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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 Analysis4 problemsPostGIS / SQL0 problemsPython2 problemsRemote Sensing0 problemsCartography5 problemsWeb GIS0 problemsData Management1 problemGIS Architecture0 problems
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SQL0 problemsPython1 problemGeoJSON1 problemAnswers3 problemsSchema0 problemsDecisions0 problemsDataset file0 problems
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Data preparation7 problemsData quality7 problemsSpatial analysis14 problemsRaster analysis6 problemsCartography5 problemsDecision support11 problemsGeospatial systems3 problemsWeb delivery6 problems
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CartographyClear all
  1. 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
  2. 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
  3. 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
  4. 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 checks

    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
  5. Not started, Show country CO2 change without losing the outlierCartographyPython

    Join two WDI observations to country polygons, preserve the true change, and cap only the display value.

    CartographyadvancedDifficulty: advanced500 XP~85 min7 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 and join two independently sourced global datasets on a stable country key
    • Calculate signed per-capita change while excluding missing year pairs
    • Publish a diverging polygon map with a disclosed display cap and the true values retained
    projectWorld BankNatural Earthdata joindiverging mapglobalAlso trains Python, Data Management
    Open problem