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A Statistical Analysis of Crime in the United States of America

A Statistical Analysis of Crime in the United States of America

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A Statistical Analysis of Crime in the United States of America

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  1. A Statistical Analysis of Crime in the United States of America

  2. Outline • Background • Motivation • Objectives • Crime Classification in United States --All States in United States --Major cities in United States • Data Collection • Results and Analysis • Conclusions • References

  3. Background • Crime • Deviant behavior that violates prevailing norms or cultural values. • Influential factors: • Social • Political • Psychological • Economical • Impact and remedies

  4. Background • Crime has a long history. • Some religious communities regard sin as a crime. • Famous historian Henry Thomas Buckle : • “Society prepares the crime, the criminal commits it.” • In 19th century onwards, development of sociological thought prompted some fresh views on crime and criminality. • Criminology, a new disciplinary, was invented to study crime in society. • In 1959, Daniel Glaser et. al. , found the strong relationship of crime and economic conditions associated with adult unemployment [1].

  5. Background • In 1976, Bruce H. Mayhew et. al., found that there will be wide variety of social interaction in higher population area and therefore increases the crime of violence [2]. • In 2004, John R. Hipp et. al. found that both the violent and property crime rates are driven by pleasant weather [3]. • In 2004, Lance Lochner et. al., found that schooling significantly reduces the probability of incarceration and arrest [4]. • In 2007, SeshaKethineni et. al., found that unemployment influences crime both directly and indirectly through social pathologies such as drug and alcohol use [5].

  6. Motivation • Need to find the influential factors of crime to help reduce the crime in a society. Objectives • Collect crime data and other influential factors of crime for United States through available sources. • Visualize and statistically analyze the crime data in major cities, states & the entire country of USA. • Determining the relation of different kind of crimes in major cities and states in USA. • Suggest some of the influential factors to help reduce the crime in the United States.

  7. Crime Classification in United States • FBI has been tabulating uniform crime reports annually since 1930. • Crime Index includes • Violent Crime • Property Crime • Violent Crime includes • Murder • Forcible rape • Aggravated assault • Robbery • Property Crime includes • Burglary • Larceny theft • Motor-vehicle theft

  8. Factors affecting crime rate • Population • Geography • Size of state or city • Illiteracy • Unemployment • Per capita • Inflation rate

  9. Data Set on Crime by Year of USA • Year covered from 1979 to 2007. • Crime data obtained from FBI source. • Other variables used are Inflation rate, Unemployment rate, Population and Per Capita of USA. (Sources * )

  10. Data Set on Crime by States of USA • Crime data was of year 2003. • All 50 states are covered. • Other variables used were Population, Illiteracy, Unemployment and Regions based on weather. • States were divided into South, West, Midwest and Northeast regions.

  11. Data Set on Crime by Major Cities of USA • Crime data for cities in USA (2003) • Data of five major cities for each state in USA • Some of the variables in the dataset include Population,Murder,Arson etc. • Cities were divided into three categories: Large, Medium and Small.

  12. Crime Index By Years In USA 1980 • In 1980 - the Cold War ended between USA and USSR • In 1984, • US presidential election • (Ronald Reagan was re-elected) • Summer Olympics, Los Angeles California, USA • In 1991 - Gulf War in the Middle East. • In 2001 - Terrorist attack in the World Trade Center and Pentagon on September 11. • In 2002- Department of Homeland Security was formed on November 25. • In 2003 - Drop down of crime index. • In 2007 - The beginning of recession in the United States. 1991 1984 2001 2002

  13. Inflation Rate and Crime Index by Year • Crime index is positively correlated with inflation rate.

  14. Unemployment • From the above plot, unemployment rate was at its peak in 1980. • The pattern decreased with a range from 1990 to 2007.

  15. Crime vs. Year Property crime vs. Year Violent crime vs. Year Property crime has decreased linearly over the years as compared to Violent crime which has a peak at 1991.

  16. Vehicle Theft Vs. Unemployment Rate There is no significant correlation between vehicle theft and unemployment rate (r=0.1123)

  17. Regions of USA • Divided into 4 Regions: • West (Blue) • South (Green) • Northeast (Red) • Midwest (Yellow) • Divided into 50 states.

  18. Unemployment and Illiteracy in All Regions • South region had higher unemployment and illiteracy. • Midwest region was better among all regions.

  19. Illiteracy Vs. Crime Index For Year 2003 • Crime and illiteracy rate were high in populated states. • Higher illiteracy rate yielded higher crime in all regions. Size of bubble indicates population of a state.

