1 + 1[1] 2Hate Crimes in NY from 2010-2016
Quarto
Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.
Running Code
When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:
You can add options to executable code like this
[1] 4The echo: false option disables the printing of code (only output is displayed).
Hate Crimes Data set
This data set looks at all types of hate crimes in New York counties by the type of hate crime from 2010 to 2016.
Flawed hate crime data collection - we should know how the data was collected
So now 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#tinytex::install_tinytex()
#library(tinytex)
setwd("~/Desktop/Data 110")
hatecrimes <- read_csv("hateCrimes2010.csv")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
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","_",names(hatecrimes))
head(hatecrimes)# A tibble: 6 × 44
county year crime_type `anti-male` `anti-female` `anti-transgender`
<chr> <dbl> <chr> <dbl> <dbl> <dbl>
1 Albany 2016 Crimes Against Pe… 0 0 0
2 Albany 2016 Property Crimes 0 0 0
3 Allegany 2016 Property Crimes 0 0 0
4 Bronx 2016 Crimes Against Pe… 0 0 4
5 Bronx 2016 Property Crimes 0 0 0
6 Broome 2016 Crimes Against Pe… 0 0 0
# ℹ 38 more variables: `anti-gender_identity_expression` <dbl>,
# `anti-age*` <dbl>, `anti-white` <dbl>, `anti-black` <dbl>,
# `anti-american_indian/alaskan_native` <dbl>, `anti-asian` <dbl>,
# `anti-native_hawaiian/pacific_islander` <dbl>,
# `anti-multi-racial_groups` <dbl>, `anti-other_race` <dbl>,
# `anti-jewish` <dbl>, `anti-catholic` <dbl>, `anti-protestant` <dbl>,
# `anti-islamic_(muslim)` <dbl>, `anti-multi-religious_groups` <dbl>, …##Select only certain hate-crimes
summary(hatecrimes) county year crime_type 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-gender_identity_expression
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-american_indian/alaskan_native 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-native_hawaiian/pacific_islander anti-multi-racial_groups anti-other_race
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-religious_groups 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-religious_practice_generally anti-other_religion 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-eastern_orthodox_(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-jehovahs_witness anti-mormon anti-other_christian 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-other_ethnicity/national_origin
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-gay_male anti-gay_female
Min. :0 Min. : 0.000 Min. :0.0000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000
Median :0 Median : 0.000 Median :0.0000
Mean :0 Mean : 1.499 Mean :0.2411
3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000
Max. :0 Max. :36.000 Max. :8.0000
anti-gay_(male_and_female) anti-heterosexual anti-bisexual
Min. :0.0000 Min. :0.000000 Min. :0.000000
1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000
Median :0.0000 Median :0.000000 Median :0.000000
Mean :0.1017 Mean :0.002364 Mean :0.004728
3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :4.0000 Max. :1.000000 Max. :1.000000
