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HPPXXX10.1177/1524839913505283He alth Promotion Practice / Month XXXXChaney, Kim / Characterizing Bicycle Collisions 2013
Injury Prevention
Characterizing Bicycle Collisions by Neighborhood in a Large Midwestern City Robert A. Chaney, MS1 Changjoo Kim, PhD1
Introduction. Local environmental factors provide important contributions to bicycle safety. The purpose of this study was to characterize bicycle collisions by neighborhood in Cincinnati, Ohio. Background. The majority of prior bicycle safety research has focused on helmet use, especially among youth. Studies that have considered the neighborhood have centered on the built environment and its facilitation of bicycling (e.g., connectivity of roads and road conditions). Other broad conditions may be associated with injury beyond the use of protective equipment and the physical environment. Method. This study sought to determine spatial clustering, local patterning, temporal differences (time of day and season of year), and significant neighborhood-level predictors of bicycle collisions. Bicycle collision data were obtained from the Cincinnati, Ohio Police Department. Conclusions. This study showed that collisions occur at higher rates in the south-central and southwest neighborhoods of Cincinnati, Ohio. There were seasonal and time-of-day differences with respect to collision rates with summer and afternoon being the most common collision times. Neighborhood ethnicity, population density and presence of public transportation were all significant predictors of bicycle collisions. These findings will be disseminated to local city authorities and bicycle advocacy groups. Keywords: injury; prevention; bicycle; collisions; geographic information system (GIS)
Introduction >> Individual rider behavior is an important component of bicycle injury prevention (Jacobson, Blizzard, & Dwyer, 1998; J. Kim, Kim, Ulfarsson, & Porrello, 2006). However, individual rider behavior can be associated with the social and physical environment (Reynolds, Harris, Teschke, Cripton, & Winters, 2009). There are roughly 40,000 preventable bicycle injures and about 700 deaths per year in the United States (BicyclingInfo. org, n.d.), and these numbers are expected to proportionally swell. A large range of factors affecting individual bicycle use and safety includes demographic, socioeconomic, environmental, and policy-related determinants that either discourage or encourage bike use. Moreover, a growing literature demonstrates the importance of neighborhood context on the transportation collision, including bike collisions (Jones & Jorgensen, 2003; Laflamme & Diderichsen, 2000; Lenguerrand, Martin, & Laumon, 2006). Ethnic or racial differences in traffic collisions have been studied frequently in the literature (Subramanian, Chen, Rehkopf, Waterman, & Krieger, 2005). A meaningful example is provided by Rietveld and Daniel (2004) who showed that ethnicity is associated with the likelihood to bike, in preference to use public transportation or a car. The number of bus stops in a neighborhood may also influence bicycle safety in that bike collisions have a much higher possibility to occur on-street than off-street in close proximity to bus stops. Neighborhoods far from public transportation will result in greater dependence on the car and will discourage individuals from using any other mode of transport 1
University of Cincinnati, Cincinnati, OH, USA
Health Promotion Practice March 2014 Vol.15, No. 2 232–242 DOI: 10.1177/1524839913505283 © 2013 Society for Public Health Education
Authors’ Note: Address correspondence to Robert A. Chaney, MS, University of Cincinnati, 2600 Clifton Avenue, ML #0068, Cincinnati OH, 45221, USA; e-mail: [email protected].
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(Hsiao, Lu, Sterling, & Weatherford, 1997). Length of bus routes per neighborhood might affect bike use and bike collisions because bus routes are often through areas with attractions, and higher traffic volume areas, which are associated with higher likelihood of bicycle collisions (K. Kim, Pant, & Yamashita, 2010). The urban structure also influences the likelihood of bike use through several factors, such as population and job densities, and land-use mixture (Kitamura, Mokhtarian, & Laidet, 1997; Verhetsel & Vanelslander, 2010). Socioeconomic and demographic variables influencing bicycle use refer to many variables such as age, income, gender, education, job, family structure, and the neighborhood characteristics, which may be associated with bicycle use (Moudon et al., 2005). Neighborhoods having higher proportion of individuals commuting to work are also likely to have more bicycle users (Plaut, 2005). Contrarily, neighborhoods with higher motorized transportation rates are less likely to have high rates of bicycle users (Kingham, Dickinson, & Copsey, 2001). The physical abilities of individuals are well represented by their age. For example, neighborhoods with more young population are more likely to bike more. Bicycle use is generally high in academic towns (Rodriguez & Joo, 2004). Education has a strong influence on bicycle use depending on the area being studied. In North America, a high level of education is positively associated with bike use (Zahran, Brody, Maghelal, Prelog, & Lacy, 2008). The majority of previous bicycle safety research has focused on helmet use (Lohse, 2003; Nagel, Hankenhof, Kimmel, & Saxe, 2003). The central finding has been that helmet use helps prevent serious head injuries. However, there may be other broader conditions that are associated with injury beyond the use of protective equipment. This study seeks to examine spatial patterns and neighborhood characteristics as they are associated with bicycle collisions. Prior research has demonstrated that vehicle-bicycle collisions are more likely at places where bicycles and vehicles directly cross routes: intersections, traffic circles, and even bike lanes to some extent (Harris et al., 2013; Wee, Park, Park, & Choi, 2012). Conditions that increase bicyclist exposure to more vehicles, such as greater traffic volume or population density, also pose greater risk for collisions (Kim et al., 2006). Bike collisions are also more likely in areas with hillier topography though the exact reason is not reported (Harris et al., 2013; Kiburz, Jacobs, Reckling, & Mason, 1986). Cottrill and Thakuriah (2010) provide a model for the spatial approach to collisions. In this case, pedestrian–vehicle collisions were explored in context of neighborhood characteristics.
The results of this study will be valuable for public health educators by helping them identify specific geographic factors to target with prevention programs. The topic of injury prevention among bicyclists will be of increasing importance as bicyclist/driver interactions occur more frequently due to population growth and urban and suburban development. The purpose of this study is to characterize where bicycle collisions are occurring in Cincinnati. Specific research questions this study will seek to answer are the following: (a) Are there spatial clusters of bicycle collisions across the city? (b) Is there spatial autocorrelation (i.e., patterning) of bicycle collisions by neighborhood? (c) Are there temporal differences (i.e., seasonal and time of day) in bicycle collisions per neighborhood? (d) What are significant neighborhoodlevel predictors of bicycle collisions?
