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Vedbar INSTRUCTOR: Dr. Xijin Ge MID-TERM PROJECT MATH 592: DATA MINING Akram Ameya Liming Priya 03/03/2011 Youtube Video clip:

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Presentation on theme: "Vedbar INSTRUCTOR: Dr. Xijin Ge MID-TERM PROJECT MATH 592: DATA MINING Akram Ameya Liming Priya 03/03/2011 Youtube Video clip:"— Presentation transcript:

1 Vedbar INSTRUCTOR: Dr. Xijin Ge MID-TERM PROJECT MATH 592: DATA MINING Akram Ameya Liming Priya 03/03/2011 Youtube Video clip:

2  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 2

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

4  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 19 th 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]. 4

5  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, Sesha Kethineni et. al., found that unemployment influences crime both directly and indirectly through social pathologies such as drug and alcohol use [5]. 5

6 Objectives  Need to find the influential factors of crime to help reduce the crime in a society. 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. 6

7 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 7

8  Population  Geography  Size of state or city  Illiteracy  Unemployment  Per capita  Inflation rate 8

9  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 * ) 9

10  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. 10

11 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. 11

12  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. 12 1980 1991 1984 2001 2002

13  Crime index is positively correlated with inflation rate. 13

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

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

16 There is no significant correlation between vehicle theft and unemployment rate (r=0.1123) 16

17  Divided into 4 Regions:  West (Blue)  South (Green)  Northeast (Red)  Midwest (Yellow)  Divided into 50 states. 17

18  South region had higher unemployment and illiteracy.  Midwest region was better among all regions. 18

19  Crime and illiteracy rate were high in populated states.  Higher illiteracy rate yielded higher crime in all regions. 19 Size of bubble indicates population of a state.

20 a Illiteracy Crime index

21  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 21

22 Unemployment rate Crime Index

23 Texas

24  Population and crime index are strongly correlated.  Population raises crime index in the states. 24

25 Population Crime index

26  Both illiteracy and unemployment are correlated to crime.  Illiteracy is more connected to crime than unemployment. 26

27  Mostly states having high population have high crime index, high illiteracy and high unemployment. 27

28 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.ew and state.ew includes four categories: eastern, middle, western, and other contains three categories: southern, mid, northern Adding two variables : total number of crime/population per city : number of car theft/population per city 28

29 29

30 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(  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 30

31 31

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 32

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

34  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 34

35 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. 35

36  Robbery and Aggravated Assault seems to be highly correlated.  Car Theft and Robbery have low correlation. 36

37  Murder and Robbery are highly correlated.  Burglary and Arson have low correlation. 37

38  Murder and Robbery are highly correlated.  Burglary and Arson have low correlation. 38

39 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. 39

40 1. Daniel Glaser and Kent Rice,” Crime, Age, and Employment”, American Sociological Review, Vol. 24, No. 5, pp. 679-686, Oct, 1959 2. 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 3. 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. 4. 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. 5. Sesha Kethineni 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.    40

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