title: Mapping Activity Patterns to Quantify Risk of Violent Assault in Urban Environments.
abstract: BACKGROUND
  We collected detailed activity paths of urban youth to investigate the dynamic interplay between their lived experiences, time spent in different environments, and risk of violent assault.
  
  
  METHODS
  We mapped activity paths of 10- to 24-year-olds, including 143 assault patients shot with a firearm, 206 assault patients injured with other types of weapons, and 283 community controls, creating a step-by-step mapped record of how, when, where, and with whom they spent time over a full day from waking up until going to bed or being assaulted. Case-control analyses compared cases with time-matched controls to identify risk factors for assault. Case-crossover analyses compared cases at the time of assault with themselves earlier in the day to investigate whether exposure increases acted to the trigger assault.
  
  
  RESULTS
  Gunshot assault risks included being alone (odds ratio [OR] = 1.6, 95% confidence interval [CI] = 1.3, 1.9) and were lower in areas with high neighbor connectedness (OR = 0.7, 95% CI = 0.6, 0.8). Acquiring a gun (OR = 1.4, 95% CI = 1.1, 1.6) and entering areas with more vacancy, violence, and vandalism (OR = 1.7, 95% CI = 1.1, 2.7) appeared to trigger the risk of getting shot shortly thereafter. Nongunshot assault risks included being in areas with recreation centers (OR = 1.2, 95% CI = 1.1, 1.4). Entering an area with higher truancy (OR = 1.6, 95% CI = 1.1, 2.5) and more vacancy, violence, and vandalism appeared to trigger the risk of nongunshot assault. Risks varied by age group.
  
  
  CONCLUSIONS
  We achieved a large-scale study of the activities of many boys, adolescents, and young men that systematically documented their experiences and empirically quantified risks for violence. Working at a temporal and spatial scale that is relevant to the dynamics of this phenomenon gave novel insights into triggers for violent assault.
authors.affiliation: From the aDepartment of Biostatistics and Epidemiology, Perelman School of Medicine, bDepartment of Biobehavioral and Health Sciences, School of Nursing, cDepartment of Sociology, School of Arts and Sciences, dDepartment of Emergency Medicine, Perelman School of Medicine, e Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA; and fDepartment of Surgery, The Children's Hospital of Philadelphia, Philadelphia, PA.
authors.forename: Douglas J
authors.initials: DJ
authors.lastname: Wiebe
authors.forename: Therese S
authors.initials: TS
authors.lastname: Richmond
authors.forename: Wensheng
authors.initials: W
authors.lastname: Guo
authors.forename: Paul D
authors.initials: PD
authors.lastname: Allison
authors.forename: Judd E
authors.initials: JE
authors.lastname: Hollander
authors.forename: Michael L
authors.initials: ML
authors.lastname: Nance
authors.forename: Charles C
authors.initials: CC
authors.lastname: Branas
mesh_terms: D000293:Adolescent; D016022:Case-Control Studies; D002648:Child; D005163:Factor Analysis, Statistical; D062305:Geographic Mapping; D006802:Human Activities / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D015143:Philadelphia; D012111:Residence Characteristics; D018570:Risk Assessment; D012307:Risk Factors; D013029:Space-Time Clustering; D014505:Urban Population / Q000706:statistics & numerical data*; D014754:Violence / Q000706:statistics & numerical data*; D055815:Young Adult