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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. [ABSTRACT FROM AUTHOR]

In spatial points that describe geographical events, outliers that deviate significantly from the global or local distribution indicate extraordinary geographical phenomena. Existing outlier detection methods cannot statistically identify significant outliers by considering the co‐occurrences of multiple categories of spatial points. Therefore, this study develops a non‐parametric statistical test method to detect significant cross‐outliers from two categories of spatial points divided into primary and reference ones. Firstly, the cross K function is employed to test the significance of co‐occurrences of the two categories of points, and the co‐occurrence intensity of each primary point is quantitatively defined as the number of reference points in its circular buffer. Then, the edge‐constrained Delaunay triangulation is utilized to construct a reasonable spatial neighborhood for each primary point. On this basis, the Monte Carlo method is utilized to simulate the distribution of reference points in the support domain of each primary point delineated using the α‐shape algorithm, and those primary points with significantly different local co‐occurrence intensity are determined as spatial cross‐outliers. Finally, those cross‐outliers obtained using different buffers are evaluated further by analysis of living intervals. Experiments on both simulated and real‐life datasets demonstrate the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining aims to discover a collection of Boolean spatial features, which are frequently located in close geographic proximity to each other. Existing methods for identifying spatial co-location patterns usually require users to specify two thresholds, i.e. the prevalence threshold for measuring the prevalence of candidate co-location patterns and distance threshold to search the spatial co-location patterns. However, these two thresholds are difficult to determine in practice, and improper thresholds may lead to the misidentification of useful patterns and the incorrect reporting of meaningless patterns. The multi-scale approach proposed in this study overcomes this limitation. Initially, the prevalence of candidate co-location patterns is measured statistically by using a significance test, and a non-parametric model is developed to construct the null distribution of features with the consideration of spatial auto-correlation. Next, the spatial co-location patterns are explored at multi-scales instead of single scale (or distance threshold) discovery. The validity of the co-location patterns is evaluated based on the concept of lifetime. Experiments on both synthetic and ecological datasets show that spatial co-location patterns are discovered correctly and completely by using the proposed method; on the other hand, the subjectivity in discovery of spatial co-location patterns is reduced significantly. [ABSTRACT FROM AUTHOR]

Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of (k + 1)-size co-location patterns from the sub-regions ofk-size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns. [ABSTRACT FROM AUTHOR]

Regional spatial co-location patterns refer to subsets of spatial features that often co-occur in close geographical proximity in certain localities of space. Discovering regional spatial co-location patterns is still very challenging because it is difficult to specify appropriate thresholds for prevalence measures without prior knowledge and to detect natural localities of regional spatial co-location patterns automatically. On that account, an adaptive method is proposed in this study. First, a non-parametric significance test is constructed to evaluate the prevalence of spatial co-location patterns. Then, an adaptive pattern clustering approach is developed to detect hotspots of each candidate regional spatial co-location pattern. Finally, all statistically significant regional spatial co-location patterns and their localities are detected by iteratively expanding these hotspots. Comparisons between this adaptive method and two state-of-the-art methods are carried out with both simulated and ecological datasets (i.e. a wetland species dataset in northeast China). Experiments show that the proposed adaptive method allows detecting regional spatial co-location patterns effectively and with less prior knowledge than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]