Hate Crime Data
Cade Miller, Gena Hoxha, Jose Cervantes
October 18, 2015
Introduction
According to the Federal Bureau, hate crimes include incidents and offenses that were motivated in whole or in part by a bias against the victim’s perceived race, religion, ethnicity, sexual orientation, or disability.
By conducting this study, we intend to identify how person and property hate crimes are spread geographically throughout the United States. For the purposes of this study, the data has been derived from the Federal Bureau of Investigation website and R Studio has been utilized to analyze the data and create the graphs. The FBI releases a separate annual publication on hate crime data in the United States - Hate Crime Statistics. The report is based on voluntarily submitted data from over 18,000 law enforcement agencies across the United States.
Using yearly data for various states, we have compiled an analysis of the spread of hate crimes by geographic region: West, South West, Mid West, South East, and North East. In addition to viewing the role of geography on hate crimes, we also investigated the role of population on the spread of hate crimes.
Questions Posed
Throughout this data analysis, we intend to shed some light on the following questions:
Has the occurrence of person and property hate crimes experienced any significant changes from 2012 to 2013?
How are person and property hate crimes spread throughout states in 2012 and 2013?
How are person and property hate crimes spread per geographic region in 2012 and 2013?
How does population affect the occurrence of person and property hate crimes?
The following analysis and discussion delves into these issues.
Comparing Crimes against People for the years 2012 and 2013
Personlong=gather(Person, Year, count, c(2:3))
ggplot(Personlong, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity", position="dodge") + coord_flip()+ ylab("Total") +xlab("State") +ggtitle("Person Crimes Overview")
Data Analysis: Overview
This overview graph compares the occurrence of hate crimes per state in 2012 versus 2013. As indicated in the graph, the hate crimes experienced a decrease from 2012 to 2013 in the majority of states (with the exception of North Carolina, Washington D.C., Georgia, Wisconsin, Arkansas, and Utah). One of the main reasons for this decrease is the stricter definition of hate crimes in certain states, the increased negative publicity and attention, as well as the increase in police enforcement of hate crime laws.
Moreover, as the figure demonstrates, California has the highest amount of hate crime offenses and incidents in 2012 and 2013. The most crucial fact to explain the very apparent gap between California and other states is the fact that California has one of the most comprehensive hate crime reporting schemes. Since 1995, the Office of Attorney General of California has released an annual report on the hate crime in the state. While it is true that California experiences an increased amount of hate crimes, the figure below might present a distorted view, given the fact that data submissions to the FBI are voluntary.
Person Crimes by State Population
PersonPopulation=gather(PersonPopulation, Year, count, c(2:3))
ggplot(PersonPopulation, aes(count, Population, color = Year)) +
geom_point(size=3) +coord_flip() + ylab("Population") +xlab("Total Crimes") +ggtitle("Person Crimes by State Population") +scale_y_continuous(limits=c(0,40000000), breaks=seq(0,40000000,4000000), expand=c(0,0), labels=comma)
As you may have predicted, there is a slight trend of an increase in crimes against people as the population increases. Most of the data is grouped in the lower left corner but California comes in with the highest population and the most crime and screws the data up. It will be interesting to see how the shifts for the years to come.
Person Crimes by Region
Person Crimes West
# PersonWest=PersonWest%>%group_by(State,Year)%>%summarise(count=length(count))
PersonWest=gather(PersonWest, Year, count, c(2:3))
ggplot(PersonWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") + ylab("Total") +xlab("State") +ggtitle("Person Crimes West Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
The figure above (Figure 2) shows the trend of hate crime in the West region in 2012 and 2013. As indicated in the graph, California, Washington, and Colorado have the highest occurrence of hate crimes in 2013. The surprising figure was the spike in Washington’s hate crime rate in 2013. With the exception of Washington States, in the Western region of the US, hate crimes experienced a decrease from 2012 to 2013.
Person Crimes South West
# PersonSouthWest=PersonSouthWest%>%group_by(State,Year)%>%summarise(count=length(count))
PersonSouthWest=gather(PersonSouthWest, Year, count, c(2:3))
ggplot(PersonSouthWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") + ylab("Total") +xlab("State") +ggtitle("Person Crimes South West Region") +geom_text(aes(label=count), vjust=1, colour="black",position=position_dodge(1), size=4) + coord_flip()
In the Southwest region, hate crimes against persons did not follow a certain trend. While the hate crime rates remained the same in Arizona and drastically decreased in New Mexico, they experienced a considerable increase in Texas in 2013.
