Unemployment Rates and Property Crime 2018

Introduction

In this project, I analyzed the similarities in US states when it comes to property crime rates and unemployment rates. My initial hypothesis is that the state with the highest property crime rate will also have the highest unemployment rate. I decided to compare these two topics because I think that more people engage in crimes such as burglary, larceny, and motor vehicle theft when they do not have a form of employment since they have no source of income.

Data

I made sure to use reliable sources in order to obtain my data. First, I visited the US Department of Justice Federal Bureau of Investigation’s website (https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/topic-pages/tables/table-5 ) where I found an elaborate chart of the state name, area, population, violent crime, murder, rape, robbery, aggravated assault, property crime, burglary, larceny, motor vehicle theft. The data had much more information than necessary, so I downloaded it and made my own spreadsheet from it. I copied and pasted everything over to a new Excel sheet where I only focused in on US states alongside their various property crimes (burglary, larceny, and motor vehicle). To find the unemployment data, I went to the US Bureau of Labor Statistics website (https://www.bls.gov/opub/ted/2018/unemployment-rates-down-over-the-year-in-10-states-unchanged-in-40-states-and-the-district.htm). Here I found the unemployment rates down over the year in 10 states, unchanged in 40 states and the District. Similar to the other dataset, it contained a lot of extra information so I downloaded it and deleted the July 2017 column and the over the year column. That left me with the states ranked in order from highest amount of employed to lowest, alongside their employment rate percentage in July of 2018.

Layout of Project

The layout of my project is as follows: I first provide the Tableau maps, where viewers will be able to first view a map that shows the US and the property crime. The next map focuses on the area that has the highest property crime rate and shows the various number of crimes in the state. The last map is of the US and focuses on the unemployment rate per state. Finally, I combine the data found by comparing the property crime and unemployment numbers in a chart.

library(tidyverse)
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## ✓ readr   1.4.0     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
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Map 1 - 2018 States and Property Crime

This map depicts the entire US as well as the property crime rates. It was important for me to use “property crime per 100,000 inhabitants” because every state varies in their own population. The red shades of the map symbolize the population per state. The blue circles symbolize the number of property crime per 100,000 inhabitants. When looking at the map as a whole, you’ll notice that the blue dot is the largest on the District of Columbia. The District had property crime of 4,374.

Map 2 - District of Columbia and Property Crime

Once I noticed that the District of Columbia was the US area with the highest number of property crime, I wanted to focus in on the location. My dataset contained the three areas of property crime - burglary, larceny, motor vehicle theft. I was curious what these numbers were in DC, so I adjusted my data to show the specifics of property crime in DC as a whole. It was interesting to find that the motor vehicle theft number was 369, burglary was 254.5 and larceny was extremely high at 3,750. From this, the point can be made that DC’s high property crime rate comes from larceny the most.

Map 3 - 2018 States and Unemployment Rate

Lastly, I wanted to observe the unemployment rate amongst the entire US. Similar to Map 1, The red shades of the map symbolize the population per state. I organized the dots to be a different color per state. It was found that the District of Columbia had the second highest unemployment rate in July 2018 at 5.6. This was an extremely interesting find, and proved my hypothesis correct. DC - which had the highest property crime numbers - also had a very high unemployment rate.

Comparing DC’s High Unemployment and Property Crime Rates 

library(tidytext)
library(readxl)
combo <- read_excel("~/Desktop/combo.xlsx")
 colnames(combo) <- c('State','Unemployment','Property Crime','Burglary','Larceny', 'Motor Vehicle Theft')
 
ggplot(combo, aes(Unemployment, `Property Crime`)) +  geom_point() + geom_label(label=combo$State, size= 3)

This plot depicts the overlap between the property crime and unemployment throughout the US states. It is visible that the District of Columbia is the outlier in the plot. It is at the highest point for both unemployment and property crime together, proving that in 2018 the District of Columbia faced very high unemployment and property crime rates in comparison to the other states.

Conclusion

In conclusion, it was very interesting to see that in 2018 the property crimes in DC correlated with the high unemployment rate in DC. I do not think that this is a coincidence. I think that since there was such a high rate of unemployment in the area, it made more people want to commit crimes such as burglary, larceny, and motor vehicle theft since they had no source of income. My hypothesis was centered around all US states having a correlation between high property crimes and high unemployment, but the chart depicted that it wasn’t the case for every state. Yet, for majority of the states it was - especially DC. My overall findings however solely focused on the District of Columbia, which was a proven area to have both high unemployment and high property crime in 2018. It would be interesting to see the numbers from 2020, especially the unemployment numbers due to COVID, and see if that also related to high property crimes.