This dataset looks at all types of hate crimes in New York counties by the type of hate crime from 2010 to 2016.
My caveat:
Flawed hate crime data collection - we should know how the data was collected
(Nathan Yau of Flowing Data, Dec 5, 2017)
Data can provide you with important information, but when the collection process is flawed, there’s not much you can do. Ken Schwencke, reporting for ProPublica, researched the tiered system that the FBI relies on to gather hate crime data for the United States:
“Under a federal law passed in 1990, the FBI is required to track and tabulate crimes in which there was ‘manifest evidence of prejudice’ against a host of protected groups, regardless of differences in how state laws define who’s protected. The FBI, in turn, relies on local law enforcement agencies to collect and submit this data, but can’t compel them to do so.”
This is a link to the ProPublica Article: https://www.propublica.org/article/why-america-fails-at-gathering-hate-crime-statistics
Here is a data visualization of where hate crimes do NOT get reported around the country (Ken Schwencke, 2017): https://projects.propublica.org/graphics/hatecrime-map
So now we know that there is possible bias in the dataset, what can we do with it?
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Rows: 423 Columns: 44
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): County, Crime Type
dbl (42): Year, Anti-Male, Anti-Female, Anti-Transgender, Anti-Gender Identi...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Clean up the data:
Make all headers lowercase and remove spaces
After cleaning up the variable names, look at the structure of the data. Since there are 44 variables considered in this dataset, you can use “summary” to decide which hate crimes to focus on. In the output of “summary”, look at the min/max values. Some have a max-vale of 1.
county year crimetype anti-male
Length:423 Min. :2010 Length:423 Min. :0.000000
Class :character 1st Qu.:2011 Class :character 1st Qu.:0.000000
Mode :character Median :2013 Mode :character Median :0.000000
Mean :2013 Mean :0.007092
3rd Qu.:2015 3rd Qu.:0.000000
Max. :2016 Max. :1.000000
anti-female anti-transgender anti-genderidentityexpression
Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.01655 Mean :0.04728 Mean :0.05674
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :5.00000 Max. :3.00000
anti-age* anti-white anti-black
Min. :0.00000 Min. : 0.0000 Min. : 0.000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.000
Median :0.00000 Median : 0.0000 Median : 1.000
Mean :0.05201 Mean : 0.3357 Mean : 1.761
3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 2.000
Max. :9.00000 Max. :11.0000 Max. :18.000
anti-americanindian/alaskannative anti-asian
Min. :0.000000 Min. :0.0000
1st Qu.:0.000000 1st Qu.:0.0000
Median :0.000000 Median :0.0000
Mean :0.007092 Mean :0.1773
3rd Qu.:0.000000 3rd Qu.:0.0000
Max. :1.000000 Max. :8.0000
anti-nativehawaiian/pacificislander anti-multi-racialgroups anti-otherrace
Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0.00000 Median :0
Mean :0 Mean :0.08511 Mean :0
3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :3.00000 Max. :0
anti-jewish anti-catholic anti-protestant anti-islamic(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.02364 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :1.00000 Max. :10.0000
anti-multi-religiousgroups anti-atheism/agnosticism
Min. : 0.00000 Min. :0
1st Qu.: 0.00000 1st Qu.:0
Median : 0.00000 Median :0
Mean : 0.07565 Mean :0
3rd Qu.: 0.00000 3rd Qu.:0
Max. :10.00000 Max. :0
anti-religiouspracticegenerally anti-otherreligion anti-buddhist
Min. :0.000000 Min. :0.000 Min. :0
1st Qu.:0.000000 1st Qu.:0.000 1st Qu.:0
Median :0.000000 Median :0.000 Median :0
Mean :0.007092 Mean :0.104 Mean :0
3rd Qu.:0.000000 3rd Qu.:0.000 3rd Qu.:0
Max. :2.000000 Max. :4.000 Max. :0
anti-easternorthodox(greek,russian,etc.) anti-hindu
Min. :0.000000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.000000
Median :0.000000 Median :0.000000
Mean :0.002364 Mean :0.002364
3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.000000
anti-jehovahswitness anti-mormon anti-otherchristian anti-sikh
Min. :0 Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0 Median :0.00000 Median :0
