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Measuring Crime Rates

Measuring Crime Rates

Introduction

Research on the trend of crimes in the United Kingdom from the 13th Century to 21st Century has burgeoned. This greatly increased the understanding and interpretation of the emerging trends in crime control and crimes. Even amid the decline in interpersonal violence in the sixteenth century, it is noted that crime and drugs are still great concern globally. Differences in the long-term trajectories for crime and homicide can be classified according to class, income, religion, age, ethnicity, and gender. Over the last two decades, crime rate has generally been on the rise in the entire Europe. In United Kingdom in particular, Britain is reported to have the highest number of burglary in the whole of European Union.

According to recent reports, Britain is tops the league in hate crimes and assaults. A survey by the European Union safety and crime unit cited UK as “a high crime” region except for Ireland. The survey further established that London is the “crime capital of Europe” with a higher potential of developing into petty crime capital. It was acknowledged that although the rate of crimes had significantly dropped since 1995(when it rocked the peak); the general drop in the crime rate in UK was still below the decline in crime rates as reported in other European nations. On average, alongside Ireland, Netherlands, Denmark, and Estonia, UK is named as one of crime hotspots with an average crime rate of thirty percent above the European Union average.

However, as observed in the survey, the chances of becoming a victim dealing in drugs and bribery are lowest in the United Kingdom compared to other countries within Europe. In addition, consumer fraud is not a case of concern in the UK. Despite this poor record of crime rates in the capitals of the united kingdom, the survey revealed that residence of the united kingdom are relatively satisfied with the performance and operations of the administration and the police department in dealing with crimes. Another survey conducted by Gallup Europe on behalf of the UN crime protection and prevention established that the decline in the general crime level in EU could be attributed to the fall in the population of youths and/or improvement in security measures within the capitals. Notably, crimes are common among younger generation.

The rates of crimes significantly vary with age, sex, education/profession, residence, and ethnicity. For instance, it was established that males were more prone to engage in crime compared to their female counterparts. Besides, most crimes were committed by youths between the ages of 20-30. Out of this proportion, more than three quarters of the suspects happened to be unemployed. This made them more vulnerable to engage in illegal acts as a way of earning livelihood. Of greater concern was the role of ethnicity in crimes. The study by Gallup Europe further found that blacks and Asians were toped the act. This paper therefore examines the variations in the crime measurement and crime rates among the diverse social diversity in the United Kingdom in comparison with other European Union member states.

Statement of the Problem

Social stratification is plays a major in determining crime rates in the society. In the UK, it is observed that crime rate is significantly different among different social groups in the community. For instance, based on colour, race, gender, ethnicity, age, and profession, crime rate varies. Given the rise in the level of crime in the leading urban centres in Europe, it is important to identify the source of variation in the crime rate and develop control mechanisms. The police department charged with the responsibility of serving the interest of the community and protecting the nation at large are faced with challenges in attempting to unfold crimes in the community. This calls for the general public to cooperate with the police unit in restoring safety.  

Research Objectives

The following are the objectives of this study:

  1. To determine the relationship between crime rates and previous involvement in criminal offenses.
  2. To explain why the lower-income class and the unemployed are the leading group in criminal activities in the leading states in UK.
  3. To establish the role of the youths in criminal activities.
  4. To determine the extent to which race, ethnicity and colour are associated with rising rate of crimes in the United Kingdom.

Research Questions

This research sought to answer the following questions in relation to crime rates:

  1. Is there a significant relationship between suspects and previous involvement in a criminal act?
  2. It is true that most crimes are committed by the low-income class as compared to the upper class?
  3. Why is it that most criminal offenses are committed by the youths?
  4. What is the role of racial discrimination in the rate of crimes?

Research Assumptions

  1. There is no significant relationship between previous criminal engagement and the rate of crime.
  2. The lower social class and the unemployed commonly engage in criminal acts as a way of earning living during hard economic times.
  3. Most crimes are committed by the youthful generation compared to the mature generation.
  4.  Ethnicity and race is a crucial factor behind crimes with the Backs and Asians leading in criminal activities in Britain.

