research-article

Modeling and prediction of moving region trajectories

Authors:

Conny Junghans,

Michael GertzAuthors Info & Claims

IWGS '10: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming

Pages 23 - 30

https://doi.org/10.1145/1878500.1878507

Published: 02 November 2010 Publication History

Abstract

Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet.

In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.

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Cited By

Qin WTang JLao S(2022)DeepFR: A trajectory prediction model based on deep feature representationInformation Sciences10.1016/j.ins.2022.05.019604(226-248)Online publication date: Aug-2022
https://doi.org/10.1016/j.ins.2022.05.019
Qin WTang JLu CLao S(2021)Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an exampleComputational Geosciences10.1007/s10596-021-10037-2Online publication date: 5-Mar-2021
https://doi.org/10.1007/s10596-021-10037-2
Brandt TGrawunder M(2018)GeoStreamsACM Computing Surveys10.1145/317784851:3(1-37)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3177848
Show More Cited By

Index Terms

Modeling and prediction of moving region trajectories
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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cover image ACM Conferences

IWGS '10: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming

November 2010

67 pages

ISBN:9781450304313

DOI:10.1145/1878500

Editors:
Mohamed Ali
Microsoft
,
Erik Hoel
ESRI
,
Cyrus Shahabi
University of Southern California

Copyright © 2010 ACM.

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2010

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GIS '10

Sponsor:

SIGSPATIAL

GIS '10: 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2, 2010

California, San Jose

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Citations

Cited By

Qin WTang JLao S(2022)DeepFR: A trajectory prediction model based on deep feature representationInformation Sciences10.1016/j.ins.2022.05.019604(226-248)Online publication date: Aug-2022
https://doi.org/10.1016/j.ins.2022.05.019
Qin WTang JLu CLao S(2021)Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an exampleComputational Geosciences10.1007/s10596-021-10037-2Online publication date: 5-Mar-2021
https://doi.org/10.1007/s10596-021-10037-2
Brandt TGrawunder M(2018)GeoStreamsACM Computing Surveys10.1145/317784851:3(1-37)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3177848
Davila ADaza R(2017)Spatio-temporal dinamic of geographic object by means of trajectories in spatial database2017 12th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2017.7975698(1-6)Online publication date: Jun-2017
https://doi.org/10.23919/CISTI.2017.7975698
Massaâbi MLayouni OZekri AAljeaid MAkaichi J(2017)Regions Trajectories Data: Evolution of Modeling and Construction MethodsIntelligent Interactive Multimedia Systems and Services 201710.1007/978-3-319-59480-4_34(343-352)Online publication date: 28-May-2017
https://doi.org/10.1007/978-3-319-59480-4_34
Ge XLee JRenz JZhang P(2016)Trend-based prediction of spatial changeProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060770(1074-1080)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060770
Wang XQi SGai ZLi HLiu EXie WHu D(2016)Micro-Scale Severe Weather Prediction Based on Region Trajectories Extracted from Meteorological Radar Data2016 IEEE International Conference on Computer and Information Technology (CIT)10.1109/CIT.2016.21(335-338)Online publication date: Dec-2016
https://doi.org/10.1109/CIT.2016.21
Massaâbi MAkaichi J(2016)Modeling Moving Regions: Colorectal Cancer Case StudyIntelligent Interactive Multimedia Systems and Services 201610.1007/978-3-319-39345-2_36(417-426)Online publication date: 4-Jun-2016
https://doi.org/10.1007/978-3-319-39345-2_36
Moreira JDias PPaulo L(2014)Creating Moving Objects Representations for Spatiotemporal DatabasesEncyclopedia of Information Science and Technology, Third Edition10.4018/978-1-4666-5888-2.ch163(1703-1712)Online publication date: 31-Jul-2014
https://doi.org/10.4018/978-1-4666-5888-2.ch163

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