Calculate the Global Colocation Quotient (CLQ) for a categorical variable using simulation based significance testing.
Arguments
- A
a character or factor vector.
- nb
a neighbors list e.g. created by
st_knn()orst_contiguity()- nsim
default
99. An integer representing how many simulations to run for calculating the simulated p-values.
Value
A list of two elements CLQ and p_sim containing the observed colocation quotient and the simulated p-value respectively.
Details
Definition
The CLQ is defined as \(CLQ_{Global} = \frac{\sum_{A \in X} C_{A \to A}}{\sum_{A \in X} N_A ({\frac{N_A - 1}{N-1})}}\). The numerator identifies the observed proportion of same-category neighbors while the denominator contains the expected proportion of same-category neighbors under the assumption of no spatial association. Thus the CLQ is just a ratio of observed to expected.
Inference
Inference is done using conditional permutation as suggested by Anselin 1995 where a number of replicates are created. The observed values are compared to the replicates and a the simulated p-value is the proportion of cases where the observed is more extreme as compared to replicate. The simulated p-value returns the lower p-value of either tail.
Interpretation
Given that the CLQ is a ratio of the observed to expected, we interpret values larger than one to mean that there is more colocation than to be expected under the null hypothesis of no spatial association. When the value is smaller than 0, we interpret it to mean that there is less colocation than expected under the null.
References
Leslie, T.F. and Kronenfeld, B.J. (2011), The Colocation Quotient: A New Measure of Spatial Association Between Categorical Subsets of Points. Geographical Analysis, 43: 306-326. doi:10.1111/j.1538-4632.2011.00821.x
Examples
A <- guerry$main_city
nb <- st_contiguity(sf::st_geometry(guerry))
global_colocation(A, nb, 49)
#> $CLQ
#> [1] 1.02841
#>
#> $p_sim
#> [1] 0.14
#>