Tracee Matthias

library(tidyverse)
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## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
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#tinytex::install_tinytex()
library(tinytex)
setwd("C:/Users/MCuser/Desktop/DATA110_CourseMaterials/HW4HateCrimesData")
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.

Data Clean Up

names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
str(hatecrimes)
## spec_tbl_df [423 × 44] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ county                                  : chr [1:423] "Albany" "Albany" "Allegany" "Bronx" ...
##  $ year                                    : num [1:423] 2016 2016 2016 2016 2016 ...
##  $ crimetype                               : chr [1:423] "Crimes Against Persons" "Property Crimes" "Property Crimes" "Crimes Against Persons" ...
##  $ anti-male                               : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-female                             : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-transgender                        : num [1:423] 0 0 0 4 0 0 0 0 0 0 ...
##  $ anti-genderidentityexpression           : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-age*                               : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-white                              : num [1:423] 0 0 0 1 1 0 0 0 0 0 ...
##  $ anti-black                              : num [1:423] 1 2 1 0 0 1 0 1 0 2 ...
##  $ anti-americanindian/alaskannative       : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-asian                              : num [1:423] 0 0 0 0 0 1 0 0 0 0 ...
##  $ anti-nativehawaiian/pacificislander     : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-multi-racialgroups                 : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-otherrace                          : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-jewish                             : num [1:423] 0 0 0 0 1 0 1 0 0 0 ...
##  $ anti-catholic                           : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-protestant                         : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-islamic(muslim)                    : num [1:423] 1 0 0 6 0 0 0 0 1 0 ...
##  $ anti-multi-religiousgroups              : num [1:423] 0 1 0 0 0 0 0 0 0 0 ...
##  $ anti-atheism/agnosticism                : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-religiouspracticegenerally         : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-otherreligion                      : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-buddhist                           : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-easternorthodox(greek,russian,etc.): num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-hindu                              : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-jehovahswitness                    : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-mormon                             : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-otherchristian                     : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-sikh                               : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-hispanic                           : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-arab                               : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-otherethnicity/nationalorigin      : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-non-hispanic*                      : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-gaymale                            : num [1:423] 1 0 0 8 0 1 0 0 0 0 ...
##  $ anti-gayfemale                          : num [1:423] 0 0 0 1 0 0 0 0 0 0 ...
##  $ anti-gay(maleandfemale)                 : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-heterosexual                       : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-bisexual                           : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-physicaldisability                 : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ anti-mentaldisability                   : num [1:423] 0 0 0 0 0 0 0 0 0 0 ...
##  $ totalincidents                          : num [1:423] 3 3 1 20 2 3 1 1 1 2 ...
##  $ totalvictims                            : num [1:423] 4 3 1 20 2 3 1 1 1 2 ...
##  $ totaloffenders                          : num [1:423] 3 3 1 25 2 3 1 1 1 2 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   County = col_character(),
##   ..   Year = col_double(),
##   ..   `Crime Type` = col_character(),
##   ..   `Anti-Male` = col_double(),
##   ..   `Anti-Female` = col_double(),
##   ..   `Anti-Transgender` = col_double(),
##   ..   `Anti-Gender Identity Expression` = col_double(),
##   ..   `Anti-Age*` = col_double(),
##   ..   `Anti-White` = col_double(),
##   ..   `Anti-Black` = col_double(),
##   ..   `Anti-American Indian/Alaskan Native` = col_double(),
##   ..   `Anti-Asian` = col_double(),
##   ..   `Anti-Native Hawaiian/Pacific Islander` = col_double(),
##   ..   `Anti-Multi-Racial Groups` = col_double(),
##   ..   `Anti-Other Race` = col_double(),
##   ..   `Anti-Jewish` = col_double(),
##   ..   `Anti-Catholic` = col_double(),
##   ..   `Anti-Protestant` = col_double(),
##   ..   `Anti-Islamic (Muslim)` = col_double(),
##   ..   `Anti-Multi-Religious Groups` = col_double(),
##   ..   `Anti-Atheism/Agnosticism` = col_double(),
##   ..   `Anti-Religious Practice Generally` = col_double(),
##   ..   `Anti-Other Religion` = col_double(),
##   ..   `Anti-Buddhist` = col_double(),
##   ..   `Anti-Eastern Orthodox (Greek, Russian, etc.)` = col_double(),
##   ..   `Anti-Hindu` = col_double(),
##   ..   `Anti-Jehovahs Witness` = col_double(),
##   ..   `Anti-Mormon` = col_double(),
##   ..   `Anti-Other Christian` = col_double(),
##   ..   `Anti-Sikh` = col_double(),
##   ..   `Anti-Hispanic` = col_double(),
##   ..   `Anti-Arab` = col_double(),
##   ..   `Anti-Other Ethnicity/National Origin` = col_double(),
##   ..   `Anti-Non-Hispanic*` = col_double(),
##   ..   `Anti-Gay Male` = col_double(),
##   ..   `Anti-Gay Female` = col_double(),
##   ..   `Anti-Gay (Male and Female)` = col_double(),
##   ..   `Anti-Heterosexual` = col_double(),
##   ..   `Anti-Bisexual` = col_double(),
##   ..   `Anti-Physical Disability` = col_double(),
##   ..   `Anti-Mental Disability` = col_double(),
##   ..   `Total Incidents` = col_double(),
##   ..   `Total Victims` = col_double(),
##   ..   `Total Offenders` = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>
#summary(hatecrimes)

