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Elliptical Hotspot Detection: A Summary of Results

Published: 03 November 2015 Publication History

Abstract

Given a set of points in Euclidean space, a minimum log likelihood ratio threshold, and a statistical significance threshold, Elliptical Hotspot Detection (EHD) finds elliptical hotspot areas where the concentration of activities inside is significantly higher than outside. The EHD problem is important to many fields, such as criminology, transportation engineering, and epidemiology. Related work (e.g., SatScan) enumerates only circular candidates using activities as centers, and may miss many significant ellipses. EHD problem is challenging for two reasons, namely the large enumeration space and the lack of monotonicity of the log likelihood ratio. To overcome the challenges and limitations of the related work, this paper proposes a novel algorithm for EHD. A case study on real crime data shows that our algorithm is able to find hotspots that cannot be detected by the related work. Experimental evaluation shows that the proposed algorithm saves substantial amount of computation compared to the naïve approach.

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  • (2023)Spatial Data ScienceMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_18(401-422)Online publication date: 18-Aug-2023
  • (2022)Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A SurveyACM Computing Surveys10.1145/348789355:2(1-38)Online publication date: 18-Jan-2022
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    cover image ACM Conferences
    BigSpatial '15: Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data
    November 2015
    60 pages
    ISBN:9781450339742
    DOI:10.1145/2835185
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    Published: 03 November 2015

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    Author Tags

    1. Hotspot detection
    2. Spatial data mining
    3. Statistical significance

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    Cited By

    View all
    • (2023)Spatial Data ScienceMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_18(401-422)Online publication date: 18-Aug-2023
    • (2022)Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A SurveyACM Computing Surveys10.1145/348789355:2(1-38)Online publication date: 18-Jan-2022
    • (2022)A Case Study on Periodic Spatio- Temporal Hotspot Detection in Azure Traffic Data2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00135(1037-1044)Online publication date: Nov-2022
    • (2022)Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm OptimizerApplied Geography10.1016/j.apgeog.2022.102719144(102719)Online publication date: Jul-2022
    • (2021)Effectiveness of Swarm Intelligence Algorithms for Geographically Robust Hotspot DetectionArabian Journal for Science and Engineering10.1007/s13369-021-06032-547:2(1693-1715)Online publication date: 17-Aug-2021
    • (2020)Significant lagrangian linear hotspot discoveryProceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3423457.3429368(1-10)Online publication date: 3-Nov-2020
    • (2017)Detecting Isodistance Hotspots on Spatial Networks: A Summary of ResultsAdvances in Spatial and Temporal Databases10.1007/978-3-319-64367-0_15(281-299)Online publication date: 22-Jul-2017
    • (2016)Discovering Spatial Regions of High Correlation2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0156(1082-1089)Online publication date: Dec-2016
    • (2016)Mining Network Hotspots with Holes: A Summary of ResultsGeographic Information Science10.1007/978-3-319-45738-3_4(51-67)Online publication date: 14-Sep-2016

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