2019
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A.
Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments Journal Article
In: Pervasive and Mobile Computing, vol. 53, 2019, ISSN: 15741192.
Abstract | Links | BibTeX | Tag: Crime prediction, Data analytics, smart city, Urban computing
@article{Catlett2019,
title = {Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments},
author = {C. Catlett and E. Cesario and D. Talia and A. Vinci},
doi = {10.1016/j.pmcj.2019.01.003},
issn = {15741192},
year = {2019},
date = {2019-01-01},
journal = {Pervasive and Mobile Computing},
volume = {53},
abstract = {Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.},
keywords = {Crime prediction, Data analytics, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.
2018
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A.
A data-driven approach for spatio-Temporal crime predictions in smart cities Best Paper Proceedings Article
In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, 2018, ISBN: 9781538647059.
Abstract | Links | BibTeX | Tag: Crime prediction, smart city, Urban computing
@inproceedings{Catlett2018,
title = {A data-driven approach for spatio-Temporal crime predictions in smart cities},
author = {C. Catlett and E. Cesario and D. Talia and A. Vinci},
doi = {10.1109/SMARTCOMP.2018.00069},
isbn = {9781538647059},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)},
journal = {Proceedings - 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018},
publisher = {IEEE},
abstract = {The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-Temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.},
keywords = {Crime prediction, smart city, Urban computing},
pubstate = {published},
tppubtype = {inproceedings}
}
The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-Temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.