One main goal of any law enforcement department is stop crime. In the long term though, law enforcement should also prevent crime to help the city grow. To aid in the prevention of crime, law enforcement should analyze the arrests they make, the demographic of the people they arrest, the location, etc. This information is important because it can help point out unchecked bias or it can point out specific crimes that are particularly prevalant. part of growing as a city means analyzing the crime. Different crimes can indicate issues different issues with the community.
The goal of this notebook is to generate a report containing different analysis of the recorded crimes in Boston and gather any trends about the location of the crimes.
# depiction of the Boston Crimes Data Set as a DataFrame
import pandas as pd
df_crime = pd.read_csv('crime.csv', encoding='latin1')
df_crime
| INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | I182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.357791 | -71.139371 | (42.35779134, -71.13937053) |
| 1 | I182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.306821 | -71.060300 | (42.30682138, -71.06030035) |
| 2 | I182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.346589 | -71.072429 | (42.34658879, -71.07242943) |
| 3 | I182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.334182 | -71.078664 | (42.33418175, -71.07866441) |
| 4 | I182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.275365 | -71.090361 | (42.27536542, -71.09036101) |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 319068 | I050310906-00 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | NaN | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.336951 | -71.085748 | (42.33695098, -71.08574813) |
| 319069 | I030217815-08 | 111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.255926 | -71.123172 | (42.25592648, -71.12317207) |
| 319070 | I030217815-08 | 3125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.255926 | -71.123172 | (42.25592648, -71.12317207) |
| 319071 | I010370257-00 | 3125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | NaN | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.302333 | -71.111565 | (42.30233307, -71.11156487) |
| 319072 | 142052550 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | NaN | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.333839 | -71.080290 | (42.33383935, -71.08029038) |
319073 rows × 17 columns
# Data Dictionary for the Headings of the Boston Crime Data Set
crime_attributes ={'Incident Number': 'Numerical Identifier for this specific crime. It is unique for all crimes',
'Offense_Code': 'Numerical Identifier associated for the type of crime committed',
'Offense_Code_Group': 'Overall category of the crime committed',
'Offense_Description': 'The reason for the arrest',
'District': 'The police station affialiated with the report',
'Reporting_Area': 'Place where the arrest took place',
'Shooting': 'Whether or not the crime committed was a shooting crime',
'Occured on Date': 'Date and Time the arrest took place',
'Year': 'Year the arrest took place',
'Month': 'Month the arrest took place',
'Day of Week': 'Day of the week the crime took place',
'Hour': 'Hour the crime took place (1-23)',
'UCR_Part':'Uniform Crime Reporting Program Category',
'Street': 'Street in Boston the arrest took place',
'Lat': 'Latitude of where the arrest took place',
'Long': 'Longitude of where the arrest took place',
'Location': 'Latitude and Longitude Tuple of where the crime took place'}
df_offense_codes = pd.read_csv('offense_codes.csv', encoding='latin1') df_offense_codes
# Data Dictionary for the Heading for the Offense Code DataFrame
offense_code_attributes ={'Code': 'Numerical Identifier associated with a specific crime',
'Name': 'Name of the Crime'}
Creating a visual map of where the crimes take place and grouping different crimes by area will identify areas of Boston with higher crimes in general. Knowing this information will help law enforcement investigate crimes of those specific area in more detail to figure if there is actually more crime in that area or if there is another problem occuring. Second, clustering the arrests by crime will help Boston law enforcement decide what type of crime they need to work on preventing which is important for the overall decrease of crime in the city