Brazil Conflict Analysis Report

Data Description

The Brazil Conflict Tracker (2018-2023) Kaggle dataset provides a comprehensive record of both violent and non-violent conflicts in Brazil since 2018. The data source is from the Armed Conflict Location & Event Data Project (ACLED), which tracks real-global political violence and protest in real-time. The last data update was on January 20th, 2023, and the dataset uploader Justin Oh removed ID-related variables and conflicts outside of Brazil provided by ACLED from the original dataset.

Questions

• How are various types of conflicts and fatalities spread out annually?
• Which types of conflicts have the highest amount of deaths?
• Which locations in Brazil tend to have the highest death tolls?

Python Libraries Deployed

• pandas
• folium
• matplotlib
• seaborn

Analysis

The analysis includes the following steps:

1. Import the necessary libraries and data and clean the data.

# Import libraries
import pandas as pd
import folium as flm
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns

# Import data
conflicts = pd.read_csv('Brazil Political Violence and Protests Dataset.csv')

# Clean data
conflicts = conflicts.rename(columns={'EVENT_TYPE': 'CONFLICT', 'SUB_EVENT_TYPE': 'SUBCONFLICT'})

# Extract year from event date column for later
conflicts['YEAR'] = pd.to_datetime(conflicts['EVENT_DATE']).dt.year

2. Explore the data by printing the first few rows and information about the data, print unique conflict and subconflict types, and obtain the value counts of conflict types. Create a pie chart to visualize the number of conflicts by type and provide tables for conflict and subconflict types.

# Explore data
## Print first few rows and info of the data
print(conflicts.head())
print(conflicts.info())

# Print unique conflict and subconflict types
conflict_types = conflicts['CONFLICT'].unique()
subconflict_types = conflicts['SUBCONFLICT'].unique()
print(conflict_types)
print(subconflict_types)

# Get value counts of conflict types
conflict_counts = conflicts['CONFLICT'].value_counts()

# Calculate percentage of each conflict type
conflict_p = round(conflict_counts / conflict_counts.sum() * 100,2).astype(str) + '%'

# Combine count and percentage into a dataframe
conflict_pc = pd.DataFrame({'COUNT': conflict_counts, 'PERCENTAGE': conflict_p})

# Rename the index column
conflict_pc = conflict_pc.rename_axis('CONFLICT TYPE')

# Create pie chart
plt.pie(conflict_counts, labels=conflict_counts.index, autopct='%1.1f%%')
plt.title('Main Conflict Types')

# Display the plot
plt.show()

# Display the conflict stats
print(conflict_pc)

# Get value counts of subconflict types
subconflict_counts = conflicts['SUBCONFLICT'].value_counts()

# Calculate percentage of each subconflict type
subconflict_p = round(subconflict_counts / subconflict_counts.sum() * 100,2).astype(str) + '%'

# Combine count and percentage into a dataframe
subconflict_pc = pd.DataFrame({'COUNT': subconflict_counts, 'PERCENTAGE': subconflict_p})

# Rename the index column
subconflict_pc = subconflict_pc.rename_axis('SUBCONFLICT TYPE')

# No pie chart because there are too many categories

# Display the conflict stats
print(subconflict_pc)

3. Group conflicts by year and event type, create a pivot table with years as rows and event types as columns, and output a heatmap to visualize the number of conflicts by type per year.

# Group conflicts by year and event type
conflict_counts = conflicts.groupby(['YEAR', 'CONFLICT']).size().reset_index(name='COUNT')

# Create pivot table with year as rows and event types as columns
pivot_table = conflict_counts.pivot_table(index='CONFLICT', columns='YEAR', values='COUNT', aggfunc='sum')

# Create heatmap using seaborn
plt.figure(figsize=(12,8))
sns.heatmap(pivot_table, cmap='Blues', annot=True, fmt='d', linecolor='black', linewidths=1)
plt.title('Number of Main Conflicts by Year')
plt.show()

4. Print the total number of fatalities and plot a line chart to visualize the number of fatalities per year.

# Print total number of fatalities
total_fatalities = conflicts['FATALITIES'].sum()
print(f'Total number of fatalities: {total_fatalities:,}')

# Group by year and total fatalities
fat_by_year = conflicts.groupby('YEAR')['FATALITIES'].sum()

# Create datetime objects for x-axis
x = [mdates.datetime.datetime(year, 1, 1) for year in fat_by_year.index]

# Plot line chart
fig, ax = plt.subplots()
ax.plot_date(x, fat_by_year.values, linestyle='--')

# Format x-axis as years
ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))

# Label axes and title
plt.xlabel('YEAR')
plt.ylabel('FATALITIES')
plt.title('Number of Fatalities by Year')

# Add the number of fatalities to each plot point
for i, j in enumerate(fat_by_year.values):
    ax.text(x[i], j, str(j), ha='center', va='bottom', weight='bold')

plt.show()

5. Create a pivot table with main conflicts as rows, year as columns and fatalities as values, and output a heatmap to visualize the number of conflicts by type per year.

