Asset Details
Do you wish to reserve the book?
A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania
Do you wish to request the book?
A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania
2017
Overview
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.
