Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective.
BACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported incidents of crime that occurred in the City of C...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6200217?pdf=render |
| _version_ | 1818133023067996160 |
|---|---|
| author | Sherry Towers Siqiao Chen Abish Malik David Ebert |
| author_facet | Sherry Towers Siqiao Chen Abish Malik David Ebert |
| author_sort | Sherry Towers |
| collection | DOAJ |
| description | BACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. RESULTS:We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. CONCLUSIONS:Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime. |
| first_indexed | 2024-12-11T08:46:07Z |
| format | Article |
| id | doaj.art-f8e10800659d4c538061b063109db508 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T08:46:07Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f8e10800659d4c538061b063109db5082022-12-22T01:14:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011310e020515110.1371/journal.pone.0205151Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective.Sherry TowersSiqiao ChenAbish MalikDavid EbertBACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. RESULTS:We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. CONCLUSIONS:Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime.http://europepmc.org/articles/PMC6200217?pdf=render |
| spellingShingle | Sherry Towers Siqiao Chen Abish Malik David Ebert Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. PLoS ONE |
| title | Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. |
| title_full | Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. |
| title_fullStr | Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. |
| title_full_unstemmed | Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. |
| title_short | Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective. |
| title_sort | factors influencing temporal patterns in crime in a large american city a predictive analytics perspective |
| url | http://europepmc.org/articles/PMC6200217?pdf=render |
| work_keys_str_mv | AT sherrytowers factorsinfluencingtemporalpatternsincrimeinalargeamericancityapredictiveanalyticsperspective AT siqiaochen factorsinfluencingtemporalpatternsincrimeinalargeamericancityapredictiveanalyticsperspective AT abishmalik factorsinfluencingtemporalpatternsincrimeinalargeamericancityapredictiveanalyticsperspective AT davidebert factorsinfluencingtemporalpatternsincrimeinalargeamericancityapredictiveanalyticsperspective |