anti-physical_disability anti-mental_disability total_incidents
Min. :0.00000 Min. :0.000000 Min. : 1.00
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 1.00
Median :0.00000 Median :0.000000 Median : 3.00
Mean :0.01182 Mean :0.009456 Mean : 10.09
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.: 10.00
Max. :1.00000 Max. :1.000000 Max. :101.00
total_victims total_offenders
Min. : 1.00 Min. : 1.00
1st Qu.: 1.00 1st Qu.: 1.00
Median : 3.00 Median : 3.00
Mean : 10.48 Mean : 11.77
3rd Qu.: 10.00 3rd Qu.: 11.00
Max. :106.00 Max. :113.00 hatecrimes2 <- hatecrimes |>
select(county, year, 'anti-black', 'anti-white', 'anti-jewish', 'anti-catholic', 'anti-gay_male', 'anti-hispanic') |>
group_by(county, year)
head(hatecrimes2)# A tibble: 6 × 8
# Groups: county, year [4]
county year `anti-black` `anti-white` `anti-jewish` `anti-catholic`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Albany 2016 1 0 0 0
2 Albany 2016 2 0 0 0
3 Allegany 2016 1 0 0 0
4 Bronx 2016 0 1 0 0
5 Bronx 2016 0 1 1 0
6 Broome 2016 1 0 0 0
# ℹ 2 more variables: `anti-gay_male` <dbl>, `anti-hispanic` <dbl>Check the dimensions and the summary to make sure no missing values
dim(hatecrimes2)[1] 423 8summary(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-gay_male anti-hispanic
Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median : 0.000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean : 1.499 Mean : 0.3735
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :36.000 Max. :17.0000 Convert from wide to long format
hatelong <- hatecrimes2 |>
pivot_longer(
cols =3:6,
names_to = "victim_cat",
values_to = "crimecount")Now use the long format to create a facet plot
hatecrimplot <-hatelong |>
ggplot(aes(year, crimecount))+
geom_point()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimplot
hatenew <- hatelong |>
filter( victim_cat %in% c("anti-black", "anti-jewish", "anti-gaymale"))|>
group_by(year, county) |>
arrange(desc(crimecount))
hatenew# A tibble: 846 × 6
# Groups: year, county [277]
county year `anti-gay_male` `anti-hispanic` victim_cat crimecount
<chr> <dbl> <dbl> <dbl> <chr> <dbl>
1 Kings 2012 0 0 anti-jewish 82
2 Kings 2016 2 1 anti-jewish 51
3 Suffolk 2014 2 0 anti-jewish 48
4 Suffolk 2012 2 2 anti-jewish 48
5 Kings 2011 2 0 anti-jewish 44
6 Kings 2013 1 1 anti-jewish 41
7 Kings 2010 1 0 anti-jewish 39
8 Nassau 2011 0 2 anti-jewish 38
9 Suffolk 2013 1 0 anti-jewish 37
10 Nassau 2016 0 0 anti-jewish 36
# ℹ 836 more rows##Plot these three types of hate crimes together
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
What about the counties?
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
counties <- hatenew |>
group_by(year, county)|>
summarize(sum = sum(crimecount)) |>
arrange(desc(sum))`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 124
2 2010 Kings 89
3 2016 Kings 83
4 2012 Suffolk 80
5 2014 Kings 69
6 2015 Kings 69
7 2011 Kings 68
8 2013 Kings 68
9 2014 Suffolk 65
10 2013 Suffolk 64
# ℹ 267 more rowsTop 5
counties2 <- hatenew |>
group_by(county)|>
summarize(sum = sum(crimecount)) |>
slice_max(order_by = sum, n=5)
counties2# A tibble: 5 × 2
county sum
<chr> <dbl>
1 Kings 570
