Do Bus Stops Increase Crime Opportunities?:
Using the frameworks of routine activity, crime pattern and rational choice theories, Yu investigates the relationship between bus stops, businesses, and five offense types (robbery, aggravated assault, motor vehicle theft, theft from motor vehicle, and burglary) in Newark, New Jersey. Several data...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
El Paso :
LFB Scholarly Pub. LLC,
2011.
|
| Schriftenreihe: | Criminal justice (LFB Scholarly Publishing LLC)
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Using the frameworks of routine activity, crime pattern and rational choice theories, Yu investigates the relationship between bus stops, businesses, and five offense types (robbery, aggravated assault, motor vehicle theft, theft from motor vehicle, and burglary) in Newark, New Jersey. Several data analysis methods were used to examine the impact of bus stops and businesses on crime. Overall, both bus stops and commercial establishments were associated with increased crime. Among business types, the category of food store was always related to increased crime across offense types and regressio. |
| Beschreibung: | 1 online resource (322 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781593326722 1593326726 |
Internformat
MARC
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| 245 | 1 | 0 | |a Do Bus Stops Increase Crime Opportunities? |
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| 505 | 0 | |a List of Tables; List of Figures; List of Abbreviations; Acknowledgement; Chapter 1: Introduction: Bus Stops and Crime Opportunities; Chapter 2: Crimes Around Mass Transit: Causes or Byproducts?; Chapter 3: Theoretical Framework and Research Questions; Chapter 4: Gathering and Cleaning Data to Answer Questions; Chapter 5: Plan to Address Spatial Aspect of Data; Chapter 6: Geocoding Crime; Chapter 7: Exploratory Data Analysis Results; Chapter 8: Regression Analysis Results; Chapter 9: Conclusions and Discussions; Appendix A: Data Analysis Results on Robbery. | |
| 505 | 8 | |a Appendix B: Data Analysis Results on Aggravated AssaultAppendix C: Data Analysis Results on Motor Vehicle Theft; Appendix D: Data Analysis Results on Theft From Motor Vehicle; Appendix E: Data Analysis Results on Burglary; References; Index. | |
| 520 | |a Using the frameworks of routine activity, crime pattern and rational choice theories, Yu investigates the relationship between bus stops, businesses, and five offense types (robbery, aggravated assault, motor vehicle theft, theft from motor vehicle, and burglary) in Newark, New Jersey. Several data analysis methods were used to examine the impact of bus stops and businesses on crime. Overall, both bus stops and commercial establishments were associated with increased crime. Among business types, the category of food store was always related to increased crime across offense types and regressio. | ||
| 588 | 0 | |a Print version record. | |
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| 650 | 6 | |a Modus operandi (Criminologie) |z États-Unis |v Études de cas. | |
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| author | Yu, Sung-Suk Violet |
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| contents | List of Tables; List of Figures; List of Abbreviations; Acknowledgement; Chapter 1: Introduction: Bus Stops and Crime Opportunities; Chapter 2: Crimes Around Mass Transit: Causes or Byproducts?; Chapter 3: Theoretical Framework and Research Questions; Chapter 4: Gathering and Cleaning Data to Answer Questions; Chapter 5: Plan to Address Spatial Aspect of Data; Chapter 6: Geocoding Crime; Chapter 7: Exploratory Data Analysis Results; Chapter 8: Regression Analysis Results; Chapter 9: Conclusions and Discussions; Appendix A: Data Analysis Results on Robbery. Appendix B: Data Analysis Results on Aggravated AssaultAppendix C: Data Analysis Results on Motor Vehicle Theft; Appendix D: Data Analysis Results on Theft From Motor Vehicle; Appendix E: Data Analysis Results on Burglary; References; Index. |
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| discipline | Rechtswissenschaft |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:25:03Z |
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| series | Criminal justice (LFB Scholarly Publishing LLC) |
| series2 | Criminal Justice: Recent Scholarship |