Method >> To achieve the purpose of this research project, several secondary data sources and computer programs were used. Data Sources Reports involving vehicle–bicycle collisions were obtained from the Cincinnati Police Department (CPD) from summer 2008 to summer 2012. These collision counts per neighborhood served as the dependent variable in the regression analysis. Prior to 2008 regular documentation of bike collisions did not occur with CPD. Each report included date, time, and collision location information (P. Byers, personal communication, September 13, 2012). The original data file contained 488 cases. There were two cases per single collision—one for the bicycle and one for the automobile involved. Duplicate cases involving automobiles were removed from this study, yielding 244 bicycleonly cases. Furthermore, examination of the data showed that there were 11 cases that involved a motorbike. Since this study examined onlybicycle collisions, these 11 cases were also removed. The total number of cases removed from the original data set was 255, and the total cases used for analysis was 233. The data obtained from the CPD did not contain any individuallevel information to protect identity and privacy of those involved in the bicycle collisions and as part of CPD policy for releasing data. Demographic information was obtained for census tracts within the city of Cincinnati from the U.S. Census Bureau (2012). Similarly, land-use mixture by census tract was obtained from the Arizona State University GeoDa
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Center for Geospatial Analysis and Computation (2012). Public transportation data in the form of bus routes and bus stops were obtained from Southwest Ohio Regional Transit Authority (2012). All data files were spatially joined to neighborhoods in order to consistently use the neighborhood as the geographic unit of the study. A spatial join combines attributes of multiple geographic information system (GIS) layers based on the location of the features. With the help of locations of bicycle collisions and neighborhoods, we joined attributes from individual bicycle collision locations to neighborhood boundary based on a spatial relationship. There are 48 municipalities in Hamilton County, Ohio, one of which is Cincinnati. This study only examined the 51 neighborhoods within City of Cincinnati. The neighborhood boundaries have historic origin in that they were at one time their own municipality and at some time were annexed into the city (Columbia Tusculum Community Council, n.d.). For traditional and cultural reasons the neighborhoods have retained their boundaries and characteristics that make them unique. Data Analysis Software Cloropleth and dot map creation and spatial data manipulation were performed using the open-source software QGIS for Mac OSX (2012). Descriptive statistics, comparison of means, and regression analysis was performed using R, version 2.15.1 (R Development Core Team, 2012). Spatial data analyses, including G clusters and local indicators of spatial association (LISA) analysis, were computed using GeoDa, Version 1.2.0 (Anselin, Syabri, & Kho, 2006). Assessment of Research Questions Spatial Clusters. Determining if spatial clusters of bicycle collisions existed across the city was done using the Global G statistic for clustering. The Global G statistic (Getis & Ord, 1992) measures the concentration of high or low values for a given study area depending on observed and expected z-score values for each geographical area. A map was generated displaying statistically significant clusters. These significant clusters were categorized as being high or low. High represents an unusually elevated level of clustering whereas low represents an unusually reduced level of clustering. Identifying if spatial clustering occurs within the city of Cincinnati will aid in characterizing where bicycle collisions tend to happen. Spatial Autocorrelation. The spatial autocorrelation in our data is explored by examining the different pairs of
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sample locations. If spatial autocorrelation exists, pairs of points that are close together should have less difference. As points become farther away from each other, in general, the difference squared should be greater. The Global Moran’s I measures spatial autocorrelation based on both feature locations and feature values simultaneously. Given a set of features and an associated attribute, it evaluates whether the pattern is clustered, dispersed, or random. A z-score and p-value of the Moran’s I index value is used to evaluate the significance of that index. A local Moran’s I statistic for spatial autocorrelation (Anselin, 1995) with inverse distance was further conducted to confirm the results from the Global Moran’s I statistic. LISA evaluates clustering by calculating local Moran’s I for each neighborhood and evaluates the statistical significance for each Ii. LISA determines if values are randomly distributed, dispersed, or clustered. Cluster and outlier analysis using Anselin’s local Moran’s I in Figure 3 distinguishes between a statistically significant (.05 level) cluster of high values (high–high), cluster of low values (low–low), outlier in which a high value is surrounded primarily by low values (high–low), and outlier in which a low value is surrounded primarily by high values (low–high). Temporal Differences Seasonal differences. Temporal differences in collisions by neighborhood were studied by season and time of day. For assessing seasonal differences, bicycle collisions were categorized into one of four seasons: Winter was defined as December through February, spring was defined as March through May, summer was defined as June through August, and fall was defined as September through November. Overall mean differences in collisions per neighborhood by season were assessed using analysis of variance (ANOVA). Differences between individual seasons were determined using Fisher’s least significant difference (LSD) t test for multiple comparisons. The analysis for temporal differences was conducted with the pure number of collisions because information on rider exposure could not be controlled for or obtained. Time of day differences. For time of day differences bicycle collisions were categorized into one of four times of day: Morning was defined as 6:00 a.m. to noon, afternoon was defined as noon to 6:00 p.m., evening was defined as 6:00 p.m. to midnight, and night was defined as midnight to 6:00 a.m. Overall mean differences in collisions per neighborhood by time of day were assessed using ANOVA. Differences between individual times of
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Figure 1 Quantile Map of Bicycle Collisions in Cincinnati Neighborhoods (2008-2012) NOTE: C.U.F. = Clifton Heights, University Heights, and Fairview.
day were determined using Fisher’s LSD t test for multiple comparisons. Neighborhood-Level Predictors. Linear regression analysis was used to determine significant neighborhoodlevel predictors for bicycle collisions. Specific explanatory variables used were neighborhood ethnicity (measured using percentage White), number of bus stops in the neighborhood, length of bus routes per neighborhood, population density, percent of the neighborhood in poverty, percent commuting by motorized transportation, percent commuting to work, median household income, median age, land-use mixture, and percent having graduated from high school. Backward elimination was used as a method of model selection and reduction.
Results >> There were 233 usable data points included in the study, each representing a single bicycle collision. The
most common result of the collision was injury (75.107%), followed by property damage only (24.464%), and fatality (0.429%). Neighborhoods had on average 4.588 collisions during the time frame of the study (SD = 4.521, range = 3-20). Figure 1 shows the spatial distribution of these collisions. Spatial Clusters Figure 1 displays the global clustering results. There was significant clustering of bicycle collisions located in the south-central and southwest areas of the city. Neighborhoods that possessed significantly high clusters were Madisonville, Clifton, South Fairmont, West Price Hill, Queensgate, West End, Over the Rhine, Mount Auburn, Pendleton, and Mount Adams. Neighborhoods that had significantly low clustering were Sayler Park and Mount Lookout. This analysis shows that there is a high clustering pattering near the downtown (e.g., Downtown) and southwest areas of the city (e.g., East Price Hill).
