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Kharita Challenges

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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 Analysis3 problemsPostGIS / SQL0 problemsPython1 problemRemote Sensing1 problemCartography1 problemWeb GIS0 problemsData Management3 problemsGIS Architecture1 problem
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Beginner0 problemsIntermediate0 problemsAdvanced3 problemsExpert0 problems
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SQL3 problemsPython1 problemGeoJSON3 problemsAnswers3 problemsSchema0 problemsDecisions0 problemsDataset file1 problem
Category
Data preparation1 problemData quality0 problemsSpatial analysis0 problemsRaster analysis1 problemCartography1 problemDecision support3 problemsGeospatial systems0 problemsWeb delivery2 problems
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  1. 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 checks

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

    1. Select the event queue · SQL
    2. Measure contact gaps · Answers
    3. 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
  2. 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
  3. 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