This data set looks at all types of hate crimes in New York counties by the type of hate crime from 2019 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 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 that we know that there is possible bias in the data-set, what can we do with it?
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── 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 variables’ names, look at the structure of the data. Since there are 44 variables 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-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
I decided i would only look at the hate-crimes types with a max number of 9 or more. THat way i can focus oon 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 12
# Thwew are currently 12 variables with 423 rows.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-multi-religiousgroups anti-gaymale anti-hispanic
Min. : 0.00000 Min. : 0.000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.000 Median : 0.0000
Mean : 0.07565 Mean : 1.499 Mean : 0.3735
3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :10.00000 Max. :36.000 Max. :17.0000
anti-otherethnicity/nationalorigin
Min. : 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean : 0.2837
3rd Qu.: 0.0000
Max. :19.0000
Convert from wide to long format
Look at each set of hate crimes for each year. Convert the dataset from wide to long with the pivot_longer function. It will take each column’s hate-crime type to combine them all into one column called “victim_cat”.
Look deeper into crimes against blacks, gays males, and jews
From facet_wrap plot above, anti_black, anti-gay males, and anti_jewish categoroes seem to have highest rates of offences reported. Filter out just for those 3 crimes.
Use the following commands to finalize your barplot: - position = “dodge” makes side-by-side bars, rather than stacked bars - stat = “identify” 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 = victim_cat),position ="dodge", stat ="identity") +labs(fill ="Hate Crime Type",y ="Number of Hate Crime Incidents",title ="Hate Crime Type in NY Counties Between 2010-2016",caption ="Source: NY State Division of Criminal Justice Services")plot2
We can see that hate crimes against the 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 = victim_cat),position ="dodge", stat ="identity") +labs(fill ="Hate Crime Type",y ="Number of Hate Crime Incidents",title ="Hate Crime Type in NY Counties Between 2010-2016",caption ="Source: NY State Division of Criminal Justice Services")plot3
So many counties
There are too many counties for this plot to make sense, but maybe we can just look at the five 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 the total sums of total incidents by counties in descending order.
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
counties
# A tibble: 277 × 3
# Groups: year [7]
year county sum
<dbl> <chr> <dbl>
1 2012 Kings 136
2 2010 Kings 110
3 2016 Kings 101
4 2013 Kings 96
5 2014 Kings 94
6 2015 Kings 90
7 2011 Kings 86
8 2016 New York 86
9 2012 Suffolk 83
10 2013 New York 75
# ℹ 267 more rows
Top 5
To list the five counties with the highest total incidents, change group_by to: group_by(county), then use slice_max(order_by=sum. n=5) to list the five counties with the highest total incident
# A tibble: 5 × 2
county sum
<chr> <dbl>
1 Kings 713
2 New York 459
3 Suffolk 360
4 Nassau 298
5 Queens 235
Finally, create the barplot above, but only for the five 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, amd legend title, all in one command.
plot4 <- hatenew |>filter(county %in%c("Kings", "New York", "Suffolk", "Nassau", "Queens")) |>ggplot() +geom_bar(aes(x=county, y=crimecount, fill = victim_cat),position ="dodge", stat ="identity") +labs(y ="Number of Hate Crime Incidents",title ="5 Counties in NY with Highest Incidents of Hate Crimes",subtitle ="Between 2010-2016", fill ="Hate Crime Type",caption ="Source: NY State Division of Criminal Justice Services")plot4
How would calculations be affected by looking at hate crimes in counties per year by populatins 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 country name to match the data set
Renames 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 Nypolong12 variable, county, so that it matches the counties12 variable by cutting off the “New York” portion of the count 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 were also listed in the counties with the highest number of hate crimes. Only the Bronx, which has the fifth highest population, is not on the list with the highest number of total hate crimes over the period from 2010 to 2016
Recall the total hate crime counts:
Kings 713 New York 459 Suffolk 360 Nassau Queens 235
# A tibble: 41 × 5
# Groups: year [1]
year county sum population rate
<dbl> <chr> <dbl> <dbl> <dbl>
1 2012 Suffolk 83 1499382 5.54
2 2012 Kings 136 2572282 5.29
3 2012 New York 71 1625121 4.37
4 2012 Richmond 18 470978 3.82
5 2012 Nassau 48 1350748 3.55
6 2012 Erie 28 920792 3.04
7 2012 Queens 48 2278024 2.11
8 2012 Bronx 23 1414774 1.63
9 2012 Westchester 13 961073 1.35
10 2012 Monroe 5 748947 0.668
# ℹ 31 more rows
Notice that the highest rates of hate crimes in 2012 happened in:
dt <- datajoinrate[,c("county", "rate")]dt
# A tibble: 41 × 2
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 the highest rates of hate crimes.
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 invictim-catentes of hate crimes relative to its population density in comparison to tother 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 densely 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
A short Essay on the given datasets
As mentioned in class, data set cleaning plays a significant role in the data science field. Since most data sets are already biased, unclean datasets can distort the meaning being communicated. To better understand the data set, I read the ProPublica article and examined the country chart by Ken Schwencke and Hannah Fresques for additional insight. The chart reinforces the idea that the data is heavily biased, making it remarkable that any data is collected. I have explored other methods for improved collection but have yet to find success. Even in surveys, we don’t always report accurately. We have, however, misused our collection methods, as we know that AI systems have inadvertently become biased based on historical trends and social media behavior.
Hate: I believe there is an issue with contemporary humans who are drawn to hate, fueling a fire that burns with an invisible flame.It inflicts harm, but data cannot be ethically collected to monitor it. Algorithms on the various social media platforms we use today tend to recommend the latest conflicts, celebrity deaths, or drama, as well as the details of Taylor Swift’s fuel consumption, rather than promote videos that spread love, joy, and the importance of the information age. Not because they aim to harm us, but because these algorithms are designed to keep users engaged, and after enough usage, data collection and updates reveal that we are more likely to keep watching negative videos or Twitter posts. Today, you would find a considerable amount of these patterns of hate turned into jokes and stigmas. Definition of hate crime: A potential solution would be to establish a system that records incidents. One of the questions posed is whether the datasets would improve if there were a universally accepted standard for this type of data collection. In this case, to gather this data, we must reach a universal agreement on what constitutes hate crimes. Despite our global connectivity, we have not achieved this; instead, we gain deeper insight into the divide. A significant capitalist bias: As long as money is involved, individuals will employ hate speech or similar rhetoric to gain engagement and revenue. This flawed data, which reflects this trend, fails to acknowledge that this type of hate crime was employed for monetary gain rather than out of genuine animosity toward a specific group.
Not part of essay: In this field, data collection is going to be dangerously flawed; these insights might be biased due to personal experiences. Note that I have never suffered a hate crime at the hands of a cop or any law enforcement officer, nor have I been arrested; however, such is not the case for some. Can we count on cops to give any reliable data at all? For one, police are among those departments that have devised and work solely within internal groups with similar beliefs. Within the group, you will find those who seek to “make America great again.” There are those who see nothing wrong with the atrocious things that were once done and are being referred to as “great.” In my opinion, there is no unbiased groups capable of collecting truthful or proper data; even within the FBI, groups with similar ideals exist. At a basic level, I realize these organizations function not because of an unconscious just system that they follow; they must be collective groups, and nothing holds collective groups together better than shared ideas and beliefs. Are we to believe the groups that existed in these government agencies that fueled these hate crimes not too long ago are completely gone just because we have new policies in place?
