This data contains information about crime in San Francisco. We are going to analyze the data using maps for geographical understanding.

Hypothesis: Crime rate increases during night time in suburban areas in San Francisco, police patrolling should change based on this analysis.

## # A tibble: 6 x 13
##   IncidntNum Category Descript DayOfWeek Date  Time  PdDistrict Resolution
##   <chr>      <chr>    <chr>    <chr>     <chr> <tim> <chr>      <chr>     
## 1 120058272  WEAPON … POSS OF… Friday    01/2… 11:00 SOUTHERN   ARREST, B…
## 2 120058272  WEAPON … FIREARM… Friday    01/2… 11:00 SOUTHERN   ARREST, B…
## 3 141059263  WARRANTS WARRANT… Monday    04/2… 14:59 BAYVIEW    ARREST, B…
## 4 160013662  NON-CRI… LOST PR… Tuesday   01/0… 23:50 TENDERLOIN NONE      
## 5 160002740  NON-CRI… LOST PR… Friday    01/0… 00:30 MISSION    NONE      
## 6 160002869  ASSAULT  BATTERY  Friday    01/0… 21:35 NORTHERN   NONE      
## # … with 5 more variables: Address <chr>, X <dbl>, Y <dbl>, Location <chr>,
## #   PdId <dbl>

Time Analysis of crime

District Breakup of Crimes

Recommendations Based on Findings: I suggest San Francisco PD should shift the amount of patrolling gradually to see the effects it has on each neighborhood, especially considering the fact that the amount of crime in certain neighborhoods may be low because of the amount of patrolling in the area. They should also increase the amount of patrolling at peak times in respective neighborhoods. Lastly, I would suggest offering incentives, such as cash bonuses, to precincts with lower case closing rates to motivate a higher solve rate. This information is relevant considering the upcoming election and U.S. current events

Sources: Data: https://www.kaggle.com/roshansharma/sanfranciso-crime-dataset