Quiz on GeoPandas

Check how much you remember from previous sections by answering the questions below.

What is the primary purpose of geopandas in Python?

✗To handle time series data.

To manage geospatial data with geometry support.

✗To visualize large datasets.

✗To improve data processing speed.

How does a GeoDataFrame differ from a DataFrame?

✗It supports indexing.

✗It is faster for processing.

✓It includes a geometry column with spatial data types.

✗It can only contain geometric data and cannot hold any other types of data.

Which of the following is NOT a common geometry type supported by geopandas?

✗Polygon

✗GeometryCollection

✓Surface

✗LineString

Which file format is most commonly associated with multi-layer geospatial data?

✗CSV

✗GeoJSON

✗Shapefile

✓GeoPackage

What does a spatial join operation do in geopandas?

✗Combines non-spatial data based on common columns.

✓Merges data based on their spatial relationships.

✗Creates new geometries by cutting overlapping areas.

✗Converts multipolygons into polygons.

How would you convert a MultiPolygon to a Polygon in geopandas if the geometry is multipart?

✓explode()

✗to_single()

✗union_all()

✗dissolve()

If two GeoDataFrames have different CRSs, what must be done before performing a spatial join?

✗Ensure they have the same columns.

✗Only keep the geometries and delete all attributes.

✓Reproject them to the same CRS.

✗Convert one GeoDataFrame to a DataFrame.

How would you reproject a GeoDataFrame called gdf to EPSG:3857?

✗gdf.to_crs(3857).

✗gdf.convert_crs(epsg=3857).

✗gdf.set_crs(epsg=3857).

✗gdf.to_epsg(3857).

What is the primary difference in the result between the plot() and explore() functions in geopandas

✗plot() is used for visualizing non-spatial data, while explore() is only for spatial data.

✗Both plot() and explore() create static maps, but explore() uses different color schemes.

✗plot() can only be used for polygons, while explore() can handle all geometry types.

✓plot() creates a static map, while explore() creates an interactive map that allows zooming and panning.

Which of the following statements correctly describes the difference between a spatial join and a spatial predicate in geopandas?

✗A spatial join filters data based on attribute values, while a spatial predicate performs calculations on geometric data.

✗A spatial join can only be performed on polygon geometries, whereas a spatial predicate can be applied to any geometry type (points, lines, or polygons).

✓A spatial join combines two GeoDataFrames based on a spatial relationship, while a spatial predicate is a function that returns a boolean value based on the relationship between geometries.

✗A spatial join outputs a new GeoDataFrame with merged attributes, while a spatial predicate modifies the original GeoDataFrames by adding new geometric properties.