Domestic Violence Incidents in National City
Joe Long
12/13/2020
#https://timesofsandiego.com/wp-content/uploads/2020/04/Mile-of-Cars.jpg
1.1 Problem Statement
According to a report by the San Diego Association of Governments, also known as SANDAG, revealed that the San Diego region saw a 3 percent increase in domestic violence in the first half of 2020 over the same period last year. SANDAG’s data shows more notable increases in domestic violence in certain local communities: Santee (18 percent) El Cajon (18 percent) National City (74 percent).
1.2 Focus of Analysis
National City is selected for this research analysis.
Domestic violence is the general category of this research.1.3 Data source :
Crime Victims in San Diego, from 2016 though July 2020, with UCR codes for the crime and the age, race and sex of the victim. The data below is a complete data set for victims of violent crime from ARJIS (Automated Regional Justice Information System) for San Diego County, the nation’s fifth largest County, from 2016 through 2020 https://data.sandiegodata.org/dataset/arjis-org-crime-victims-pra/
1.4 Caution:
The general category classifications of “SEXUAL ASSAULT” is subdivided into 6 different categories
- INFLICT CORPORAL INJURY ON SPOUSE/COHABITANT
- BATTERY:SPOUSE/EX SPOUSE/DATE/ETC (M)
- SPOUSAL/COHABITANT ABUSE WITH SERIOUS INJURY
- SPOUSAL/COHABITANT ABUSE WITH MINOR INJURY (F)
- RAPE
- BATTERY ON NONCOHABITATING FORMER SPOUSE
- LEWD OR LASCIVIOUS ACTS W/CHILD UNDER 14 YEARS (F)
1.5 Caution:
each incident could have multiple reports: one report for suspect and one for each victim(s)
#GPS coordinates of National City, California, United States. Latitude: 32.6692 Longitude: -117.0890.
library(leaflet)##
## Attaching package: 'leaflet'## The following object is masked from 'package:xts':
##
## addLegendleaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
setView(-117.0890, 32.6692, zoom = 12) %>%
addMarkers(lng=-117.0890, lat=32.6692, popup="National City, CA")#raw <- read.csv("C:/Users/menif/Downloads/sdcrime_16_20.csv")
all <- read.csv("C:/Users/Joe/Downloads/sdcrime_16_20.csv")
all$date <- ymd_hms(all$activitydate)
all$year <- year(all$date)
all$month <- month(all$date,label=T)
all$weekday <-wday(all$date,label = T)
all$hour <- hour(all$date)
all$date <- floor_date(all$date, "day")
kable(head(all,n=5))| pk | activitynumber | activitydate | year | agency | violationsection | violationtype | chargedescription | chargelevel | codeucr | crimecategory | personrole | race | age | sex | zipcode | censusblock | censustract | city | census_race | tract_geoid | block_geoid | intptlat | intptlon | geometry | date | month | weekday | hour |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12144871 | ‘01600014’ | 2016-01-01 00:00:00 | 2016 | NATIONAL CITY | 10851 | VC | TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | FELONY | 7A0 | Vehicle Theft | VICTIM | OTHER | NA | FEMALE | 91950 | 2000 | 22000 | NATIONAL CITY | other | 14000US06073022000 | 10100US060730220002000 | 32.67887 | -117.0875 | POINT (-117.0875061797541 32.67887038013) | 2016-01-01 | Jan | Fri | 0 |