# create pivot table for num of fatalities by main conflict by year
pivot_table2 = conflicts.pivot_table(index='CONFLICT', columns='YEAR', values='FATALITIES', aggfunc=sum)

# Create heatmap using seaborn
plt.figure(figsize=(12,8))
sns.heatmap(pivot_table2, cmap='Reds', annot=True, fmt='d', linecolor='black', linewidths=1)
plt.title('Number of Fatalities by Main Conflict and Year')
plt.show()

6. Group fatalities by conflict type, plot a bar chart of fatalities by conflict type, and add the number of fatalities to each plot point.

# Group data fatalities by conflict type
fat_by_conflict_type = conflicts.groupby('CONFLICT')['FATALITIES'].sum().reset_index()
fat_count = fat_by_conflict_type['FATALITIES'].sum()
fat_by_conflict_type['FATALITY_PERCENTAGE'] = round(fat_by_conflict_type['FATALITIES'] / fat_count * 100, 2).astype(str) + '%'

# Plot bar chart of fatalities by conflict type
plt.bar(fat_by_conflict_type['CONFLICT'], fat_by_conflict_type['FATALITIES'])
plt.xticks(rotation=90)
plt.xlabel('Conflict Type')
plt.ylabel('Fatalities')
plt.title('Number of Fatalities by Conflict')
for i, j in enumerate(fat_by_conflict_type['FATALITIES']):
    plt.text(i, j, str(j), ha='center', va='bottom')
plt.show()

print(fat_by_conflict_type)

7. Group fatalities by subconflict type, plotting a bar chart of fatalities by subconflict type, and adding the number of fatalities to each plot point.

# Group fatalities by subconflict type
fat_by_subconflict_type = conflicts.groupby('SUBCONFLICT')['FATALITIES'].sum().reset_index()
fat_count_sub = fat_by_subconflict_type['FATALITIES'].sum()
fat_by_subconflict_type['FATALITY_PERCENTAGE'] = round(fat_by_subconflict_type['FATALITIES'] / fat_count_sub * 100, 2).astype(str) + '%'

# Plot bar chart of fatalities by subconflict type
plt.bar(fat_by_subconflict_type['SUBCONFLICT'], fat_by_subconflict_type['FATALITIES'])
plt.xticks(rotation=90)
plt.xlabel('Subconflict Type')
plt.ylabel('Fatalities')
plt.title('Number of Fatalities by Subconflict')
for i, j in enumerate(fat_by_subconflict_type['FATALITIES']):
    plt.text(i, j, str(j), ha='center', va='bottom')
plt.show()

print(fat_by_subconflict_type)

8. Group the top 10 locations with the most fatalities and create a folium map to visualize their geographic locations.

# Group top 10 locations with the most fatalities
top_ten_fat = conflicts.groupby('LOCATION')['FATALITIES'].sum().nlargest(10)  

# Create a map centered on Brazil using country capital's lat and long
map = flm.Map(location=[-10.788497,-42.879873], zoom_start=4.2)

# Create a marker for each city in the top 10
for city, fatalities in top_ten_fat.items():
    # get lat and long for each city
    city_rows = conflicts.loc[conflicts['LOCATION'] == city, ['LATITUDE', 'LONGITUDE']]
    if not city_rows.empty:
        lat, long = city_rows.iloc[0]
        
        # convert lat and long to floats
        lat = float(lat)
        long = float(long)

        # create marker for city with bold text
        popup_str = "<strong>{}</strong><br>{} fatalities".format(city, fatalities)
        flm.Marker(location=[lat, long],
                      popup=popup_str,
                      icon=flm.Icon(color='red', icon='info-sign')).add_to(map)
    
# Save the map as an HTML file
map.save('top_fatalities.html')
map

9. Export the data

# Export data tables
conflict_pc.to_csv('Breakdown of Types of Main Conflicts.csv', index=True)
subconflict_pc.to_csv('Breakdown of Types of Subconflicts.csv', index=True)
fat_by_conflict_type.to_csv('Fatalities by Main Conflict Type.csv', index=False)
fat_by_subconflict_type.to_csv('Fatalities by Subconflict Type.csv', index=False)
top_ten_fat.to_csv('Top 10 Locations with the Highest Fatalities.csv', index=True)             

Key Findings

• Battles (32.4%) and Protests (32.2%) make up the majority of main conflicts
• Protests decreased between 2019 and 2020 by 61.86% and increased by 62.53% between 2020 and 2021
• Remote violence and explosives have the rarest occurences out of the other main conflicts
• Strategic development is the only main conflict that contributed to 0 fatalities while making up 3.7% of conflicts
• Violence against civilians (52.61%) has the highest rate of fatalities for main conflicts, attacks (52.46%) have the highest rate of fatalities out of any subconflict
• Manaus has the highest amount of fatalities in the country at a total of 2,344 fatalities

Further Considerations

• Create a diagram to investigation the association between "ACTOR1" and "ACTOR" and in relation to fatality rates
• Build a map to show which "CONFLICT" category is the most prevalent in certain parts of Brazil