Emergency calls during the 2013 southern Ontario ice storm: case study of Vaughan

Abstract

Purpose Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses. Design/methodology/approach The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016). Findings The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days. Originality/value This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada

“…Most disasters and emergencies, such as earthquakes, flooding, wildfires, ice storms, hurricanes and industrial accidents, create situations where the demand for emergency services increases (Asgary et al , 2018; Shafiei Sabet et al , 2019). This sudden increase in demand cannot be easily and quickly met and, as such, the response time may be expected to increase due to the lack of appropriate/adequate resources or sometimes inaccessibility of the emergency site due to the ongoing conditions.…”

“…We primarily use line/bar charts and circular time plots (see, e.g. Asgary et al , 2010; Wuschke et al , 2013; Santos, 2017; Shafiei Sabet et al , 2019) to illustrate similarities, differences and emerging patterns over time.…”

“…Earlier, Shafiei Sabet et al (2019) conducted spatiotemporal analyses of emergency incidents and responses that occurred in the City of Vaughan during the 2013 ice storm in Southern Ontario, with December 20–31, 2013, as the “study period.” Incident and response patterns were compared with those arising in the same 11 calendar day period in other years (2009–2012 and 2014–2016). In the current study, we undertake temporal analyses of data for the COVID-19 pandemic covering almost ten months (from March 17, 2020 to January 13, 2021), in comparison with data for the corresponding time periods during the three preceding years.…”

Growth of Rescues in Natural Areas during the First Summer of COVID-19 Pandemic in Catalonia

“…We compared the total number of rescues and the daily averages for both the whole period and for July and August 2020, with those corresponding to the equivalent periods in the period 2016-2019, following a similar approach to that used by Shafiei et al [58]. The analysis also took into account the following: whether rescues occurred on weekdays or at weekends; the number of people injured or killed; the time at which the rescue started; the altitude at which the rescue took place; the distance to the nearest town with over 100,000 people; and the protection status of the area in which the rescue occurred.…”

“…This enabled us to produce a series of heat maps showing the highest and lowest concentrations of rescues in each of the periods studied. Kernel density estimation is a well-established technique for conducting studies using Geographical Information Systems and spatial-analysis approaches to reveal spatial patterns of the deployment of emergency services [58].…”