| 12127327 | ‘16000042’ | 2016-01-01 00:00:00 | 2016 | SAN DIEGO | 459 | PC | BURGLARY/UNSPECIFIED | FELONY | 5A6 | Non Res Burglary | nan | nan | NA | nan | 92109 | 20000 | 7907 | SAN DIEGO | unknown | 14000US06073007907 | 10100US0607300790720000 | NaN | NaN | nan | 2016-01-01 | Jan | Fri | 0 |
| 12278698 | ‘16005661’ | 2016-01-01 00:00:00 | 2016 | SAN DIEGO | 488 | PC | PETTY THEFT | MISDEMEANOR | 6DG | Larceny < $400 | SUSPECT | WHITE | 28 | MALE | 92115 | 20230 | 2902 | SAN DIEGO | nhwhite | 14000US06073002902 | 10100US0607300290220230 | NaN | NaN | nan | 2016-01-01 | Jan | Fri | 0 |
| 12278698 | ‘16005661’ | 2016-01-01 00:00:00 | 2016 | SAN DIEGO | 488 | PC | PETTY THEFT | MISDEMEANOR | 6DG | Larceny < $400 | VICTIM/WITNESS | HISPANIC | 74 | MALE | 92115 | 20230 | 2902 | SAN DIEGO | hisp | 14000US06073002902 | 10100US0607300290220230 | NaN | NaN | nan | 2016-01-01 | Jan | Fri | 0 |
| 12364997 | ‘16008822’ | 2016-01-01 00:00:00 | 2016 | SAN DIEGO | 487(A) | PC | GRAND THEFT:MONEY/LABOR/PROPERTY OVER $950 | FELONY | 6AE | Larceny >= $400 | VICTIM/WITNESS | WHITE | 70 | MALE | 92109 | 20120 | 7905 | SAN DIEGO | nhwhite | 14000US06073007905 | 10100US0607300790520120 | NaN | NaN | nan | 2016-01-01 | Jan | Fri | 0 |
colnames(all)## [1] "pk" "activitynumber" "activitydate"
## [4] "year" "agency" "violationsection"
## [7] "violationtype" "chargedescription" "chargelevel"
## [10] "codeucr" "crimecategory" "personrole"
## [13] "race" "age" "sex"
## [16] "zipcode" "censusblock" "censustract"
## [19] "city" "census_race" "tract_geoid"
## [22] "block_geoid" "intptlat" "intptlon"
## [25] "geometry" "date" "month"
## [28] "weekday" "hour"all <- all[,c(1,8:9,11:20,23:25,4,26:29)]
names(all)[c(1,14:15)] <- c("ID","latitude","longitute")
colnames(all)## [1] "ID" "chargedescription" "chargelevel"
## [4] "crimecategory" "personrole" "race"
## [7] "age" "sex" "zipcode"
## [10] "censusblock" "censustract" "city"
## [13] "census_race" "latitude" "longitute"
## [16] "geometry" "year" "date"
## [19] "month" "weekday" "hour"kable(head(all,n=5))| ID | chargedescription | chargelevel | crimecategory | personrole | race | age | sex | zipcode | censusblock | censustract | city | census_race | latitude | longitute | geometry | year | date | month | weekday | hour |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12144871 | TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | FELONY | Vehicle Theft | VICTIM | OTHER | NA | FEMALE | 91950 | 2000 | 22000 | NATIONAL CITY | other | 32.67887 | -117.0875 | POINT (-117.0875061797541 32.67887038013) | 2016 | 2016-01-01 | Jan | Fri | 0 |
| 12127327 | BURGLARY/UNSPECIFIED | FELONY | Non Res Burglary | nan | nan | NA | nan | 92109 | 20000 | 7907 | SAN DIEGO | unknown | NaN | NaN | nan | 2016 | 2016-01-01 | Jan | Fri | 0 |
| 12278698 | PETTY THEFT | MISDEMEANOR | Larceny < $400 | SUSPECT | WHITE | 28 | MALE | 92115 | 20230 | 2902 | SAN DIEGO | nhwhite | NaN | NaN | nan | 2016 | 2016-01-01 | Jan | Fri | 0 |
| 12278698 | PETTY THEFT | MISDEMEANOR | Larceny < $400 | VICTIM/WITNESS | HISPANIC | 74 | MALE | 92115 | 20230 | 2902 | SAN DIEGO | hisp | NaN | NaN | nan | 2016 | 2016-01-01 | Jan | Fri | 0 |
| 12364997 | GRAND THEFT:MONEY/LABOR/PROPERTY OVER $950 | FELONY | Larceny >= $400 | VICTIM/WITNESS | WHITE | 70 | MALE | 92109 | 20120 | 7905 | SAN DIEGO | nhwhite | NaN | NaN | nan | 2016 | 2016-01-01 | Jan | Fri | 0 |
