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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.

2 of 59 problems

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Track
Spatial Analysis2 problemsPostGIS / SQL1 problemPython5 problemsRemote Sensing0 problemsCartography2 problemsWeb GIS1 problemData Management4 problemsGIS Architecture0 problems
Level
Beginner1 problemIntermediate4 problemsAdvanced2 problemsExpert2 problems
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SQL4 problemsPython2 problemsGeoJSON8 problemsAnswers6 problemsSchema0 problemsDecisions0 problemsDataset file4 problems
Category
Data preparation0 problemsData quality0 problemsSpatial analysis1 problemRaster analysis0 problemsCartography0 problemsDecision support1 problemGeospatial systems0 problemsWeb delivery0 problems
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Spatial AnalysisAdvancedPythonClear all
  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, 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 checks

    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