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

No. Panggil :	eja-21-0254
Nama Orang :	Lee, Yong Jei O, SooHyun






Penerbitan :	[Place of publication not identified] : Sage - Crime and Justice, 2020

Info Lainnya

Abstrak	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.
Entri Tambahan Nama Orang
001 Hak Akses (open/membership)	membership
Kata Kunci	Crime hot spot, forecasting, population heterogeneity, state dependence, Excel
ISSN
Tahun Terbit	2020
No. Induk	eja-21-0254
Entri Sumber Data	Sage - Crime and Justice
Entri Utama Nama orang	Lee, Yong Jei; O, SooHyun
Volume, Nomor, Tahun dan Hlm.	vol. 22, no. 1, p. 4-15
Barcode	eja-21-0254
Subjek Topik
Judul Utama	Flag and boost theories for hot spot forecasting: An application of NIJ s Real-Time Crime forecasting algorithm using Colorado Springs crime data
Kode Bahasa	eng
Sumber Koleksi	Perpustakaan Nasional