SPATIAL ANALYSIS OF FIRES IN VILNIUS CITY IN 2010–2012

Vasiliauskas, Darius; Beconytė, Giedrė · 2015 · Crossref

DOI: 10.3846/20296991.2015.1011862

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Summary

This study investigates the spatial distribution and determinants of urban fires in Vilnius, Lithuania, during the 2010–2012 period. Motivated by the significant socio-economic damage and fatalities caused by urban fires, and a lack of geographic research on this phenomenon in Lithuania, the authors aimed to identify high-risk areas and analyze the factors influencing fire incidence. The research sought to determine whether fire patterns correlated with socio-demographic variables, construction materials, or criminal activity, thereby informing more efficient prevention strategies. The methodology involved geocoding 4,524 registered fire incidents and analyzing them using Geographic Information Systems (GIS), cartographic methods, and statistical analysis. The data were aggregated into 500x500 meter grid cells to assess relative fire density. The researchers categorized fires by type (e.g., abandoned buildings, open spaces, tower blocks, garbage cans, vehicles, arsons), cause, and location. To test specific hypotheses, fire data were compared against two primary datasets: a digital database of constructional materials derived from realty registers and municipal data, and registered criminal incidents from 2012 provided by police authorities. The analysis included correlation tests, location quotient maps, and hot-spot analysis to identify spatial anomalies. The results revealed that fire distribution in Vilnius is strongly influenced by socio-demographic factors rather than construction materials. While general fire density maps mirrored population density, the correlation between residents and residential fires was barely significant (r = 0.55). Contrary to expectations, constructional materials had little impact on fire frequency; for instance, areas with wooden constructions had higher fire rates per resident, but arson was not the primary cause as commonly believed. The correlation between fires and criminal activities was positive but statistically insignificant (r = 0.44). Temporal analysis showed peaks in April (linked to dry weather) and October–November (linked to heating season), with afternoon hours and Sundays seeing higher incident rates. Distinct spatial patterns emerged: garbage container fires dominated in districts with lower social standards or high homeless populations, while arsons were prevalent in prestigious districts. Hot-spot analysis identified four significant clusters of increasing risk, largely associated with flats and garbage containers in specific residential areas. The study concludes that urban fire distribution patterns are highly specific to individual cities and cannot be explained by general assumptions or single factors like building materials. The findings challenge common beliefs, such as the link between wooden structures and arson, and highlight the complex interplay of socio-demographic and environmental factors. The authors emphasize that combining spatial, statistical, and cartographic analyses is essential for reliable generalizations. These insights suggest that fire prevention measures must be tailored to specific districts based on their unique risk profiles, such as replacing plastic garbage containers in areas prone to vandalism or addressing socio-economic vulnerabilities in high-risk zones.

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• urban rural setting