Flag and Boost Theories for Hot Spot Forecasting: An Application of NIJ’s Real-Time Crime Forecasting Algorithm using Colorado Springs Crime Data
Document Type
Article
Publication Date
2020
Keywords
Crime hot spot, forecasting, population heterogeneity, state dependence, Excel®
Digital Object Identifier (DOI)
https://doi.org/10.1177/1461355719864367
Abstract
By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
International Journal of Police Science & Management, v. 22, issue 1, p. 4-15
Scholar Commons Citation
Lee, YongJei and O, SooHyun, "Flag and Boost Theories for Hot Spot Forecasting: An Application of NIJ’s Real-Time Crime Forecasting Algorithm using Colorado Springs Crime Data" (2020). Criminology Sarasota Manatee Campus Faculty Publications. 61.
https://digitalcommons.usf.edu/cjp_facpub_sm/61
