Practical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.
Practical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.
Produktdetails
Produktdetails
Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
This book is based on teaching materials developed by the authors. Professor Juanjo Medina is Senior Distinguished Researcher at the Department of Criminal Law and Crime Sciences at the University of Seville. Previously he was Professor of Quantitative Criminology at the University of Manchester where he taught data analysis and crime mapping for 20 years. Dr Reka Solymosi is a Senior Lecturer in Quantitative Methods at the University of Manchester where she has been teaching data analysis and crime mapping since 2016.
Inhaltsangabe
1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis
1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis
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