2 Suffolk 317
3 Nassau 283
4 New York 266
5 Queens 175plot4 <- 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 population densitites
setwd("~/Desktop/Data 110")
nypop <- read_csv("newyorkpopulation.csv")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
nypop$Geography <- gsub(" , New York", "", nypop$Geography)
nypop$Geography <- gsub("County", "", nypop$Geography)
nypoplong <- nypop |>
rename(county = Geography) |>
gather("year", "population", 2:8)
nypoplong$year <- as.double(nypoplong$year)
head(nypoplong)# 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 79844Focus on 2012
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 470978Filter hate crimes just for 2012 as well
counties12 <- counties |>
filter(year == 2012) |>
arrange(desc(sum))
counties12# A tibble: 41 × 3
# Groups: year [1]
year county sum
<dbl> <chr> <dbl>
1 2012 Kings 124
2 2012 Suffolk 80
3 2012 New York 54
4 2012 Nassau 48
5 2012 Queens 38
6 2012 Erie 20
7 2012 Bronx 19
8 2012 Richmond 16
9 2012 Multiple 14
10 2012 Dutchess 9
# ℹ 31 more rowsJoin the Hate Crimes data with NY population data for 2012
datajoin <- counties12 |>
full_join(nypoplong12, by=c("county", "year"))
datajoin# A tibble: 41 × 4
# Groups: year [1]
year county sum population
<dbl> <chr> <dbl> <dbl>
1 2012 Kings 124 2572282
2 2012 Suffolk 80 1499382
3 2012 New York 54 1625121
4 2012 Nassau 48 1350748
5 2012 Queens 38 2278024
6 2012 Erie 20 920792
7 2012 Bronx 19 1414774
8 2012 Richmond 16 470978
9 2012 Multiple 14 NA
10 2012 Dutchess 9 NA
# ℹ 31 more rowsCalculate the rate of incidents per 100,000. Then arrange in descending order
datajoinrate <- datajoin |>
mutate(rate = sum/population*100000) |>
arrange(desc(rate))
datajoinrate# A tibble: 41 × 5
# Groups: year [1]
year county sum population rate
<dbl> <chr> <dbl> <dbl> <dbl>
1 2012 Suffolk 80 1499382 5.34
2 2012 Kings 124 2572282 4.82
3 2012 Nassau 48 1350748 3.55
4 2012 Richmond 16 470978 3.40
5 2012 New York 54 1625121 3.32
6 2012 Erie 20 920792 2.17
7 2012 Queens 38 2278024 1.67
8 2012 Bronx 19 1414774 1.34
9 2012 Monroe 5 748947 0.668
10 2012 Westchester 6 961073 0.624
# ℹ 31 more rowsdt <- datajoinrate[,c("county","rate")]
dt# A tibble: 41 × 2
county rate
<chr> <dbl>
1 Suffolk 5.34
2 Kings 4.82
3 Nassau 3.55
4 Richmond 3.40
5 New York 3.32
6 Erie 2.17
7 Queens 1.67
8 Bronx 1.34
9 Monroe 0.668
10 Westchester 0.624
# ℹ 31 more rowsAggregating some of the categories
aggregategroups <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount"
)
unique(aggregategroups$victim_cat) [1] "anti-male"
[2] "anti-female"
[3] "anti-transgender"
[4] "anti-gender_identity_expression"
[5] "anti-age*"
[6] "anti-white"
[7] "anti-black"
[8] "anti-american_indian/alaskan_native"
[9] "anti-asian"
[10] "anti-native_hawaiian/pacific_islander"
[11] "anti-multi-racial_groups"
[12] "anti-other_race"
[13] "anti-jewish"
[14] "anti-catholic"
[15] "anti-protestant"
[16] "anti-islamic_(muslim)"
[17] "anti-multi-religious_groups"
[18] "anti-atheism/agnosticism"
[19] "anti-religious_practice_generally"
[20] "anti-other_religion"
[21] "anti-buddhist"
[22] "anti-eastern_orthodox_(greek,_russian,_etc.)"