Selected Hate Crimes

hatecrimes2 <- hatecrimes %>%
  select(county, year, 'anti-black', 'anti-white', 'anti-jewish', 'anti-catholic', 'anti-age*', 'anti-islamic(muslim)', 'anti-gaymale', 'anti-hispanic') %>%
  group_by(county, year)
head(hatecrimes2)
## # A tibble: 6 × 10
## # 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
## # … with 4 more variables: `anti-age*` <dbl>, `anti-islamic(muslim)` <dbl>,
## #   `anti-gaymale` <dbl>, `anti-hispanic` <dbl>

Dimensions and Summary Check

dim(hatecrimes2)
## [1] 423  10
# There are currently 13 varaibles 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-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

hatecrimeslong <- hatecrimes2 %>%
  tidyr::gather("id", "crimecount", 3:10)
hatecrimesplot <-hatecrimeslong %>%
  ggplot(., aes(year, crimecount))+
  geom_point()+
  aes(color = id)+
  facet_wrap(~id)
hatecrimesplot

Deeper Look at Crimes Against Black People, Jewish People, and Gay Males

hatenew <- hatecrimeslong %>%
  filter( id== "anti-black" | id == "anti-jewish" | id == "anti-gaymale")%>%
  group_by(year, county)%>%
  arrange(desc(crimecount))
hatenew
## # A tibble: 1,269 × 4
## # Groups:   year, county [277]
##    county   year id          crimecount
##    <chr>   <dbl> <chr>            <dbl>
##  1 Kings    2012 anti-jewish         82
##  2 Kings    2016 anti-jewish         51
##  3 Suffolk  2014 anti-jewish         48
##  4 Suffolk  2012 anti-jewish         48
##  5 Kings    2011 anti-jewish         44
##  6 Kings    2013 anti-jewish         41
##  7 Kings    2010 anti-jewish         39
##  8 Nassau   2011 anti-jewish         38
##  9 Suffolk  2013 anti-jewish         37
## 10 Nassau   2016 anti-jewish         36
## # … with 1,259 more rows

Plotting Three Types of Hate Crimes Together

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

What About the Counties?

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

Five Counties

counties <- hatenew %>%
  group_by(county, year)%>%
  summarize(sum = sum(crimecount)) %>%
  arrange(desc(sum))
## `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
## # … with 267 more rows
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 Crimes Incidents",
       title = "Five Counties in NY with Highest Incidents of Hate Crimes", 
       subtitle = "Between 2010-2016", 
       fill = "Hate Crime Type")
plot4

## Hate Crimes in Counties Per Year by Population Density

setwd("C:/Users/MCuser/Desktop/DATA110_CourseMaterials/HW4HateCrimesData")
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 Up County Names to Match

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      79844

Focus 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     470978

2012 Hate Crimes

counties12 <- counties %>%
  filter(year == 2012) %>%
  arrange(desc(sum))
counties12
## # A tibble: 41 × 3
## # Groups:   county [41]
##    county       year   sum
##    <chr>       <dbl> <dbl>
##  1 Kings        2012   136
##  2 Suffolk      2012    83
##  3 New York     2012    71
##  4 Nassau       2012    48
##  5 Queens       2012    48
##  6 Erie         2012    28
##  7 Bronx        2012    23
##  8 Richmond     2012    18
##  9 Multiple     2012    14
## 10 Westchester  2012    13
## # … with 31 more rows

Joint 2012 Hate Crimes and NY Population Data

datajoin <- counties12 %>%
  full_join(nypoplong12, by=c("county", "year"))
datajoin
## # A tibble: 41 × 4
## # Groups:   county [41]
##    county       year   sum population
##    <chr>       <dbl> <dbl>      <dbl>
##  1 Kings        2012   136    2572282
##  2 Suffolk      2012    83    1499382
##  3 New York     2012    71    1625121
##  4 Nassau       2012    48    1350748
##  5 Queens       2012    48    2278024
##  6 Erie         2012    28     920792
##  7 Bronx        2012    23    1414774
##  8 Richmond     2012    18     470978
##  9 Multiple     2012    14         NA
## 10 Westchester  2012    13     961073
## # … with 31 more rows

