The goal of this study is to explore the relationship between pedestrian injuries and socioeconomic characteristics.
Pedestrian collisions were identified in the data of the California Statewide Integrated Traffic Records System (SWITRS), which is assembled from police crash reports by the California Highway Patrol Information Services Unit. Four thousand crashes were identified and geocoded within the census tracts in a county population of 2,846,289 over a 5-year period. Population and population characteristics for census tracts were obtained from the 2000 U.S. Census.
The percentage of the population living in households with low income (less than 185% of the federal poverty level) was the strongest predictor of pedestrian injuries. One fourth of census tracts had less than 8.7 percent of residents with low income and averaged 11 per 100,000 pedestrian crashes annually. One fourth of the census tracts had more than 32.2 percent of residents with low income and an average of 44 pedestrian crashes per 100,000 annually. Negative binomial regression showed that with each 1 percent increase in the percentage of residents with low income was associated with a 2.8 percent increase in pedestrian crashes. The percentage of residents age 14 years or less, adult residents who had not completed high school, residents who spoke English less than "very well" and spoke another language at home, and the population density were each associated with a higher frequency of pedestrian crashes. However, when low income was added to these 4 regression models, the relationship between low income and pedestrian crashes increased.
Our study showed that pedestrian crashes are 4 times more frequent in poor neighborhoods and that neither age of the population, education, English language fluency, nor population density explained the effect of poverty.
"In the United States, children from families earning less than US$20,000/year are seven times more likely to be injured than children from families earning more than US$30,000/year (Mueller, Rivara, and Bergman 1988). Chakravarthy et al. (2010) found that the percentage of the population living in low-income households is the strongest predictor of pedestrian injuries, with pedestrian crashes four times more likely in poor neighborhoods. This finding is repeated in a study that found that the risk of injury for children in the lowest socioeconomic stratum is more than twice that of children in higher SES categories (Roberts et al. 1995). "
[Show abstract][Hide abstract] ABSTRACT: Urban and regional planning has a contribution to make toward improving pedestrian safety, particularly in view of the fact that about 273,000 pedestrians were killed in road traffic crashes in 2010. The road is a built environments that should enhance safety and security for pedestrians, but this ideal is not always the case. This article presents an overview of the evidence on the risks that pedestrians face in the built environment. This article shows that design of the roadway and development of different land uses can either increase or reduce pedestrian road traffic injury. Planners need to design or modify the built environment to minimize risk for pedestrians.
Journal of Planning Literature 08/2015; DOI:10.1177/0885412215595438 · 1.19 Impact Factor
"At the aggregate level, place-based and trip-based measures have been widely used to estimate exposure (Wundersitz and Hutchinson, 2008; Greene-Roesel et al., 2007). Examples of place-based methods include the number of population living within a certain predefined areal units like census blocks (Wier et al., 2009; Chakravarthy et al., 2010) and population density computed at census tract level (Cottrill and Thakuriah, 2010). An advantage of using such methods is that one can make efficient use of readily available data sources. "
[Show abstract][Hide abstract] ABSTRACT: Modeling pedestrian-vehicle crashes is a spatially complex and temporally dynamic process. Examining the probability of and degree to which pedestrians are exposed to pedestrian-vehicle crash risk has important implications for formulating effective road safety measures. Pedestrian exposure can be a useful explanatory variable for modeling crashes but this piece of information is often difficult and costly to collect. The study attempts to take advantage of time geography and travel activity data to propose a new pedestrian exposure metric. Making use of the concept of potential path tree (PPT), this paper developed an individual-based and network constrained pedestrian exposure measure. Using negative binomial regressions to examine crash frequency with exposure, roadway and environmental variables, the proposed metric is compared with other existing pedestrian exposure methods to examine its applicability and potential in road safety analysis.
"The socioeconomic inequalities in traffic injuries and fatalities have been demonstrated by previous studies (Hyder and Peden, 2003; Nantulya and Reich, 2003; Sethi et al., 2006; Laflamme et al., 2009; Chen et al., 2010). Risks for road traffic injuries and fatalities are higher among disadvantaged groups with less education (Murray, 1998; Ferrando et al., 2005; Park et al., 2010), unskilled occupation (Hasselberg and Laflamme, 2003, 2004, 2008), lower income (Hasselberg and Laflamme, 2004; Chakravarthy et al., 2010), or lower SES in general (Chen et al., 2010; Hanna et al., 2010). However, these studies were mainly conducted in developed countries, and the situation in developing countries, including China, is still under-investigated (Ameratunga et al., 2006). "
[Show abstract][Hide abstract] ABSTRACT: Traffic crashes have become the fifth leading cause of burden of diseases and injuries in China. More importantly, it may further aggravate the degree of health inequality among Chinese population, which is still under-investigated. Based on a nationally representative data, we calculated the concentration index (CI) to measure the socioeconomic inequality in traffic-related disability (TRD), and decomposed CI into potential sources of the inequality. Results show that more than 1.5 million Chinese adults were disabled by traffic crashes and the adults with financial disadvantage bear disproportionately heavier burden of TRD. Besides, strategies of reducing income inequality and protecting the safety of poor road users, are of great importance. Residence appears to counteract the socioeconomic inequality in TRD, however, it does not necessarily come to an optimistic conclusion. In addition to the worrying income gap between rural and urban areas, other possible mechanisms, e.g. the low level of post-crash medical resources in rural area, need further studies. China is one of the developing countries undergoing fast motorization and our findings could provide other countries in similar context with some insights about how to maintain socioeconomic equality in road safety.
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