A large-scale spatio-temporal data analytics system for wildfire risk management

Ziyuan Wang, Hoang Tam Vo, Mahsa Salehi, Laura Irina Rusu, Claire Reeves, Anna Phan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Wildfires have been a significant concern for communities and fire response agencies in many countries. Hence, it is critical to
be able to predict the fire risk in a timely and accurate manner and at granular level. However, this requires accessing and processing large amounts of spatial and temporal data from a number of sources in near real-time, while ensuring the immediate availability of risk measurement results. In this paper, we describe a large-scale data-driven system for personalized risk mitigation, fire response’s resource optimization and dynamic evacuation planning. It leverages large spatial and temporal datasets to provide predictive analytics in near real-time and to deliver tailored insights to government agencies, communities and individuals.
LanguageEnglish
Title of host publicationGeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data
Subtitle of host publicationIn Conjunction with SIGMOD 2017, May 14th 2017
EditorsPanagiotis Bouros, Mohamed Sarwat
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages19-24
Number of pages6
ISBN (Electronic)9781450350471
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventInternational ACM Workshop on Managing and Mining Enriched Geo-Spatial Data 2015 - Melbourne Convention & Exhibition Centre, Melbourne, Australia
Duration: 31 May 201531 May 2015
Conference number: 2
http://www.dbs.ifi.lmu.de/georich15/

Conference

ConferenceInternational ACM Workshop on Managing and Mining Enriched Geo-Spatial Data 2015
Abbreviated titleGeoRich 2015
CountryAustralia
CityMelbourne
Period31/05/1531/05/15
Internet address

Cite this

Wang, Z., Vo, H. T., Salehi, M., Rusu, L. I., Reeves, C., & Phan, A. (2017). A large-scale spatio-temporal data analytics system for wildfire risk management. In P. Bouros, & M. Sarwat (Eds.), GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data: In Conjunction with SIGMOD 2017, May 14th 2017 (pp. 19-24). [4] New York NY USA : Association for Computing Machinery (ACM). https://doi.org/10.1145/3080546.3080549
Wang, Ziyuan ; Vo, Hoang Tam ; Salehi, Mahsa ; Rusu, Laura Irina ; Reeves, Claire ; Phan, Anna. / A large-scale spatio-temporal data analytics system for wildfire risk management. GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data: In Conjunction with SIGMOD 2017, May 14th 2017. editor / Panagiotis Bouros ; Mohamed Sarwat. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 19-24
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title = "A large-scale spatio-temporal data analytics system for wildfire risk management",
abstract = "Wildfires have been a significant concern for communities and fire response agencies in many countries. Hence, it is critical tobe able to predict the fire risk in a timely and accurate manner and at granular level. However, this requires accessing and processing large amounts of spatial and temporal data from a number of sources in near real-time, while ensuring the immediate availability of risk measurement results. In this paper, we describe a large-scale data-driven system for personalized risk mitigation, fire response’s resource optimization and dynamic evacuation planning. It leverages large spatial and temporal datasets to provide predictive analytics in near real-time and to deliver tailored insights to government agencies, communities and individuals.",
author = "Ziyuan Wang and Vo, {Hoang Tam} and Mahsa Salehi and Rusu, {Laura Irina} and Claire Reeves and Anna Phan",
year = "2017",
doi = "10.1145/3080546.3080549",
language = "English",
pages = "19--24",
editor = "Bouros, {Panagiotis } and Sarwat, {Mohamed }",
booktitle = "GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data",
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Wang, Z, Vo, HT, Salehi, M, Rusu, LI, Reeves, C & Phan, A 2017, A large-scale spatio-temporal data analytics system for wildfire risk management. in P Bouros & M Sarwat (eds), GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data: In Conjunction with SIGMOD 2017, May 14th 2017., 4, Association for Computing Machinery (ACM), New York NY USA , pp. 19-24, International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data 2015, Melbourne, Australia, 31/05/15. https://doi.org/10.1145/3080546.3080549

A large-scale spatio-temporal data analytics system for wildfire risk management. / Wang, Ziyuan; Vo, Hoang Tam; Salehi, Mahsa; Rusu, Laura Irina; Reeves, Claire; Phan, Anna.

GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data: In Conjunction with SIGMOD 2017, May 14th 2017. ed. / Panagiotis Bouros; Mohamed Sarwat. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 19-24 4.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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Wang Z, Vo HT, Salehi M, Rusu LI, Reeves C, Phan A. A large-scale spatio-temporal data analytics system for wildfire risk management. In Bouros P, Sarwat M, editors, GeoRich - Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data: In Conjunction with SIGMOD 2017, May 14th 2017. New York NY USA : Association for Computing Machinery (ACM). 2017. p. 19-24. 4 https://doi.org/10.1145/3080546.3080549