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

6 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 2017 - Chicago, United States of America
Duration: 14 May 201714 May 2017
Conference number: 4th
https://dl.acm.org/doi/proceedings/10.1145/3080546 (Proceedings)

Workshop

WorkshopInternational ACM Workshop on Managing and Mining Enriched Geo-Spatial Data 2017
Abbreviated titleGeoRich 2017
CountryUnited States of America
CityChicago
Period14/05/1714/05/17
Internet address

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