| spelling | Yu, Sung-Suk Violet. Do Bus Stops Increase Crime Opportunities? El Paso : LFB Scholarly Pub. LLC, 2011. 1 online resource (322 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier polychrome. rdacc http://rdaregistry.info/termList/RDAColourContent/1003 text file rdaft http://rdaregistry.info/termList/fileType/1002 Criminal Justice: Recent Scholarship List of Tables; List of Figures; List of Abbreviations; Acknowledgement; Chapter 1: Introduction: Bus Stops and Crime Opportunities; Chapter 2: Crimes Around Mass Transit: Causes or Byproducts?; Chapter 3: Theoretical Framework and Research Questions; Chapter 4: Gathering and Cleaning Data to Answer Questions; Chapter 5: Plan to Address Spatial Aspect of Data; Chapter 6: Geocoding Crime; Chapter 7: Exploratory Data Analysis Results; Chapter 8: Regression Analysis Results; Chapter 9: Conclusions and Discussions; Appendix A: Data Analysis Results on Robbery. Appendix B: Data Analysis Results on Aggravated AssaultAppendix C: Data Analysis Results on Motor Vehicle Theft; Appendix D: Data Analysis Results on Theft From Motor Vehicle; Appendix E: Data Analysis Results on Burglary; References; Index. Using the frameworks of routine activity, crime pattern and rational choice theories, Yu investigates the relationship between bus stops, businesses, and five offense types (robbery, aggravated assault, motor vehicle theft, theft from motor vehicle, and burglary) in Newark, New Jersey. Several data analysis methods were used to examine the impact of bus stops and businesses on crime. Overall, both bus stops and commercial establishments were associated with increased crime. Among business types, the category of food store was always related to increased crime across offense types and regressio. Print version record. Includes bibliographical references and index. English. Criminal methods United States Case studies. Criminal behavior, Prediction of United States Case studies. Crime forecasting United States Case studies. Modus operandi (Criminologie) États-Unis Études de cas. Criminalité Prévision États-Unis Études de cas. SOCIAL SCIENCE Criminology. bisacsh Crime forecasting fast Criminal behavior, Prediction of fast Criminal methods fast United States fast https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq Case studies fast Case studies. lcgft http://id.loc.gov/authorities/genreForms/gf2017026140 Études de cas. rvmgf has work: Do bus stops increase crime opportunities? (Text) https://id.oclc.org/worldcat/entity/E39PCFt9tWmmmbXkFtDqyj6Pcd https://id.oclc.org/worldcat/ontology/hasWork Print version: Yu, Sung-Suk Violet. Do Bus Stops Increase Crime Opportunities? El Paso : LFB Scholarly Publishing LLC, ©2011 9781593324551 Criminal justice (LFB Scholarly Publishing LLC) http://id.loc.gov/authorities/names/n2001094770 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=520577 Volltext |
| spellingShingle | Yu, Sung-Suk Violet Do Bus Stops Increase Crime Opportunities? Criminal justice (LFB Scholarly Publishing LLC) List of Tables; List of Figures; List of Abbreviations; Acknowledgement; Chapter 1: Introduction: Bus Stops and Crime Opportunities; Chapter 2: Crimes Around Mass Transit: Causes or Byproducts?; Chapter 3: Theoretical Framework and Research Questions; Chapter 4: Gathering and Cleaning Data to Answer Questions; Chapter 5: Plan to Address Spatial Aspect of Data; Chapter 6: Geocoding Crime; Chapter 7: Exploratory Data Analysis Results; Chapter 8: Regression Analysis Results; Chapter 9: Conclusions and Discussions; Appendix A: Data Analysis Results on Robbery. Appendix B: Data Analysis Results on Aggravated AssaultAppendix C: Data Analysis Results on Motor Vehicle Theft; Appendix D: Data Analysis Results on Theft From Motor Vehicle; Appendix E: Data Analysis Results on Burglary; References; Index. Criminal methods United States Case studies. Criminal behavior, Prediction of United States Case studies. Crime forecasting United States Case studies. Modus operandi (Criminologie) États-Unis Études de cas. Criminalité Prévision États-Unis Études de cas. SOCIAL SCIENCE Criminology. bisacsh Crime forecasting fast Criminal behavior, Prediction of fast Criminal methods fast |
| subject_GND | http://id.loc.gov/authorities/genreForms/gf2017026140 |
| title | Do Bus Stops Increase Crime Opportunities? |
| title_auth | Do Bus Stops Increase Crime Opportunities? |
| title_exact_search | Do Bus Stops Increase Crime Opportunities? |
| title_full | Do Bus Stops Increase Crime Opportunities? |
| title_fullStr | Do Bus Stops Increase Crime Opportunities? |
| title_full_unstemmed | Do Bus Stops Increase Crime Opportunities? |
| title_short | Do Bus Stops Increase Crime Opportunities? |
| title_sort | do bus stops increase crime opportunities |
| topic | Criminal methods United States Case studies. Criminal behavior, Prediction of United States Case studies. Crime forecasting United States Case studies. Modus operandi (Criminologie) États-Unis Études de cas. Criminalité Prévision États-Unis Études de cas. SOCIAL SCIENCE Criminology. bisacsh Crime forecasting fast Criminal behavior, Prediction of fast Criminal methods fast |
| topic_facet | Criminal methods United States Case studies. Criminal behavior, Prediction of United States Case studies. Crime forecasting United States Case studies. Modus operandi (Criminologie) États-Unis Études de cas. Criminalité Prévision États-Unis Études de cas. SOCIAL SCIENCE Criminology. Crime forecasting Criminal behavior, Prediction of Criminal methods United States Case studies Case studies. Études de cas. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=520577 |
| work_keys_str_mv | AT yusungsukviolet dobusstopsincreasecrimeopportunities |