Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities
The increasing number of crimes affecting urban areas requires the adoption of countermeasures to tackle this problem from different perspectives, including the technological one. Currently, there are many research initiatives with the goal of applying machine or deep learning techniques leveraging...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/6/1/10 |
| _version_ | 1827755482874380288 |
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| author | Rosario Minardi Maria Luisa Villani Antonio De Nicola |
| author_facet | Rosario Minardi Maria Luisa Villani Antonio De Nicola |
| author_sort | Rosario Minardi |
| collection | DOAJ |
| description | The increasing number of crimes affecting urban areas requires the adoption of countermeasures to tackle this problem from different perspectives, including the technological one. Currently, there are many research initiatives with the goal of applying machine or deep learning techniques leveraging historical data to predict the occurrence of crime incidents. Conversely, there is a lack of tools aiming at crime risk assessment, in particular, by supporting the police in conceiving what could be the crime incidents affecting a given city area. To this purpose, we propose the Crime Prevention System, a modular software application for qualitative crime risk assessment. This consists of an ontology of crime risk, a module to retrieve contextual data from OpenStreetMap, semantics reasoning functionalities, and a GIS interface. We discuss how this system can be used through a case study related to the Italian city of Syracuse. |
| first_indexed | 2024-03-11T08:09:58Z |
| format | Article |
| id | doaj.art-8856ca93f0e7404680406a86bd268c81 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-11T08:09:58Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-8856ca93f0e7404680406a86bd268c812023-11-16T23:15:04ZengMDPI AGSmart Cities2624-65112023-01-016117919510.3390/smartcities6010010Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart CitiesRosario Minardi0Maria Luisa Villani1Antonio De Nicola2Department of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00198 Rome, ItalyENEA-Centro Ricerche Casaccia, Via Anguillarese 301, 00123 Rome, ItalyENEA-Centro Ricerche Casaccia, Via Anguillarese 301, 00123 Rome, ItalyThe increasing number of crimes affecting urban areas requires the adoption of countermeasures to tackle this problem from different perspectives, including the technological one. Currently, there are many research initiatives with the goal of applying machine or deep learning techniques leveraging historical data to predict the occurrence of crime incidents. Conversely, there is a lack of tools aiming at crime risk assessment, in particular, by supporting the police in conceiving what could be the crime incidents affecting a given city area. To this purpose, we propose the Crime Prevention System, a modular software application for qualitative crime risk assessment. This consists of an ontology of crime risk, a module to retrieve contextual data from OpenStreetMap, semantics reasoning functionalities, and a GIS interface. We discuss how this system can be used through a case study related to the Italian city of Syracuse.https://www.mdpi.com/2624-6511/6/1/10crimerisk assessmentontologysemantic reasoningcomputational creativitygeographic information systems |
| spellingShingle | Rosario Minardi Maria Luisa Villani Antonio De Nicola Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities Smart Cities crime risk assessment ontology semantic reasoning computational creativity geographic information systems |
| title | Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities |
| title_full | Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities |
| title_fullStr | Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities |
| title_full_unstemmed | Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities |
| title_short | Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities |
| title_sort | semantic reasoning for geolocalized assessment of crime risk in smart cities |
| topic | crime risk assessment ontology semantic reasoning computational creativity geographic information systems |
| url | https://www.mdpi.com/2624-6511/6/1/10 |
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