A decision framework to assess urban fire vulnerability in cities of developing nations: empirical evidence from Mumbai

Abstract

The article aims to analyze the various causes and relationships of fire with the urban pattern. Various spatial analytics and geostatistical techniques are applied to reveal spatiotemporal variations in the datasets. The novel machine learning framework models important variables identified by random forest technique as predictors to develop GWR models. The framework is applied to establish the relationship between fire vulnerability and urban patterns for two periods (day and night) for the southern region of Mumbai city. We found that the urban pattern has a strong relationship with fire vulnerability, especially during the day time. High R-square values of 0.9086 and 0.7448 are achieved for the day and night periods, respectively. Further, significant differences in the influence of the predictors is observed during the periods. As cities are becoming more prone to fire, this study has the potential to help decision-makers with proactive measures over time and space.