RESULTS AND FINDINGS

Univariate Analysis of Variance

Notes

Output Created

04-Jan-2013 22:56:21

Comments

Input

Data

C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

10609

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on all cases with valid data for all variables in the model.

Syntax

UNIANOVA SusWork BY Reason Satisfied Worry WITH Weapon

  /RANDOM=Worry

  /METHOD=SSTYPE(3)

  /INTERCEPT=INCLUDE

  /CRITERIA=ALPHA(0.05)

  /DESIGN=Weapon Reason Satisfied Worry Reason*Satisfied Reason*Worry Satisfied*Worry Reason*Satisfied*Worry.

Resources

Processor Time

0:00:00.140

Elapsed Time

0:00:00.187

[DataSet1] C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Between-Subjects Factors

Value Label

N

Reason for stop/search

1

Officer Intuition

93

2

Suspect acting suspiciously

55

3

Called to Scene

42

4

Prior Information

24

5

Public Complaint

19

If complaint made, how satisfied was suspect with response

1

Very satisfied

28

2

Satisfied

77

3

Neither satisfied/unsatisfied

65

4

Dissatisfied

27

5

Very dissatisfied

36

How worried was suspect about crime in their area

1

Very worried

28

2

Fairly worried

19

3

Not too worried

34

4

Not at all worried

45

5

Not applicable

107

Tests of Between-Subjects Effects

Dependent Variable:Suspects employment

Source

Type III Sum of Squares

df

Mean Square

F

Sig.

Intercept

Hypothesis

5.658

1

5.658

4.008

.047

Error

216.560

153.419

1.412a

Weapon

Hypothesis

2.492

1

2.492

1.769

.185

Error

211.278

150

1.409b

Reason

Hypothesis

5.989

4

1.497

1.622

.198

Error

24.633

26.682

.923c

Satisfied

Hypothesis

1.504

4

.376

.516

.724

Error

21.586

29.644

.728d

Worry

Hypothesis

6.716

4

1.679

3.632

.182

Error

1.200

2.596

.462e

Reason * Satisfied

Hypothesis

15.872

16

.992

.903

.574

Error

30.113

27.402

1.099f

Reason * Worry

Hypothesis

12.337

15

.822

.733

.735

Error

37.044

33.030

1.122g

Satisfied * Worry

Hypothesis

9.903

16

.619

.554

.895

Error

35.777

32.007

1.118h

Reason * Satisfied * Worry

Hypothesis

23.569

22

1.071

.761

.769

Error

211.278

150

1.409b

a. .012 MS(Worry) – .000 MS(Reason * Worry) + 3.49E-006 MS(Satisfied * Worry) + .001 MS(Reason * Satisfied * Worry) + .987 MS(Error)

b.  MS(Error)

c. .836 MS(Reason * Worry) – .014 MS(Reason * Satisfied * Worry) + .178 MS(Error)

d. .859 MS(Satisfied * Worry) + .007 MS(Reason * Satisfied * Worry) + .134 MS(Error)

e. .874 MS(Reason * Worry) + .872 MS(Satisfied * Worry) – .755 MS(Reason * Satisfied * Worry) + .009 MS(Error)

f. .918 MS(Reason * Satisfied * Worry) + .082 MS(Error)

g. .851 MS(Reason * Satisfied * Worry) + .149 MS(Error)

h. .862 MS(Reason * Satisfied * Worry) + .138 MS(Error)

Expected Mean Squaresa,b

Source

Variance Component

Var(Worry)

Var(Reason * Worry)

Var(Satisfied * Worry)

Var(Reason * Satisfied * Worry)

Var(Error)