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Figure 2 Global G Statistic Clustering for Bicycle Collisions in Cincinnati, Ohio (2008-2012)
Spatial Autocorrelation Figure 2 displays the LISA grouping results. There were 10 neighborhoods out of 51 that had a significant patterning effect. Those that had a large number of collisions with neighbors also having a high number (high–high) were West Price Hill (I = 1.262), West End (I = 1.491), Over the Rhine (I = 1.351), and Pendleton (I = −1.335). Those that had a low number of collisions but where their neighbors had high numbers of collisions (low–high) were South Fairmount (I = −0.409), Queensgate (I = −0.471), Clifton (I = −0.080), Mount Auburn (I = −0.392), and Mount Adams (I = −0.877). There was one high–low grouping signifying that the neighborhood had a high number of collisions and its neighbors had a low number of collisions: Oakley (I = −1.078). There were no statistically significant low–low groupings. This analysis shows there is a clear local patterning effect of bicycle collisions within the city, especially in the south-central (e.g., West End, Over the Rhine) and
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southwest areas of the city (e.g., South Fairmount, West Price Hill). Temporal Differences Seasonal Differences. An ANOVA was used to determine if mean number of collisions per neighborhood differed by season. This analysis showed that there was a statistically significant difference between seasons with respect to mean number of collisions per neighborhood, F(3, 188) = 13.166, p < .001. Specifying the differences between the seasons was done using Fisher’s LSD t test for multiple comparisons. The results of between-season analysis is presented in Table 1. Summer had the largest number average collisions (2.313), followed by spring (1.375) and fall (0.896), and winter had the least (0.250). Time of Day Differences. Likewise, an ANOVA was used to determine if mean number of collisions per neighborhood differed by time of day. This analysis
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Figure 3 Local Indicators of Spatial Analysis (LISA) for Bicycle Collisions by Neighborhood in Cincinnati, Ohio (2008-2012)
showed that there was a statistically significant difference between times of day with respect to mean number of collisions per neighborhood, F(3, 188) = 19.251, p < .001. Specifying the differences between the times of day was done using Fisher’s LDS t test for multiple comparisons. The results of between times of day analysis are presented in Table 1. It appears that afternoon had the largest number of collisions, whereas night had the least; and morning and evening were in the middle and were not statistically different. Neighborhood-Level Predictors The full regression model containing each collected variable was fit using linear regression. Assumptions for this analysis were verified. Results are presented in Table 1. The adjusted R2 was .545, indicating that this set of variables explains just more than half of bicycle collisions per neighborhood. There were several predictor variables that did not appear to be significant contributors to the regression
model. For this reason, backward elimination was used to reduce the number of explanatory variables in the model. Explanatory variables were removed from the model one by one, and the model was rerun in each intermediate step (Faraway, 2004). Variables were removed in the following order: poverty (p = .973), percent commuting by motorized transportation (p = .881), median household income (p = .824), percentage commuting to work (p = .805), median age (p = .435), land-use mixture (p = .181), percentage having graduated from high school (p = .126), length of bus routes per neighborhood (p = .069). The full model was reduced to the model presented in Table 1. The three remaining explanatory variables were number of bus stops, neighborhood ethnicity, and population density. The reduced model had an R2adj of .534, which was a 0.011 reduction from the full model. This signifies that these three explanatory variables are the best at explaining bicycle collisions, among those collected.
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Table 1 Mean Differences per Season and Time of Day and Predictors of Collisions per Cincinnati Neighborhood Temporal Differences
Regression Analysis Predictor
β
Reduced model (Intercept) Number of bus stops Population density Neighborhood ethnicity F(11, 38) = 20.09, p < .001, R2adj = .534
−1.743 0.026 0.001 3.564
−1.419 5.884*** 3.008** 2.303*
Full model (Intercept) Population density Number of bus stops Neighborhood ethnicity Bus route lengths Land-use mixture Percentage high school graduates Median age Percentage commuting to work Median household income Percentage motor commute Percentage living in poverty F(11, 38) = 6.335, p < .001, R2adj = .545
−9.997 0.001 0.175 5.061 7.01 × 10−5 10.800 −2.941 −0.388 −0.355 −6.85 × 10−6 0.715 −0.313
−1.05 3.194** 2.374* 2.349* 1.637 1.534 −0.825 −0.328 −0.259 −0.208 0.143 −0.034
t
Seasons Spring > Winter Summer > Winter Summer > Spring Fall > Winter Summer > Fall Fall = Spring Time of day Morning > Night Afternoon > Night Afternoon > Morning Evening > Night Evening = Morning Afternoon > Evening
3.920*** 5.860*** 2.110* 3.570*** −3.700*** −1.470 −2.910** −6.437*** −4.507*** −4.682*** −1.695 3.267***
t
NOTE: Night = 12 a.m. to 6 a.m.; Morning = 6 a.m. to 12 p.m.; Afternoon = 12 p.m. to 6 p.m.; Evening = 6 p.m. to 12 a.m. *p < .05. **p < .01. ***p < .001.
Discussion >> The most salient findings from this study are threefold: (a) There is a clear spatial pattern to where collisions occur, (b) there are temporal differences in collision occurrence, and (c) significant predictors of collisions include neighborhood ethnicity, population density, and presence of public transportation. Both Global G cluster analysis and LISA spatial autocorrelation analysis produced nearly identical results. There was high level of collisions near downtown Cincinnati, the University of Cincinnati, and extending toward the southwest region of the city (i.e., Price Hill). Prior research has shown that students tend to bicycle more than the average adult (Gordon-Larsen, Nelson, & Beam, 2005). More riders on the road in a densely populated area is one set of conditions that could be associated with more observed collisions.
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This may explain why there was the large number of collisions in the downtown area of the city; population density, mixed with high traffic volume and a large low-income population. Previous research indicated that low income is one predictor of bicycle use (Larsen et al., 2009), which may explain the high numbers of collisions seen in the southwest area of the town. However, this research found income to be nonsignificant in both regression models (see Table 1). For reference to local conditions, Table 2 presents a collection of characteristics of significantly clustered or autocorrelated neighborhoods from Figures 2 and 3. A clear difference was observed with regard to the temporality of collisions. The times that seem most high risk for collisions were summer and afternoon. Confirming previous findings, cyclists are outside recreating more in the summer during nicer weather (Kim et al., 2006).
