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Kharita Challenges

Problems

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

11 of 59 problems

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Track
Spatial Analysis15 problemsPostGIS / SQL0 problemsPython0 problemsRemote Sensing5 problemsCartography3 problemsWeb GIS0 problemsData Management11 problemsGIS Architecture1 problem
Level
Beginner2 problemsIntermediate1 problemAdvanced8 problemsExpert0 problems
Hand in
SQL7 problemsPython10 problemsGeoJSON11 problemsAnswers10 problemsSchema1 problemDecisions2 problemsDataset file5 problems
Category
Data preparation1 problemData quality1 problemSpatial analysis3 problemsRaster analysis2 problemsCartography0 problemsDecision support4 problemsGeospatial systems0 problemsWeb delivery0 problems
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Data ManagementGeoJSONClear all
  1. 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
  2. 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
  3. 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
  4. 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 checks

    Worked in 3 steps, each checked before the next one opens.

    1. Audit the vegetation rasters · Answers
    2. Deliver the borough evidence table · Dataset file
    3. 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
  5. 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 checks

    Worked in 4 steps, each checked before the next one opens.

    1. Audit both incoming sources · Answers
    2. Build the common asset model · Dataset file
    3. Verify and round coordinates · GeoJSON
    4. 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
  6. 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 checks

    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
  7. 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 checks

    Worked in 3 steps, each checked before the next one opens.

    1. Classify the snapshot · SQL
    2. Map feasible transfers · GeoJSON
    3. 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
  8. 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 checks

    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
  9. 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 checks

    Worked in 3 steps, each checked before the next one opens.

    1. Audit the class chip · Answers
    2. Hand over the measured grid · Dataset file
    3. 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
  10. 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 checks

    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
  11. 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 checks

    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