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Algorithms for hotspot computation on trajectory data

Published: 05 November 2013 Publication History

Abstract

We study one of the basic tasks in moving object analysis, namely the location of hotspots. A hotspot is a (small) region in which an entity spends a significant amount of time. Finding such regions is useful in many applications, for example in segmentation, clustering, and locating popular places. We may be interested in locating a minimum size hotspot in which the entity spends a fixed amount of time, or locating a fixed size hotspot maximizing the time that the entity spends inside it. Furthermore, we can consider the total time, or the longest contiguous time the entity spends in the hotspot. We solve all four versions of the problem. For a square hotspot, we can solve the contiguous-time versions in O(nlogn) time, where n is the number of trajectory vertices. The algorithms for the total-time versions are roughly quadratic. Finding a hotspot containing relatively the most time, compared to its size, takes O(n3) time. Even though we focus on a single moving entity, our algorithms immediately extend to multiple entities. Finally, we consider hotspots of different shape.

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    cover image ACM Conferences
    SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2013
    598 pages
    ISBN:9781450325219
    DOI:10.1145/2525314
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 05 November 2013

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    Author Tags

    1. geometric algorithms
    2. hotspot
    3. moving entity
    4. trajectory

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    • (2023)Approximating the packedness of polygonal curvesComputational Geometry: Theory and Applications10.1016/j.comgeo.2022.101920108:COnline publication date: 1-Jan-2023
    • (2022)Recommending Popular Locations Based on Collected Trajectories2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE57176.2022.9960068(181-189)Online publication date: 17-Nov-2022
    • (2022)Covering a set of line segments with a few squaresTheoretical Computer Science10.1016/j.tcs.2022.04.053Online publication date: May-2022
    • (2022)Sampling and sparsification for approximating the packedness of trajectories and detecting gatheringsInternational Journal of Data Science and Analytics10.1007/s41060-021-00301-015:2(201-216)Online publication date: 29-Jan-2022
    • (2021)Covering a Set of Line Segments with a Few SquaresAlgorithms and Complexity10.1007/978-3-030-75242-2_20(286-299)Online publication date: 4-May-2021
    • (2020)Approximate Discontinuous Trajectory HotspotsOpen Computer Science10.1515/comp-2020-017610:1(444-449)Online publication date: 18-Nov-2020
    • (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
    • (2019)An Experimental Evaluation of Grouping Definitions for Moving EntitiesProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359346(89-98)Online publication date: 5-Nov-2019
    • (2019)Detecting Hot Spots Using the Data Field MethodThe 8th International Conference on Computer Engineering and Networks (CENet2018)10.1007/978-3-030-14680-1_7(51-58)Online publication date: 13-Apr-2019
    • (2018)Hot Spot Analysis over Big Trajectory Data2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622376(761-770)Online publication date: Dec-2018
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