Many Candidates

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


This chapter explores the challenge where the search space itself may have potentially large number of candidates when summarizing spatial network data. For example, in a transportation planning scenario, there may be as many as \(10^{16}\) shortest paths in a given dataset with hundreds of millions of activities or road network nodes. For large roadmaps such as the 100 million road-segments in the US, this results in prohibitive shortest path computation times. This challenge may be formalized as the significant route discovery problem where given a spatial network, a collection of activities (e.g., pedestrian fatality reports, crime reports), and a likelihood threshold \(\theta \), the goal is to find all shortest paths in the spatial network where the concentration of activities is unusually high (i.e., statistically significant) and the likelihood exceeds \(\theta \). Depending on the domain, an activity may be the location of a pedestrian fatality, a carjacking, a train accident, etc.


Likelihood Ratio Short Path Road Network Activity Density Spatial Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.ESRIRedlandsUSA

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