GIS and Crime Mapping
(Sprache: Englisch)
GIS & Crime covers the theoretical principles, data processing solutions, partnership working, analytical methods, problem-solving approaches, and map design for GIS and crime in a manner that underpins GIS application use in three broad areas...
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Klappentext zu „GIS and Crime Mapping “
GIS & Crime covers the theoretical principles, data processing solutions, partnership working, analytical methods, problem-solving approaches, and map design for GIS and crime in a manner that underpins GIS application use in three broad areas (operational, tactical and strategic). The book also includes short case studies at relevant sections to help demonstrate how the principles and concepts are essential for successful application development. These case studies bring a richness and depth to the text and draw from the authors' international contacts and knowledge of excellence in the field to offer a global tone to the book.The authors are well-placed to write the leading text on GIS and crime. Spencer Chainey is a leading consultant and academic at the Jill Dando Crime Research Institute, and current vice-president of the AGI, and his experience is complemented by Jerry Ratcliffe, an academic in police studies in Australia and a former PC and crime analyst with the London Metropolitan Police Force. Case studies provide further insight into specific issues of interest to readers.
* Provides readers with a complete start-to-finish coverage of crime mapping: fundamental theory of mapping and criminology, scientific methodologies, analysis and design techniques, and applications.
* Incorporates global examples and case studies from international contributors to demonstrate best and worst practice
Inhaltsverzeichnis zu „GIS and Crime Mapping “
Acknowledgements.1 Introduction.
1.1 The geography of crime.
1.2 A brief history of GIS and crime mapping.
1.3 Using GIS in policing and to prevent crime.
1.4 The audience for this book.
1.5 The content and structure of the book.
1.6 Putting it all in perspective.
Case study: Crime mapping in Lincoln, Nebraska.
Further reading.
References.
2 Mapping and the Criminal Justice Environment.
Learning Objectives.
2.1 Introduction.
2.2 The terminology of services in the criminal justiceenvironment.
2.3 The spatial hierarchy of the criminal justice system andcrime reduction services.
Case study: Policing across the spatial hierarchy in the UK- The National Intelligence Model.
2.4 The geographical jurisdiction of law enforcement and crimereduction services.
2.5 The use of crime mapping in law enforcement and crimereduction.
Case study: Using GIS to monitor the effect of alley-gatingschemes.
2.6 Summary.
Further reading.
References.
3 The Basics of Crime Mapping.
Learning Objectives.
3.1 What is a GIS?
3.2 How does a GIS work?
3.3 GIS files.
3.4 Coordinate systems and projections.
3.5 Getting crime data into a GIS.
Case study: Using GPS technology to capture environmental crimeincidents in North London, England.
3.6 Geocoding in the real world.
3.7 Address data cleaning.
3.8 Address reference files.
Case study: Geocoding crime data at the point of record entry inDumfries and Galloway, Scotland.
3.9 Geocoding functions.
3.10 Geocoding and fitness for purpose.
3.11 Measuring geocoding accuracy.
Case study: Handling uncertainty and incompleteness in crimerecords.
3.12 Mapping and unreported crime data.
3.13 Editing data in a GIS.
3.14 Performing queries on data in a GIS.
3.15 Performing spatial functions and integrating data in aGIS.
3.16 Asking spatial questions before mapping or analysingdata.
3.17 Summary.
Further reading.
References.
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Spatial Theories of Crime.
Learning Objectives.
4.1 Introduction.
4.2 Early environmental criminology.
4.3 The space and time of offences.
4.4 Offender-offence interaction.
4.5 Spatial crime theory in practice.
4.6 Summary.
Further reading.
References.
5 Spatial Statistics for Crime Analysis.
Learning Objectives.
5.1 Introduction.
5.2 Spatial processes.
5.3 Centrographic statistics.
5.4 Estimates of spatial dependence.
Case study: The application of Moran's I onburglary at the state level in the United States of America.
5.5 Spatial regression models.
Case study: A spatial lag model of anonymous narcotics tips inPhiladelphia, USA.
Case study: Local spatial processes with Geographically WeightedRegression.
5.6 Summary.
Further reading.
References.
6 Identifying Crime Hotspots.
Learning Objectives.
6.1 Introduction.
6.2 When is a hotspot 'hot'?
6.3 Point maps.
6.4 Geographic boundary thematic mapping.
6.5 Grid thematic mapping.
6.6 Continuous surface smoothing methods.
Case study: Mapping hotspots of thefts of vehicles in Camden,London.
6.7 Local Indicators of Spatial Association (LISA)statistics.
6.8 Considering the underlying population.
Case study: Identifying street crime risk hotspots in the WestEnd of London using pedestrian counts.
6.9 Predictive crime mapping.
6.10 Summary.
Further reading.
References.
7 Mapping Crime with Local Community Data.
Learning Objectives.
7.1 Introduction.
7.2 What are crime reduction partnerships?
7.3 Mapping and the benefits of partnership working.
Case study: Comparing the perception of where crime happens withwhere crime actually happens.
7.4 Partnership data.
Case study: Crime And Disorder Information Exchange (CADDIE),Sussex, England.
7.5 Information sharing.
Case study: The Amethyst Crime and Disorder Information Hub,Cornwall, England.
7.6 Combining data from different geographic uni
Learning Objectives.
4.1 Introduction.
4.2 Early environmental criminology.
4.3 The space and time of offences.
4.4 Offender-offence interaction.
4.5 Spatial crime theory in practice.
4.6 Summary.
Further reading.
References.
5 Spatial Statistics for Crime Analysis.
Learning Objectives.
5.1 Introduction.
5.2 Spatial processes.
5.3 Centrographic statistics.
5.4 Estimates of spatial dependence.
Case study: The application of Moran's I onburglary at the state level in the United States of America.
5.5 Spatial regression models.
Case study: A spatial lag model of anonymous narcotics tips inPhiladelphia, USA.
Case study: Local spatial processes with Geographically WeightedRegression.
5.6 Summary.
Further reading.
References.
6 Identifying Crime Hotspots.
Learning Objectives.
6.1 Introduction.
6.2 When is a hotspot 'hot'?
6.3 Point maps.
6.4 Geographic boundary thematic mapping.
6.5 Grid thematic mapping.
6.6 Continuous surface smoothing methods.
Case study: Mapping hotspots of thefts of vehicles in Camden,London.
6.7 Local Indicators of Spatial Association (LISA)statistics.
6.8 Considering the underlying population.
Case study: Identifying street crime risk hotspots in the WestEnd of London using pedestrian counts.
6.9 Predictive crime mapping.
6.10 Summary.
Further reading.
References.
7 Mapping Crime with Local Community Data.
Learning Objectives.
7.1 Introduction.
7.2 What are crime reduction partnerships?
7.3 Mapping and the benefits of partnership working.
Case study: Comparing the perception of where crime happens withwhere crime actually happens.
7.4 Partnership data.
Case study: Crime And Disorder Information Exchange (CADDIE),Sussex, England.
7.5 Information sharing.
Case study: The Amethyst Crime and Disorder Information Hub,Cornwall, England.
7.6 Combining data from different geographic uni
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Bibliographische Angaben
- Autoren: Spencer Chainey , Jerry Ratcliffe
- 2005, 1. Auflage, 456 Seiten, Maße: 23 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470860995
- ISBN-13: 9780470860991
Sprache:
Englisch
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