Nonparametric Test for Detecting Significant Spatial Co-location Patterns
Nonparametric Test for Detecting Significant Spatial Co-location Patterns
By Jiannan Cai, Apr. 10, 2019
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Background
Background
Due to the induced spatial auto-correlation among different features, they tend to be located together in close geographic proximity. Spatial co-location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for identifying spatial co-location patterns usually require users to specify two thresholds, i.e. the prevalence threshold and distance threshold. However, these two thresholds are difficult to determine in practice, and improper thresholds may lead to wrong decisions. On that account, the discovery of spatial co-location patterns is modelled as an nonparametric significant test problem by considering the distribution characteristics (e.g. auto-correlation) of each feature. In addition, the validity of the co-location patterns is evaluated based on scale-invariance principle of human perception and cognition.
Innovations
Innovations
The spatial co-location patterns are interpreted as collections of interactive spatial features where the spatial auto-correlation of each feature is “induced” by underlying spatially auto-correlated variables, and the significance test is employed to determine significant patterns.
The null model of independency is constructed by reproducing several summary characteristics of the observed dataset of each spatial feature.
Figure 1. Construction of the null model based on pattern reconstruction method.
Spatial co-location patterns are further discovered at multi-scales, and the concept of lifetime is used to evaluate the validity of multi-scales co-location patterns.
Applications
Applications
Case study 1: The proposed approach was further used to investigate the symbiotic relationships between species in a wetland located in the northeast of China based on the spatial co-location patterns discovered at multi-scales.
Case study 2: The nonparametric test was extended to discover network-constrained spatial co-location patterns among urban facilities in south Toronto. The precision and recall values of mining results detected by our method were higher than those of two state-of-the-art methods.
Figure 2. Distribution of urban facility POIs and road networks in south Toronto. (Cai et al., 2019)
Figure 3. Multi-scale 3-size co-location patterns among the urban facilities. (Cai et al., 2019)
References
References
Jiannan Cai, Min Deng, Qiliang Liu, Zhanjun He, Jianbo Tang and Xuexi Yang., 2019. Nonparametric significance test for discovery of network-constrained spatial co-location patterns. Geographical Analysis, 51(1), 3-22. (Link)
Min Deng, Zhanjun He, Qiliang Liu, Jiannan Cai and Jianbo Tang., 2017. Multi‐scale approach to mining significant spatial co‐location patterns. Transactions in GIS, 21(5), 1023-1039. (Link)
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