Exploratory and Inferential Methods for Spatio-Temporal Analysis of Residential Fire Clustering in Urban Areas

TL;DR

This paper explores various exploratory and inferential methods for analyzing the spatio-temporal clustering of residential fires in urban areas, providing guidelines for detecting patterns and changes over time to aid fire management.

Contribution

It introduces a comprehensive framework combining exploratory analysis and second-order spatial statistics for understanding fire clustering and its temporal evolution.

Findings

Identified significant fire clustering patterns in the case study.

Demonstrated the effectiveness of Diggle's D-function in detecting clustering.

Showed how space-time interaction analysis reveals changes over time.

Abstract

The spatio-temporal analysis of residential fires could allow decision makers to plan effective resource allocations in fire management according to fire clustering levels in space and time. In this study, we provide guidelines for the use of various methods in detecting the differences in clustering patterns of fire and non-fire (i.e., background residential) locations and how these patterns change over time. As a preliminary analysis step, various exploratory data analysis methods, such as, intensity plots (i.e., kernel density estimates) are used. Moreover, the use of Diggle's D-function (a second order analysis technique) is proposed for detecting the clustering of residential fire locations (if any) and whether there is additional clustering (or regularity) in the locations of the fires compared to background residential pattern. A test for trend over time (in years, months, and weeks) of the fire location patterns are provided with a space-time interaction analysis by spatio-temporal K-function. Residential fire data from \c{C}ankaya Municipality of Ankara, Turkey is used as an illustrative example. The presented methodology is also applicable to residential fire data from similar urban settings.