Person Crimes Mid West
# PersonMidWest=PersonMidWest%>%group_by(State,Year)%>%summarise(count=length(count))
PersonMidWest=gather(PersonMidWest, Year, count, c(2:3))
ggplot(PersonMidWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Person Crimes Mid West Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
In the Mid West region, however, the hate crime rate experienced an increase in the majority of states in 2013. As the figure below indicates, Michigan has the highest occurrence number of hate crimes against persons in 2012 and 2013. FBI data indicates that while the overall rate of violent hate crime has decreased in 2013 for the state as a whole, Detroit still remains among the nation’s most dangerous cities.
Person Crimes South East
# PersonSouthEast=PersonSouthEast%>%group_by(State,Year)%>%summarise(count=length(count))
PersonSouthEast=gather(PersonSouthEast, Year, count, c(2:3))
ggplot(PersonSouthEast, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Person Crimes South East Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
In the South East region, crimes tended to remain fairly constant in states such as Alabama and Kentucky. It is in the states Tennessee and Virginia that we see a real shift happening. In Virginia crime decreased significantly, whereas in Tennessee, crime increased significantly. We could attribute these shifts to skews in the data or perhaps there was regulation put in place to reduce crime. As for the increase in crime in Tennessee, it could be attributed to their acute poverty and their lower high school and college graduation rates. Either way, something needs to be addressed so this trend does not continue.
Person Crimes North East
# PersonNorthEast=PersonNorthEast%>%group_by(State,Year)%>%summarise(count=length(count))
PersonNorthEast=gather(PersonNorthEast, Year, count, c(2:3))
ggplot(PersonNorthEast, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Person Crimes North East Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
The North East region saw some significant spikes in their results. States such as New York, New Jersey, Pennsylvania and Massachusetts all saw significant increases in crimes from the previous years. One source explained that this trend could be because of the declining trend in crime over the past twenty years and the sudden increase may not be that much respectively but because of previous years being so low, even a small increase in violent victimization becomes a large percentage change.
Comparing Crimes against Property for the years 2012 and 2013
# Property=Property%>%group_by(State,Year)%>%summarise(count=length(count))
Propertylong=gather(Property, Year, count, c(2:3))
ggplot(Propertylong, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") + coord_flip()+ ylab("Total") +xlab("State") +ggtitle("Property Crimes Overview")
Data Analysis: Overview
This graph showcases crime property in the United States in 2012 and 2013. The data here is more inconstant than that of hate crimes with a near 50/50 spread in terms of decreases/increases in reported incidents. The FBI collection agency reports only a 4.1% decrease nationwide though most of this can be attributed to NM (from 427 to below 5). A more in-depth look by regions shows a better picture of how the US crime property has risen or fallen. Factors contributing towards an increase or decrease in incidents and prevention (thereby lack of incident) attributes largely to the size of the population, rate of drug related crimes (narconon.org), size of the middleclass in said state, filings procedures and what is reported voluntarily.
Property Crimes by State Population
PropertyPopulation=gather(PropertyPopulation, Year, count, c(2:3))
ggplot(PropertyPopulation, aes(count, Population, color = Year)) +
geom_point(size=3) +coord_flip() + ylab("Population") +xlab("Total Crimes") +ggtitle("Property Crimes by State Population") +scale_y_continuous(limits=c(0,40000000), breaks=seq(0,40000000,4000000), expand=c(0,0), labels=comma)
Again, as in the first scatterplot, there is a trend of an increase in crimes against property as the population increases. Most of the data is grouped in the lower left corner but California comes in with the highest population and the most crime and screws the data up. It will be interesting to see how the shifts for the years to come.
Property Crimes By Region
Property Crimes West
# PropertyWest=PropertyWest%>%group_by(State,Year)%>%summarise(count=length(count))
PropertyWest=gather(PropertyWest, Year, count, c(2:3))
ggplot(PropertyWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Property Crimes West Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
This graphic shows the West Region with California leading both the region and country in property crimes in 2013 (379) and was second in the US in 2012 (387). The most notable change from year to year for any state in the region was Washington with near 1000% increase (from 16 to 128 incidents). Some of this may attribute to the new legislation that passed for marijuana to be legalized for recreational use. Though the drug itself is likely not the direct cause, the effect around for other drugs to be a greater attributer towards more violent crimes and thereby leading to increase in property damage from said incidents. Colorado meanwhile saw the biggest decrease in % and numbers (45%, down to 48 from 91). This is a stat to note though since much of this is from a decrease in burglary (down 3.52%) which is the biggest by volume for property crimes whereas larceny and auto theft increased by 2.27% and 3.76% respectively (cpr.org).
