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Significant Route Discovery: A Summary of Results

  • Dev Oliver
  • Shashi Shekhar
  • Xun Zhou
  • Emre Eftelioglu
  • Michael R. Evans
  • Qiaodi Zhuang
  • James M. Kang
  • Renee Laubscher
  • Christopher Farah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)

Abstract

Given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports), Significant Route Discovery (SRD) finds all shortest paths in the spatial network where the concentration of activities is unusually high (i.e., statistically significant). SRD is important for societal applications in transportation safety, public safety, or public health such as finding routes with significant concentrations of accidents, crimes, or diseases. SRD is challenging because 1) there are a potentially large number of candidate routes (~1016) in a given dataset with millions of activities or road network nodes and 2) significance testing does not obey the monotonicity property. Previous work focused on finding circular areas of concentration, limiting its usefulness for finding significant linear routes on a network. SaTScan may miss many significant routes since a large fraction of the area bounded by circles for activities on a path will be empty. This paper proposes a novel algorithm for discovering statistically significant routes. To improve performance, the proposed algorithm features algorithmic refinements that prune unlikely paths and speeds up Monte Carlo simulation. We present a case study comparing the proposed statistically significant network-based analysis (i.e., shortest paths) to a statistically significant geometry-based analysis (e.g., circles) on pedestrian fatality data. Experimental results on real data show that the proposed algorithm, with our algorithmic refinements, yields substantial computational savings without reducing result quality.

Keywords

Likelihood Ratio Short Path Road Network Active Node Route Discovery 
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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References

  1. 1.
    Ernst, M., Lang, M., Davis, S.: Dangerous by design: Solving the epidemic of preventable pedestrian deaths. Transportation for America: Surface Transportation Policy Partnership, Washington, DC (2011)Google Scholar
  2. 2.
    National Highway Traffic Safety Administration (NHTSA): Fatality Analysis Reporting System (FARS) Encyclopedia, http://www.nhtsa.gov/FARS
  3. 3.
    Kulldorff, M.: A spatial scan statistic. Communications in Statistics-Theory and Methods 26(6), 1481–1496 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Neill, D.B., Moore, A.W.: Rapid detection of significant spatial clusters. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 256–265. ACM (2004)Google Scholar
  5. 5.
    Kulldorff, M., Mostashari, F., Duczmal, L., Katherine Yih, W., Kleinman, K., Platt, R.: Multivariate scan statistics for disease surveillance. Statistics in Medicine 26(8), 1824–1833 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Kulldorff, M.: Spatial scan statistics: Models, calculations, and applications. In: Scan Statistics and Applications, pp. 303–322. Springer (1999)Google Scholar
  7. 7.
    Costa, M.A., Assunção, R.M., Kulldorff, M.: Constrained spanning tree algorithms for irregularly-shaped spatial clustering. Computational Statistics & Data Analysis 56(6), 1771–1783 (2012)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Duczmal, L., Assuncao, R.: A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. Computational Statistics & Data Analysis 45(2), 269–286 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Shi, L., Janeja, V.P.: Anomalous window discovery for linear intersecting paths. IEEE Transactions on Knowledge and Data Engineering 23(12), 1857–1871 (2011)CrossRefGoogle Scholar
  10. 10.
    Janeja, V.P., Atluri, V.: Ls 3: A linear semantic scan statistic technique for detecting anomalous windows. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 493–497. ACM (2005)Google Scholar
  11. 11.
    Li, X., Han, J., Lee, J.-G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 441–459. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  13. 13.
    MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  14. 14.
    Oliver, D., Bannur, A., Kang, J.M., Shekhar, S., Bousselaire, R.: A K-Main Routes Approach to Spatial Network Activity Summarization: A Summary of Results. In: IEEE International Conference on Data Mining Workshops (ICDMW), pp. 265–272 (2010)Google Scholar
  15. 15.
    Buchin, K., Cabello, S., Gudmundsson, J., Löffler, M., Luo, J., Rote, G., Silveira, R.I., Speckmann, B., Wolle, T.: Finding the most relevant fragments in networks. J. Graph Algorithms Appl. 14(2), 307–336 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Chawla, S., Roughgarden, T.: Single-source stochastic routing. In: Díaz, J., Jansen, K., Rolim, J.D.P., Zwick, U. (eds.) APPROX/RANDOM 2006. LNCS, vol. 4110, pp. 82–94. Springer, Heidelberg (2006)Google Scholar
  17. 17.
    Shekhar, S., Liu, D.: CCAM: A connectivity-clustered access method for networks and network computations. IEEE Transactions on Knowledge and Data Engineering 9(1), 102–119 (1997)CrossRefGoogle Scholar
  18. 18.
    Cormen, T.: Introduction to algorithms. The MIT press (2001)Google Scholar
  19. 19.
    Kulldorff, M., Rand, K., Gherman, G., Williams, G., DeFrancesco, D.: SaTScan v 2.1: Software for the spatial and space-time scan statistics. National Cancer Institute, Bethesda (1998)Google Scholar
  20. 20.
    The QGIS Project: Quantum GIS OpenLayers Plugin, http://plugins.qgis.org/plugins/openlayers_plugin/ (accessed: January 23, 2014)
  21. 21.
    US Census Bureau: Census TIGER/Line Shapefiles (2010), http://www.census.gov/geo/maps-data/data/tiger-line.html (accessed: January 23, 2014)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dev Oliver
    • 1
  • Shashi Shekhar
    • 1
  • Xun Zhou
    • 1
  • Emre Eftelioglu
    • 1
  • Michael R. Evans
    • 1
  • Qiaodi Zhuang
    • 1
  • James M. Kang
    • 2
  • Renee Laubscher
    • 2
  • Christopher Farah
    • 2
  1. 1.Department of Computer ScienceUniversity of MinnesotaUSA
  2. 2.National Geospatial-Intelligence AgencyUSA

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