Quiz on spatial graphs

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

What can a spatial weights matrix represent?

✗The physical distance between geographic points.

✗The number of neighbors for each observation.

✗The strength of spatial interaction between observations.

✓All of the above.

What does contiguity mean in spatial analysis?

✗A measure of distance between non-adjacent areas.

✗A calculation of attribute similarity between spatial units.

✓The relationship between geographically adjacent areas.

✗A method for analyzing data across time.

What is a graph in PySAL used for?

✗It is a structure where nodes represent spatial units, and edges represent spatial relationships.

✗It represents geographic areas and their attributes.

✗It stores geographic coordinates of all points in a dataset.

✗It is a structure where nodes represent spatial relationships, and edges represent geometries.

What is the difference between Queen, Rook, and Bishop contiguity?

✗Queen and Rook both consider only shared edges, while Bishop considers both edges and vertices.

✓Queen considers neighbors that share edges and vertices, Rook considers only shared edges, Bishop considers shared vertices only.

✗Queen considers shared edges, Rook shared vertices, and Bishop considers diagonal neighbors.

✗Queen considers only shared vertices, Rook considers both edges and vertices, and Bishop considers shared distances.

How do k-nearest neighbor (KNN) spatial weights differ from contiguity-based methods?

✗KNN considers only neighboring regions with shared boundaries.

✓KNN connects an observation to a fixed number of closest neighbors.

✗KNN uses a binary approach for weighting.

✗KNN focuses only on Euclidean distance in geographic space.

Which of the following statements best describes block weights and how they differ from distance and contiguity weights?

✗Block weights assign a weight of one to neighboring observations based on their physical distance, while distance weights assign weights according to geographical proximity.

✗Block weights define spatial connections through shared boundaries, similar to contiguity weights, making them suitable for polygon analysis.

✗Block weights utilize a continuous scale for weights, whereas distance weights only consider categorical group memberships.

✓Block weights connect observations in the same category with a weight of one, regardless of distance, while distance weights assign weights based on how far apart observations are.

What is the key difference between perimeter-weighted contiguity and a distance-weighted distance band?

✓Perimeter-weighted contiguity assigns weights based on the shared perimeter length between neighboring units, while a distance-weighted distance band assigns weights that decrease as the distance between units increases.

✗Perimeter-weighted contiguity uses the Euclidean distance between spatial units, while a distance-weighted distance band considers the Manhattan distance.

✗Perimeter-weighted contiguity calculates weights based on distance decay functions, while a distance-weighted distance band calculates weights based on the area of each unit.

✗Perimeter-weighted contiguity only works for irregular polygons, while a distance-weighted distance band applies only to regular grids.

What does the function queen.to_parquet("queen.parquet") do in PySAL?

✗It converts a spatial weights object into a queen contiguity matrix and displays it.

✗It exports a queen contiguity matrix to a CSV file format for analysis.

✓It saves a queen contiguity weights object as a compressed Parquet file for efficient storage.

✗It converts a spatial weights object into a shapefile for geographic data visualization.

What is a spatial lag in the context of spatial analysis?

✗The influence of neighboring observations on the value of a variable at a given location.

✓The weighted average of a variable in neighboring observations.

✗The total distance between all observations and their neighbors.

✗The time it takes for spatial relationships to manifest.