San Diego Crime Incidents 2016_2020
Joe Long
December 15 2020
Background:
National City is a city located in the South Bay region of the San Diego metropolitan area, in southwestern San Diego County, California.
The population was 58,582 at the 2010 census, up from 54,260 at the 2000 census. National City is the second-oldest city in San Diego County, having been incorporated in 1887
Data Source:
One recent 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); and National City (74 percent). Meanwhile, statistics cited by the National Coalition Against Domestic Violence indicate that one in three female murder victims are killed by intimate partners and that an abuser’s access to a firearm increases the risk of femicide significantly.
Santee (18 percent) El Cajon (18 percent) National City (74 percent)
Reading 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
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 data: all city/county law enforcement agencies
From 2016 to July 2020
## pk activitynumber activitydate year agency
## 1 12144871 '01600014' 2016-01-01 00:00:00 2016 NATIONAL CITY
## 2 12127327 '16000042' 2016-01-01 00:00:00 2016 SAN DIEGO
## 3 12278698 '16005661' 2016-01-01 00:00:00 2016 SAN DIEGO
## 4 12278698 '16005661' 2016-01-01 00:00:00 2016 SAN DIEGO
## 5 12364997 '16008822' 2016-01-01 00:00:00 2016 SAN DIEGO
## violationsection violationtype chargedescription
## 1 10851 VC TAKE VEHICLE W/O OWNER'S CONSENT/VEHICLE THEFT
## 2 459 PC BURGLARY/UNSPECIFIED
## 3 488 PC PETTY THEFT
## 4 488 PC PETTY THEFT
## 5 487(A) PC GRAND THEFT:MONEY/LABOR/PROPERTY OVER $950
## chargelevel codeucr crimecategory personrole race age sex
## 1 FELONY 7A0 Vehicle Theft VICTIM OTHER NA FEMALE
## 2 FELONY 5A6 Non Res Burglary nan nan NA nan
## 3 MISDEMEANOR 6DG Larceny < $400 SUSPECT WHITE 28 MALE
## 4 MISDEMEANOR 6DG Larceny < $400 VICTIM/WITNESS HISPANIC 74 MALE
## 5 FELONY 6AE Larceny >= $400 VICTIM/WITNESS WHITE 70 MALE
## zipcode censusblock censustract city census_race tract_geoid
## 1 91950 2000 22000 NATIONAL CITY other 14000US06073022000
## 2 92109 20000 7907 SAN DIEGO unknown 14000US06073007907
## 3 92115 20230 2902 SAN DIEGO nhwhite 14000US06073002902
## 4 92115 20230 2902 SAN DIEGO hisp 14000US06073002902
## 5 92109 20120 7905 SAN DIEGO nhwhite 14000US06073007905
## block_geoid intptlat intptlon
## 1 10100US060730220002000 32.67887 -117.0875
## 2 10100US0607300790720000 NaN NaN
## 3 10100US0607300290220230 NaN NaN
## 4 10100US0607300290220230 NaN NaN
## 5 10100US0607300790520120 NaN NaN
## geometry date month weekday hour
## 1 POINT (-117.0875061797541 32.67887038013) 2016-01-01 1 6 0
## 2 nan 2016-01-01 1 6 0
## 3 nan 2016-01-01 1 6 0
## 4 nan 2016-01-01 1 6 0
## 5 nan 2016-01-01 1 6 0Data Quality might change due to available information at different crime scenarios
filtering data with identifiable suspects
- Clarification : multiple crime report IDs are generated for each crime, with victim(s) and with suspect(s).
- Some crime incidents might have victims not, with no identifiable suspects, such as car theft
- Counting all crime report might lead to double counting number of some crime incidents, especially domestic crimes.
## [1] "Example of counting differences:"## [1] "Double counting of crime incidents could lead to misunderstanding"##
## Aggravated Assault Armed Robbery Arson Larceny
## 68300 14799 2180 18
## Larceny < $400 Larceny >= $400 Murder Non Res Burglary
## 125234 101260 1029 25591
## Rape Res Burglary Simple Assault Strong ArmRobbery
## 6672 40304 198196 19528
## Vehicle Theft
## 73133## [1] "all crime reports including all suspects and victims"##
## Aggravated Assault Armed Robbery Arson Larceny
## 32911 8607 983 10
## Larceny < $400 Larceny >= $400 Murder Non Res Burglary
## 50227 31086 524 11924
## Rape Res Burglary Simple Assault Strong ArmRobbery
## 3405 10916 96245 11283
## Vehicle Theft
## 15726## [1] "crime incidents with identifiable suspects"##
## Aggravated Assault Armed Robbery Arson Larceny
## 25241 4550 1370 2
## Larceny < $400 Larceny >= $400 Murder Non Res Burglary
## 79908 65967 363 14752
## Rape Res Burglary Simple Assault Strong ArmRobbery
## 2827 19596 79552 6722
## Vehicle Theft
## 40717## [1] "crime incidents by crime report"Sexual Violence Incidents in National City, California
- Crime descriptions: All Domestic Violence descriptions
- with identifiable suspects
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)
Caution: each incident could have multiple reports: one report for suspect and one for each victim(s)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## 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.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `summarise()` ungrouping output (override with `.groups` argument)
** Rape Incidents **
- Selecting only report ID for each case.
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Vehicle Theft
- Selecting only report ID for each case.
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Armed Robbery
- Segregate data into suspect and victim
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
Monthly
## `summarise()` ungrouping output (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Domestic Sexual assaults in San Diego City with identifiable suspects
## `summarise()` ungrouping output (override with `.groups` argument)