  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.
