Neighbourhood Marginalization as a Risk Factor for Pedestrian & Cyclist Collisions in Toronto Intersections Jordan D. Silverman Dr. Michael Cusimano, Dr. Michael Hutchison
Outline Objectives Background & Motivation Methodology Results Interpretation & Significance Questions
Objectives 1) To identify socio-demographic factors associated with pedestrian & cyclist collisions in Toronto intersections 2) To demonstrate methodology for use of ON-Marg Index and GIS-based approach to study road collisions
Background Canada among OECD Countries 150/ yr / million vehicles 90 / yr / million population In 2006: 2889 fatalities 199 337 injuries
Background Vulnerable road users in Toronto November 1 October 20 October 9 September 18 October 16 October 30
Background Risk factors for urban pedestrian & cyclist collisions Road factors residential, speed limits > 50 km/h, no pedestrian signals Driver factors alcohol, driving record Injured person factors unsupervised, male, Aboriginal, children, low education, poor English skills, low income & home values Intersections Complex, high collision potential 50% of urban collisions Toronto High cycling rates leads Canada in cycling infrastructure
Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)
Methods 1 Identify intersections & collect collision data 114 intersections in Toronto Collisions from TTDC database (2001-2006) 114 Intersections Studied Cyclist collisions (2001-2006) Pedestrian & cyclist collisions within 20m of intersections
Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)
Methods 2a Identify Dissemination Areas Dissemination Area (DA): Smallest geographical area for which census data are available
Methods 2b Identify Dissemination Areas ArcMap GIS: Find latitude & longitude of each intersection Draw 250m buffer Determine DAs that buffer overlaps & area of overlap DA 1 DA 2 250m DA 3
Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)
Methods 2b Calculate weighted ON-Marg scores What is the? To understand trends in: marginalization, health outcomes Based on 2006 Canadian census Scoring DAs: mean = 0, SD = 1, = more marginalized Four dimensions of marginalization
Methods 2b Calculate weighted ON-Marg scores Residential Instability (RI)» Population fluctuation over time Material Deprivation (MD)» Socio-economic status Dependency (DP)» Reliance on work force Ethnic Concentration (EC)» Immigrant populations
Methods 2b Calculate weighted ON-Marg scores For each intersection, 4 ON-Marg scores were calculated (weighted average based on buffer area overlapping DAs) I x = where: 1:y (ON-Marg DAy *A DAx,y ) 1:y (A DAx,y ) 250m x = 1 : 114 y = 1: # DAs overlapping buffer I x = RI/MD/DP/EC for intersection x ON-Marg DAy = RI/MD/DP/EC of each DA A DAxy = Area of DA y -buffer x overlap
Methods 2b Calculate weighted ON-Marg scores I x = 1:y (ON-Marg DAy *A DAx,y ) 1:y (A DAx,y ) MD DA1 = 0.5 MD DA2 = 1.0 250m Example: MD for Intersection 7 (0.5*100,000 + 1.0*50,000 + 1.5*50,000) MD 7 = (100,000 + 50,000 + 50,000) Intersection 7 MD DA3 = 1.5 = 1.125 A DA7,1 = 100 000m 2 MD DA7,1 = 0.5 MD DA7,2 = 1.0 MD DA7,3 = 1.5 A DA7,2 = 50 000m 2 A DA7,3 = 50 000m 2
Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)
Methods 3 ON-Marg vs collision frequency Ordered logistic regression Intersection risk quartiles α collision frequency: Risk Quartile Pedestrian Collisions Cyclist Collisions Total Collisions 1 (low) 0-2 0 0-3 2 3-5 1-2 4-7 3 6-8 3-4 8-12 4 (high) 9+ 5+ 13+ Regression Output: Odds Ratio (OR), 95% CI likelihood of an intersection to be in higher risk quartile based on unit increases in ON-Marg index
Results Collisions summary (2001-2006, 114 intersections) Risk Quartile Pedestrian Collisions Cyclist Collisions Total Collisions Total 738 456 1194 Median per intersection 5 (0-32) 2.5 7 Range 0-32 0-23 0-55 Eg. Cyclist Collisions across Toronto intersections 2006 2005 2004 2003 2002 2001
Results Logistic Regression Model ON-Marg Index Ped (OR, 95% CI) Cyclist ((OR, 95% CI) Total (OR, 95% CI) Residential Instability 1.84 (1.21-2.84) ** 2.04 (1.34-3.16) ** 2.16 (1.43-3.38) ** Material Deprivation 1.19 (0.74-1.94) 1.01 (0.62-1.66) 1.18 (0.73-1.95) Dependency 1.28 (0.66-2.53) 0.89 (0.45-1.73) 1.19 (0.62-2.33) Ethnic Concentration 1.41 (0.96-2.11) * 1.42 (0.96-2.15) * 1.56 (1.05-2.37) ** ** Statistically significant (p < 0.05) * Marginally significant (0.05 < p < 0.10) Summary: RI & EC are associated with pedestrian and cyclist collisions DP & MD are not significant in this model
Interpretation & Significance Why ethnic concentration? children, familiarity with road rules, cultural norms, signs tendency for poorer road safety infrastructure Why residential instability? apartments: walking, cycling, traffic volumes, lanes, transit stops, bike lanes, crosswalks Future work: Study physical infrastructure in vulnerable intersections Possible interventions to collisions: simple signage, separated bicycle tracks, maintained, well-marked sidewalks & cross-walks
Interpretation & Significance Can results be applied in different cities? ON-Marg Index 114 Intersections Ontario Average Chi-squared p-value Residential Instability 1.08 ± 0.95-0.16 ± 0.95 p < 0.001 Material Deprivation -0.21 ± 0.91-0.16 ± 0.89 p = 0.57 Dependency -0.29 ± 0.54 0.03 ± 0.94 p < 0.001 Ethnic Concentration 1.22 ± 1.00 0.20 ± 1.13 p < 0.001 Limitations:» Probably large multicultural cities Inadequate flow data Relative infrequency of collisions underreporting short observation time not directly considering victim, road & driver characteristics
Methodology: Summary ON-Marg & GIS-based approach may be used to study collisions in urban settings Results: Residential instability (RI) & ethnic concentration (EC) are associated with collisions in Toronto Significance: Intersections in marginalized neighbourhoods can be targeted for strategies to reduce collision risk and prevent injury
Nov 7, 2011: Jenna Morrison
Acknowledgments Injury Prevention Research Office staff: Dr. Michael Cusimano Dr. Stanley Zhang Dr. Michael Hutchison Andreea Andrei Keenan Research Centre Dr. Flora Matheson Rosane Nisenbaum Map & Data Library, University of Toronto Gerald Romme Funding sources: CIHR, Ontario Neurotrauma Foundation
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Neighbourhood Marginalization as a Risk Factor for Pedestrian & Cyclist Collisions in Toronto Intersections Jordan D. Silverman Dr. Michael Cusimano, Dr. Michael Hutchison