Spatial analysis of fires in Vilnius city in 2010–2012

The paper describes the results of investigation into urban fires in the city of Vilnius, Lithuania in the three-year period of 2010–2012. Cartographic and geospatial analysis of fires is needed due to dynamism of this phenomenon, risks for inhabitants, importance to city's socio-economic development and lack of geographic approach to research of urban fires in Lithuania. The registered fires were mapped and grouped by their type (abandoned building fires, open space fires, fires in tower blocks of flats, garbage can fires, vehicle fires and arsons), cause, location type (open space and premises) and by fatality rate. Spatial distribution of fires at different scales was analysed using cartographic method and spatial analysis with GIS. Some unexpected patterns have been revealed, analysed and compared with building materials that dominate in different areas of the city. It was found out that relative frequency of fires depends on complex parameters of socio-demographic environment whereas constructional materials have little or no impact. We expected to observe a relationship between criminal activities and fires due to similar influencing socio-demographic factors. Positive correlation, though insignificant, supported this hypothesis. The study showed that fire distribution patterns may be very specific for an individual city and difficult to explain by general assumptions. Different methods of spatial, statistical and cartographic analysis must be combined in order to make reliable generalisations

Modelling risk factors in urban residential fires in Helsinki

Asuinrakennuksissa syttyvät tulipalot aiheuttavat merkittäviä henkilö- ja omaisuusvahinkoja erityisesti kaupungeissa. Palojen esiintyvyydellä on todettu olevan voimakas yhteys alueiden ja alueiden asukkaiden piirteisiin, kuten sosioekonomiseen asemaan sekä kotitalouksien ja rakennusten ominaisuuksiin. Vaikuttavat tekijät ovat kuitenkin monimutkaisia ja usein toisiinsa kytkeytyneitä, mikä on vaikeuttanut tarkkojen ennusteiden tekemistä. Riskimallinnus ja paikkatietoanalyysit tarjoavat entistä tehokkaampia ja käytännöllisiä keinoja ilmiön tutkimiseen, erityisesti onnettomuuksien ennaltaehkäisyn ja varautumisen näkökulmasta. Tähän mennessä asuinrakennuspalojen alueelliseen esiintyvyyteen vaikuttavien riskitekijöiden tuntemus Helsingissä on ollut rajallista, mihin tällä tutkielmalla on pyritty tuomaan uutta empiiristä tietoa. Tässä tutkielmassa analysoitiin Helsingissä syttyneitä asuinrakennuspaloja vuosina 2014–2018 250 x 250 metrin ruututasolla. Tulipalojen alueellista riippuvuutta tutkittiin havainnoimalla tilastollisesti merkittäviä palojen keskittymiä. Lisäksi luotiin riskimalli, jolla pyrittiin tunnistamaan tulipalojen alueelliseen esiintyvyyteen vaikuttavia naapurustojen rakenteellisia, sosioekonomisia ja väestöllisiä piirteitä. Menetelminä käytettiin lineaarista regressiota ja spatiaalisen heterogeenisyyden huomioivaa Geographically Weighted Regression (GWR) -menetelmää. Tulokset osoittivat, että asuinrakennuspalot ovat alueellisesti klusteroituneita Helsingissä. Merkittävä suuri keskittymä löytyi kantakaupungin alueelta ja pienempiä keskittymiä Itä-Helsingistä. Tulosten perusteella naapuruston rakenteellisilla piirteillä, sosioekonomisella asemalla ja kotitalouksien ominaisuuksilla on vaikutusta asuinrakennuspalojen esiintyvyyden todennäköisyyteen sekä paloriskiä lisäävinä että vähentävinä tekijöinä. Naapurustotasolla tilastollisesti merkittäviä paloriskiä lisääviä selittäviä muuttujia olivat väestöntiheys, alhainen koulutustaso, työttömyys, asumisväljyys sekä omistusasuminen. Negatiivisesti paloriskiin vaikuttavia tekijöitä olivat asuinrakennusten tiheys, alueen rakennuskannan ikä, korkea koulutustaso sekä myös omistusasuminen. Yleisesti tutkimusalueella tämä kahdeksan muuttujaa selittivät noin puolet asuinrakennuspalojen vaihtelusta. Mallien välisessä vertailussa GWR:n selitysaste oli lineaarista regressiota parempi, ja se myös pystyi tunnistamaan merkittäviä paikallisia eroja selittävien muuttujien vaikutuksissa paloriskiin. Asuinrakennuspalojen riskiin vaikuttavien tekijöiden kokonaisvaltainen ymmärtäminen aluetasolla on tärkeää pelastustoimelle erityisesti valmiuden mitoittamisen ja resurssien tehokkaamman kohdentamisen kannalta. Jatkossa tulisikin kehittää tarkempia malleja, jotta saavutettaisiin entistä kattavampi kokonaiskuva paloriskistä ja siihen vaikuttavista tekijöistä. Erityisesti huomiota tulee kiinnittää tarkemman ja monipuolisemman aineiston ja menetelmien hyödyntämiseen, sekä myös tulipalojen ajallisen ulottuvuuden ja palojen seurauksien sisällyttämiseen mallinnuksessa.Fires in residential buildings can lead to significant personal injury and property damage, especially in cities. Fire incidence has been found to have a strong connection with the characteristics of neighbourhoods and their inhabitants, such as with socioeconomic status and the features of households and buildings. However, the influencing factors are complex and often interconnected, which has made it difficult to make accurate predictions. Risk modelling and spatial data analysis provide effective and practical means of studying the phenomenon, especially from the point of view of accident prevention and preparedness. To date, knowledge of the spatial risk factors affecting residential fire incidence is yet limited in Helsinki. Thus, this study has sought to bring new empirical evidence on the matter. This study analysed residential fires in Helsinki from 2014 to 2018 at a 250 x 250 m grid level. The spatial dependence of fires was investigated by observing statistically significant clusters of fires. In this study, a risk model was created that sought to identify the underlying structural, socioeconomic, and household characteristics of neighbourhoods that affect the likelihood of residential fire incidence. The methods used were linear regression and the Geographically Weighted Regression (GWR), which takes spatial heterogeneity into account. The results showed that residential fires are spatially clustered in Helsinki. A significant large concentration of fires was found in the inner-city area and smaller concentrations in eastern Helsinki. The results indicate that the structural features of the neighbourhoods, socioeconomic status, and household circumstances have an impact on the likelihood of residential fire incidence by both increasing and decreasing the risk of fire. At the neighbourhood level, statistically significant explanatory variables that increased fire risk were population density, low education, unemployment, occupancy rate of dwellings, and home ownership. A negative relationship with fire risk was found with residential building density, age of the buildings, high education, as well as home ownership. Overall, in the study area, these eight variables explained about half of the variance of residential fire incidence. In a comparison between the models, the explanatory power of the GWR was better than linear regression, and it was also able to identify significant local variations in the effects of explanatory variables on fire risk. A comprehensive understanding of the factors influencing residential fire risk at local levels is important for rescue services, especially in terms of planning response readiness and efficient allocation of resources. In the future, more precise models should be developed in order to achieve a more comprehensive understanding of fire risk and the factors affecting it. Particular attention should be paid to the use of more precise and diverse data and methods in modelling, as well as to the temporal dimension and the consequences of fires