Property Crimes South West
# PropertySouthWest=PropertySouthWest%>%group_by(State,Year)%>%summarise(count=length(count))
PropertySouthWest=gather(PropertySouthWest, Year, count, c(2:3))
ggplot(PropertySouthWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") + ylab("Total") +xlab("State") +ggtitle("Property Crimes South West Region") +geom_text(aes(label=count), vjust=1, colour="black",position=position_dodge(1), size=4) + coord_flip()
This highlights the South West region with one key large change. New Mexico nearly has under 5 reported incidents in 2013 as compared to 2012 (427). This large dip is considered by the state to be somewhat more of an anomaly and absence of proper reporting (FBI.gov). Apart from this rather vague reason the cause for such a decline remains a very curious question that can use more analysis based on other variables.
Property Crimes Mid West
# PropertyMidWest=PropertyMidWest%>%group_by(State,Year)%>%summarise(count=length(count))
PropertyMidWest=gather(PropertyMidWest, Year, count, c(2:3))
ggplot(PropertyMidWest, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Property Crimes Mid West Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
This visual represents property crime in the Mid West region with several states that show a large change year to year. Michigan saw more than a 33% decrease in property crimes from 2012 to 2013 (157 to 98). North Dakota and South Dakota saw an even sharper fall in property crime from near 120 to 17 and 103 to 7 respectively. The largest rise in property crime in the region was Ohio with over 180 reported incidents in 2013 compared to 5 in 2012.
Property Crimes South East
# PropertySouthEast=PropertySouthEast%>%group_by(State,Year)%>%summarise(count=length(count))
PropertySouthEast=gather(PropertySouthEast, Year, count, c(2:3))
ggplot(PropertySouthEast, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Property Crimes South East Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
Property Crimes in the South East Region is shown here. In this region there are 2 states that have a sharp decrease in property crimes while two show a large increase. Virginia leads the region with over 125 crimes in 2012 but fell to second in 2013 with a decrease of nearly 50% (to 68). Florida was third in the region in 2012 with 57 incidents but fell to under 35 in 2013. Tennessee and North Carolina saw the largest increase in incidents going up 46 from 25 and 49 from 21 respectively. These states became the third and fourth highest for incidents in the region.
Property Crimes North East
# PropertyNorthEast=PropertyNorthEast%>%group_by(State,Year)%>%summarise(count=length(count))
PropertyNorthEast=gather(PropertyNorthEast, Year, count, c(2:3))
ggplot(PropertyNorthEast, aes(x=reorder(State,count),y=count,fill=Year)) + geom_bar(stat = "identity",position="dodge") +ylab("Total") +xlab("State") +ggtitle("Property Crimes North East Region") +geom_text(aes(label=count), vjust=0.5, colour="black",position=position_dodge(1), size=3) + coord_flip()
This last graph shows the last region, North East, for property crimes. Here there are 4 states show the most change from year to year. New York was in the mid tiers for property crimes in 2012 but rose up to second in report incidents in the country at 321. New Jersey saw a very large increase in incidents at 151 from nearly 0 in 2012. The fact that both these states are next to each other suggest a correlation with the large increase and should be noted for further analysis. The two states with notable decreases in property crime in this region is Rhode Island and Vermont. Here both states drop from 78 and 75 to 5 and 6 reported incidents from 2012 to 2013.
Limitations
The nature of property crime as shown in the data shows some rather large changes from year to year. Gathering data across several years would show a more consistent direction as to the rate of property crime in the states. The large variations can also be attributed to the nature of voluntarily filing said crimes, new legislations and initiatives for reporting said crimes or not and whether certain counties in said states report offenses or not depending on severity or arrest record. Heighten awareness in reporting said crimes year to year can also attribute to a state either seeing a large increase (newly passed initiative) or large decrease (results from heighten awareness in previous years leading to low rates the following year). Since the data provided in Hate Crime Statistics is voluntarily submitted to the FBI, the data on hate crime is widely underrepresents the true extent of hate crime in the United States. This is due to lack of participation, lax recording of hate crime incidents and offenses, and different hate crime classification practices of law enforcement agencies in various states.
Summary
Based on the two main crime reports (person and property) there is a fairly high correlation between the two types of crimes as far as change from year to year. This is especially true with the highest change in crime in states such as New York, New Jersey, California, New Mexico and Washington. This suggest that the two crimes are either often part of the same police and voluntary report or that the rates are close to the same because the criminals that perform one crime may also perform another the crime due to their disregard for the law and rights of the people around them. Looking at only one criteria instead of both, the crime rates for each state from year to year did not show a strong pull in either an increase or decrease in crime as a whole. States are subject to their own demographics and state legislations and that of neighboring states. It is very hard to try and decrease the US rate of population as a whole with each state having either different reporting styles, new initiatives to decrease rates or even polarized events that insinuate more violence and crime (such as riots and major events that cause anger against the government). The data and graphs show enough to suggest that it is worthwhile, however, to look at individual states and see why the dramatic changes occurred from 2012 to 2013.