Quiz on spatial autocorrelation

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

What is the purpose of clustering in multivariate analysis

✗To increase the number of variables in the dataset.

✓To reduce the complexity by grouping similar observations.

✗To eliminate irrelevant variables from the dataset.

✗To focus on a single variable for analysis.

Which of the following clustering methods assigns each observation to the nearest centroid?

✗Agglomerative clustering

✗DBSCAN

✓K-means

✗Spectral clustering

Regionalization refers to:

✗Dividing a country into equal-sized regions for data analysis.

✓Aggregating small geographic units into larger regions based on attribute and spatial similarity.

✗Reducing the dimensionality of non-geographic data.

✗Assigning each data point to a predefined region without considering clustering.

Which clustering method is most suitable when we want to ensure that neighboring geographic units are more likely to be in the same cluster?

✗K-means

✗Spectral clustering

✗Agglomerative hierarchical clustering

✓Spatially lagged clustering

What is the primary purpose of standardizing variables before applying a clustering algorithm like K-means?

✗To normalize the dataset to only include integer values.

✗To remove outliers from the dataset.

✗To reduce the number of variables.

✓To ensure all variables contribute equally to the distance calculations.

Attribute-based clustering focuses on which aspect of the dataset?

✗The time-based patterns within the dataset.

✓The similarity of values for specific attributes or variables.

✗The hierarchical relationships between categories.

✗The geographic location of the data points.

In K-means clustering, the number of clusters (k) is typically:

✗Determined automatically by the algorithm.

✓Specified by the user.

✗Based on the number of rows in the dataset.

✗Based on the number of columns in the dataset.

What is a key limitation of K-means clustering when applied to spatial data?

✗K-means cannot handle datasets with more than 10,000 points.

✗K-means requires all variables to be categorical.

✓K-means does not consider spatial proximity between points.

✗K-means works only with time-series data.

In spatially lagged clustering, which of the following statements is true?

✗It focuses on clustering attributes regardless of spatial proximity.

✗It is a form of hierarchical clustering.

✗It creates non-overlapping clusters of equal size.

✓It considers the relationship between a location and its neighbors.

What is the main advantage of agglomerative clustering?

✗It automatically determines the optimal number of clusters

✓It builds a hierarchy of clusters that can be interpreted at different levels.

✗To works best with geographic data.

✗It requires very little computational power.

What is the purpose of this code _ = sns.pairplot(simd[subranks])?

✓To visualize bivariate correlations between the sub-ranks using scatter plots.

✗To display the spatial distribution of the sub-ranks on a map.

✗To overlay different layers of geographic information.

✗To generate a histogram for each variable in the dataset.

What is the key difference between spatially lagged K-means and spatially constrained clustering (regionalization)?

✗Spatially lagged K-means enforces spatial contiguity, while spatially constrained clustering allows non-contiguous areas to be grouped together.

✗Spatially constrained clustering uses hierarchical methods, while spatially lagged K-means uses distance-based methods.

✓Spatially lagged K-means focuses on clustering based on both attributes and spatial relationships, while spatially constrained clustering ensures that neighboring areas are grouped into the same cluster.

✗Spatially lagged K-means requires prior knowledge of the number of clusters, while spatially constrained clustering automatically determines the number of clusters.