Mean :0 Mean :0 Mean :0.01655 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :0 Max. :3.00000 Max. :0
anti-hispanic anti-arab anti-otherethnicity/nationalorigin
Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 0.3735 Mean :0.06619 Mean : 0.2837
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :17.0000 Max. :2.00000 Max. :19.0000
anti-non-hispanic* anti-gaymale anti-gayfemale anti-gay(maleandfemale)
Min. :0 Min. : 0.000 Min. :0.0000 Min. :0.0000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0 Median : 0.000 Median :0.0000 Median :0.0000
Mean :0 Mean : 1.499 Mean :0.2411 Mean :0.1017
3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :0 Max. :36.000 Max. :8.0000 Max. :4.0000
anti-heterosexual anti-bisexual anti-physicaldisability
Min. :0.000000 Min. :0.000000 Min. :0.00000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00000
Median :0.000000 Median :0.000000 Median :0.00000
Mean :0.002364 Mean :0.004728 Mean :0.01182
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00000
Max. :1.000000 Max. :1.000000 Max. :1.00000
anti-mentaldisability totalincidents totalvictims totaloffenders
Min. :0.000000 Min. : 1.00 Min. : 1.00 Min. : 1.00
1st Qu.:0.000000 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :0.000000 Median : 3.00 Median : 3.00 Median : 3.00
Mean :0.009456 Mean : 10.09 Mean : 10.48 Mean : 11.77
3rd Qu.:0.000000 3rd Qu.: 10.00 3rd Qu.: 10.00 3rd Qu.: 11.00
Max. :1.000000 Max. :101.00 Max. :106.00 Max. :113.00
Select only certain hate-crimes
I decided I would only look at the hate-crime types with a max number or 9 or more. That way I can focus on the most prominent types of hate-crimes.
Check the dimensions and the summary to make sure no missing values
Also check the dimensions to count how many variables remain
dim(hatecrimes2)
[1] 423 10
summary(hatecrimes2)
county year anti-black anti-white
Length:423 Min. :2010 Min. : 0.000 Min. : 0.0000
Class :character 1st Qu.:2011 1st Qu.: 0.000 1st Qu.: 0.0000
Mode :character Median :2013 Median : 1.000 Median : 0.0000
Mean :2013 Mean : 1.761 Mean : 0.3357
3rd Qu.:2015 3rd Qu.: 2.000 3rd Qu.: 0.0000
Max. :2016 Max. :18.000 Max. :11.0000
anti-jewish anti-catholic anti-age* anti-islamic(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.05201 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :9.00000 Max. :10.0000
anti-gaymale anti-hispanic
Min. : 0.000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000
Mean : 1.499 Mean : 0.3735
3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :36.000 Max. :17.0000
Use Facet_Wrap
Look at each set of hate-crimes for each type for each year. Use the package “tidyr” to convert the dataset from wide to long with the command “gather”. It will take each column’s hate-crime type combine them all into one column called “id”. Then each cell count will go into the new column, “crimecount”. Finally, we are only doing this for the quantitiative variables, which are in columns 3 - 10. Note the command facet_wrap requires (~) before “id”.
Look deeper into crimes against blacks, gay males, and jews
From the facet_wrap plot above, anti-black, anti-gay males, and anti-jewish categories seem to have highest rates of offenses reported. Filter out just for those 3 crimes.
hatenew <- hatecrimeslong %>%filter( id=="anti-black"| id =="anti-jewish"| id =="anti-gaymale") %>%group_by(year, county) %>%arrange(desc(crimecount))hatenew
Use the following commands to finalize your barplot: - position = “dodge” makes side-by-side bars, rather than stacked bars - stat = “identity” allows you to plot each set of bars for each year between 2010 and 2016 - ggtitle gives the plot a title - labs gives a title to the legend
plot2 <- hatenew %>%ggplot() +geom_bar(aes(x=year, y=crimecount, fill = id),position ="dodge", stat ="identity") +ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +ylab("Number of Hate Crime Incidents") +labs(fill ="Hate Crime Type")plot2
We can see that hate crimes against jews spiked in 2012. All other years were relatively consistent with a slight upward trend. There was also an upward trend in hate crimes against gay males. Finally, there appears to be a downward trend in hate crimes against blacks during this period.
