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2.5 Influence of Neighbourhood Social and Economic Characteristics on Crashes

The sociodemographic characteristics of the neighbourhood where the person lives and where

the crash occurs contribute to road users’ injury severity (Factor et al., 2008). A wide range
of variables have been used in previous studies of the influence of neighbourhood

socioeconomic factors on traffic safety, including average family size, home density,

(Lovegrove and Sayed, 2006), car ownership (Jones et al., 2008; Schneider et al., 2010;
Pirdavani et al., 2016), marital status (Lascala et al., 2001; Steinbach et al., 2010), ethnic

group (Lascala et al., 2001), level of education (Lascala et al., 2001; Huang et al. 2010;
Dapilah et al., 2016; Haustein and Møller, 2016), driving licence (Pirdavani et al., 2016),

gender (Lascala et al., 2001; Schneider et al., 2010), income (Lascala et al., 2001; Schneider
et al., 2010; Guliani et al., 2015 ), age (Lascala et al., 2001; Schneider et al., 2010), residents’

occupation or employment (Lascala et al., 2001; Hadayeghi et al., 2010; Pirdavani et al.,

2016), and population (Lovegrove and Sayed, 2006; Jones et al.,2008; Pirdavani et al., 2016).

Several studies have examined the influence of the neighbourhood socio-demographic

characteristics of the crash location on traffic safety. For instance, Lovegrove and Sayed

(2006) developed an aggregate or macro-level collision prediction model. They found that
increases in the number of crashes were associated with an increase in job density, population

density and unemployment in the neighbourhood. In another study, Spoerri et al. (2011)
found that traffic mortality increased with a decrease in the population density of study areas

in motor vehicle occupants and motorcyclists but not for cyclists or pedestrians. Pirdavani et

al. (2016) found that average car ownership and household income of the traffic analysis
zones in Flanders, Belgium, had a significant influence on the frequency of crashes in those

zones. Jones et al. (2008) found that the average number of cars per capita and the
depravation scores of local areas in England and Wales were correlated with the frequency of

crashes in those areas.

Schneider et al. (2010) examined the association between intersection characteristics and
pedestrian crash risk. They found that neighbourhoods with higher populations of children

were more likely to have a higher frequency of pedestrian crashes. Using a geostatistical

analysis, Lascala et al. (2001) examined the influence of neighbourhood characteristics, such
as alcohol availability and alcohol consumption patterns, on pedestrian injury crashes

involving single motor vehicles. They found that alcohol- related pedestrian crashes occurred

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