  20. Illiteracy Crime index aa

  21. Unemployment Vs. Crime Index For Year 2003 • Crime and unemployment rate were high in populated states. • Higher unemployment rate yielded higher crime in all regions. • Population were higher in states with pleasant weather. Pleasant environment leads to high population and so high crime rate

  22. Unemployment rate Crime Index

  23. Less crime in winter, more in the summer Texas http://www.txdps.state.tx.us/crimereports/09/citCh2.pdf

  24. Crime Index Vs Population for 2003 • Population and crime index are strongly correlated. • Population raises crime index in the states.

  25. Population Crime index

  26. Crime Index Vs Unemployment and Illiteracy in 2003 • Both illiteracy and unemployment are correlated to crime. • Illiteracy is more connected to crime than unemployment.

  27. Heat Map Plot of United States • Mostly states having high population have high crime index, high illiteracy and high unemployment.

  28. Preparing For City Level Data Converting city variable into categorical: large, middle, and small cities by people large: population>150000 middle: 150000>=population>=45000 small: population<45000 Converting state variable into two variables: state.ewand state.sn state.ewincludes four categories: eastern, middle, western, and other state.sn contains three categories: southern, mid, northern Adding two variables ratio.total: total number of crime/population per city ratio.car: number of car theft/population per city

  29. Histograms

  30. Comparing ratios of crime across different cities a. Ratio of total crime across city variable ● ANOVA: Analysis of variance ● H0: There is no difference in ratio of total crime across large, middle, and small cities. ● H0 is rejected based on the output of R below. There is significant difference. > aov(formula=ratio.total~city) • Df Sum Sq Mean Sq F value Pr(>F) • city 2 0.036206 0.0181030 35.364 3.869e-14 • Residuals 233 0.119276 0.0005119

  31. Total ratios of large city are greater than that of middle cities, which are greater than small cities.

  32. b. Ratio of car theft crime across city variable Df Sum Sq Mean Sq F value Pr(>F) • city 2 0.036206 0.0181030 35.364 3.869e-14 • Residuals 233 0.119276 0.0005119

  33. Bubble Plot • Proportion of violent crime increases with total crime. • States with high population seem to have a higher crime rate. Size: population Color : City

  34. BubblePlot • Property crime has a perfect correlation with total crime. • R value shows that it is highly correlated as compared to violent crime. Size: population Color : City

  35. Scatter Plot for City Data 1. When Population increases total crime increases.2. Correlation of larger city is more than small and middle cities but small cities are more correlated than middle cities.

  36. Heat Map (Large Cities) • Robbery and Aggravated Assault seems to be highly correlated. • Car Theft and Robbery have low correlation.

  37. Heat Map (Medium Cities) • Murder and Robbery are highly correlated. • Burglary and Arson have low correlation.

  38. Heat Map (Small Cities) • Murder and Robbery are highly correlated. • Burglary and Arson have low correlation.

  39. Conclusions • Major events or crises in a country can influence the crime index of a country. • Inflation rate and crime index are positively correlated. • Population was positively correlated with crime rate in both state and city level. • Larger cities have high crime rate. • Car Theft is high in large cities. • Murder and Robbery seem to have high correlation in small and medium cities. • Regions or states having pleasant weather have bigger population and crime index. • Illiteracy rate is more highly correlated to crime index than unemployment rate. • Government should focus more on lowering illiteracy rate first than solving unemployment to reduce crime index.

  40. References • Daniel Glaser and Kent Rice,” Crime, Age, and Employment”, American Sociological Review, Vol. 24, No. 5, pp. 679-686, Oct, 1959 • Bruce H. Mayhew and Roger L. Levinger, “Size and the Density of Interaction in Human Aggragates.” ,The American Journal of Sociology, Vol. 82. No. 1, pp. 86-110, Jul. 1976 • John R. Hipp, Daniel J. Bauer, Patrick J. Curran and Kenneth A. Bollen, “Crimes of Opportunity or Crimes of Emotion? Testing Two Explanations of Seasonal Change in Crime”, Social Forces, Vol. 82, No. 4, pp. 1333-1372, Jun. 2004. • Lance Lochner and Enrico Moretti, “The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports” ,The American Economic Review, Vol. 94, No. 1, pp. 155-189, Mar., 2004. • SeshaKethineni and David N. Falcone, “Employment and ex-offenders in the United States: Effects of legal and extra legal factors.”, The Journal of Community and Criminal Justice, Vol. 54(1), pp. 36-51, 2007 * Source: • Crime in the United States, 2007, FBI, Uniform Crime Reports. • http://www.disastercenter.com/crime/wicrime.htm • http://www.infoplease.com/ipa/A0004902.html#ixzz1ESnn3Tf9 • http://www.miseryindex.us