In recent years, safety has been a significant concern for many residents in the United States, and crime rates in the city are much higher when compared to the suburbs. In an article written by James H Anderson, the violent-crime rate in urban areas was between 29 percent and 42 percent higher than the rate in rural areas. In a large metropolitan city like Boston, there are many crimes and dangers hiding throughout the city, so finding out which districts have the highest crime rates can really help residents of Boston in avoiding danger.
import pandas as pd
df_2 = pd.read_csv('tmpdfeo3qy2.csv')
df = pd.read_csv('rmscrimeincidentfieldexplanation.xlsx - Sheet1 (2).csv')
#data too big cant show the whole data set
print(df_2[0:20])
print(df)
INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \
0 222076257 619 NaN
1 222053099 2670 NaN
2 222039411 3201 NaN
3 222011090 3201 NaN
4 222062685 3201 NaN
5 222040307 3115 NaN
6 222023700 2670 NaN
7 222027838 3114 NaN
8 222002890 1109 NaN
9 222000003 423 NaN
10 222000387 2647 NaN
11 222000182 3201 NaN
12 222000309 3201 NaN
13 222000181 3201 NaN
14 222000004 1402 NaN
15 222000027 3114 NaN
16 222000011 423 NaN
17 222000006 423 NaN
18 222000010 3802 NaN
19 222000025 3115 NaN
OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \
0 LARCENY ALL OTHERS D4 167 0
1 HARASSMENT/ CRIMINAL HARASSMENT A7 0
2 PROPERTY - LOST/ MISSING D14 778 0
3 PROPERTY - LOST/ MISSING B3 465 0
4 PROPERTY - LOST/ MISSING B3 465 0
5 INVESTIGATE PERSON A1 954 0
6 HARASSMENT/ CRIMINAL HARASSMENT D4 0
7 INVESTIGATE PROPERTY B3 0
8 FRAUD - WIRE C6 200 0
9 ASSAULT - AGGRAVATED D4 0
10 THREATS TO DO BODILY HARM A1 77 0
11 PROPERTY - LOST/ MISSING A1 77 0
12 PROPERTY - LOST/ MISSING A15 41 0
13 PROPERTY - LOST/ MISSING A1 0
14 VANDALISM E5 0
15 INVESTIGATE PROPERTY A1 0
16 ASSAULT - AGGRAVATED A1 0
17 ASSAULT - AGGRAVATED A1 93 0
18 M/V ACCIDENT - PROPERTY DAMAGE D4 0
19 INVESTIGATE PERSON E18 519 0
OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \
0 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
1 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
2 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
3 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
4 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
5 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
6 2022-01-01 00:00:00 2022 1 Saturday 0 NaN
7 2022-01-01 00:01:00 2022 1 Saturday 0 NaN
8 2022-01-01 00:01:00 2022 1 Saturday 0 NaN
9 2022-01-01 00:29:00 2022 1 Saturday 0 NaN
10 2022-01-01 00:30:00 2022 1 Saturday 0 NaN
11 2022-01-01 00:30:00 2022 1 Saturday 0 NaN
12 2022-01-01 00:30:00 2022 1 Saturday 0 NaN
13 2022-01-01 00:30:00 2022 1 Saturday 0 NaN
14 2022-01-01 00:33:00 2022 1 Saturday 0 NaN
15 2022-01-01 00:44:00 2022 1 Saturday 0 NaN
16 2022-01-01 00:46:00 2022 1 Saturday 0 NaN
17 2022-01-01 00:48:00 2022 1 Saturday 0 NaN
18 2022-01-01 00:53:00 2022 1 Saturday 0 NaN
19 2022-01-01 00:57:00 2022 1 Saturday 0 NaN
STREET Lat Long \
0 HARRISON AVE 42.339542 -71.069409
1 BENNINGTON ST 42.377246 -71.032597
2 WASHINGTON ST 42.349056 -71.150498
3 BLUE HILL AVE 42.284826 -71.091374
4 BLUE HILL AVE 42.284826 -71.091374
5 FULTON ST 42.362936 -71.052538
6 HARRISON AVE 42.339542 -71.069409
7 SELDEN ST 42.280894 -71.080375
8 W BROADWAY 42.341288 -71.054679
9 BROOKLINE AVE 42.346251 -71.099539
10 NEW SUDBURY ST 42.361839 -71.059765
11 NEW SUDBURY ST 42.361839 -71.059765
12 VINE ST 42.376632 -71.055932
13 NEW SUDBURY ST 42.361839 -71.059765
14 DELFORD ST 42.292092 -71.121089
15 MELROSE ST 42.348965 -71.068485
16 SCHOOL ST 42.357546 -71.058820
17 UNION ST 42.360688 -71.056873
18 SAINT BOTOLPH ST 42.346175 -71.079259
19 PIERCE ST 42.257114 -71.117191
Location
0 (42.33954198983014, -71.06940876967543)
1 (42.37724638479816, -71.0325970804128)
2 (42.34905600030506, -71.15049849975023)
3 (42.28482576580488, -71.09137368938802)
4 (42.28482576580488, -71.09137368938802)
5 (42.36293610909294, -71.0525379472723)
6 (42.33954198983014, -71.06940876967543)
7 (42.280893655822176, -71.0803746810546)
8 (42.341287504390436, -71.05467932649397)
9 (42.34625079905638, -71.09953855872904)
10 (42.361838566564714, -71.05976489094158)
11 (42.361838566564714, -71.05976489094158)
12 (42.37663166813234, -71.05593195497872)
13 (42.361838566564714, -71.05976489094158)
14 (42.292092069228836, -71.12108876777533)
15 (42.34896523984558, -71.06848460481795)
16 (42.35754619923963, -71.0588195319583)
17 (42.36068786991018, -71.05687293370812)
18 (42.346174755447535, -71.07925920373032)
19 (42.25711405538539, -71.11719142973159)
Field Name, Data Type, Required \
0 [incident_num]
1 [offense_code]
2 [Offense_Code_Group_Description]
3 [Offense_Description]
4 [district]
5 [reporting_area]
6 [shooting]
7 [occurred_on] [datetime2]
8 [Month]
9 [year]
Description
0 Internal BPD report number
1 Numerical code of offense description
2 Internal categorization of [offense_description]
3 Primary descriptor of incident
4 What district the crime was reported in
5 RA number associated with the where the crime ...
6 Indicated a shooting took place.
7 Earliest date and time the incident could have...
8 Which the crime was reported in
9 which year the crime was reported in
/var/folders/rg/6rp05ksn32sgc45qc8ftq6gm0000gn/T/ipykernel_69007/3560792410.py:3: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
df_2 = pd.read_csv('tmpdfeo3qy2.csv')
We will be creating graphs indicating what crimes were reported in which area, and we will also be seeing during which seasons have higher crime rates. With this information, we can provide a guide to residents of Boston on which areas to avoid and at what time or seasons they should avoid going out.