ANALYZING CRIME IN LOS ANGELES
Los Angeles, California 😎. The City of Angels. Tinseltown. The Entertainment Capital of the World!
Known for its warm weather, palm trees, sprawling coastline, and Hollywood, along with producing some of the most iconic films and songs. However, as with any highly populated city, it isn't always glamorous and there can be a large volume of crime. That's where you can help!
You have been asked to support the Los Angeles Police Department (LAPD) by analyzing crime data to identify patterns in criminal behavior. They plan to use your insights to allocate resources effectively to tackle various crimes in different areas.
The Data
They have provided you with a single dataset to use. A summary and preview are provided below.
It is a modified version of the original data, which is publicly available from Los Angeles Open Data.
crimes.csv
| Column | Description |
|---|---|
'DR_NO' | Division of Records Number: Official file number made up of a 2-digit year, area ID, and 5 digits. |
'Date Rptd' | Date reported - MM/DD/YYYY. |
'DATE OCC' | Date of occurrence - MM/DD/YYYY. |
'TIME OCC' | In 24-hour military time. |
'AREA NAME' | The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example, the 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles. |
'Crm Cd Desc' | Indicates the crime committed. |
'Vict Age' | Victim's age in years. |
'Vict Sex' | Victim's sex: F: Female, M: Male, X: Unknown. |
'Vict Descent' | Victim's descent:
|
'Weapon Desc' | Description of the weapon used (if applicable). |
'Status Desc' | Crime status. |
'LOCATION' | Street address of the crime. |
1. Importing libraries and reading data into a DataFrame
#Â Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns# Cheking data
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()2. Finding the frequencies of crimes by the hour of occurrence
# Creating 'HOUR OCC' column
crimes['HOUR OCC'] = crimes['TIME OCC'].str[:2].astype('int64')
crimes# Plotting the frequencies
sns.countplot(data=crimes, x='HOUR OCC', palette='flare')
plt.title('The number of crimes in hours')
plt.xlabel('Hours')
plt.ylabel('Number of crimes')
plt.show()# Hour that has the highest frequency of crimes
peak_crime_hour = 123. Identifying the area with the most night crime
# Creating a new DataFrame for mignight-crimes
night_time = crimes[crimes['HOUR OCC'].isin([22,23,0,1,2,3])]
night_time.head(10)# Area has the highest number of mid-night crimes
area_midnight_crimes = night_time.groupby('AREA NAME')['HOUR OCC'].count().sort_values(ascending=False)
peak_night_crime_location = area_midnight_crimes.index[0]
peak_night_crime_location4. Crimes by age group
# Creating bins and labels
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]
age_labels = ['0-18', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Creating a new column for Age group
crimes['Age Bracket'] = pd.cut(crimes['Vict Age'], bins=age_bins, labels=age_labels)
crimes.head(10)# Showing the number of victims for each Age bracket
victim_ages = crimes[['Age Bracket']].value_counts()
print(victim_ages)‌
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