Flag and boost theories for hot spot forecasting: An application of NIJ’s Real-Time Crime forecasting algorithm using Colorado Springs crime data

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...

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Bibliographic Details
Published inInternational journal of police science & management Vol. 22; no. 1; pp. 4 - 15
Main Authors Lee, YongJei, O, SooHyun
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.03.2020
Sage Publications Ltd
Subjects
Algorithms
Crime
Forecasting
Spatial analysis
United States > US
Colorado
state dependence
Crime hot spot
Excel
population heterogeneity
forecasting
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Summary: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.
ISSN:1461-3557
1478-1603
DOI:10.1177/1461355719864367