Does Weather Influence Crime?

Introduction 

The rate of crime is a matter of concern to most people, with the security agents and bodies talking the highest interested in the manner of crime waves. As a result, the factors that may influence the nature and manner of crime committed among a population of people is the very vital knowledge that can determine how well the security personnel can be able to address the increasing case of crime. One of the factors that may influence the prevalence of crime committed among a population of people is the weather. The weather has the capability of either accelerating or discouraging crime among people. Therefore, it is vital that the security agencies understand how the changes in weather may affect the crime and the specific kinds of crimes that can be attributed to the changes in the weather patterns. 

The security personnel and agencies must adequately address different kinds of crime. One of the major security threats that are prevalent in developed countries like the USA is a homicide. The security personnel is interested in understanding whether homicide can be accelerated or discourages by different changes in the weather patterns. Other different nature of crime include the elements of forcible rape, Sodomy and other kinds of sexual assaults that may be reported among a community of people. 

Elements like forcible fondling and incest are other criminal elements that may be deponent on the nature of weather patterns and weather changes reports in a community. There are also other crimes like robbery with violence, suicide cases and murders that can also be reported based on the kind of weather the community experiences at any particular time. According to the research study that was undertaken by Ransom (2014), it was understandable that the elements of different kinds of crime took a near definite pattern in their occurrences and reporting. 

The police departments like the homeland security and the anticrime units are interested in understanding how the changes in the weather patterns would influence every crime. It is such patterns that the law enforcement officers need to understand as a way of curbing the escalating incidences of crime. The same research was confirmed by Linning, Andresen, and Brantingham (2017), who found that there exists a close correlaation between the number of criminal cases reported and the nature of the weather activities in any state in America. 

Problem Statement 

The number of criminal cases reported among populations should be curtailed and reduced to the bare minimum. The law enforcement officers are interested in determining the possible causes of the high frequencies of the criminal cases. There is a possibility that the changes in the weather patterns may have a significant influence on the manner and the kind of criminal cases reported among a population. Therefore, it is useful if the security enforcing agencies are well informed about the possible correlation between the weather patterns and the frequency of the criminal cases reported. Such knowledge about the possible correlation between the weather patterns and the crime rates reports cannot be as a result of dead reckoning but must be based on the right information or knowledge received as a result of the empirical analysis. 

Objectives of the Study 

The objectives of the study include 

To determine the influence that prevalence of precipitation has on the number of crime committed 

To determine how the change in temperature may influence the prevalence of crime cases 

Study Hypothesis 

H0: There is no relationship between the rate of precipitation and the frequency of crime committed 

H0: There is no relationship between the change in temperature and the prevalence of crime cases 

The methodology of the Study 

The research model will be qualitative, making use of primary data to model the kind of relationships that may exist between the weather of a place and the criminal cases in the place. To model, the kind of relationships that would exist between the weather and the crimes committed, the data published by the Louisville department of police, the arm of the state dealing in crimes would be useful. The data include the continuous publications that have been done since the year 2003 to 2018. The data contains many different kinds of criminal cases that have been reported to the police departments most of the time with corresponding cases of weather patterns on the dates when the crimes were committed. For the study, random sampling was conducted to find the averages of the scores. Moreover, only one case of crime, robbery with violence was sampled for the development of a representational study analysis. The data used in the study were therefore exclusively police reported cases based on the robbery with violence as a type of crime. The data is available on the Louisville police website (Lousviille Police Department, 2018) and also from the college scorecard database (US Departement of Education, 2018) . 

The kinds of analysis done on the data are both descriptive and inferential. The descriptive data will involve the determination of the mean, mode, variances as measures of central tendency. On the other hand, the inferential analysis will include the regression analysis and ANOVA that illustrates the deeper meaning of the data. 

Descriptive Statistics 

Below is the illustrated table that informs the kin of descriptive analysis that was possible in the data collected by the police department pertaining to the crime cases in the years between 2003 and 2018.

Statistics
			Average Temperature		Average Precipitation		Average Crimes Reported
N	Valid		7594		7594		7594
	Missing		0		0		0
Mean		20.4333		2621.7667		4157.7000
Median		16.5000		1778.0000		1940.5000
Std. Deviation		12.52496		2749.93411		6566.57284
Variance		156.875		7562137.633		4.312E7

From the above illustration, the mean mode and the variances of the response data indicates that the data is evenly distributed with not cases of data outliers, since the average scores in each case is consistent. Based on the possibility of determining correlationship between the independent weather variables, precipitation and temperature, the following correlational analysis were conducted to determine the level of correlation. 

Correlations
			Average Crimes Reported	Average Temperature
Average Crimes Reported		Pearson Correlation	1	.659 **
		Sig. (2-tailed)		.000
		N	30	30
Average Temperature		Pearson Correlation	.659 **	1
		Sig. (2-tailed)	.000
		N	30	30
**. Correlation is significant at the 0.01 level (2-tailed).

