Abstract
The dispatch of emergency services is a complex cognitive task. Current decision support methods rely heavily on manual analysis of maps and map overlays. This paper aims to use call for service/emergency (CFS) dispatch data from various cities to look for patterns not usually amenable to visual analysis that could be used to create decision support tools or methods for dispatchers who must allocate first responder resources under emergency conditions. The authors have collected from the Police Data Initiative, a publicly available government repository that contains millions of annotated 911 dispatch records. The authors have selected three major American cities (Hartford, CT; Lincoln, NE; and Orlando, FL). Three experiments are performed to assess possible benefits of augmenting conventional manual methods with automated analysis derived using methods of data science. In particular, high-dimensional and non-linearly coded information not amenable to manual analysis are considered.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Theodoridis, S.: Pattern Recognition. Elsevier, Amsterdam (2008). ISBN 9780080949123
Pao, Y.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Boston (1989). ISBN 0-201-12584-6
Hanlon, et al.: Feedback control for optimizing human wellness. In: Proceedings of 22nd
International Human-Computer Interaction Conference, Copenhagen, Denmark, July 2020
https://www.policedatainitiative.org/datasets/calls-for-service/Additional
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hancock, M., Hancock, K., Tree, M., Kirshner, M., Bowles, B. (2020). Information-Theoretic Methods Applied to Dispatch of Emergency Services Data. In: Schmorrow, D., Fidopiastis, C. (eds) Augmented Cognition. Human Cognition and Behavior. HCII 2020. Lecture Notes in Computer Science(), vol 12197. Springer, Cham. https://doi.org/10.1007/978-3-030-50439-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-50439-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50438-0
Online ISBN: 978-3-030-50439-7
eBook Packages: Computer ScienceComputer Science (R0)