Over the past couple of months we have observed a numerous amount of crime cases related to universities around the United States. This has left a students not only within the campuses affected however throughout the country it has caused students to fear for their safety within their community. With Boston being home to around 350 thousand students spread across numerous universities and colleges it is appropriate to observe and analyze the crimes that exist in boston the magnitude of them, frecuency and where they take place. With this it can help students feel more comfotable and aware of their surroundings as well as help ensure they know they are safe whereever they go. To solve this real life problem on the crime rate in Boston I will be able to use data science to create graphs, conduct any necessary calculations aswell as allow for user interaction (for example a user may input where they are going to go and the return can be a safety rating index depending on the time of day). While the links below describe more severe cases of crimes, there are still many smaller crimes that can put people in danger that we often dont take into consideration, thereby these more severe ones prompted me to take this topic into concern to ensure all students in Bostons safety is maximised.
Below are two images showing the districts in boston and a map of the districts that have universities in them.
Michigan State Shooting: https://apnews.com/article/michigan-state-shooting-58d87c54210d30f9514f6b350e4f929d Idaho Murders: https://www.idahostatesman.com/news/northwest/idaho/article272572530.html UVA shooting: https://www.espn.com/college-football/story/_/id/35211743/suspect-deadly-virginia-football-shooting-appears-court
import pandas as pd
df = pd.read_csv("Boston Incidents.csv")
df.head(5)
| OFFENSE_NAME | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | DISTRICT_NAME | REPORTING_AREA | SHOOTING | DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | HOUR_RANGE | STREET | Lat | Long | Location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | FRAUD - IMPERSONATION | 1107 | Fraud | FRAUD - IMPERSONATION | C11 | Dorchester | 352 | Not reported | 2016-01-01 00:00:00 | 2016 | January | Friday | 0 | Midnight - 4:00am | DITSON ST | 42.301085 | -71.063908 | (42.30108481, -71.06390782) |
| 1 | FRAUD - LARCENY BY SCHEME | 1107 | Fraud | FRAUD - IMPERSONATION | C11 | Dorchester | 352 | Not reported | 2016-01-01 00:00:00 | 2016 | January | Friday | 0 | Midnight - 4:00am | DITSON ST | 42.301085 | -71.063908 | (42.30108481, -71.06390782) |
| 2 | HARASSMENT | 2629 | Harassment | HARASSMENT | D4 | South End/Kenmore | 161 | Not reported | 2016-01-01 00:00:00 | 2016 | January | Friday | 0 | Midnight - 4:00am | TREMONT ST | 42.347017 | -71.068862 | (42.34701666, -71.06886238) |
| 3 | EMBEZZLEMENT | 1201 | Embezzlement | EMBEZZLEMENT | A1 | Central Boston | 122 | Not reported | 2016-01-01 00:00:00 | 2016 | January | Friday | 0 | Midnight - 4:00am | WASHINGTON ST | 42.350005 | -71.063591 | (42.35000492, -71.06359053) |
| 4 | ASSAULT - AGGRAVATED | 423 | Aggravated Assault | ASSAULT - AGGRAVATED | B2 | Roxbury | 330 | Not reported | 2016-01-01 00:00:00 | 2016 | January | Friday | 0 | Midnight - 4:00am | COLUMBIA RD | 42.305248 | -71.080894 | (42.30524752, -71.08089418) |
df.keys()
Index(['OFFENSE_NAME', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP',
'OFFENSE_DESCRIPTION', 'DISTRICT', 'DISTRICT_NAME', 'REPORTING_AREA',
'SHOOTING', 'DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR',
'HOUR_RANGE', 'STREET', 'Lat', 'Long', 'Location'],
dtype='object')
data_dict = {}
data_dict['Offense name'] = 'What type of crime took place'
data_dict['offense code'] = 'code associated with the type of crime that took place'
data_dict['offense code group'] = 'the group of crime that the offense code falls under'
data_dict['offense description'] = 'how did the crime take place, what was done'
data_dict['distric'] = 'the letter number code associated with each district'
data_dict['district name'] = 'the name of the district'
data_dict['reporting area'] = 'area reffering to which police station is closest to the crime'
data_dict['shooting'] = 'where any shots fired during the crime'
data_dict['date'] = 'the month, day, year reporting of the incident'
data_dict['year'] = 'the year of the crime'
data_dict['month'] = 'the month of the crime'
data_dict['day of week'] = 'what day of the week did the crime occur on'
data_dict['hour'] = 'what hour of the day did the crime happen at'
data_dict['hour range '] = 'wha 4 hour range did it occur in'
data_dict['street'] = 'what street did it happen on'
data_dict['lat, long, location'] = 'the latitude and the longitued of the crime'
data_dict
{'Offense name': 'What type of crime took place',
'offense code': 'code associated with the type of crime that took place',
'offense code group': 'the group of crime that the offense code falls under',
'offense description': 'how did the crime take place, what was done',
'distric': 'the letter number code associated with each district',
'district name': 'the name of the district',
'reporting area': 'area reffering to which police station is closest to the crime',
'shooting': 'where any shots fired during the crime',
'date': 'the month, day, year reporting of the incident',
'year': 'the year of the crime',
'month': 'the month of the crime',
'day of week': 'what day of the week did the crime occur on',
'hour': 'what hour of the day did the crime happen at',
'hour range ': 'wha 4 hour range did it occur in',
'street': 'what street did it happen on',
'lat, long, location': 'the latitude and the longitued of the crime'}
Using this data we will be able to map out crime count per district name, develop a safety index for each district based on ranking the crimes that occur in each district and the time of day they occur as well as observe the crime rate in Boston over the years. With all of this we will be able to raise awareness to college students in Boston of the crimes and safety of areas surrounding them which will help them know where to be careful and where they can feel safe going to.