TY  - JOUR
AU  - Milić, Nenad
AU  - Đurđević, Zoran
AU  - Mijalković, Saša
AU  - Erkić, Dražan
PY  - 2020
UR  - http://jakov.kpu.edu.rs/handle/123456789/1350
AB  - Throughout history, police have dedicated their attention to locations with a high incidence of crime and/or disorder.
These efforts have become more systematic since the introduction of hotspot policing in the 1990s. A large body of
research conducted over the last three decades has shown that focusing police resources on areas characterised by
high concentration of crime (hotspots) may have positive preventive effects. If crime hotspots inaccurately reflect
the spatial patterning of crime, (intense) criminal activity may not attract police attention. The progress of the crime
mapping discipline, spurred by the development of information technology (e.g. GIS) and spatial statistics, enabled
crime analysts to identify hotspots more accurately than before. When identifying hotspots, crime analysts use spatial
statistical methods which are based on the assumption of a 2D homogeneous Euclidean space. However, in an urban
setting, the street network may significantly affect the patterning of crime. Crime may be concentrated along the
streets, thus forming linear hotspots. When such linear distribution is analysed, the assumption of homogeneity
of the 2D space, which is inherent in “traditional” spatial statistical methods, could lead to a false conclusion. In
order to avoid that from happening, these methods should be extended to a network space. This paper explains the
specifics of spatial analysis in a network environment and its relevance to the crime analysis practice. Finally, the
network extension of one of the most popular methods of hotspot identification, i.e. the kernel density estimation
(KDE) method, was applied in the present paper. Having compared the network and planar KDE outputs, the authors
concluded that the network-based KDE allowed a more accurate identification of linear hotspots, resulting in a more
effective deployment of resources.
PB  - Ljubljana : Ministrstvo za notranje zadeve Republike Slovenije, Policija
T2  - Revija za kriminalistiko in kriminologijo
T1  - Identifying Street Hotspots Using a Network Kernel Density Estimation
T1  - Prepoznavanje uličnih kriminalnih žarišč z uporabo Network Density Estimation
VL  - 71
IS  - 4
SP  - 257
EP  - 272
UR  - https://hdl.handle.net/21.15107/rcub_jakov_1350
ER  - 

@article{
author = "Milić, Nenad and Đurđević, Zoran and Mijalković, Saša and Erkić, Dražan",
year = "2020",
abstract = "Throughout history, police have dedicated their attention to locations with a high incidence of crime and/or disorder.
These efforts have become more systematic since the introduction of hotspot policing in the 1990s. A large body of
research conducted over the last three decades has shown that focusing police resources on areas characterised by
high concentration of crime (hotspots) may have positive preventive effects. If crime hotspots inaccurately reflect
the spatial patterning of crime, (intense) criminal activity may not attract police attention. The progress of the crime
mapping discipline, spurred by the development of information technology (e.g. GIS) and spatial statistics, enabled
crime analysts to identify hotspots more accurately than before. When identifying hotspots, crime analysts use spatial
statistical methods which are based on the assumption of a 2D homogeneous Euclidean space. However, in an urban
setting, the street network may significantly affect the patterning of crime. Crime may be concentrated along the
streets, thus forming linear hotspots. When such linear distribution is analysed, the assumption of homogeneity
of the 2D space, which is inherent in “traditional” spatial statistical methods, could lead to a false conclusion. In
order to avoid that from happening, these methods should be extended to a network space. This paper explains the
specifics of spatial analysis in a network environment and its relevance to the crime analysis practice. Finally, the
network extension of one of the most popular methods of hotspot identification, i.e. the kernel density estimation
(KDE) method, was applied in the present paper. Having compared the network and planar KDE outputs, the authors
concluded that the network-based KDE allowed a more accurate identification of linear hotspots, resulting in a more
effective deployment of resources.",
publisher = "Ljubljana : Ministrstvo za notranje zadeve Republike Slovenije, Policija",
journal = "Revija za kriminalistiko in kriminologijo",
title = "Identifying Street Hotspots Using a Network Kernel Density Estimation, Prepoznavanje uličnih kriminalnih žarišč z uporabo Network Density Estimation",
volume = "71",
number = "4",
pages = "257-272",
url = "https://hdl.handle.net/21.15107/rcub_jakov_1350"
}

Milić, N., Đurđević, Z., Mijalković, S.,& Erkić, D.. (2020). Identifying Street Hotspots Using a Network Kernel Density Estimation. in Revija za kriminalistiko in kriminologijo
Ljubljana : Ministrstvo za notranje zadeve Republike Slovenije, Policija., 71(4), 257-272.
https://hdl.handle.net/21.15107/rcub_jakov_1350

Milić N, Đurđević Z, Mijalković S, Erkić D. Identifying Street Hotspots Using a Network Kernel Density Estimation. in Revija za kriminalistiko in kriminologijo. 2020;71(4):257-272.
https://hdl.handle.net/21.15107/rcub_jakov_1350 .

Milić, Nenad, Đurđević, Zoran, Mijalković, Saša, Erkić, Dražan, "Identifying Street Hotspots Using a Network Kernel Density Estimation" in Revija za kriminalistiko in kriminologijo, 71, no. 4 (2020):257-272,
https://hdl.handle.net/21.15107/rcub_jakov_1350 .