2. Data selection:
- City: National city
- Suspect: Known suspect ( domestic relation with the victim)
- Chages : SPOUSAL/COHABITANT ABUSE WITH SERIOUS INJURY SPOUSAL/COHABITANT ABUSE WITH MINOR INJURY (F) INFLICT CORPORAL INJURY ON SPOUSE/COHABITANT BATTERY:SPOUSE/EX SPOUSE/DATE/ETC (M)
battery <- all %>%
filter(city == "NATIONAL CITY" & personrole=="SUSPECT") %>%
filter(chargedescription== "INFLICT CORPORAL INJURY ON SPOUSE/COHABITANT" |
chargedescription=="BATTERY:SPOUSE/EX SPOUSE/DATE/ETC (M)" |
chargedescription=="SPOUSAL/COHABITANT ABUSE WITH SERIOUS INJURY (F)" |
chargedescription=="SPOUSAL/COHABITANT ABUSE WITH MINOR INJURY (F)" )
kable(table(battery$sex)) | Var1 | Freq |
|---|---|
| FEMALE | 250 |
| MALE | 734 |
| nan | 0 |
| NONBINARY | 0 |
| UNKNOWN | 3 |
kable(table(battery$race)) | Var1 | Freq |
|---|---|
| ASIAN INDIAN | 1 |
| BLACK | 162 |
| CAMBODIAN | 0 |
| CHINESE | 2 |
| EAST AFRICAN | 0 |
| FILIPINO | 33 |
| GUAMANIAN | 0 |
| HAWAIIAN | 0 |
| HISPANIC | 650 |
| INDIAN | 1 |
| JAPANESE | 0 |
| KOREAN | 0 |
| LAOTIAN | 0 |
| MIDDLE EASTERN | 1 |
| nan | 3 |
| OTHER | 9 |
| OTHER ASIAN | 9 |
| PACIFIC ISLANDER | 4 |
| SAMOAN | 0 |
| VIETNAMESE | 0 |
| WHITE | 112 |
nc_month <- battery %>%
group_by(year,month) %>%
summarise(Total = n())## `summarise()` regrouping output by 'year' (override with `.groups` argument)nc_month$date <- paste(nc_month$year, nc_month$month, sep="-")
nc_month$date <- parse_date_time(nc_month$date,"Ym")
tseries <- xts(nc_month$Total, order.by=as.POSIXct(nc_month$date))
hchart(tseries, name = "Domestic Battery") %>%
hc_add_theme(hc_theme_darkunica()) %>%
hc_credits(enabled = TRUE, text = "Sources: SANDAG",
style = list(fontSize = "12px")) %>%
hc_title(text = "Domestic Battery in National City, California") %>%
hc_subtitle(text="Monthly counts from January 2016 to July 2020")## Warning: `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated. #hc_legend(enabled = TRUE)
nc_month %>% ggplot(aes(x=date,y=Total)) +
geom_bar(stat="identity",fill="darkblue",alpha=.7) +
geom_smooth(color="red")+
labs(x="Monthly",y="Count by Suspect", title = "Domestic Battery Incidents in National City, California",
subtitle = "Monthly counts from January 2016 to July 2020",
caption = "Data source: SANDAG\nIllustration by @JoeLongSanDiego ")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Domestic Violence incidents in 2020 in National City, CA
battery_2020 <- filter(battery,year==2020)
ggplot(data=battery_2020,aes(x=month,fill=chargedescription)) + geom_bar() +
labs(title = "Charge Description",subtitle = "2020 in National City, CA ",x="Month",y="Count" )
ggplot(data=battery_2020,aes(x=month,fill=sex)) + geom_bar() +
labs(title = "Gender of suspects",subtitle = "2020 in National City, CA ",x="Month",y="Count" )
battery_2020race <- battery_2020 %>% group_by(race) %>% summarise(count=n()) %>%
arrange(desc(count)) ## `summarise()` ungrouping output (override with `.groups` argument)ggplot(data=battery_2020race,aes(x=reorder(race,-count),y=count,fill=race)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90))
Each domestic violence incident might have more than one victim.