Quadratic Term

Intercept

.253

.054

.052

.023

1.000

Intercept, Reason, Satisfied, Reason * Satisfied

Weapon

.000

.000

.000

.000

1.000

Weapon

Reason

.000

4.280

.000

1.633

1.000

Reason, Reason * Satisfied

Satisfied

.000

.000

4.226

1.749

1.000

Satisfied, Reason * Satisfied

Worry

20.700

4.473

4.293

1.734

1.000

Reason * Satisfied

.000

.000

.000

2.149

1.000

Reason * Satisfied

Reason * Worry

.000

5.120

.000

1.992

1.000

Satisfied * Worry

.000

.000

4.922

2.018

1.000

Reason * Satisfied * Worry

.000

.000

.000

2.340

1.000

Error

.000

.000

.000

.000

1.000

a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell.

b. Expected Mean Squares are based on the Type III Sums of Squares.

GET   FILE=’C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav’. UNIANOVA SusWork BY Reason Satisfied Worry WITH Weapon   /RANDOM=Worry   /METHOD=SSTYPE(3)   /INTERCEPT=INCLUDE   /CRITERIA=ALPHA(0.05)   /DESIGN=Weapon Reason Satisfied Worry Reason*Satisfied Reason*Worry Satisfied*Worry Reason*Satisfied*Worry.

 

Univariate Analysis of Variance

Notes

Output Created

04-Jan-2013 22:56:21

Comments

Input

Data

C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

10609

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on all cases with valid data for all variables in the model.

Syntax

UNIANOVA SusWork BY Reason Satisfied Worry WITH Weapon

  /RANDOM=Worry

  /METHOD=SSTYPE(3)

  /INTERCEPT=INCLUDE

  /CRITERIA=ALPHA(0.05)

  /DESIGN=Weapon Reason Satisfied Worry Reason*Satisfied Reason*Worry Satisfied*Worry Reason*Satisfied*Worry.

Resources

Processor Time

0:00:00.140

Elapsed Time

0:00:00.187

[DataSet1] C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

  

Between-Subjects Factors

Value Label

N

Reason for stop/search

1

Officer Intuition

93

2

Suspect acting suspiciously

55

3

Called to Scene

42

4

Prior Information

24

5

Public Complaint

19

If complaint made, how satisfied was suspect with response

1

Very satisfied

28

2

Satisfied

77

3

Neither satisfied/unsatisfied

65

4

Dissatisfied

27

5

Very dissatisfied

36

How worried was suspect about crime in their area

1

Very worried

28

2

Fairly worried

19

3

Not too worried

34

4

Not at all worried

45

5

Not applicable

107

Tests of Between-Subjects Effects

Dependent Variable:Suspects employment

Source

Type III Sum of Squares

df

Mean Square

F

Sig.

Intercept

Hypothesis

5.658

1

5.658

4.008

.047

Error

216.560

153.419

1.412a

Weapon

Hypothesis

2.492

1

2.492

1.769

.185

Error

211.278

150

1.409b

Reason

Hypothesis

5.989

4

1.497

1.622

.198

Error

24.633

26.682

.923c

Satisfied

Hypothesis

1.504

4

.376

.516

.724

Error

21.586

29.644

.728d

Worry

Hypothesis

6.716

4

1.679

3.632

.182

Error

1.200

2.596

.462e

Reason * Satisfied

Hypothesis

15.872

16

.992

.903

.574

Error

30.113

27.402

1.099f

Reason * Worry

Hypothesis

12.337

15

.822

.733

.735

Error

37.044

33.030

1.122g

Satisfied * Worry

Hypothesis

9.903

16

.619

.554

.895

Error

35.777

32.007

1.118h

Reason * Satisfied * Worry

Hypothesis

23.569

22

1.071

.761

.769

Error

211.278

150

1.409b

a. .012 MS(Worry) – .000 MS(Reason * Worry) + 3.49E-006 MS(Satisfied * Worry) + .001 MS(Reason * Satisfied * Worry) + .987 MS(Error)

b.  MS(Error)

c. .836 MS(Reason * Worry) – .014 MS(Reason * Satisfied * Worry) + .178 MS(Error)

d. .859 MS(Satisfied * Worry) + .007 MS(Reason * Satisfied * Worry) + .134 MS(Error)

e. .874 MS(Reason * Worry) + .872 MS(Satisfied * Worry) – .755 MS(Reason * Satisfied * Worry) + .009 MS(Error)

f. .918 MS(Reason * Satisfied * Worry) + .082 MS(Error)

g. .851 MS(Reason * Satisfied * Worry) + .149 MS(Error)

h. .862 MS(Reason * Satisfied * Worry) + .138 MS(Error)

