Patterns of Urban Violent Injury: A Spatio-Temporal Analysis

Abstract

ObjectivesInjury related to violent acts is a problem in every society. Although some authors have examined the geography of violent crime, few have focused on the spatio-temporal patterns of violent injury and none have used an ambulance dataset to explore the spatial characteristics of injury. The purpose of this study was to describe the combined spatial and temporal characteristics of violent injury in a large urban centre.Methodology/Principal FindingsUsing a geomatics framework and geographic information systems software, we studied 4,587 ambulance dispatches and 10,693 emergency room admissions for violent injury occurrences among adults (aged 18–64) in Toronto, Canada, during 2002 and 2004, using population-based datasets. We created kernel density and choropleth maps for 24-hour periods and four-hour daily time periods and compared location of ambulance dispatches and patient residences with local land use and socioeconomic characteristics. We used multivariate regressions to control for confounding factors. We found the locations of violent injury and the residence locations of those injured were both closely related to each other and clearly clustered in certain parts of the city characterised by high numbers of bars, social housing units, and homeless shelters, as well as lower household incomes. The night and early morning showed a distinctive peak in injuries and a shift in the location of injuries to a “nightlife” district. The locational pattern of patient residences remained unchanged during those times.Conclusions/SignificanceOur results demonstrate that there is a distinctive spatio-temporal pattern in violent injury reflected in the ambulance data. People injured in this urban centre more commonly live in areas of social deprivation. During the day, locations of injury and locations of residences are similar. However, later at night, the injury location of highest density shifts to a “nightlife” district, whereas the residence locations of those most at risk of injury do not change.

Improving the coding and classification of ambulance data through the application of International Classification of Disease 10th revision

“…1 The most common methods were either using a telephone triagederived dispatch determinant (most commonly the Medical Priority Dispatch System (MPDS)) [2][3][4][5] or 'a system devised by the authors'. [6][7][8][9][10][11] Categorisation using MPDS occurs at the point-of-call receipt, before ambulance arrival.…”

Tavern proximity, tavern density and socio-economic status as predictors of assault occurrence within New Zealand: a temporal comparison

“…One such Philadelphia based study conducted by Morrison et al (2017) showed that proximity to bars and restaurants was associated with decreased odds of (non-gun) assault before 1pm but with increased odds of assault after 7pm. Another Toronto based study (Cusimano et al 2010) used ambulance data to map incidents of violent injury across the city by time of day. The study found that during day time hours the greatest concentration of assaults were found in areas characterised by high proportions of social housing communities, homeless shelters, high unemployment rates and very low mean household incomes, i.e.…”

Environmental characteristics of hotspots of alcohol-related health incidents outside of the entertainment districts of Amsterdam

“…In Worcester (United Kingdom) patterns of alcohol-related crime and disorder in the city centre were associated with various characteristics of the functional environment, with higher rates along the key route ways towards residential areas and retail zones, and close to functional sites such as the railway station and the shopping centre (Bromley and Nelson 2002). Similar studies on hotspots of violent injuries identified environmental features such as a high number of bars, recreation facilities, transit nodes, high density housing, public restrooms and homeless shelters (Braga et al 1999;Cusimano et al 2010;Walker and Schuurman 2012).…”

“…We selected functions where alcohol can be bought or functions that attract risk populations such as homeless people. Relevant characteristics of the functional, social and built environment were selected based on results of previous studies mentioned in the introduction (for example Bromley and Nelson 2002;Braga et al 1999;Cusimano et al 2010;Walker and Schuurman 2012).…”