Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Pablo Robles; Andrés Tello; Lizandro Solano-Quinde; Miguel Zúñiga-Prieto

doi:10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Pablo Robles
, Andrés Tello
, Lizandro Solano-Quinde
, Miguel Zúñiga-Prieto

Universidad de Cuenca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.

Original language	English
Title of host publication	Advances in Emerging Trends and Technologies Volume 1
Editors	Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	136-145
Number of pages	10
ISBN (Print)	9783030320218
DOIs	https://doi.org/10.1007/978-3-030-32022-5_13
State	Published - 2020
Event	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador Duration: 29 May 2019 → 31 May 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1066
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Country/Territory	Ecuador
City	quito
Period	29/05/19 → 31/05/19

Keywords

911 calls
ARIMA
Emergency calls
GP
Temporal models

Access to Document

10.1007/978-3-030-32022-5_13

Cite this

Robles, P., Tello, A., Solano-Quinde, L., & Zúñiga-Prieto, M. (2020). Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. In M. Botto-Tobar, J. León-Acurio, A. Díaz Cadena, & P. Montiel Díaz (Eds.), Advances in Emerging Trends and Technologies Volume 1 (pp. 136-145). (Advances in Intelligent Systems and Computing; Vol. 1066). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32022-5_13

Robles, Pablo ; Tello, Andrés ; Solano-Quinde, Lizandro et al. / Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. Advances in Emerging Trends and Technologies Volume 1. editor / Miguel Botto-Tobar ; Joffre León-Acurio ; Angela Díaz Cadena ; Práxedes Montiel Díaz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 136-145 (Advances in Intelligent Systems and Computing).

@inproceedings{0555954b8f554a9bac5304b15d5994f6,

title = "Temporal Analysis of 911 Emergency Calls Through Time Series Modeling",

abstract = "We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.",

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author = "Pablo Robles and Andr{\'e}s Tello and Lizandro Solano-Quinde and Miguel Z{\'u}{\~n}iga-Prieto",

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doi = "10.1007/978-3-030-32022-5\_13",

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Robles, P, Tello, A, Solano-Quinde, L & Zúñiga-Prieto, M 2020, Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. in M Botto-Tobar, J León-Acurio, A Díaz Cadena & P Montiel Díaz (eds), Advances in Emerging Trends and Technologies Volume 1. Advances in Intelligent Systems and Computing, vol. 1066, Springer Science and Business Media Deutschland GmbH, pp. 136-145, 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019, quito, Ecuador, 29/05/19. https://doi.org/10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. / Robles, Pablo; Tello, Andrés; Solano-Quinde, Lizandro et al.
Advances in Emerging Trends and Technologies Volume 1. ed. / Miguel Botto-Tobar; Joffre León-Acurio; Angela Díaz Cadena; Práxedes Montiel Díaz. Springer Science and Business Media Deutschland GmbH, 2020. p. 136-145 (Advances in Intelligent Systems and Computing; Vol. 1066).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

AU - Robles, Pablo

AU - Tello, Andrés

AU - Solano-Quinde, Lizandro

AU - Zúñiga-Prieto, Miguel

PY - 2020

Y1 - 2020

N2 - We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.

AB - We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.

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KW - ARIMA

KW - Emergency calls

KW - GP

KW - Temporal models

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DO - 10.1007/978-3-030-32022-5_13

M3 - Contribución a la conferencia

AN - SCOPUS:85075643669

SN - 9783030320218

T3 - Advances in Intelligent Systems and Computing

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EP - 145

BT - Advances in Emerging Trends and Technologies Volume 1

A2 - Botto-Tobar, Miguel

A2 - León-Acurio, Joffre

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T2 - 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019

Y2 - 29 May 2019 through 31 May 2019

ER -

Robles P, Tello A, Solano-Quinde L, Zúñiga-Prieto M. Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. In Botto-Tobar M, León-Acurio J, Díaz Cadena A, Montiel Díaz P, editors, Advances in Emerging Trends and Technologies Volume 1. Springer Science and Business Media Deutschland GmbH. 2020. p. 136-145. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Predictive analysis of the occurrence of emergency events in the province of Azuay

Cite this

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Predictive analysis of the occurrence of emergency events in the province of Azuay

Cite this