Geovisualization and Geographical Analysis for Fire Prevention

Abstract

Swedish emergency services still have relatively limited resources and time for proactive fire prevention. As a result of this, there is an extensive need for strategic working methods and knowledge to take advantage of spatial analyses. In addition, decision-making based on visualizations and analyses of their own collected data has the potential to increase the validity of strategic decisions. The objective of this paper is to critically examine how some different geovisualization techniques—point data, kernel density and choropleth mapping—actively can complement each other and be applied in fire preventive work. The results show that each technique itself has limitations, but that, in combination, they increase the scope for interpretation and the possibilities of targeting different forms of preventive measures. The investigated geovisualization techniques facilitate various forms of fire prevention such as identifying which areas to prioritize for outreach, home visits, identification and targeting of different risk groups and customized information campaigns about certain types of fires in risk-prone areas. Furthermore, fairly simple mapping techniques can be utilized directly to evaluate incident reports and increase the quality of geocoded fire incidents. The study also shows how some of these techniques can be applied when analyzing residential fire incidents and their relation to underlying structural and socio-economic factors as well as spatio-temporal dimensions of fire incident data. The spatial analyses and supporting maps can help find and predict risk areas for residential fires or be used directly to formulate hypotheses on fire patterns. The generic functionality of the visualization methods makes them also useful for visual analysis of other types of incidents, such as reported crimes and accidents. Finally, the results are applicable to a work process adapted to the Swedish legislation on confidential data.

Residential Fires in Metropolitan Areas: Living Conditions and Fire Prevention

“…Different methods for geovisualisation -point data, kernel density and choropleth mapping -have been applied to increase the possibility for spatial analysis of residential fires (section 4.3). Earlier studies shows that each method itself has limitations regarding analytical depth and visualisation of fires, but that, in combination, they can improve the possibilities of targeting different forms of area-based fire preventive measures [5]. The kernel density map is combined with a map layer over living conditions, which is an indexation based on eight socio-economic and demographic variables from 2016-2018.…”

“…GIS-based analyses of fire incidents in urban contexts are quite frequent worldwide and in the research literature [1][2][3]. Different geovisualisation methods are used to clarify patterns of residential fires or other incidents in different metropolitan areas [4,5]. Spatial approaches are also applied for theoretical and practical purposes, such as risk modeling, linking fires to socio-economic variables, theorizing and developing hypotheses about the causes of fires, and to analyse different types of fire incidents on various geographical scales within a selected area, e.g.…”

“…In this context, geovisualisation is an appropriate visual method for presenting and communicating large datasets and the result of spatial analyses [19,20]. Fire risks or fire incidents are often envisioned through maps, diagrams and figures, where reliability of these visualisations is affected by the quality of data, accumulated errors in the analysis process, and different settings such as used spatial method, scale, bandwidth and choice of visualisation technique [4,5,21].…”

Residential Fires in Metropolitan Areas - Living Conditions and Fire Prevention

“…GIS-based analyses of fire incidents in urban contexts are quite frequent worldwide and in the research literature [1][2][3]. Different geovisualisation methods are used to clarify patterns of residential fires or other incidents in different metropolitan areas [4,5]. Spatial approaches are also applied for theoretical and practical purposes, such as risk modeling, linking fires to socio-economic variables, theorizing and developing hypotheses about the causes of fires, and to analyse different types of fire incidents on various geographical scales within a selected area, e.g.…”

Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

“…However, prevention is better than a cure, so fire prediction techniques may help to identify which areas that are at higher risk of fires. For example, Guldaker [ 10 ] found and predicted risk areas for residential fires based on geo-visualization techniques. Regarding forest fire prediction, parameters such as previous weather conditions, humidity and cumulative precipitation can offer a risk estimate by means of artificial intelligence methods, such as support vector machines and artificial neural networks [ 11 , 12 ], and fire ignitions caused by negligence and pyromaniacal behavior can also provide valuable information in the prediction [ 13 ].…”