San Diego THEFT Crimes 2017_2020
Joe Long
4/22/2022
Source: general data set from SANDAG AGENCY, San Diego county**
From https://data.sandiegodata.org/dataset/sandag-gov-crime-2007e2013/
San Diego county crime incidents,From SANDAG http://www.sandag.org/programs/public_safety/arjis/CrimeData/crimedata.zip
sdcrime_16_20.csv
larceny: the unlawful taking of personal property with intent to deprive the rightful owner of it permanently
Cautions:
Crime incident reports are collected and reported by field officers from various different law enforcement agencies
Data is collected by 19 different agencies. Each law enforcement departments practice different policies, that can result in different categorizations, codifications.
Many incident reports could be generated using the same single location block. Because all of the crimes on a block are geocoded to the middle of the block, many incidents will appear as a single point.
Some incidents are not geocoded.
Some incident might have multipple report IDs due to multiple suspects or/and victims or multiple crimes
Some incidents might have victims but no suspects
Some incidents might have suspect but no victim
all <- read.csv("D:/Dataset_main/sd_crime_data.csv")
all %>% filter(year==2018) %>% group_by(CrimeCategory) %>% summarise(Total=n()) %>%
arrange(desc(Total))## # A tibble: 12 x 2
## CrimeCategory Total
## <chr> <int>
## 1 Simple Assault 43242
## 2 Larceny < $400 27250
## 3 Larceny >= $400 22654
## 4 Vehicle Theft 14706
## 5 Aggravated Assault 14691
## 6 Res Burglary 8278
## 7 Non Res Burglary 5176
## 8 Strong ArmRobbery 4791
## 9 Armed Robbery 3070
## 10 Rape 1501
## 11 Arson 466
## 12 Murder 211all %>% filter(year==2019) %>% group_by(CrimeCategory) %>% summarise(Total=n()) %>%
arrange(desc(Total))## # A tibble: 13 x 2
## CrimeCategory Total
## <chr> <int>
## 1 Simple Assault 44707
## 2 Larceny < $400 27050
## 3 Larceny >= $400 23669
## 4 Aggravated Assault 15472
## 5 Vehicle Theft 14731
## 6 Res Burglary 7664
## 7 Non Res Burglary 5731
## 8 Strong ArmRobbery 4360
## 9 Armed Robbery 3330
## 10 Rape 1503
## 11 Arson 380
## 12 Murder 210
## 13 Larceny 18all %>% filter(year==2020) %>% group_by(CrimeCategory) %>% summarise(Total=n()) %>%
arrange(desc(Total))## # A tibble: 12 x 2
## CrimeCategory Total
## <chr> <int>
## 1 Simple Assault 23828
## 2 Larceny < $400 13847
## 3 Larceny >= $400 11720
## 4 Aggravated Assault 9228
## 5 Vehicle Theft 8382
## 6 Res Burglary 3799
## 7 Non Res Burglary 3502
## 8 Armed Robbery 1977
## 9 Strong ArmRobbery 1968
## 10 Rape 623
## 11 Arson 410
## 12 Murder 161theft<- all %>%
filter(City == "SAN DIEGO") %>%
filter(CrimeCategory=="Larceny<$400" | CrimeCategory=="Larceny>$400" |
CrimeCategory=="Vehicle Theft" | CrimeCategory=="Res Burglary")
t2017 <- theft %>% filter(year==2017) %>% group_by(chargeDescription) %>% summarise(Total=n()) %>%
arrange(desc(Total))
t2018 <- theft %>% filter(year==2018) %>% group_by(chargeDescription) %>% summarise(Total=n()) %>%
arrange(desc(Total))
t2019<- theft %>% filter(year==2019) %>% group_by(chargeDescription) %>% summarise(Total=n()) %>%
arrange(desc(Total))
t2020 <- theft %>% filter(year==2020) %>% group_by(chargeDescription) %>% summarise(Total=n()) %>%
arrange(desc(Total))
head(t2017,n=10) %>%
kable(caption = "Theft incidences in 2017") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") %>%
footnote(general = "Sub-categories under THEFT")| chargeDescription | Total |
|---|---|
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | 6844 |
| BURGLARY/UNSPECIFIED | 2775 |
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT (F) | 741 |
| BURGLARY/FIRST DEGREE | 369 |
| ATTEMPTED BURGLARY:DEGREE UNSPECIFIED | 195 |
| BURGLARY:UNSPECIFIED | 150 |
| BURGLARY/SECOND DEGREE | 96 |
| ATTEMPT OPERATE VEHICLE W/O OWNER’S CONSENT | 24 |
| ATTEMPT BURGLARY | 13 |
| BURGLARY (RESIDENTIAL) (F) | 13 |
| Note: | |
| Sub-categories under THEFT |
head(t2018,n=10) %>%