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209 126 126 642 105 42 234 9 69 77 151 410 156
72.70 49.10 72.10 60.50 52.00 92.80 26.30 21.40 31.70 37.40 9.60 45.90 69.90
6.81 3.94 7.32 14.83 7.45 5.76 5.93 0.51 8.41 5.33 9.39 14.81 9.08
0.915 0.887 0.92 0.88 0.92 0.88 0.837 0.75 0.5 0.94 0.7 0.86 0.88
Neighborhood Bus Route Number of Ethnicity (% Lengths Land-Use Bus Stops White) (Miles) Mixture 88.70 86.70 94.00 82.90 64.40 97.10 76.00 95.10 68.80 52.00 67.00 85.10 80.00
24.4 29.17 33 34.7 29.6 34.7 32.35 34.7 30.75 30 30.75 31.87 33
29.00 39.00 nd 50.00 25.00 29.00 50.00 nd 33.00 50.00 40.00 38.00 22.00
20,650 18,119 35,834 45,849 28,425 99,125 14,517 13,397 nd 24,395 12,808 33,922 37,720
Percentage High Percentage Median School Median Commuting to Household Graduates Age Work Income ($)
NOTE: C.U.F. = Clifton Heights, University Heights, and Fairview; nd = no data.
7,101 3,702 2,264 2,622 3,743 1,607 7,031 7,335 56 1,037 4,377 3,020 3151
Population Density (Population/ Square Mile) 66.60 64.00 79.40 42.90 77.10 82.60 54.50 50.50 nd 75.00 48.20 85.60 85.60
Percentage Motor Commute
Table 2 Characteristics of Neighborhoods Where Bicycle Collisions Cluster
8.00 11.60 19.80 6.80 23.10 26.30 7.60 20.40 nd 24.60 23.30 22.20 9.00
Percentage Living in Poverty
C.U.F. Corryville Clifton Downtown East Price Hill Mt. Adams Over The Rhine Pendleton Queensgate South Fairmount West End Westwood West Price Hill
Predictors/ Neighborhoods
Based on the regression analysis, only three explanatory variables were kept in the final reduced model: neighborhood ethnicity, bus stops, and population density. In fact, the more densely populated, the less diverse the neighborhood (higher percentage of White), and the more bus stops, the higher the number of collisions would be expected. Population density is associated with collisions by overcrowding the shared motorway. Ethnicity may be associated with collisions because White populations are more likely to use private transportation (Gordon-Larsen et al., 2005), so there are more cars on the road posing risk to any cyclist. Bus stops as a predictor of collisions is an interesting finding of this study. One explanation of how this variable is associated with collisions can be understood when reflecting on where cyclists typically ride on the road. The bicyclist typically rides on the far right of the road. This is also a space that the bus enters and exits regularly as it makes scheduled stops. The actual bus stop is not likely to contribute to the collisions, rather the interaction of the bus and the bicyclist in the shared side-of-the-road space. This could be especially problematic when one party is trying to pass the other on a narrow, busy road. It is possible that public transportation presence is related to population density, which could expound the problem of bicycle safety on a shared road. Further explanation to the differences in bicycle collisions by neighborhood in Cincinnati can be viewed through the lens of the right to the city as originated by Henri Lefebvre, but expounded on by David Harvey in Rebel Cities (2012). When reviewing the findings of this study one cannot help but notice that hot spots for bike collisions are occurring in certain neighborhoods. Based on Harvey’s analysis, it is unlikely that some neighborhoods will receive much attention due to their socioeconomic character and influence among those in power. In this study there appears to be a disconnect between bicycle collision location and city investment in the built environment to prevent injury. Some of the neighborhoods with the most collisions have no bicycle lanes or signed bike routes. Examples of this phenomenon include the Westwood and East Price Hill neighborhoods. Another recent example is city investment in the Clifton neighborhood near Ludlow Avenue and Central Parkway where enlarged and brightly colored bike lanes have been installed. It is possible a sizable ridership passes through this corridor, but bicycle safety concerns not addressed by city investment exist elsewhere in the city. One can further explore whether it is the bicyclist’s right to the road in context of the finding that more bus stops are being associated with more collisions. The transportation system seems to be set up to accommodate
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motorized transportation and only marginally recognizes bicyclists’ use of the road. Unless bicyclists contribute to the construction and maintenance of roads through their payment of taxes, the right to the city (or the road) will remain superficial and in idea only. There are inherent limitations to this study. Primary among them is that aggregated data were used for all variables. For this reason, every important variable may not have been included in the analysis. Also, this study was a secondary data analysis—no primary data were collected. This could pose a bias when aggregating variables to geographic locations. However, precautions were taken to ensure that only quality data from the representative neighborhoods and time periods were used. This research has important implications for public health education. By identifying the specific locations within Cincinnati that collisions tend to happen, injury prevention efforts can now happen in a more focused manner. This approach may be applicable in other municipalities or regions. By identifying where collisions are happening, resources allocation and efforts to alert drivers and bicycle riders can be more focused. This can be at the general neighborhood level or the local street level by addressing how buses interact with bicyclists. The characterization that buses potentially influence bicycle collisions is a key finding and valuable for the promotion of bicycle safety. Little research has been conducted with adult cyclist populations or within the scope of public health education. Future research to further characterize bicycle collisions would be a correlation analysis with traffic volume and traffic collisions. This may shed light on geographic conditions that are contributing to an unsafe riding environment. An assessment of bike lane distribution in relation to collisions as well as an assessment of the protocol used for deciding where bike lanes are installed would be another valuable contribution to the field. Last, it would be important to apply the assessments from this study to other geographic locations for the purpose of validating these findings and further characterizing bicycle safety.
Conclusion >> The purpose of this study was to characterize where bicycle collisions are occurring in Cincinnati. This study helps fill a gap in the scientific literature because it focuses on the spatial distribution of collisions and its application to injury prevention. Key findings from this study include higher rates of collisions that occur in the south-central and southwest neighborhoods on Cincinnati. Time of day and season are also important
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factors that are associated with collision rates. Neighborhood ethnicity, population density, and number of bus stops are all significant predictors of neighborhood bicycle collisions. This approach can be useful for other municipalities or regions by identifying where collisions occur and allocating resources thereto. This allocation could be on a macro level (e.g., neighborhood) or on a micro level (e.g., street). In general, these findings will be of value when applied to promoting the safety of bicyclists using a shared motorway. The results of this study will be disseminated to local policy makers within the City of Cincinnati and with local bicycle advocacy groups. This will promote bicycle safety through informed decision-making and collaboration. References Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27, 93-115. doi:10.1111/j.1538-4632.1995. tb00338.x Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38, 5-22. ASU GeoDa Center. (2012). Spatial impact data. Retrieved from https://geodacenter.asu.edu/geodata BicyclingInfo.org, (n.d.). Bicycle crash facts. Retrieved from http:// www.bicyclinginfo.org/facts/crash-facts.cfm Columbia Tusculum Community Council. (n.d.). Neighborhood history. Retrieved from http://columbiatusculum.org/about-theneighborhood/history/ Cottrill, C. D., & Thakuriah, P. V. (2010). Evaluating pedestrian crashes in areas with high low-income or minority populations. Accident Analysis & Prevention, 42, 1718-1728. doi:10.1016/ j.aap.2010.04.012 Faraway, J. J. (2004). Linear models with R. Boca Raton, FL: Chapman & Hall/CRC Press. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189-206. doi:10.1111/j.1538-4632.1992.tb00261.x Gordon-Larsen, P., Nelson, M. C., & Beam, K. (2005). Associations among active transportation, physical activity, and weight status in young adults. Obesity Research, 13, 868-875.
