MAD-STEC: a method for multiple automatic detection of space-time emerging clusters
Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Veloso, Bráulio M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Statistics and computing - Springer US, 1991, 27(2016), 4 vom: 16. Juni, Seite 1099-1110 |
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| Übergeordnetes Werk: |
volume:27 ; year:2016 ; number:4 ; day:16 ; month:06 ; pages:1099-1110 |
| Links: |
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| DOI / URN: |
10.1007/s11222-016-9673-y |
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OLC2033749096 |
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| 520 | |a Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. | ||
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| 700 | 1 | |a Oliveira, Gabriel F. |4 aut | |
| 700 | 1 | |a Tavares, Andréa I. |4 aut | |
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10.1007/s11222-016-9673-y doi (DE-627)OLC2033749096 (DE-He213)s11222-016-9673-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Veloso, Bráulio M. verfasserin aut MAD-STEC: a method for multiple automatic detection of space-time emerging clusters 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. Surveillance Point pattern Prospective space-time surveillance Space-time clustering Correa, Thais R. aut Prates, Marcos O. aut Oliveira, Gabriel F. aut Tavares, Andréa I. aut Enthalten in Statistics and computing Springer US, 1991 27(2016), 4 vom: 16. Juni, Seite 1099-1110 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:27 year:2016 number:4 day:16 month:06 pages:1099-1110 https://doi.org/10.1007/s11222-016-9673-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4126 AR 27 2016 4 16 06 1099-1110 |
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10.1007/s11222-016-9673-y doi (DE-627)OLC2033749096 (DE-He213)s11222-016-9673-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Veloso, Bráulio M. verfasserin aut MAD-STEC: a method for multiple automatic detection of space-time emerging clusters 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. Surveillance Point pattern Prospective space-time surveillance Space-time clustering Correa, Thais R. aut Prates, Marcos O. aut Oliveira, Gabriel F. aut Tavares, Andréa I. aut Enthalten in Statistics and computing Springer US, 1991 27(2016), 4 vom: 16. Juni, Seite 1099-1110 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:27 year:2016 number:4 day:16 month:06 pages:1099-1110 https://doi.org/10.1007/s11222-016-9673-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4126 AR 27 2016 4 16 06 1099-1110 |
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10.1007/s11222-016-9673-y doi (DE-627)OLC2033749096 (DE-He213)s11222-016-9673-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Veloso, Bráulio M. verfasserin aut MAD-STEC: a method for multiple automatic detection of space-time emerging clusters 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. Surveillance Point pattern Prospective space-time surveillance Space-time clustering Correa, Thais R. aut Prates, Marcos O. aut Oliveira, Gabriel F. aut Tavares, Andréa I. aut Enthalten in Statistics and computing Springer US, 1991 27(2016), 4 vom: 16. Juni, Seite 1099-1110 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:27 year:2016 number:4 day:16 month:06 pages:1099-1110 https://doi.org/10.1007/s11222-016-9673-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4126 AR 27 2016 4 16 06 1099-1110 |
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Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. © Springer Science+Business Media New York 2016 |
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Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. © Springer Science+Business Media New York 2016 |
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Abstract Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil. © Springer Science+Business Media New York 2016 |
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