[23] "anti-hindu"
[24] "anti-jehovahs_witness"
[25] "anti-mormon"
[26] "anti-other_christian"
[27] "anti-sikh"
[28] "anti-hispanic"
[29] "anti-arab"
[30] "anti-other_ethnicity/national_origin"
[31] "anti-non-hispanic*"
[32] "anti-gay_male"
[33] "anti-gay_female"
[34] "anti-gay_(male_and_female)"
[35] "anti-heterosexual"
[36] "anti-bisexual"
[37] "anti-physical_disability"
[38] "anti-mental_disability"
[39] "total_incidents"
[40] "total_victims"
[41] "total_offenders" aggregategroups <- aggregategroups |>
mutate(group = case_when(
victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual") ~ "anti-lgbtq",
victim_cat %in% c("anti-multi-racialgroups", "anti-jewish", "anti-protestant", "anti-multi-religousgroups", "anti-religiouspracticegenerally", "anti-buddhist", "anti-hindu", "anti-mormon", "anti-sikh", "anti-catholic", "anti-islamic(muslim)", "anti-atheism/agnosticism", "anti-otherreligion", "anti-easternorthodox(greek,russian,etc.)", "anti-jehovahswitness", "anti-otherchristian") ~ "anti-religion",
victim_cat %in% c("anti-asian", "anti-arab", "anti-non-hispanic", "anti-white", "anti-americanindian/alaskannative", "anti-nativehawaiian/pacificislander", "anti-otherrace", "anti-hispanic", "anti-otherethnicity/nationalorigin") ~ "anti-ethnicity",
victim_cat %in% c("anti-physicaldisability", "anti-mentaldisability") ~ "anti-disability",
victim_cat %in% c("anti-female", "anti-male") ~ "anti-gender",
TRUE ~ "others"))
aggregategroups# A tibble: 17,343 × 6
county year crime_type victim_cat crimecount group
<chr> <dbl> <chr> <chr> <dbl> <chr>
1 Albany 2016 Crimes Against Persons anti-male 0 anti…
2 Albany 2016 Crimes Against Persons anti-female 0 anti…
3 Albany 2016 Crimes Against Persons anti-transgender 0 anti…
4 Albany 2016 Crimes Against Persons anti-gender_identity_ex… 0 othe…
5 Albany 2016 Crimes Against Persons anti-age* 0 othe…
6 Albany 2016 Crimes Against Persons anti-white 0 anti…
7 Albany 2016 Crimes Against Persons anti-black 1 othe…
8 Albany 2016 Crimes Against Persons anti-american_indian/al… 0 othe…
9 Albany 2016 Crimes Against Persons anti-asian 0 anti…
10 Albany 2016 Crimes Against Persons anti-native_hawaiian/pa… 0 othe…
# ℹ 17,333 more rowsor create subset with jusr LGBTQ
lgbtq <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount") |>
filter(victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual"))
lgbtq# A tibble: 846 × 5
county year crime_type victim_cat crimecount
<chr> <dbl> <chr> <chr> <dbl>
1 Albany 2016 Crimes Against Persons anti-transgender 0
2 Albany 2016 Crimes Against Persons anti-bisexual 0
3 Albany 2016 Property Crimes anti-transgender 0
4 Albany 2016 Property Crimes anti-bisexual 0
5 Allegany 2016 Property Crimes anti-transgender 0
6 Allegany 2016 Property Crimes anti-bisexual 0
7 Bronx 2016 Crimes Against Persons anti-transgender 4
8 Bronx 2016 Crimes Against Persons anti-bisexual 0
9 Bronx 2016 Property Crimes anti-transgender 0
10 Bronx 2016 Property Crimes anti-bisexual 0
# ℹ 836 more rowsThe data set on hate crimes in New York from 2010 to 2016 provides valuable data on hate crimes committed across different counties. It has several advantages, such as offering a wide array of hate crime data, including categories like anti-male, anti-female, anti-Black, and anti-Jewish. Another advantage is that the data set includes data from many counties, allowing for a broader analysis rather than focusing only on the most densely populated areas.
However, this dataset also has limitations. One significant issue is that the data can be skewed and may not reflect the actual number of hate crimes. Victims of hate crimes may choose not to report incidents due to fear or lack of trust in authorities. Another factor contributing to biased data is that the New York Police Department has the authority to decide what qualifies as a hate crime, which may lead to inconsistencies in classification.
One potential research path I would explore is analyzing crime types and their relationship to anti-female hate crimes. By plotting crime types with anti-female incidents, I could determine whether specific types of crimes are more commonly committed against women. Another research path would involve graphing the anti-transgender and anti-gay hate crime data to identify any correlation between the two types of hate crimes and determine if they share any patterns.
After reviewing the hate crimes tutorial, two follow-up actions I would take include investigating the reasons behind the spike in anti-Jewish hate crimes. By researching historical and political contexts, I could understand potential factors contributing to increases in these incidents. Additionally, I would examine whether any community interventions were implemented when hate crime rates decreased, as this could provide insights into effective strategies for reducing hate crimes.