- Counting incidents by number of victims
v_battery <- all %>%
filter(city == "NATIONAL CITY" & personrole=="VICTIM") %>%
filter(chargedescription== "INFLICT CORPORAL INJURY ON SPOUSE/COHABITANT" |
chargedescription=="BATTERY:SPOUSE/EX SPOUSE/DATE/ETC (M)" |
chargedescription=="SPOUSAL/COHABITANT ABUSE WITH SERIOUS INJURY (F)" |
chargedescription=="SPOUSAL/COHABITANT ABUSE WITH MINOR INJURY (F)" )
v_battery_2020 <- filter(v_battery, year==2020)
nc_month <- v_battery_2020 %>%
group_by(month,chargedescription) %>%
summarise(Total = n())## `summarise()` regrouping output by 'month' (override with `.groups` argument)nc_month %>% ggplot(aes(x=month,y=Total,fill=chargedescription)) +
geom_bar(stat = "identity") +
labs(x="Monthly",y="Count by Victims", title = "Victims of Domestic Violence in National City, California",
subtitle = "Monthly counts from January 2020 to July 2020",
caption = "Data source: SANDAG\nIllustration by @JoeLongSanDiego ")
#-------------------
race_nc <- v_battery %>% group_by(race) %>% summarise(count=n()) %>% arrange(desc(count)) ## `summarise()` ungrouping output (override with `.groups` argument)race_nc %>%
kable(caption = "Race of victim _ Domestic Violence") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") | race | count |
|---|---|
| HISPANIC | 668 |
| WHITE | 122 |
| BLACK | 112 |
| FILIPINO | 47 |
| OTHER | 27 |
| OTHER ASIAN | 9 |
| PACIFIC ISLANDER | 3 |
| ASIAN INDIAN | 2 |
| CHINESE | 2 |
| MIDDLE EASTERN | 2 |
| SAMOAN | 1 |
vrace_nc <- battery %>% group_by(race) %>% summarise(count=n()) %>% arrange(desc(count)) ## `summarise()` ungrouping output (override with `.groups` argument)vrace_nc %>%
kable(caption = "Race of SUSPECT _ Domestic Violence") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") | race | count |
|---|---|
| HISPANIC | 650 |
| BLACK | 162 |
| WHITE | 112 |
| FILIPINO | 33 |
| OTHER | 9 |
| OTHER ASIAN | 9 |
| PACIFIC ISLANDER | 4 |
| nan | 3 |
| CHINESE | 2 |
| ASIAN INDIAN | 1 |
| INDIAN | 1 |
| MIDDLE EASTERN | 1 |
#---- suspect
age_suspect <- battery %>%
group_by(age) %>%
summarise(total = n()) %>%
na.omit()## `summarise()` ungrouping output (override with `.groups` argument)age_suspect %>%
ggplot(aes(x = age, y = total)) +
geom_line(color="blue") +
geom_point(size=age_suspect$total/4,color="red") +
labs(title = "Age of Suspects _ Domestic Violence Incidents\nNational City",
x = "Age", y = "Total", subtitle = "Youngest = 16 Oldest = 85")
#--------------- VICTIMS IN 2020
victim_2020 <- filter(v_battery, year==2020)
age <- victim_2020 %>%
group_by(age) %>%
summarise(total = n()) %>%
na.omit()## `summarise()` ungrouping output (override with `.groups` argument)age %>%
ggplot(aes(x = age, y = total)) +
geom_line(color="blue") +
geom_point(size=age$total,color="red") +
labs(title = "Age of Victim _ 2020 Domestic Violence",
