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

4 Citations (Scopus)

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.
Original 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] Association for Computing Machinery (ACM). https://doi.org/10.1145/3080546.3080549