Incident Rate per 100,000

datajoinrate <- datajoin %>%
  mutate(rate = sum/population*100000) %>%
  arrange(desc(rate))
datajoinrate
## # 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
## # … with 31 more rows

Incident Rate per 100,000 in Descending Order

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
## # … with 31 more rows

Follow Up

Aggregating some of the categories

aggregategroups <- hatecrimes %>%
  tidyr::gather("id", "crimecount", 4:44)
unique(aggregategroups$id)
##  [1] "anti-male"                               
##  [2] "anti-female"                             
##  [3] "anti-transgender"                        
##  [4] "anti-genderidentityexpression"           
##  [5] "anti-age*"                               
##  [6] "anti-white"                              
##  [7] "anti-black"                              
##  [8] "anti-americanindian/alaskannative"       
##  [9] "anti-asian"                              
## [10] "anti-nativehawaiian/pacificislander"     
## [11] "anti-multi-racialgroups"                 
## [12] "anti-otherrace"                          
## [13] "anti-jewish"                             
## [14] "anti-catholic"                           
## [15] "anti-protestant"                         
## [16] "anti-islamic(muslim)"                    
## [17] "anti-multi-religiousgroups"              
## [18] "anti-atheism/agnosticism"                
## [19] "anti-religiouspracticegenerally"         
## [20] "anti-otherreligion"                      
## [21] "anti-buddhist"                           
## [22] "anti-easternorthodox(greek,russian,etc.)"
## [23] "anti-hindu"                              
## [24] "anti-jehovahswitness"                    
## [25] "anti-mormon"                             
## [26] "anti-otherchristian"                     
## [27] "anti-sikh"                               
## [28] "anti-hispanic"                           
## [29] "anti-arab"                               
## [30] "anti-otherethnicity/nationalorigin"      
## [31] "anti-non-hispanic*"                      
## [32] "anti-gaymale"                            
## [33] "anti-gayfemale"                          
## [34] "anti-gay(maleandfemale)"                 
## [35] "anti-heterosexual"                       
## [36] "anti-bisexual"                           
## [37] "anti-physicaldisability"                 
## [38] "anti-mentaldisability"                   
## [39] "totalincidents"                          
## [40] "totalvictims"                            
## [41] "totaloffenders"
aggregrategroups <- aggregategroups %>%
  mutate(group = case_when(
id %in% c("anti-transgender", "anti-gayfemale", "anti-genderidendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual") ~"anti-lgbtq",
id %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",
id %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",
id %in% c("anti-physicaldisability", 
"anti-mentaldisability") ~ "anti-disability", 
id %in% c("anti-female", "anti-male") ~ "anti-gender",
TRUE ~ "others"))
aggregategroups
## # A tibble: 17,343 × 5
##    county    year crimetype              id        crimecount
##    <chr>    <dbl> <chr>                  <chr>          <dbl>
##  1 Albany    2016 Crimes Against Persons anti-male          0
##  2 Albany    2016 Property Crimes        anti-male          0
##  3 Allegany  2016 Property Crimes        anti-male          0
##  4 Bronx     2016 Crimes Against Persons anti-male          0
##  5 Bronx     2016 Property Crimes        anti-male          0
##  6 Broome    2016 Crimes Against Persons anti-male          0
##  7 Cayuga    2016 Property Crimes        anti-male          0
##  8 Chemung   2016 Crimes Against Persons anti-male          0
##  9 Chemung   2016 Property Crimes        anti-male          0
## 10 Chenango  2016 Crimes Against Persons anti-male          0
## # … with 17,333 more rows

LGBTQ Subset

lgbtq <- hatecrimes %>%
tidyr::gather("id", "crimecount", 4:44) %>%
filter(id %in% c("anti-transgender", "anti-gayfemale", "anti-genderidendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual"))
lgbtq
## # 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
## # … with 1,682 more rows

Based on what we discussed in class, write a paragraph that incorporates the following:

1. Positive and negative aspect of this dataset

2. List two different paths you would like to (hypothetically) study about this dataset.

3. Describe two things you would do to follow up after seeing these results.

The dataset was easy to understand. It included categories that are federal protected classes and makes it possible to compare data from similarly situated communities in other states. The dataset does not include information about the police force including filed complaints, police response times, and crime report initiated enforcement vs. police initiated enforcement.

The dataset shows a higher number of anti-Jewish hate crimes. In Brooklyn, there are private Jewish patrols that work alongside the New York City Police Department. These patrol guards are unarmed, receive private financing, and serve as a sort of auxiliary police force. I think this volunteer force contributes to the higher rate of anti-Jewish hate crimes recorded in the dataset.

Using community activists, I would meet with community leaders to create coalitions that work with police to curtail hate crime and increase reporting and investigative efforts. I would compare the data with similarly situated cities with and without the private Jewish patrols to observe the incident rates of anti-Jewish hate crimes. The findings might support a narrative about the impact of the private Jewish patrols.