Predictive ability for emergency service requests represents a great potential for a safer community and cost-savings. The ability to predict the everyday variety of crimes, fires, and emergency medical calls is within reach. After 9-11, the trend toward information analysis and intelligence in law enforcement accelerated rapidly. Business intelligence analysis software and geographic information system technology has found its way into policing, not just in large urban areas but in small towns as well. National databases and information sharing among all levels of law enforcement make it possible to reduce the risk of terrorism threats.

It also works for the crimes that a city such as Richmond, Virginia experiences routinely. An information management system for predictive crime analysis includes elements for data mining, reporting, and mapping with GIS software. Police officers receive the estimations or predictions for crime hot spots before their shift begins. The result is positive action taken to prevent crimes rather than a reaction to a crime already committed. Using the system, the city lowered its dangerous city rating in one year, dropping from fifth highest to number fifteen. The goal of these systems is to replicate the “intuitive nature” of a highly experienced police officer. Data collection is the key. Without baseline data, such systems have no predictive value also critical is a records management system that facilitates data mining.   

While this approach has application for arson crimes, attempting the same for building fires is unfortunately more problematic. Some progress in this regard is underway as a team of Australian geographers works with the Queensland Fire and Rescue Service for the purpose of better allocating fire service resources and save lives. In the terminology of geographic analysis, the research team is investigating the spatial-temporal arrangement of urban fires and their association with weather conditions, calendar events, and socio-economic conditions. The area protected by this particular fire service has a large migrant population. The budgets of urban fire-rescue services are limited and thus essential that managers and planners understand the underlying forces that drive where, when and why fires start.        

Using disaggregated fire incident data form Queensland Fire and Rescue Service subsequently aggregated to the Statistical Local Area, the team used the Australian Bureau of Statistics’ defined index of socio-economic disadvantage (SEIFA) as the basis to identify relationships between socio-economic disadvantage and building fires. They then used a regression model to develop predictions for the incidence of building fires over a range of socio-economic variables.

The geographers identified five significant predictors: percentage of unemployed, proportion of indigenous population, families living in separate dwellings, one parent, and parent families with children less than fifteen years of age. This study shows that mapping urban (building) fires for informed decision-making and resource allocation has potential for further application in other areas to validate the results.