Skip to content
Project: 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. |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes = pd.read_csv("crimes.csv", dtype={"TIME OCC": str})
crimes.head()# Which hour has the highest frequency of crimes?
#Group times, count number of occurences of time, reset time as index
time_labels = ["00", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23"]
bins = ["00", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"]
crimes['TimeGr'] = pd.cut(crimes['TIME OCC'], bins=bins, labels=time_labels, right=False)
peak = crimes.groupby("TimeGr").size().reset_index(name="Total Count")
peak_time = peak.sort_values("Total Count", ascending=False)
peak_crime_hour = peak_time.iloc[0,0]
print(peak_crime_hour)f = sns.catplot(data=peak_time, x="TimeGr", y="Total Count", kind="bar", palette="flare")
f.set(xlabel="Hour of the Day", ylabel="Crimes Committed")
plt.xticks(rotation=90)
f.legend.remove()
plt.show()#Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)?
#Filter for time, merge tables
night = crimes[crimes["TIME OCC"] >= "2200"]
night2 = crimes[crimes["TIME OCC"] <= "0359"]
night_crime= pd.concat([night, night2], axis=0)
#Group times, count number of occurences of time, reset time as index
peak_area = night_crime.groupby(["AREA NAME"]).size().reset_index(name="Total Count")
peak_area = peak_area.sort_values("Total Count", ascending=False)
peak_night_crime_location = peak_area.iloc[0,0]
print(peak_night_crime_location)#Identify the number of crimes committed against victims of different age groups. Series with age group labels as the index and the frequency of crimes as the values.
age_labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"] #index
bins = [0, 18, 26, 35, 45, 55, 65, 110]
crimes['AgeGr'] = pd.cut(crimes['Vict Age'], bins=bins, labels=age_labels, right=False)
vic = crimes.groupby("AgeGr").size().reset_index(name="Total Count")
vic = vic.set_index("AgeGr")
victim_ages = vic["Total Count"]
print(victim_ages)g =sns.catplot(data=vic, x="AgeGr", y="Total Count", kind="bar", palette="flare", hue= "AgeGr")
g.set(xlabel="Victim Age Group", ylabel="Crimes Committed")
g.fig.suptitle("Crimes Commited Against Varying Age Groups", y=1.03)
g.legend.remove()
plt.xticks(rotation=90)
plt.show()