Spatial Network Big Databases by KwangSoo Yang & Shashi Shekhar

Author:KwangSoo Yang & Shashi Shekhar
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

4.1.4 Literature Review

Circle covering problems have been studied to find complete and partial spatial covering on a geometric space [1, 5, 6, 11, 11, 14, 22]. However, geometrical approaches (e.g., Euclidean distance) are not ideal for spatial networks [3, 23]. Metric k-center problems can find complete coverage of spatial events, which minimizes the longest edge between the center and spatial event locations [12, 15, 16]. However, these approaches are not designed to honor distance constraints between two nodes in a sub-network and cover all spatial events, leading to a limitation of the detection of distance-constrained spatial sub-networks. Clustering methods have been widely used in related research on a partial coverage problem [2, 9, 13, 26]. However, these methods do not consider distance constraints to build sub-networks. There exists a significant body of research on spatial network analysis. The K-function has been applied to spatial networks to analyze the distribution of events and detect clusters [19, 20, 25, 27]. Scan statistics has been used to detect anomalies on networks [17, 21]. The concept of spatial auto-correlation has been used to analyze the correlation between two variables on spatial networks [4, 7]. Network kernel density estimation analyzes the probability distributions of events and provides visual patterns of relative density on spatial networks [8, 18]. There is a network-based variable-distance clumping method that can discover multi-scale network-based clumps [24]. None of these approaches, however,consider distance constraints in its problem formulation. The Distance-Constrained k Spatial Sub-Networks (DCSSN) approach presented here honors distance constraints while also maximizing coverage of spatial events.

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