What about the counties?
I have not dealt with the counties, but I think that is the next place to explore. I can make bar graphs by county instead of by year.
plot3 <- hatenew %>%ggplot() +geom_bar(aes(x=county, y=crimecount, fill = id),position ="dodge", stat ="identity") +ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +ylab("Number of Hate Crime Incidents") +labs(fill ="Hate Crime Type")plot3
So many counties
There are too many counties for this plot to make sense, but maybe we can just look at the 5 counties with the highest number of incidents. - use “group_by” to group each row by counties - use summarize to get the total sum of incidents by county - use arrange(desc) to arrange those sums of total incidents by counties in descending order - use top_n to list the 5 counties with highest total incidents
`summarise()` has grouped output by 'county'. You can override using the
`.groups` argument.
counties
# A tibble: 277 × 3
# Groups: county [60]
county year sum
<chr> <dbl> <dbl>
1 Kings 2012 136
2 Kings 2010 110
3 Kings 2016 101
4 Kings 2013 96
5 Kings 2014 94
6 Kings 2015 90
7 Kings 2011 86
8 New York 2016 86
9 Suffolk 2012 83
10 New York 2013 75
# ℹ 267 more rows
Finally, create the barplot above, but only for the 5 counties in 2012 with the highest incidents of hate-crimes. The command “labs” is nice, because you can get a title, subtitle, y-axis label, and legend title, all in one command.
plot4 <- hatenew %>%filter(county =="Kings"| county =="New York"| county =="Suffolk"| county =="Nassau"| county =="Queens") %>%ggplot() +geom_bar(aes(x=county, y=crimecount, fill = id),position ="dodge", stat ="identity") +labs(ylab ="Number of Hate Crime Incidents",title ="5 Counties in NY with Highest Incidents of Hate Crimes", subtitle ="Between 2010-2016", fill ="Hate Crime Type")plot4
How would calculations be affected by looking at hate crimes in counties per year by population densities?
Bring in census data for populations of New York counties. These are estimates from the 2010 census.
Rows: 62 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Geography
dbl (7): 2010, 2011, 2012, 2013, 2014, 2015, 2016
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Clean the county name to match the other dataset
Rename the variable “Geography” as “county” so that it matches in the other dataset.
# A tibble: 6 × 3
county year population
<chr> <dbl> <dbl>
1 Albany , New York 2010 304078
2 Allegany , New York 2010 48949
3 Bronx , New York 2010 1388240
4 Broome , New York 2010 200469
5 Cattaraugus , New York 2010 80249
6 Cayuga , New York 2010 79844
Focus on 2012
Since 2012 had the highest counts of hate crimes, let’s look at the populations of the counties in 2012.
Clean the nypoplong12 variable, county, so that matches the counties12 variable by Cutting off the “, New York” portion of the county listing
nypoplong12 <- nypoplong %>%filter(year ==2012) %>%arrange(desc(population)) %>%head(10)nypoplong12$county<-gsub(" , New York","",nypoplong12$county)nypoplong12
# A tibble: 10 × 3
county year population
<chr> <dbl> <dbl>
1 Kings 2012 2572282
2 Queens 2012 2278024
3 New York 2012 1625121
4 Suffolk 2012 1499382
5 Bronx 2012 1414774
6 Nassau 2012 1350748
7 Westchester 2012 961073
8 Erie 2012 920792
9 Monroe 2012 748947
10 Richmond 2012 470978
Not surprisingly, 4/5 of the counties with the highest populations also were listed in the counties with the highest number of hate crimes. Only the Bronx, which has the fifth highest population is not in the list with the highest number of total hate crimes over the period from 2010 to 2016.