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Harris, M. A., Reynolds, C. C. O., Winters, M., Cripton, P. A., Shen, H., Chipman, M. L., & . . .Teschke, K. (2013). Comparing the effects of infrastructure on bicycling injury at intersections and non-intersections using a case-crossover design. Injury Prevention. Advance online publication. doi:10.1136/injuryprev-2012-040561
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HPPXXX10.1177/1524839913505283He alth Promotion Practice / Month XXXXChaney, Kim / Characterizing Bicycle Collisions 2013
Injury Prevention
Characterizing Bicycle Collisions by Neighborhood in a Large Midwestern City Robert A. Chaney, MS1 Changjoo Kim, PhD1
Introduction. Local environmental factors provide important contributions to bicycle safety. The purpose of this study was to characterize bicycle collisions by neighborhood in Cincinnati, Ohio. Background. The majority of prior bicycle safety research has focused on helmet use, especially among youth. Studies that have considered the neighborhood have centered on the built environment and its facilitation of bicycling (e.g., connectivity of roads and road conditions). Other broad conditions may be associated with injury beyond the use of protective equipment and the physical environment. Method. This study sought to determine spatial clustering, local patterning, temporal differences (time of day and season of year), and significant neighborhood-level predictors of bicycle collisions. Bicycle collision data were obtained from the Cincinnati, Ohio Police Department. Conclusions. This study showed that collisions occur at higher rates in the south-central and southwest neighborhoods of Cincinnati, Ohio. There were seasonal and time-of-day differences with respect to collision rates with summer and afternoon being the most common collision times. Neighborhood ethnicity, population density and presence of public transportation were all significant predictors of bicycle collisions. These findings will be disseminated to local city authorities and bicycle advocacy groups. Keywords: injury; prevention; bicycle; collisions; geographic information system (GIS)
Introduction >> Individual rider behavior is an important component of bicycle injury prevention (Jacobson, Blizzard, & Dwyer, 1998; J. Kim, Kim, Ulfarsson, & Porrello, 2006). However, individual rider behavior can be associated with the social and physical environment (Reynolds, Harris, Teschke, Cripton, & Winters, 2009). There are roughly 40,000 preventable bicycle injures and about 700 deaths per year in the United States (BicyclingInfo. org, n.d.), and these numbers are expected to proportionally swell. A large range of factors affecting individual bicycle use and safety includes demographic, socioeconomic, environmental, and policy-related determinants that either discourage or encourage bike use. Moreover, a growing literature demonstrates the importance of neighborhood context on the transportation collision, including bike collisions (Jones & Jorgensen, 2003; Laflamme & Diderichsen, 2000; Lenguerrand, Martin, & Laumon, 2006). Ethnic or racial differences in traffic collisions have been studied frequently in the literature (Subramanian, Chen, Rehkopf, Waterman, & Krieger, 2005). A meaningful example is provided by Rietveld and Daniel (2004) who showed that ethnicity is associated with the likelihood to bike, in preference to use public transportation or a car. The number of bus stops in a neighborhood may also influence bicycle safety in that bike collisions have a much higher possibility to occur on-street than off-street in close proximity to bus stops. Neighborhoods far from public transportation will result in greater dependence on the car and will discourage individuals from using any other mode of transport 1
University of Cincinnati, Cincinnati, OH, USA
Health Promotion Practice March 2014 Vol.15, No. 2 232–242 DOI: 10.1177/1524839913505283 © 2013 Society for Public Health Education
Authors’ Note: Address correspondence to Robert A. Chaney, MS, University of Cincinnati, 2600 Clifton Avenue, ML #0068, Cincinnati OH, 45221, USA; e-mail: [email protected].
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(Hsiao, Lu, Sterling, & Weatherford, 1997). Length of bus routes per neighborhood might affect bike use and bike collisions because bus routes are often through areas with attractions, and higher traffic volume areas, which are associated with higher likelihood of bicycle collisions (K. Kim, Pant, & Yamashita, 2010). The urban structure also influences the likelihood of bike use through several factors, such as population and job densities, and land-use mixture (Kitamura, Mokhtarian, & Laidet, 1997; Verhetsel & Vanelslander, 2010). Socioeconomic and demographic variables influencing bicycle use refer to many variables such as age, income, gender, education, job, family structure, and the neighborhood characteristics, which may be associated with bicycle use (Moudon et al., 2005). Neighborhoods having higher proportion of individuals commuting to work are also likely to have more bicycle users (Plaut, 2005). Contrarily, neighborhoods with higher motorized transportation rates are less likely to have high rates of bicycle users (Kingham, Dickinson, & Copsey, 2001). The physical abilities of individuals are well represented by their age. For example, neighborhoods with more young population are more likely to bike more. Bicycle use is generally high in academic towns (Rodriguez & Joo, 2004). Education has a strong influence on bicycle use depending on the area being studied. In North America, a high level of education is positively associated with bike use (Zahran, Brody, Maghelal, Prelog, & Lacy, 2008). The majority of previous bicycle safety research has focused on helmet use (Lohse, 2003; Nagel, Hankenhof, Kimmel, & Saxe, 2003). The central finding has been that helmet use helps prevent serious head injuries. However, there may be other broader conditions that are associated with injury beyond the use of protective equipment. This study seeks to examine spatial patterns and neighborhood characteristics as they are associated with bicycle collisions. Prior research has demonstrated that vehicle-bicycle collisions are more likely at places where bicycles and vehicles directly cross routes: intersections, traffic circles, and even bike lanes to some extent (Harris et al., 2013; Wee, Park, Park, & Choi, 2012). Conditions that increase bicyclist exposure to more vehicles, such as greater traffic volume or population density, also pose greater risk for collisions (Kim et al., 2006). Bike collisions are also more likely in areas with hillier topography though the exact reason is not reported (Harris et al., 2013; Kiburz, Jacobs, Reckling, & Mason, 1986). Cottrill and Thakuriah (2010) provide a model for the spatial approach to collisions. In this case, pedestrian–vehicle collisions were explored in context of neighborhood characteristics.
