| bibtype |
J -
Journal Article
|
| ARLID |
0587295 |
| utime |
20240903170602.1 |
| mtime |
20240701235959.9 |
| WOS |
001260088200002 |
| DOI |
10.14311/NNW.2024.34.006 |
| title
(primary) (eng) |
3D Local Crime Type Models Based on Crime Hotspot Detection |
| specification |
| page_count |
22 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0290321 |
| ISSN |
1210-0552 |
| title
|
Neural Network World |
| volume_id |
34 |
| volume |
2 (2024) |
| page_num |
89-110 |
| publisher |
| name |
Ústav informatiky AV ČR, v. v. i. |
|
|
| keyword |
crime location |
| keyword |
crime type |
| keyword |
cluster analysis |
| keyword |
recursive Bayesian mixture estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0383037 |
| name1 |
Uglickich |
| name2 |
Evženie |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
8A21009 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0432581 |
|
| abstract
(eng) |
This paper deals with the analysis of the relationship between locations and types of crime observed in the Czech Republic. Cluster analysis of crime data based on the recursive Bayesian mixture estimation algorithm is used to identify crime hotspots and estimate local models of crime type. The experiments report that the 2D configuration of the algorithm allows the detection of crime hotspots online. The 3D configuration provides 29% more accurate crime type models than 2D clustering and alternative data mining algorithms. For the data set used, it was determined in which crime hotspots the most serious and most frequent types of crime can be expected to occur with the highest probability. The limitation of the study is the artificial support of the 3D clusters by the fully continuous data vector with the recoded values of the crime type. The potential use of the algorithm is expected in online web applications for sharing information on criminal offenses managed by the Police of the Czech Republic with the public and local government entities in the Czech Republic.\n |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2025 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0355026 |
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.164 |
| mrcbT16-D |
Q4 |
| arlyear |
2024 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001260088200002 WOS |
| mrcbU63 |
cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 34 č. 2 2024 89 110 Ústav informatiky AV ČR, v. v. i. |
|