An analysis of Spatio–temporal patterns of fires in an Iranian city

Abstract

Exploring Spatio–temporal patterns of fire incidents provides important information to help develop strategies to prevent and mitigate fire risk based on geographical knowledge. This study aims to map and analyse the Spatio–temporal patterns of urban fire incidents. Fire incident data were obtained from Ardabil Municipality Fire Department and Emergency Services and analysed using radial shape charts, kernel density estimation and average nearest neighbour to quantify spatial and Spatio–temporal patterns of fires. The results show that the Spatio–temporal fire patterns vary, depending on time, their types and causes. Interestingly, results indicate that fires are most likely to occur on Tuesdays and Thursdays and during summer. The study provides evidence to enhance decision-making on resource allocation in terms of establishing new fire stations and deploying an additional workforce to more vulnerable localities, or to formulate prevention strategies, such as education campaigns, to mitigate fire risk.

A decade of urban fires: Portuguese events between 2013 and 2022

“… 10 examined the use of machine learning techniques to identify risk factors for unintentional house fires. Other current research trend focuses on the spatio–temporal analysis which provides a framework for understanding the distribution, patterns, and dynamics of fires 11 . Despite these methodological advances, there is still a dearth of available information in particular with regard to urban fires 12 , 13 , which often leads to unreliable results, making it more challenging to adopt and apply evaluation tools in different urban contexts.…”

“…Additionally, the combination of fire data with information on, e.g., weather and social-economic factors can further enhance the understanding of urban fires dynamics. With the integration of spatial planning to manage fire risk becoming a priority for many local authorities 11 , publishing data descriptors on urban fire occurrences indexed both in time and space, such as this one, may support evidence-based policymaking playing a crucial role in promoting the safety and well-being of the communities.…”