GIS for crime analysis
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/158324 |
Resumo: | The term crime analysis refers to a concept and to a discipline practiced in the policing community. It includes analysis of more than just a crime, which is why some authors refer to it as public safety analysis. However, over the last few years crime an alysis has become a general term that includes a lot of research subcategories: intelligence analysis, criminal investigative analysis, tactical crime analysis, strategic crime analysis, operation analysis and administrative crime analysis. Crime mapping and spatial analysis complements all of them and plays a crucial role in defining new forms of representation and visualization to better understand crime and to respond adequately to the problem of criminality. A new worldwide socio‑economical order lead to an increasing number on crime rates and raised the need to find new ways to handle information about criminality. To better understand its causes, local, regional and national security authorities turned to new decision support tools such as Geographi c Information Systems (GIS) and other information technologies to find better solutions. To understand the magnitude of all the variables involved it is necessary to spatially capture and correlate them. Only by doing that it´s possible to quantify and qualify some hidden aspects of the phenomena. The city of Lisbon with is new proposed administrative division, reducing from 53 to 24 freguesias (minimum administrative division and similar to parishs) implies an enormous degree of uncertainty in the observation and location of criminal data. As the crime is not treated with an exact point, but at the level of parish, it implies that larger parishes are treated by the average crime regardless of place of occurrence. This research combines statistica l methods (cluster analysis) and spatial models created with GIS, based on police crime reports. It also details a framework for short‑term tactical deployment of police resources in which the objective is the identification of areas where the crime lev els are high (enough) to enable accurate predictive models as well as to produce rigorous thematic maps. In recent years police services have engaged on proactive and Intelligence‑Led Policing (ILP) methods. This advance was coincident with the recogn ition of law‑enforcement solutions at local level. This paper also engages an approach to ILP as a methodology to provide the necessary tools for Decision Support System (DSS) of police departments. |
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GIS for crime analysisGeography for predictive modelsSDG 16 - Peace, Justice and Strong InstitutionsThe term crime analysis refers to a concept and to a discipline practiced in the policing community. It includes analysis of more than just a crime, which is why some authors refer to it as public safety analysis. However, over the last few years crime an alysis has become a general term that includes a lot of research subcategories: intelligence analysis, criminal investigative analysis, tactical crime analysis, strategic crime analysis, operation analysis and administrative crime analysis. Crime mapping and spatial analysis complements all of them and plays a crucial role in defining new forms of representation and visualization to better understand crime and to respond adequately to the problem of criminality. A new worldwide socio‑economical order lead to an increasing number on crime rates and raised the need to find new ways to handle information about criminality. To better understand its causes, local, regional and national security authorities turned to new decision support tools such as Geographi c Information Systems (GIS) and other information technologies to find better solutions. To understand the magnitude of all the variables involved it is necessary to spatially capture and correlate them. Only by doing that it´s possible to quantify and qualify some hidden aspects of the phenomena. The city of Lisbon with is new proposed administrative division, reducing from 53 to 24 freguesias (minimum administrative division and similar to parishs) implies an enormous degree of uncertainty in the observation and location of criminal data. As the crime is not treated with an exact point, but at the level of parish, it implies that larger parishes are treated by the average crime regardless of place of occurrence. This research combines statistica l methods (cluster analysis) and spatial models created with GIS, based on police crime reports. It also details a framework for short‑term tactical deployment of police resources in which the objective is the identification of areas where the crime lev els are high (enough) to enable accurate predictive models as well as to produce rigorous thematic maps. In recent years police services have engaged on proactive and Intelligence‑Led Policing (ILP) methods. This advance was coincident with the recogn ition of law‑enforcement solutions at local level. This paper also engages an approach to ILP as a methodology to provide the necessary tools for Decision Support System (DSS) of police departments.Departamento de Geografia e Planeamento Regional (DGPR)e-GEO -Centro de Estudos de Geografia e Planeamento RegionalRUNFerreira, JorgeJoão, PauloMartins, José2023-09-26T22:20:43Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/158324eng1566-6379PURE: 72378018info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:40:38Zoai:run.unl.pt:10362/158324Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:04.332277Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
GIS for crime analysis Geography for predictive models |
title |
GIS for crime analysis |
spellingShingle |
GIS for crime analysis Ferreira, Jorge SDG 16 - Peace, Justice and Strong Institutions |
title_short |
GIS for crime analysis |
title_full |
GIS for crime analysis |
title_fullStr |
GIS for crime analysis |
title_full_unstemmed |
GIS for crime analysis |
title_sort |
GIS for crime analysis |
author |
Ferreira, Jorge |
author_facet |
Ferreira, Jorge João, Paulo Martins, José |
author_role |
author |
author2 |
João, Paulo Martins, José |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Departamento de Geografia e Planeamento Regional (DGPR) e-GEO -Centro de Estudos de Geografia e Planeamento Regional RUN |
dc.contributor.author.fl_str_mv |
Ferreira, Jorge João, Paulo Martins, José |
dc.subject.por.fl_str_mv |
SDG 16 - Peace, Justice and Strong Institutions |
topic |
SDG 16 - Peace, Justice and Strong Institutions |
description |
The term crime analysis refers to a concept and to a discipline practiced in the policing community. It includes analysis of more than just a crime, which is why some authors refer to it as public safety analysis. However, over the last few years crime an alysis has become a general term that includes a lot of research subcategories: intelligence analysis, criminal investigative analysis, tactical crime analysis, strategic crime analysis, operation analysis and administrative crime analysis. Crime mapping and spatial analysis complements all of them and plays a crucial role in defining new forms of representation and visualization to better understand crime and to respond adequately to the problem of criminality. A new worldwide socio‑economical order lead to an increasing number on crime rates and raised the need to find new ways to handle information about criminality. To better understand its causes, local, regional and national security authorities turned to new decision support tools such as Geographi c Information Systems (GIS) and other information technologies to find better solutions. To understand the magnitude of all the variables involved it is necessary to spatially capture and correlate them. Only by doing that it´s possible to quantify and qualify some hidden aspects of the phenomena. The city of Lisbon with is new proposed administrative division, reducing from 53 to 24 freguesias (minimum administrative division and similar to parishs) implies an enormous degree of uncertainty in the observation and location of criminal data. As the crime is not treated with an exact point, but at the level of parish, it implies that larger parishes are treated by the average crime regardless of place of occurrence. This research combines statistica l methods (cluster analysis) and spatial models created with GIS, based on police crime reports. It also details a framework for short‑term tactical deployment of police resources in which the objective is the identification of areas where the crime lev els are high (enough) to enable accurate predictive models as well as to produce rigorous thematic maps. In recent years police services have engaged on proactive and Intelligence‑Led Policing (ILP) methods. This advance was coincident with the recogn ition of law‑enforcement solutions at local level. This paper also engages an approach to ILP as a methodology to provide the necessary tools for Decision Support System (DSS) of police departments. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 2012-01-01T00:00:00Z 2023-09-26T22:20:43Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/158324 |
url |
http://hdl.handle.net/10362/158324 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1566-6379 PURE: 72378018 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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13 application/pdf |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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