Table 1.

Summary of the relevant literatures.

Theoretical categories	Authors	Sketches	Methods
Frequent item sets	Shekhal et al. (2001) [12]	Propose a notion of user-specified neighbourhoods in place of transactions to specify groups of items.	Transaction-based approach
Density-based algorithm	Cheruiyot et al. (2022) [15]	Calculate the network kernel density of spatial features and mine spatial colocation patterns.	Density-based algorithm
Location quotient	Leslie et al. (2011) [50]	Present the colocation quotient (CLQ) to quantify spatial association between categories of a population.	CLQ
	Wang et al. (2017) [41]	Develop a simulation-based statistical test for the local indicator of colocation quotient (LCLQ).	LCLQ
	Chen et al. (2023) [42]	Present the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city by POI data and LCLQ method.	LCLQ
Local Moran's I	Zhang et al. (2022) [44]	Propose a method for association rule mining based on spatial autocorrelation clustering events and apply it to polymetallic ore deposits.	Adopt local Moran's I, Apriori algorithm
	Sansuk et al. (2023) [46]	Explore the spatial autocorrelation between socioeconomic factors, health service factors and sepsis mortality.	Local indicators of spatial association(LISA)
Nonparametric significance test	Cai et al. (2019) [51]	Develop both point-dependent and location-dependent network-constrained summary statistics to construct the null model of the test, and model the degree of co-location patterns' prevalence as the significance level.	Network-constrained summary statistics
Spatiotemporal episode pattern mining	He et al. (2020) [48]	Propose a novel complex event-based spatiotemporal association pattern mining framework.	An adaptive spatiotemporal episode pattern mining algorithm