# A tibble: 41 × 5
# Groups: county [41]
county year sum population rate
<chr> <dbl> <dbl> <dbl> <dbl>
1 Suffolk 2012 83 1499382 5.54
2 Kings 2012 136 2572282 5.29
3 New York 2012 71 1625121 4.37
4 Richmond 2012 18 470978 3.82
5 Nassau 2012 48 1350748 3.55
6 Erie 2012 28 920792 3.04
7 Queens 2012 48 2278024 2.11
8 Bronx 2012 23 1414774 1.63
9 Westchester 2012 13 961073 1.35
10 Monroe 2012 5 748947 0.668
# ℹ 31 more rows
Notice that the highest rates of hate crimes in 2012 happened in: suffolk
dt <- datajoinrate[,c("county","rate")]dt
# A tibble: 41 × 2
# Groups: county [41]
county rate
<chr> <dbl>
1 Suffolk 5.54
2 Kings 5.29
3 New York 4.37
4 Richmond 3.82
5 Nassau 3.55
6 Erie 3.04
7 Queens 2.11
8 Bronx 1.63
9 Westchester 1.35
10 Monroe 0.668
# ℹ 31 more rows
But the highest populated counties were: Kings (Brooklyn), Queens, New York, Suffolk (Long Island), Bronx, and Nassau. They do not correspond directly, though they are similar, to the counties with highest rates of hate crimes.
# A tibble: 1,692 × 5
county year crimetype id crimecount
<chr> <dbl> <chr> <chr> <dbl>
1 Albany 2016 Crimes Against Persons anti-transgender 0
2 Albany 2016 Property Crimes anti-transgender 0
3 Allegany 2016 Property Crimes anti-transgender 0
4 Bronx 2016 Crimes Against Persons anti-transgender 4
5 Bronx 2016 Property Crimes anti-transgender 0
6 Broome 2016 Crimes Against Persons anti-transgender 0
7 Cayuga 2016 Property Crimes anti-transgender 0
8 Chemung 2016 Crimes Against Persons anti-transgender 0
9 Chemung 2016 Property Crimes anti-transgender 0
10 Chenango 2016 Crimes Against Persons anti-transgender 0
# ℹ 1,682 more rows
So what does all of this mean?
Important Findings:
I wonder what the data would look like if there was a universally accepted requirement for this type of data collection.
The Bronx appears to have much lower than expected incidents of hate crimes relative to its population density in comparison to other NY counties.
In Kings County, NY (which is home to Brooklyn; according to Wikipedia, it is New York’s most populous borough and the second most densly populated county in the US) in 2012, there was a spike in hate crimes against jews.
All of these findings are corroborated in Hate Crime in New York State 2012 Annual Report: https://www.criminaljustice.ny.gov/crimnet/ojsa/hate-crime-in-nys-2012-annual-report.pdf
Thank you!!! Questions?
HW Questions!
Positive and negative aspects of this dataset.
I think one of the more positive impacts of this dataset is that it provides many variables. Meaning, that someone who analyzes this dataset has the ability to explore different variables and compare/contrast depending on their curiosity. There are multiple pathways for someone to examine to get results for different purposes.
A negative aspect is that this dataset has bias within it. This is because the dataset may have false/missing information due to these numbers being left to the state to report. As a result, there’s not much we can do except try to analyze with the data given.
List 2 different paths you would like to (hypothetically) study about this dataset.
As a Latino myself, I would like to explore the amount of hate crimes towards Hispanics across New York. I would like to examine each county of New York and compare/contrast which county would appear to be more safe for a Hispanic. Additionally, comparing crime rates between Hispanics to other races would help identify who is more targeted.
A second path i would explore is religious hate crimes. This is because I am a Christian and would like to see if being a Christian in New York is dangerous. I would also compare Christianity to other religions to see measure the rate of hate crimes between all of them.
Describe 2 things you would do to follow up after seeing these results.
After seeing which county has the most hate crimes towards Hispanics. I would target high rate counties and protest/raise awareness to county leaders to provide more safety measures. Specifically, by examining low crime rate counties and see if those counties have any different laws/police regulations that would affect change.
After analyzing the amount of religious hate crimes. I would try to connect to local police stations to perhaps provide more security to areas where churches or other religious groups gather. Especially, after seeing on the news that many shooters target religious gatherings. Hopefully, using this data can help inform police which religious groups are most likely to be targeted.
