    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
  32. Not started, Which places can the emissions change map actually compare?Data qualityPython

    Audit two-date indicator coverage and map the places excluded before showing a climate trend.

    Data ManagementintermediateDifficulty: intermediate290 XP~45 min6 checks

    You hand in a Python script.

    Datasets

    • Countries · MultiPolygon · 241 features · EPSG:4326
    • CO2 per capita by country, 2010 and 2022 · Point · 241 features · EPSG:4326

    What you’ll do

    • Audit missingness before a two-date map is interpreted
    • Prevent a many-to-many join on nonstandard ISO codes
    • Show complete and incomplete coverage on real country polygons
    World Bankdata qualitymissing datacategorical mapglobalAlso trains Python, Cartography
    Open problem
  33. Not started, Which ports are within 25 km of a capital?Spatial analysisSQL

    ST_DWithin on geography: a proximity join in true metres that the index can still answer.

    PostGIS / SQLintermediateDifficulty: intermediate220 XP~35 min5 checks

    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_DWithingeographyGiSTproximityglobal
    Open problem
  34. 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 checks

    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
  35. Not started, Why a cloud-optimised GeoTIFF is cheap to serveWeb deliveryAnswers

    Read a COG's internal structure — tiles, overviews, bytes — and turn it into the numbers a tile server's capacity plan rests on.

    GIS ArchitectureintermediateDifficulty: intermediate200 XP~30 min4 checks

    You hand in your answers as values.

    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

    What you’ll do

    • Read a GeoTIFF's block size and overview structure
    • Count internal tiles from dimensions and block size
    • Relate the structure to what a tile request costs
    COGGeoTIFFtile servercapacity planningrasterAlso trains Web GIS
    Open problem
  36. Not started, Are these country polygons fit to assign cities to?Data qualitySQL

    A point-in-polygon audit that exposes what 1:50m generalisation does to a coastline — and which places the attribute and the geometry disagree about.

    PostGIS / SQLadvancedDifficulty: advanced300 XP~45 min5 checks

    You hand in a spatial SQL query.

    Datasets

    • Populated places · Point · 1,251 features · EPSG:4326
    • Countries · MultiPolygon · 241 features · EPSG:4326

    What you’ll do

    • Join points to the polygon containing them with an outer join
    • Compare an attribute to a derived value with NULL-safe semantics
    • Return the disagreement list, including the points nothing contains
    ST_Containsouter joinIS DISTINCT FROMgeneralisationdata qualityAlso trains Data Management
    Open problem
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36 of 59 problems shown