Based on the two tailed correlational analysis between the average crime cases reported and the average weather temperature, it can be determined that the correlation coefficient is 0.659, which is a strong positive correlation. It means that an increase in temperature is likely to increase the level of violent crime, and a decrease in the temperature would lead to a reduced case of violent crime reported. 

Correlations
		Average Crimes Reported	Average Precipitation
Average Crimes Reported	Pearson Correlation	1	-.221
	Sig. (2-tailed)		.240
	N	30	30
Average Precipitation	Pearson Correlation	-.221	1
	Sig. (2-tailed)	.240
	N	30	30

The two tailed Pearson correlation coefficient illustrated din the table above depicts that there is a weak negative correlation between the level of precipitation and the cases of violent crimes conducted. It means that an increase in precipitation reduces cases of violence as a crime. Below is a chart that summarizes the distribution of the scores of reported violence based on the temperature levels and the precipitation levels. 

Regression Analysis 

Regression analysis gives a more accurate interpretation of correlation between the dependent and the independent variables. In the regressional analysis, a model summary, Anova analysis and regression models are used to determine the direction and the strength of the correlation. 

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.669 a	.447	.406	5060.35791
a. Predictors: (Constant), Average Precipitation, Average Temperature
b. Dependent Variable: Average Crimes Reported

The above is a regression model, with the value of R given as 0.669. This means that there is a 66.9% chances that the independent variables, the level of precipitation and the level of temperature can influence the rate of crime, with only 33.1% cues of crime attributed to other factors. Below is a summary of the Anova analysis. 

ANOVA b
Model		Sum of Squares		df	Mean Square			F	Sig.
1	Regression		5.591E8			2	2.795E8			10.916	.000 a
	Residual		6.914E8			27	2.561E7
	Total		1.250E9			29
a. Predictors: (Constant), Average Precipitation, Average Temperature
b. Dependent Variable: Average Crimes Reported

The Anova analysis above indicates that the significance interval is 0.000, which means that integrity of the research data used in this analysis can be able to yield at least 95% confidence level. This is a significant score that alludes to the credibility of the research outcome. 

Coefficients
Model		Unstandardized Coefficients	Standardized Coefficients		t		Sig.
B	Std. Error		Beta
1	(Constant)			1977.621		2136.242				-.926	.363
	Average Temperature			335.490		76.077		.640		4.410	.000
	Average Precipitation			-.275		.347		-.115		-.792	.435
a. Dependent Variable: Average Crimes Reported

Using the g=regression model Y= C + A1X1 + A2X2, the regressional analysis can be determined as 

Y is the Y- Intercept 

C is the regression constant, 1977.621 

A is the coefficient of the first independent variable, temperature, 335.490 

B is the regression coefficient of the second independent variable, precipitation, -.275 

Therefore, the regression model is 1977.621 + 335.490 Average Temperature – 0. 275 Average Precipitation 

Results Discussion 

The security department and the other law enforcement agencies are normally interested in undemanding how the weather experienced in a day may be an influence in the possible change in the frequency of the crime cases reported. As a result, it is important to find objective and empirical information for where they can base their analysis and understanding of the crime wave rates in their areas of jurisdiction. This is particularly important when there is need for the development of policies to counter the possible increase in the crime prevalence among the population. Therefore, this research study will play a vital role in determine the correlations between the weather experienced in place and the frequency of the crime cases reported in the place. 

As a results it was useful to evaluate how the research addressed the hypothesis the study. The first hypothesis was; 

H 0 : There is no relationship between the rate of precipitation and the frequency of crime committed 

H 0 : There is no relationship between the change in temperature and the prevalence of crime cases 

From the study, it can be noted that the null hypothesis, there is no relationship between the rate of precipitation and the frequency of crime committed is dropped for the alternative hypothesis, “There is relationship between the rate of precipitation and the frequency of crime committed”. Moreover, the other null hypothesis, There is no relationship between the change in temperature and the prevalence of crime cases, is also dropped for the alternate hypothesis, “There is relationship between the change in temperature and the prevalence of crime cases” 

References 

Linning, S. J., Andresen, M. A., & Brantingham, P. J. (2017). Crime seasonality: Examining the temporal fluctuations of property crime in cities with varying climates.  International journal of offender therapy and comparative criminology ,  61 (16), 1866-1891. 

Lousviille Police Department. (2018). Crime Data . Retrieved from Lousviille Open Data: Police Department : https://data.louisvilleky.gov/dataset/crime-data 

Ranson, M. (2014). Crime, weather, and climate change.  Journal of environmental economics and management ,  67 (3), 274-302. 

US Departement of Education . (2018). Data Documentation . Retrieved from US Departement of Education : https://collegescorecard.ed.gov/data/