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Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results

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Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

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Abstract

Spatial hotspot detection aims to find regions of interest with statistically significant high concentration of activities. In recent years, it has presented significant value in many critical application domains such as epidemiology, criminology and transportation engineering. However, existing spatial hotspot detection approaches focus on either on Euclidean space or are unable to find the entire set of hotspots. In this paper, we first formulate the problem of Network Isodistance Hotspot Detection (NIHD) as finding all sub-networks whose nodes and edges are reachable from a activity center and have significantly high concentration of activities. Then, we propose a novel algorithm based on network partitioning and pruning (NPP) which overcomes the computational challenges due to the high costs from candidate enumeration and statistical significance test based on randomization. Theoretical and experimental analysis show that NPP substantially improves the scalability over the baseline approach while keeping the results correct and complete. Moreover, case studies on real crime datasets show that NPP detects hotspots with higher accuracy and is able to reveal the hotspots that are missed by existing approaches.

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Acknowledgement

This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818, IIS-1218168, and IIS-1541876, the USDOD under Grant No. HM0210-13-1-0005, the ARPA-E under Grand No. DE-AR0000795, and the University of Minnesota under the OVPR U-Spatial. We are particularly grateful to Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their valuable comments.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Xun Tang, Emre Eftelioglu & Shashi Shekhar

Authors
  1. Xun Tang
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  2. Emre Eftelioglu
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  3. Shashi Shekhar
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Corresponding author

Correspondence to Xun Tang .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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Tang, X., Eftelioglu, E., Shekhar, S. (2017). Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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