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Project: Analyzing Crime in Los Angeles

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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

ColumnDescription
'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:
  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian
'Weapon Desc'Description of the weapon used (if applicable).
'Status Desc'Crime status.
'LOCATION'Street address of the crime.
# Re-run this cell
#Β Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()
# Title: Comprehensive Analysis of Crime Data

# Summary: This code offers a thorough examination of crime data, starting with the import of necessary libraries and the loading of crime data from a CSV file. It includes visualizations to display the distribution of crimes by the hour of occurrence, highlights the peak crime hour, and determines the maximum frequency for that hour. The code also delves into area-wise crime statistics and demographics, grouping crimes by area, and categorizing victim ages into different age groups, providing valuable insights into crime patterns and demographics.

# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Read the crime data from a CSV file, parse date columns, and set data types
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})

# Display the first few rows of the DataFrame
print(crimes.head())

# Extract the hour of occurrence from the 'TIME OCC' column and convert it to an integer
Hour_Occ = crimes['TIME OCC'].str[:2].astype(int)

# Create a count plot to visualize the frequencies of crimes by the hour of occurrence
sns.countplot(x=Hour_Occ)
plt.title('Frequencies of crimes by the hour of occurrence')
plt.xlabel('Hours')
plt.ylabel('Frequency')
plt.xticks(rotation=0)
plt.show()

# Find the peak crime hour (hour with the highest frequency)
peak_crime_hour = int(Hour_Occ.value_counts().idxmax())
print(peak_crime_hour)

# Find the maximum frequency of crimes for the peak hour
peak_crime_max = int(Hour_Occ.value_counts().max())
print(peak_crime_max)

# Define a list of night hours (10pm to 4am)
night_hours_list = ['22', '23', '0', '1', '2', '3', '4']

# Filter the data for night hours and create a DataFrame
night_hours_df = Hour_Occ[Hour_Occ.isin(night_hours_list)]
print(night_hours_df)

# Group the data by 'AREA NAME' and 'Crm Cd Desc', and count the number of crimes
area_crime_count = crimes.groupby('AREA NAME')['Crm Cd Desc'].value_counts().reset_index(name='Count')
print(area_crime_count)

# Sort the area crime counts in descending order
area_crime_count_sorted = area_crime_count.sort_values(by='Count', ascending=False)
print(area_crime_count_sorted)

# Extract the first row (highest count) and get the 'AREA NAME'
first_row = area_crime_count_sorted.iloc[0]
peak_night_crime_location = first_row['AREA NAME']
print(peak_night_crime_location)

# Define age labels and age bins for victim age groups
age_labels = ['<18', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]

# Create age brackets and count the number of crimes in each age group
crimes['Age_Bracket'] = pd.cut(crimes['Vict Age'], bins=age_bins, labels=age_labels, right=False)
crime_count_by_age = crimes['Age_Bracket'].value_counts()
print(crime_count_by_age)
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