x = "Age", y = "Total", subtitle = "Youngest = 0 Oldest = 75\nNational City")
#---------SUSPECT IN 2020
suspect_2020 <- filter(battery, year==2020)
age <- suspect_2020 %>%
group_by(age) %>%
summarise(total = n()) %>%
na.omit()## `summarise()` ungrouping output (override with `.groups` argument)age %>%
ggplot(aes(x = age, y = total)) +
geom_line(color="blue") +
geom_point(size=age$total,color="red") +
labs(title = "Age of Suspect _ 2020 Domestic Violence in National city, CA",
x = "Age", y = "Total", subtitle = "Youngest = 17 Oldest = 76\nCircle sizes reflect count totals")
##Incident Mapping of Domestic Violence in 2020 in National City, CA **
library(leaflet)
suspect_2020 %>%
leaflet() %>%
addTiles() %>%
addMarkers(~longitute, ~latitude,clusterOptions = markerClusterOptions())## Warning in validateCoords(lng, lat, funcName): Data contains 5 rows with either
## missing or invalid lat/lon values and will be ignoredOverall Incident reports in 2020 from National City, CA
charge_2020 <- all %>%
filter(city == "NATIONAL CITY" & year==2020 )
charge_2020ID <- charge_2020[!duplicated(charge_2020$ID),]
charge_2020 <- charge_2020ID %>% group_by(chargedescription) %>% summarise(Total=n()) %>%
arrange(desc(Total))## `summarise()` ungrouping output (override with `.groups` argument)head(charge_2020,n=20) %>%
kable() %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") %>%
row_spec(c(4,6,13),bold=TRUE) %>%
footnote(general = "Three descriptions could be re-classified under DOMESTIC VIOLENCE
* if combining the three charges, total count could be higher")| chargedescription | Total |
|---|---|
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT (F) | 162 |
| BURGLARY (VEHICLE) (F) | 133 |
| PETTY THEFT(All Other Larceny) (M) | 72 |
| BATTERY:SPOUSE/EX SPOUSE/DATE/ETC (M) | 67 |
| SIMPLE BATTERY (M) | 54 |
| SPOUSAL/COHABITANT ABUSE WITH MINOR INJURY (F) | 54 |
| ROBBERY (F) | 53 |
| GRAND THEFT:MONEY/LABOR/PROPERTY (F) | 45 |
| PETTY THEFT(from Veh) (M) | 44 |
| BURGLARY (COMMERCIAL) (F) | 41 |
| ASSAULT W/DEADLY WEAPON:NOT F/ARM (F) | 38 |
| SHOPLIFTING (M) | 32 |
| SPOUSAL/COHABITANT ABUSE WITH SERIOUS INJURY (F) | 31 |
| PETTY THEFT(from Building) (M) | 30 |
| BURGLARY (RESIDENTIAL) (F) | 25 |
| PETTY THEFT(Shoplift) (M) | 24 |
| OTHER AGENCY VEHICLE THEFT/RECOVERY (F) | 21 |
| WILLFUL CRUELTY TO CHILD WITHOUT INJURY OR DEATH (F) | 17 |
| GRAND THEFT (Theft From Mot Veh) (F) | 13 |
| BATTERY ON PERSON (M) | 12 |
| Note: | |
|
Three descriptions could be re-classified under DOMESTIC VIOLENCE * if combining the three charges, total count could be higher |
ggplot(data=head(charge_2020,n=10), aes(x=reorder(chargedescription,Total), y=Total)) +
geom_bar(stat = "identity",fill="blue")+
labs(title = "Ten Top Crime Incidents _ 2020 National City, CA",
x="",y="Count") +
coord_flip()