A GIS-Based Risk Assessment for Fire Departments: Case Study of Richland County, SC

Risk assessments enable fire departments to be better prepared for future incidents and to engage in more effective prevention activities. A combination of physical, demographic, and behavioral risk factors combined form a community’s level of risk. This research shows how spatial and nonspatial statistical methods can be used within a GIS framework to create such a risk assessment, with the Columbia-Richland Fire Department in Richland County, SC being used as a case study. Hot spot analysis and thematic mapping of incident rates were used to assess the first research question – what is the spatial variability of structure fires, carbon monoxide incidents, and emergency medical calls? Correlation analysis, principal component analysis (PCA), and factor analysis were applied to a few dozen social and physical risk factors at the block group level to assess the second research question - how are the risk factors correlated with each other, and how are these risk factors varied across the county? The results of all types of methods were compared against each other to assess how risk factors correlated with incident types. These methods were able to map hot and cold spots of incidents, identify the most relevant risk factors, and show which risk factors were most prevalent in hot spot areas. The primary hot spot for EMS and fire incidents was found in northern Columbia, with a secondary hot spot located in far Lower Richland. PCA identified nine primary factors, the top three of which were related to systematic hard times, older homeowners, and rural location. Factor analysis was able to cluster block groups into fourteen groupings of similar risk traits. There were very clear differences in incident rates between the fourteen groupings, although hot spots contained block groups from multiple groupings. Given the snapshot in time nature of risk assessments, this research builds a baseline for future risk assessments, both in terms of methods and results