Geographically Robust Hotspot Detection: A Summary of Results

Emre Eftelioglu; Xun Tang; Shashi Shekhar

doi:10.1109/ICDMW.2015.159

Geographically Robust Hotspot Detection: A Summary of Results

Emre Eftelioglu, Xun Tang, Shashi Shekhar

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Given a set of points in two dimensional space, a minimum radius, a minimum log likelihood ratio and a significance threshold, Geographically Robust Hotspot Detection (GRHD) finds hotspot areas where the concentration of points inside is significantly high. The GRHD problem is societally important for many applications including environmental criminology, epidemiology, etc. GRHD is computationally challenging due to the difficulty of enumerating all possible candidate hotspots and the lack of monotonicity property for the interest measure, namely the log likelihood ratio test. Related work may miss hotspots when hotspots are divided by geographic barriers (the road network, rivers etc.) or when hotspot centers are close to parks, lakes, mountains, etc. To address these limitations, a novel approach is proposed based on two ideas: cubic grid circle enumeration and a grid log likelihood ratio upper bound. A case study on real crime data shows that the proposed approach finds hotspots which cannot be discovered by the related work. Experimental results show that the proposed algorithm yields substantial computational savings compared to the related work.

Original language	English (US)
Title of host publication	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Editors	Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1447-1456
Number of pages	10
ISBN (Electronic)	9781467384926
DOIs	https://doi.org/10.1109/ICDMW.2015.159
State	Published - Jan 29 2016
Event	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015

Publication series

Name	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/Territory	United States
City	Atlantic City
Period	11/14/15 → 11/17/15

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, USDOD under Grant No. HM1582-08-1-0017, HM0210-13-1-0005, and University of Minnesota via U-Spatial. We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.

Keywords

Hotspot Detection
Spatial Data Mining
Spatial Scan Statistics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1109/ICDMW.2015.159

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Cite this

Eftelioglu, E., Tang, X., & Shekhar, S. (2016). Geographically Robust Hotspot Detection: A Summary of Results. In X. Wu, A. Tuzhilin, H. Xiong, J. G. Dy, C. Aggarwal, Z-H. Zhou, & P. Cui (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 1447-1456). [7395840] (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2015.159

Geographically Robust Hotspot Detection: A Summary of Results. / Eftelioglu, Emre; Tang, Xun; Shekhar, Shashi.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1447-1456 7395840 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Eftelioglu, E, Tang, X & Shekhar, S 2016, Geographically Robust Hotspot Detection: A Summary of Results. in X Wu, A Tuzhilin, H Xiong, JG Dy, C Aggarwal, Z-H Zhou & P Cui (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015., 7395840, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1447-1456, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDMW.2015.159

Eftelioglu E, Tang X, Shekhar S. Geographically Robust Hotspot Detection: A Summary of Results. In Wu X, Tuzhilin A, Xiong H, Dy JG, Aggarwal C, Zhou Z-H, Cui P, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1447-1456. 7395840. (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). doi: 10.1109/ICDMW.2015.159

Eftelioglu, Emre ; Tang, Xun ; Shekhar, Shashi. / Geographically Robust Hotspot Detection : A Summary of Results. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. editor / Xindong Wu ; Alexander Tuzhilin ; Hui Xiong ; Jennifer G. Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1447-1456 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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ER -

Geographically Robust Hotspot Detection: A Summary of Results

Abstract

Publication series

Other

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

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