The results of this study will be valuable for public health educators by helping them identify specific geographic factors to target with prevention programs. The topic of injury prevention among bicyclists will be of increasing importance as bicyclist/driver interactions occur more frequently due to population growth and urban and suburban development. The purpose of this study is to characterize where bicycle collisions are occurring in Cincinnati. Specific research questions this study will seek to answer are the following: (a) Are there spatial clusters of bicycle collisions across the city? (b) Is there spatial autocorrelation (i.e., patterning) of bicycle collisions by neighborhood? (c) Are there temporal differences (i.e., seasonal and time of day) in bicycle collisions per neighborhood? (d) What are significant neighborhoodlevel predictors of bicycle collisions?
Method >> To achieve the purpose of this research project, several secondary data sources and computer programs were used. Data Sources Reports involving vehicle–bicycle collisions were obtained from the Cincinnati Police Department (CPD) from summer 2008 to summer 2012. These collision counts per neighborhood served as the dependent variable in the regression analysis. Prior to 2008 regular documentation of bike collisions did not occur with CPD. Each report included date, time, and collision location information (P. Byers, personal communication, September 13, 2012). The original data file contained 488 cases. There were two cases per single collision—one for the bicycle and one for the automobile involved. Duplicate cases involving automobiles were removed from this study, yielding 244 bicycleonly cases. Furthermore, examination of the data showed that there were 11 cases that involved a motorbike. Since this study examined onlybicycle collisions, these 11 cases were also removed. The total number of cases removed from the original data set was 255, and the total cases used for analysis was 233. The data obtained from the CPD did not contain any individuallevel information to protect identity and privacy of those involved in the bicycle collisions and as part of CPD policy for releasing data. Demographic information was obtained for census tracts within the city of Cincinnati from the U.S. Census Bureau (2012). Similarly, land-use mixture by census tract was obtained from the Arizona State University GeoDa
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Center for Geospatial Analysis and Computation (2012). Public transportation data in the form of bus routes and bus stops were obtained from Southwest Ohio Regional Transit Authority (2012). All data files were spatially joined to neighborhoods in order to consistently use the neighborhood as the geographic unit of the study. A spatial join combines attributes of multiple geographic information system (GIS) layers based on the location of the features. With the help of locations of bicycle collisions and neighborhoods, we joined attributes from individual bicycle collision locations to neighborhood boundary based on a spatial relationship. There are 48 municipalities in Hamilton County, Ohio, one of which is Cincinnati. This study only examined the 51 neighborhoods within City of Cincinnati. The neighborhood boundaries have historic origin in that they were at one time their own municipality and at some time were annexed into the city (Columbia Tusculum Community Council, n.d.). For traditional and cultural reasons the neighborhoods have retained their boundaries and characteristics that make them unique. Data Analysis Software Cloropleth and dot map creation and spatial data manipulation were performed using the open-source software QGIS for Mac OSX (2012). Descriptive statistics, comparison of means, and regression analysis was performed using R, version 2.15.1 (R Development Core Team, 2012). Spatial data analyses, including G clusters and local indicators of spatial association (LISA) analysis, were computed using GeoDa, Version 1.2.0 (Anselin, Syabri, & Kho, 2006). Assessment of Research Questions Spatial Clusters. Determining if spatial clusters of bicycle collisions existed across the city was done using the Global G statistic for clustering. The Global G statistic (Getis & Ord, 1992) measures the concentration of high or low values for a given study area depending on observed and expected z-score values for each geographical area. A map was generated displaying statistically significant clusters. These significant clusters were categorized as being high or low. High represents an unusually elevated level of clustering whereas low represents an unusually reduced level of clustering. Identifying if spatial clustering occurs within the city of Cincinnati will aid in characterizing where bicycle collisions tend to happen. Spatial Autocorrelation. The spatial autocorrelation in our data is explored by examining the different pairs of
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sample locations. If spatial autocorrelation exists, pairs of points that are close together should have less difference. As points become farther away from each other, in general, the difference squared should be greater. The Global Moran’s I measures spatial autocorrelation based on both feature locations and feature values simultaneously. Given a set of features and an associated attribute, it evaluates whether the pattern is clustered, dispersed, or random. A z-score and p-value of the Moran’s I index value is used to evaluate the significance of that index. A local Moran’s I statistic for spatial autocorrelation (Anselin, 1995) with inverse distance was further conducted to confirm the results from the Global Moran’s I statistic. LISA evaluates clustering by calculating local Moran’s I for each neighborhood and evaluates the statistical significance for each Ii. LISA determines if values are randomly distributed, dispersed, or clustered. Cluster and outlier analysis using Anselin’s local Moran’s I in Figure 3 distinguishes between a statistically significant (.05 level) cluster of high values (high–high), cluster of low values (low–low), outlier in which a high value is surrounded primarily by low values (high–low), and outlier in which a low value is surrounded primarily by high values (low–high). Temporal Differences Seasonal differences. Temporal differences in collisions by neighborhood were studied by season and time of day. For assessing seasonal differences, bicycle collisions were categorized into one of four seasons: Winter was defined as December through February, spring was defined as March through May, summer was defined as June through August, and fall was defined as September through November. Overall mean differences in collisions per neighborhood by season were assessed using analysis of variance (ANOVA). Differences between individual seasons were determined using Fisher’s least significant difference (LSD) t test for multiple comparisons. The analysis for temporal differences was conducted with the pure number of collisions because information on rider exposure could not be controlled for or obtained. Time of day differences. For time of day differences bicycle collisions were categorized into one of four times of day: Morning was defined as 6:00 a.m. to noon, afternoon was defined as noon to 6:00 p.m., evening was defined as 6:00 p.m. to midnight, and night was defined as midnight to 6:00 a.m. Overall mean differences in collisions per neighborhood by time of day were assessed using ANOVA. Differences between individual times of
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Figure 1 Quantile Map of Bicycle Collisions in Cincinnati Neighborhoods (2008-2012) NOTE: C.U.F. = Clifton Heights, University Heights, and Fairview.
day were determined using Fisher’s LSD t test for multiple comparisons. Neighborhood-Level Predictors. Linear regression analysis was used to determine significant neighborhoodlevel predictors for bicycle collisions. Specific explanatory variables used were neighborhood ethnicity (measured using percentage White), number of bus stops in the neighborhood, length of bus routes per neighborhood, population density, percent of the neighborhood in poverty, percent commuting by motorized transportation, percent commuting to work, median household income, median age, land-use mixture, and percent having graduated from high school. Backward elimination was used as a method of model selection and reduction.
