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Kharita Challenges

Problems

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

6 of 59 problems

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Track
Spatial Analysis6 problemsPostGIS / SQL0 problemsPython6 problemsRemote Sensing0 problemsCartography0 problemsWeb GIS0 problemsData Management3 problemsGIS Architecture0 problems
Level
Beginner1 problemIntermediate2 problemsAdvanced1 problemExpert2 problems
Hand in
SQL4 problemsPython6 problemsGeoJSON4 problemsAnswers0 problemsSchema0 problemsDecisions0 problemsDataset file0 problems
Category
Data preparation2 problemsData quality1 problemSpatial analysis6 problemsRaster analysis0 problemsCartography1 problemDecision support3 problemsGeospatial systems0 problemsWeb delivery1 problem
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Spatial analysisPythonClear all
  1. 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
  2. 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
  3. 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
  4. 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
  5. Not started, River kilometres per country, measured on the ellipsoidSpatial analysisPython

    Clip the river centrelines to the country polygons and measure each piece geodesically — no projection is right for a layer that spans the planet.

    PythonexpertDifficulty: expert400 XP~70 min6 checks

    You hand in a Python script.

    Datasets

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

    What you’ll do

    • Find every river–country pair that intersects
    • Cut each river to the country polygon
    • Measure the pieces geodesically and sum per country
    GeoPandaspyprojgeodesicoverlayhydrologyglobalAlso trains Spatial Analysis, Data Management
    Open problem
  6. Not started, Screen rivers for a shared-water annexSpatial analysisPython

    Measure mapped river lengths by country and screen the rivers whose courses are meaningfully shared.

    PythonexpertDifficulty: expert650 XP~120 min7 checks

    You hand in a Python script.

    Datasets

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

    What you’ll do

    • Intersect every river with every country it touches and measure each piece geodesically
    • Aggregate the pieces per river into a count of meaningful holders and a dominance share
    • Assign the shared rivers, ranked by how evenly they are shared, to result
    GeoPandaspyprojgeodesicoverlayhydrologyglobalAlso trains Spatial Analysis, Data Management
    Open problem