View source: R/local-colocation-quotient-impl.R
local_colocation | R Documentation |
The local indicator of the colocation quotient (LCLQ) is a Local Indicator of Spatial Association (LISA) that evaluates if a given observation's subcategory in A is colocated with subcategories in B. Like the CLQ, the LCLQ provides insight into the asymmetric relationships between subcategories of A and B (where B can also equal A) but at the local level.
The LCLQ is defined using Gaussian kernel weights and an adaptive bandwidth (see st_kernel_weights()
). However, any type of weights list can be used. Kernel weights are used to introduce a decay into the calculation of the CLQ. This ensures that points nearer to the focal point have more influence than those that are more distant.
local_colocation(A, B, nb, wt, nsim)
A |
a character or factor vector. |
B |
a character or factor vector. |
nb |
a neighbors list e.g. created by |
wt |
a weights list. Recommended that it is a Gaussian kernel weights list using an adaptive bandwidth e.g. created by |
nsim |
default |
The LCLQ is defined as LCLQ_{A_i \to B} = \frac{N_{A_i \to B}}{N_B / (N - 1)} where N_{A_i \to B} = ∑_{j = 1(j \ne i)}^{N}(\frac{w_{ij}f_{ij}}{∑_{j = 1(j \ne i)}^{N}w_{ij}}). And the weights matrix, wij, uses adaptive bandwidth Gaussian kernel weights.
LCLQ is only calculated for those subcategories which are present in the neighbor list. If a subcategory is not present, then the resultant LCLQ and simulated p-value will be NA
.
a data frame with as many rows as observations in A and two times as many columns as unique values in B. Columns contain each unique value of B as well as the simulated p-value for each value of B.
Fahui Wang, Yujie Hu, Shuai Wang & Xiaojuan Li (2017) Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities, The Professional Geographer, 69:1, 22-31, doi: 10.1080/00330124.2016.1157498
A <- guerry$main_city B <- guerry$region geo <- sf::st_centroid(sf::st_geometry(guerry)) nb <- include_self(st_knn(geo, 5)) wt <- st_kernel_weights(nb, geo, "gaussian", adaptive = TRUE) res <- local_colocation(A, B, nb, wt, 9) tail(res)
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