Results >> There were 233 usable data points included in the study, each representing a single bicycle collision. The
most common result of the collision was injury (75.107%), followed by property damage only (24.464%), and fatality (0.429%). Neighborhoods had on average 4.588 collisions during the time frame of the study (SD = 4.521, range = 3-20). Figure 1 shows the spatial distribution of these collisions. Spatial Clusters Figure 1 displays the global clustering results. There was significant clustering of bicycle collisions located in the south-central and southwest areas of the city. Neighborhoods that possessed significantly high clusters were Madisonville, Clifton, South Fairmont, West Price Hill, Queensgate, West End, Over the Rhine, Mount Auburn, Pendleton, and Mount Adams. Neighborhoods that had significantly low clustering were Sayler Park and Mount Lookout. This analysis shows that there is a high clustering pattering near the downtown (e.g., Downtown) and southwest areas of the city (e.g., East Price Hill).
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Figure 2 Global G Statistic Clustering for Bicycle Collisions in Cincinnati, Ohio (2008-2012)
Spatial Autocorrelation Figure 2 displays the LISA grouping results. There were 10 neighborhoods out of 51 that had a significant patterning effect. Those that had a large number of collisions with neighbors also having a high number (high–high) were West Price Hill (I = 1.262), West End (I = 1.491), Over the Rhine (I = 1.351), and Pendleton (I = −1.335). Those that had a low number of collisions but where their neighbors had high numbers of collisions (low–high) were South Fairmount (I = −0.409), Queensgate (I = −0.471), Clifton (I = −0.080), Mount Auburn (I = −0.392), and Mount Adams (I = −0.877). There was one high–low grouping signifying that the neighborhood had a high number of collisions and its neighbors had a low number of collisions: Oakley (I = −1.078). There were no statistically significant low–low groupings. This analysis shows there is a clear local patterning effect of bicycle collisions within the city, especially in the south-central (e.g., West End, Over the Rhine) and
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southwest areas of the city (e.g., South Fairmount, West Price Hill). Temporal Differences Seasonal Differences. An ANOVA was used to determine if mean number of collisions per neighborhood differed by season. This analysis showed that there was a statistically significant difference between seasons with respect to mean number of collisions per neighborhood, F(3, 188) = 13.166, p < .001. Specifying the differences between the seasons was done using Fisher’s LSD t test for multiple comparisons. The results of between-season analysis is presented in Table 1. Summer had the largest number average collisions (2.313), followed by spring (1.375) and fall (0.896), and winter had the least (0.250). Time of Day Differences. Likewise, an ANOVA was used to determine if mean number of collisions per neighborhood differed by time of day. This analysis
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Figure 3 Local Indicators of Spatial Analysis (LISA) for Bicycle Collisions by Neighborhood in Cincinnati, Ohio (2008-2012)
showed that there was a statistically significant difference between times of day with respect to mean number of collisions per neighborhood, F(3, 188) = 19.251, p < .001. Specifying the differences between the times of day was done using Fisher’s LDS t test for multiple comparisons. The results of between times of day analysis are presented in Table 1. It appears that afternoon had the largest number of collisions, whereas night had the least; and morning and evening were in the middle and were not statistically different. Neighborhood-Level Predictors The full regression model containing each collected variable was fit using linear regression. Assumptions for this analysis were verified. Results are presented in Table 1. The adjusted R2 was .545, indicating that this set of variables explains just more than half of bicycle collisions per neighborhood. There were several predictor variables that did not appear to be significant contributors to the regression
model. For this reason, backward elimination was used to reduce the number of explanatory variables in the model. Explanatory variables were removed from the model one by one, and the model was rerun in each intermediate step (Faraway, 2004). Variables were removed in the following order: poverty (p = .973), percent commuting by motorized transportation (p = .881), median household income (p = .824), percentage commuting to work (p = .805), median age (p = .435), land-use mixture (p = .181), percentage having graduated from high school (p = .126), length of bus routes per neighborhood (p = .069). The full model was reduced to the model presented in Table 1. The three remaining explanatory variables were number of bus stops, neighborhood ethnicity, and population density. The reduced model had an R2adj of .534, which was a 0.011 reduction from the full model. This signifies that these three explanatory variables are the best at explaining bicycle collisions, among those collected.
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Table 1 Mean Differences per Season and Time of Day and Predictors of Collisions per Cincinnati Neighborhood Temporal Differences
Regression Analysis Predictor
β
Reduced model (Intercept) Number of bus stops Population density Neighborhood ethnicity F(11, 38) = 20.09, p < .001, R2adj = .534
−1.743 0.026 0.001 3.564
−1.419 5.884*** 3.008** 2.303*
Full model (Intercept) Population density Number of bus stops Neighborhood ethnicity Bus route lengths Land-use mixture Percentage high school graduates Median age Percentage commuting to work Median household income Percentage motor commute Percentage living in poverty F(11, 38) = 6.335, p < .001, R2adj = .545
−9.997 0.001 0.175 5.061 7.01 × 10−5 10.800 −2.941 −0.388 −0.355 −6.85 × 10−6 0.715 −0.313
−1.05 3.194** 2.374* 2.349* 1.637 1.534 −0.825 −0.328 −0.259 −0.208 0.143 −0.034
t
Seasons Spring > Winter Summer > Winter Summer > Spring Fall > Winter Summer > Fall Fall = Spring Time of day Morning > Night Afternoon > Night Afternoon > Morning Evening > Night Evening = Morning Afternoon > Evening
3.920*** 5.860*** 2.110* 3.570*** −3.700*** −1.470 −2.910** −6.437*** −4.507*** −4.682*** −1.695 3.267***
t
NOTE: Night = 12 a.m. to 6 a.m.; Morning = 6 a.m. to 12 p.m.; Afternoon = 12 p.m. to 6 p.m.; Evening = 6 p.m. to 12 a.m. *p < .05. **p < .01. ***p < .001.
Discussion >> The most salient findings from this study are threefold: (a) There is a clear spatial pattern to where collisions occur, (b) there are temporal differences in collision occurrence, and (c) significant predictors of collisions include neighborhood ethnicity, population density, and presence of public transportation. Both Global G cluster analysis and LISA spatial autocorrelation analysis produced nearly identical results. There was high level of collisions near downtown Cincinnati, the University of Cincinnati, and extending toward the southwest region of the city (i.e., Price Hill). Prior research has shown that students tend to bicycle more than the average adult (Gordon-Larsen, Nelson, & Beam, 2005). More riders on the road in a densely populated area is one set of conditions that could be associated with more observed collisions.
