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A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania


Urban crime may be an important but overlooked public health impact of rising ambient temperatures. We conducted a time series analysis of associations between temperature and crimes in Philadelphia, PA, for years 2006–2015. We obtained daily crime data from the Philadelphia Police Department, and hourly temperature and dew point data from the National Centers for Environmental Information. We calculated the mean daily heat index and daily deviations from each year’s seasonal mean heat index value. We used generalized additive models with a quasi-Poisson distribution, adjusted for day of the week, public holiday, and long-term trends and seasonality, to estimate relative rates (RR) and 95% confidence intervals. We found that the strongest associations were with violent crime and disorderly conduct. For example, relative to the median of the distribution of mean daily heat index values, the rate of violent crimes was 9% (95% CI 6–12%) higher when the mean daily heat index was at the 99th percentile of the distribution. There was a positive, linear relationship between deviations of the daily mean heat index from the seasonal mean and rates of violent crime and disorderly conduct, especially in cold months. Overall, these analyses suggest that disorderly conduct and violent crimes are highest when temperatures are comfortable, especially during cold months. This work provides important information regarding the temporal patterns of crime activity.

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This work was funded, in part, by a pilot grant from the Drexel University Dornsife School of Public Health Urban Health Collaborative, internal funding number 282671.

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Correspondence to Leah H. Schinasi.

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Schinasi, L.H., Hamra, G.B. A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania. J Urban Health 94, 892–900 (2017).

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  • Crime
  • Violent crime
  • Violence
  • Temperature
  • Heat index
  • Public health
  • Epidemiology
  • Time series analysis
  • Generalized additive models