kable(caption = "Theft incidences in 2018") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") %>%
footnote(general = "Sub-categories under THEFT")| chargeDescription | Total |
|---|---|
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | 4052 |
| BURGLARY/UNSPECIFIED | 2630 |
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT (F) | 2291 |
| BURGLARY/FIRST DEGREE | 389 |
| ATTEMPTED BURGLARY:DEGREE UNSPECIFIED | 145 |
| BURGLARY:UNSPECIFIED | 133 |
| BURGLARY/SECOND DEGREE | 117 |
| BURGLARY (RESIDENTIAL) (F) | 48 |
| ATTEMPT BURGLARY | 15 |
| ATTEMPT OPERATE VEHICLE W/O OWNER’S CONSENT | 15 |
| Note: | |
| Sub-categories under THEFT |
head(t2019,n=10) %>%
kable(caption = "Theft incidences in 2019") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") %>%
footnote(general = "Sub-categories under THEFT")| chargeDescription | Total |
|---|---|
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT (F) | 4495 |
| BURGLARY (RESIDENTIAL) (F) | 2164 |
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | 1698 |
| BURGLARY/UNSPECIFIED | 887 |
| BURGLARY/FIRST DEGREE | 172 |
| OTHER AGENCY VEHICLE THEFT/RECOVERY (F) | 101 |
| ATTEMPTED BURGLARY:DEGREE UNSPECIFIED | 55 |
| BURGLARY/SECOND DEGREE | 46 |
| BURGLARY:UNSPECIFIED | 35 |
| BURGLARY (COMMERCIAL) (F) | 17 |
| Note: | |
| Sub-categories under THEFT |
head(t2020,n=10) %>%
kable(caption = "Theft incidences in 2020") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
column_spec(1:2, color = "black", background = "lightblue") %>%
footnote(general = "Sub-categories under THEFT")| chargeDescription | Total |
|---|---|
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT (F) | 3639 |
| BURGLARY (RESIDENTIAL) (F) | 1829 |
| OTHER AGENCY VEHICLE THEFT/RECOVERY (F) | 204 |
| TAKE VEHICLE W/O OWNER’S CONSENT/VEHICLE THEFT | 53 |
| BURGLARY (COMMERCIAL) (F) | 39 |
| TAKE VEHICLE WITHOUT OWNER CONSENT WITH SPECIFIC PRIORS (F) | 15 |
| GRAND THEFT:MONEY/LABOR/PROPERTY (F) | 6 |
| BURGLARY DURING STATE OF EMERGENCY - LOOTING (F) | 5 |
| VEH THEFT:SPECIFIC PRS (F) | 5 |
| GRAND THEFT (Mot Veh Theft) (F) | 4 |
| Note: | |
| Sub-categories under THEFT |
theft$activityDate <- ymd_hm(theft$activityDate)
sd <- theft %>% filter(year==2017) %>%
group_by(chargeDescription) %>%
summarise(Total=n()) %>%
arrange(-Total)
head(sd,n=10) %>% ggplot(aes(x = reorder(chargeDescription,Total), y = Total)) +
geom_bar(stat = "identity", fill = "#756bb1") +
geom_text(aes(x = chargeDescription, y = Total, label = Total)) +
labs(x ="Charge Description", y = "Count", title = "2017 Top THEFT Crimes in San Diego city, CA",
subtitle = " ") +
coord_flip()
sd <- theft %>% filter(year==2018) %>%
group_by(chargeDescription) %>%
summarise(Total=n()) %>%
arrange(-Total)
head(sd,n=10) %>% ggplot(aes(x = reorder(chargeDescription,Total), y = Total)) +
geom_bar(stat = "identity", fill = "#756bb1") +
geom_text(aes(x = chargeDescription, y = Total, label = Total)) +
labs(x ="Charge Description", y = "Count", title = "2018 Top THEFT Crimes\nSan Diego city, CA",
subtitle = " ") +
coord_flip()
sd <- theft %>% filter(year==2019) %>%
group_by(chargeDescription) %>%
summarise(Total=n()) %>%
arrange(-Total)
head(sd,n=10) %>% ggplot(aes(x = reorder(chargeDescription,Total), y = Total)) +
geom_bar(stat = "identity", fill = "#756bb1") +
geom_text(aes(x = chargeDescription, y = Total, label = Total)) +
labs(x ="Charge Description", y = "Count", title = "2019 Top THEFT Crimes\nSan Diego city, CA",
subtitle = " ") +
coord_flip()
sd <- theft %>% filter(year==2020) %>%
group_by(chargeDescription) %>%
summarise(Total=n()) %>%
arrange(-Total)
head(sd,n=10) %>% ggplot(aes(x = reorder(chargeDescription,Total), y = Total)) +
geom_bar(stat = "identity", fill = "#756bb1") +
geom_text(aes(x = chargeDescription, y = Total, label = Total)) +
labs(x ="Charge Description", y = "Count", title = "2020 Top THEFT Crimes\nSan Diego city, CA",
subtitle = " ") +
coord_flip()