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)


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)
Number of pages6
ISBN (Electronic)9781450350471
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)


WorkshopInternational ACM Workshop on Managing and Mining Enriched Geo-Spatial Data 2017
Abbreviated titleGeoRich 2017
CountryUnited States of America
Internet address

Cite this