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This may explain why there was the large number of collisions in the downtown area of the city; population density, mixed with high traffic volume and a large low-income population. Previous research indicated that low income is one predictor of bicycle use (Larsen et al., 2009), which may explain the high numbers of collisions seen in the southwest area of the town. However, this research found income to be nonsignificant in both regression models (see Table 1). For reference to local conditions, Table 2 presents a collection of characteristics of significantly clustered or autocorrelated neighborhoods from Figures 2 and 3. A clear difference was observed with regard to the temporality of collisions. The times that seem most high risk for collisions were summer and afternoon. Confirming previous findings, cyclists are outside recreating more in the summer during nicer weather (Kim et al., 2006).
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209 126 126 642 105 42 234 9 69 77 151 410 156
72.70 49.10 72.10 60.50 52.00 92.80 26.30 21.40 31.70 37.40 9.60 45.90 69.90
6.81 3.94 7.32 14.83 7.45 5.76 5.93 0.51 8.41 5.33 9.39 14.81 9.08
0.915 0.887 0.92 0.88 0.92 0.88 0.837 0.75 0.5 0.94 0.7 0.86 0.88
Neighborhood Bus Route Number of Ethnicity (% Lengths Land-Use Bus Stops White) (Miles) Mixture 88.70 86.70 94.00 82.90 64.40 97.10 76.00 95.10 68.80 52.00 67.00 85.10 80.00
24.4 29.17 33 34.7 29.6 34.7 32.35 34.7 30.75 30 30.75 31.87 33
29.00 39.00 nd 50.00 25.00 29.00 50.00 nd 33.00 50.00 40.00 38.00 22.00
20,650 18,119 35,834 45,849 28,425 99,125 14,517 13,397 nd 24,395 12,808 33,922 37,720
Percentage High Percentage Median School Median Commuting to Household Graduates Age Work Income ($)
NOTE: C.U.F. = Clifton Heights, University Heights, and Fairview; nd = no data.
7,101 3,702 2,264 2,622 3,743 1,607 7,031 7,335 56 1,037 4,377 3,020 3151
Population Density (Population/ Square Mile) 66.60 64.00 79.40 42.90 77.10 82.60 54.50 50.50 nd 75.00 48.20 85.60 85.60
Percentage Motor Commute
Table 2 Characteristics of Neighborhoods Where Bicycle Collisions Cluster
8.00 11.60 19.80 6.80 23.10 26.30 7.60 20.40 nd 24.60 23.30 22.20 9.00
Percentage Living in Poverty
C.U.F. Corryville Clifton Downtown East Price Hill Mt. Adams Over The Rhine Pendleton Queensgate South Fairmount West End Westwood West Price Hill
Predictors/ Neighborhoods
Based on the regression analysis, only three explanatory variables were kept in the final reduced model: neighborhood ethnicity, bus stops, and population density. In fact, the more densely populated, the less diverse the neighborhood (higher percentage of White), and the more bus stops, the higher the number of collisions would be expected. Population density is associated with collisions by overcrowding the shared motorway. Ethnicity may be associated with collisions because White populations are more likely to use private transportation (Gordon-Larsen et al., 2005), so there are more cars on the road posing risk to any cyclist. Bus stops as a predictor of collisions is an interesting finding of this study. One explanation of how this variable is associated with collisions can be understood when reflecting on where cyclists typically ride on the road. The bicyclist typically rides on the far right of the road. This is also a space that the bus enters and exits regularly as it makes scheduled stops. The actual bus stop is not likely to contribute to the collisions, rather the interaction of the bus and the bicyclist in the shared side-of-the-road space. This could be especially problematic when one party is trying to pass the other on a narrow, busy road. It is possible that public transportation presence is related to population density, which could expound the problem of bicycle safety on a shared road. Further explanation to the differences in bicycle collisions by neighborhood in Cincinnati can be viewed through the lens of the right to the city as originated by Henri Lefebvre, but expounded on by David Harvey in Rebel Cities (2012). When reviewing the findings of this study one cannot help but notice that hot spots for bike collisions are occurring in certain neighborhoods. Based on Harvey’s analysis, it is unlikely that some neighborhoods will receive much attention due to their socioeconomic character and influence among those in power. In this study there appears to be a disconnect between bicycle collision location and city investment in the built environment to prevent injury. Some of the neighborhoods with the most collisions have no bicycle lanes or signed bike routes. Examples of this phenomenon include the Westwood and East Price Hill neighborhoods. Another recent example is city investment in the Clifton neighborhood near Ludlow Avenue and Central Parkway where enlarged and brightly colored bike lanes have been installed. It is possible a sizable ridership passes through this corridor, but bicycle safety concerns not addressed by city investment exist elsewhere in the city. One can further explore whether it is the bicyclist’s right to the road in context of the finding that more bus stops are being associated with more collisions. The transportation system seems to be set up to accommodate
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motorized transportation and only marginally recognizes bicyclists’ use of the road. Unless bicyclists contribute to the construction and maintenance of roads through their payment of taxes, the right to the city (or the road) will remain superficial and in idea only. There are inherent limitations to this study. Primary among them is that aggregated data were used for all variables. For this reason, every important variable may not have been included in the analysis. Also, this study was a secondary data analysis—no primary data were collected. This could pose a bias when aggregating variables to geographic locations. However, precautions were taken to ensure that only quality data from the representative neighborhoods and time periods were used. This research has important implications for public health education. By identifying the specific locations within Cincinnati that collisions tend to happen, injury prevention efforts can now happen in a more focused manner. This approach may be applicable in other municipalities or regions. By identifying where collisions are happening, resources allocation and efforts to alert drivers and bicycle riders can be more focused. This can be at the general neighborhood level or the local street level by addressing how buses interact with bicyclists. The characterization that buses potentially influence bicycle collisions is a key finding and valuable for the promotion of bicycle safety. Little research has been conducted with adult cyclist populations or within the scope of public health education. Future research to further characterize bicycle collisions would be a correlation analysis with traffic volume and traffic collisions. This may shed light on geographic conditions that are contributing to an unsafe riding environment. An assessment of bike lane distribution in relation to collisions as well as an assessment of the protocol used for deciding where bike lanes are installed would be another valuable contribution to the field. Last, it would be important to apply the assessments from this study to other geographic locations for the purpose of validating these findings and further characterizing bicycle safety.
Conclusion >> The purpose of this study was to characterize where bicycle collisions are occurring in Cincinnati. This study helps fill a gap in the scientific literature because it focuses on the spatial distribution of collisions and its application to injury prevention. Key findings from this study include higher rates of collisions that occur in the south-central and southwest neighborhoods on Cincinnati. Time of day and season are also important
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