Abstract
Stochastic dynamics involved in ecological count data require distribution fitting procedures to model and make informed judgments. The study provides empirical research, focused on the provision of an early warning system and a spatial graph that can detect societal fire risks. It offers an opportunity for communities, organizations, risk managers, actuaries and governments to be aware of, and understand fire risks, so that they will increase the direct tackling of the threats posed by fire. Statistical distribution fitting method that best helps identify the stochastic dynamics of fire count data is used. The aim is to provide a fire-prediction model and fire spatial graph for observed fire count data. An empirical probability distribution model is fitted to the fire count data and compared to the theoretical probability distribution of the stochastic process of fire count data. The distribution fitted to the fire frequency count data helps identify the class of models that are exhibited by the fire and provides time leading decisions. The research suggests that fire frequency and loss (fire fatalities) count data in Ghana are best modelled with a Negative Binomial Distribution. The spatial map of observed fire frequency and fatality measured over 5 years (2007–2011) offers in this study a first regional assessment of fire frequency and fire fatality in Ghana.
Highlights
As population growth rates increase, large numbers of people and infrastructure within urban centres and disaster-prone areas are affected when events such as natural disasters occur (Grid-Arendal and UNEP 2002)
221.18 28.38 147.90 45.33 147.27 20.74 ND 42.19 151.70 121.16 285.43. It is proven by the log-likelihood statistics, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) goodness of fit test that the Negative Binomial Distribution that emerged is a better model for observed fire data amongst the three candidate models
We argue that the Negative Binomial model is good for modelling stochastic variability of count data around a theoretical expectation, taking into account the over-dispersion that arises from the data
Summary
As population growth rates increase, large numbers of people and infrastructure within urban centres and disaster-prone areas are affected when events such as natural disasters occur (Grid-Arendal and UNEP 2002). Issues of fire-disaster risk have received a lot of attention (Mckenzie et al 2000; Wang et al 2005; Cheng and Wang 2008; Bistinas et al 2014) Most of these studies focused on providing models that helped statistically quantify the likelihood of the frequency of fire for fire prevention (Zhang et al 2011; Bistinas et al 2014), whereas others facilitated the understanding of the information needed for fire management decisions (Wang et al 2005; Taylor et al 2013). It empirically fits an appropriate statistical distribution to fire count data, assesses the likelihood of a fire risk affecting properties and the economy, and determines the fatality that can be expected through fire occurrence.
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