Expected Mean Squaresa,b

Source

Variance Component

Var(Worry)

Var(Reason * Worry)

Var(Satisfied * Worry)

Var(Reason * Satisfied * Worry)

Var(Error)

Quadratic Term

Intercept

.253

.054

.052

.023

1.000

Intercept, Reason, Satisfied, Reason * Satisfied

Weapon

.000

.000

.000

.000

1.000

Weapon

Reason

.000

4.280

.000

1.633

1.000

Reason, Reason * Satisfied

Satisfied

.000

.000

4.226

1.749

1.000

Satisfied, Reason * Satisfied

Worry

20.700

4.473

4.293

1.734

1.000

Reason * Satisfied

.000

.000

.000

2.149

1.000

Reason * Satisfied

Reason * Worry

.000

5.120

.000

1.992

1.000

Satisfied * Worry

.000

.000

4.922

2.018

1.000

Reason * Satisfied * Worry

.000

.000

.000

2.340

1.000

Error

.000

.000

.000

.000

1.000

a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell.

b. Expected Mean Squares are based on the Type III Sums of Squares.

REGRESSION   /MISSING LISTWISE   /STATISTICS COEFF OUTS R ANOVA   /CRITERIA=PIN(.05) POUT(.10)   /NOORIGIN   /DEPENDENT StopLocation   /METHOD=ENTER SusWork.

Regression

Notes

Output Created

05-Jan-2013 06:48:57

Comments

Input

Data

C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

10609

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on cases with no missing values for any variable used.

Syntax

REGRESSION

  /MISSING LISTWISE

  /STATISTICS COEFF OUTS R ANOVA

  /CRITERIA=PIN(.05) POUT(.10)

  /NOORIGIN

  /DEPENDENT StopLocation

  /METHOD=ENTER SusWork.

Resources

Processor Time

0:00:00.032

Elapsed Time

0:00:00.047

Memory Required

1820 bytes

Additional Memory Required for Residual Plots

0 bytes

[DataSet1] C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

Suspects employmenta

.

Enter

a. All requested variables entered.

b. Dependent Variable: Location of stop/search

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.010a

.000

.000

1.948

a. Predictors: (Constant), Suspects employment

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3.922

1

3.922

1.033

.309a

Residual

40270.242

10607

3.797

Total

40274.164

10608

a. Predictors: (Constant), Suspects employment

b. Dependent Variable: Location of stop/search

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

5.602

.056

99.315

.000

Suspects employment

-.016

.016

-.010

-1.016

.309

a. Dependent Variable: Location of stop/search

CORRELATIONS   /VARIABLES=StopLocation SusAge SusActivity SusWork Weapon OffAge Complaint Trust SusGender   /PRINT=TWOTAIL NOSIG   /MISSING=PAIRWISE.

Correlations

Notes

Output Created

05-Jan-2013 06:51:55

Comments

Input

Data

C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

10609

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics for each pair of variables are based on all the cases with valid data for that pair.

Syntax

CORRELATIONS

  /VARIABLES=StopLocation SusAge SusActivity SusWork Weapon OffAge Complaint Trust SusGender

  /PRINT=TWOTAIL NOSIG

  /MISSING=PAIRWISE.

Resources

Processor Time

0:00:00.046

Elapsed Time

0:00:00.031

[DataSet1] C:UsersmoseAppDataLocalTempLynfield Stop Data(1).sav

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