Time-Series Features for Predictive Policing
| Autor: | Leonardo C. T. Bezerra, Michael Beigl, Allan de Medeiros Martins, Nelio Cacho, Adelson de Araujo, Julio Borges, Daniel Ziehr, Simon Geisler |
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| Přispěvatelé: | Digital Society Institute, Instructional Technology |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Law enforcement 020207 software engineering 02 engineering and technology Data science Task (project management) Capacity planning Work (electrical) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Predictive policing Time series Goal setting |
| Zdroj: | ISC2 2018 IEEE International Smart Cities Conference, ISC2 2018 |
| DOI: | 10.1109/isc2.2018.8656731 |
| Popis: | Forecasting when and where crimes are more likely to occur based on years of historical record analysis is becoming a task which is increasingly helping cities' safety departments with capacity planning, goal setting, and anomaly detection. Crime is a geographically concentrated phenomena and varies in intensity and category over time. Despite its importance, there are serious challenges associated with producing reliable forecasts such as sub-regions with sparse crime incident information. In this work, we address these challenges proposing a crime prediction model which leverages features extracted from time series patterns of criminal records based on spatial dependencies. Our results benchmarked against the state of the art and evaluated on two real world datasets, one from San Francisco, US, and another from Natal, Brazil, show how crime forecasting can be enhanced by leveraging Spatio-Temporal dependencies improving our understanding of such models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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