Nonparametric Significance Test for Discovery of Network-Constrained Spatial Co-Location Patterns

Spatial co-location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co-location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user-specified parameters or priori assumptions for determining prevalent co-location patterns are still subjective. As a result, some co-location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point-dependent and location-dependent network-constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network-constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co-location patterns is modeled as the significance level. The significance test is evaluated using the facility points-of-interest data sets. Experiments and comparisons show that the significance test can effectively detect network-constrained spatial co-location patterns with less priori knowledge and outperforms two state-of-the-art methods in excluding spurious patterns.