Urban fire station location planning using predicted demand and service quality index

Dey, Arnab; Andrew, Heger,; England, Darin

doi:10.1007/s41060-022-00328-x

“…osrm is already used in various fields such as transport (Danesi & Tengattini, 2020;Fernandes Barroso et al, 2021;Savaria et al, 2021), education (Odell, 2017), health (Chen et al, 2021;Rice et al, 2021;Snyder & Smucker, 2022;Wisch et al, 2022), applied geography (Kandlbinder et al, 2019;Oberst & Voigtländer, 2021;Wieland, 2018Wieland, , 2021, environmental science (Hickisch et al, 2019;E. Walker et al, 2021), urban planning (Ballantyne et al, 2022;Dey et al, 2022;Xu, 2019) and linguistics (Jeszenszky et al, 2019).…”

Section: Statement Of Needmentioning

confidence: 99%

osrm: Interface Between R and the OpenStreetMap-Based Routing Service OSRM

Giraud¹

2022

JOSS

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“…As another example of actionable knowledge discovery, Dey et al [19] proposed a systematic approach for fire station location planning. As urban fires could adversely affect the socio-economic growth and ecosystem health of our communities, the authors applied various data mining and machine learning models in working with the Victoria Fire Department to make important decisions for selecting location of a new fire station.…”

Section: Interpretable and Actionable Predictionsmentioning

confidence: 99%

“…Another contributor to this proliferation is the increase in the quantity of data collected, stored, and appropriately documented for mining since the benefits of leveraging this data has become more apparent. Some of the works in this special issue demonstrated how data mining techniques can be applied in agriculture [2], healthcare and medicine [22,52], and city planning [19].…”

Section: New Trends From the Industry Perspectivementioning

confidence: 99%

Recent advances in domain-driven data mining

Liu

¹

,

Fakharizadi

²

,

Xu

³

et al. 2022

Complex Intell. Syst.

0

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Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.

show abstract

“…As another example of actionable knowledge discovery, Dey et al [19] proposed a systematic approach for fire station location planning. As urban fires could adversely affect the socioeconomic growth and ecosystem health of our communities, the authors applied various data mining and machine learning models in working with the Victoria Fire Department to make important decisions for selecting location of a new fire station.…”

Section: Interpretable and Actionable Predictionsmentioning

confidence: 99%

“…Another contributor to this proliferation is the increase in the quantity of data collected, stored, and appropriately documented for mining since the benefits of leveraging this data has become more apparent. Some of the works in this special issue demonstrated how data mining techniques can be applied in agriculture [2], health care and medicine [22,48], and city planning [19].…”

Section: New Trends From the Industry Perspectivementioning

confidence: 99%

Recent advances in domain-driven data mining

Liu

¹

,

Fakharizadi

²

,

Xu

³

et al. 2022

Int J Data Sci Anal

1

0

View full text Add to dashboard Cite

Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.

show abstract

Urban fire station location planning using predicted demand and service quality index

Cited by 7 publications

References 33 publications

osrm: Interface Between R and the OpenStreetMap-Based Routing Service OSRM

osrm: Interface Between R and the OpenStreetMap-Based Routing Service OSRM

Recent advances in domain-driven data mining

Recent advances in domain-driven data mining

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