import pandas as pd
import plotly.express as px
import numpy as np
from plotly.subplots import make_subplots

Lethal attacks against land and environmental defenders between 2012 and 2021

source: Global Witness (www.globalwitness.org)

# Loading data from Global Witness (https://www.globalwitness.org/en/campaigns/environmental-activists/numbers-lethal-attacks-against-defenders-2012/)
gw_data = pd.read_csv("global_witness_led_22-09-22.csv")

Columns of the CSV file:

gw_data.columns

Index(['id', 'date', 'name', 'gender', 'age', 'person_characteristics',
       'industry_driver', 'perpetrator_type', 'continent', 'country',
       'country_numeric', 'local_region', 'number_of_victims'],
      dtype='object')

Number of rows, columns:

gw_data.shape

(1733, 13)

The data looks like this (minus a couple of columns):

gw_data.head(10).style\
    .hide(axis="columns", subset=["id", "country_numeric"])\
    .hide(axis="index")\
    .set_properties(**{'background-color': 'RGBA(232, 232, 250, .5)',
                                 'border': '1.5px solid white',
                                  'color': '#495057'})\
    .set_table_styles(
        [{'selector': 'tr:hover',
          'props': [('background-color', '#fffacd')]},
        {'selector': 'th',
          'props': [('background-color', 'RGBA(180, 142, 173, .5)')]}]
     )

date	name	gender	age	person_characteristics	industry_driver	perpetrator_type	continent	country	local_region	number_of_victims
2016-02-18	Aníbal Coronado Madera	Male	49.000000	Other	Sector could not be confirmed*	Private military actors	Americas	Colombia	Córdoba	1
2016-10-16	Yimer Chávez Rivera	Male	31.000000	Small-scale farmer	Sector could not be confirmed*	Private military actors	Americas	Colombia	Cauca	1
2015-02-10	Héctor William Cabrera Suárez	Male	58.000000	Other	Sector could not be confirmed*	Unspecified	Americas	Colombia	Caquetá	1
2016-04-27	Rolan Lonin Casiano	Male	32.000000	Small-scale farmer	Sector could not be confirmed*	Armed forces	Asia	Philippines	Bicol	1
2015-05-22	Benilda Santos	Female	43.000000	nan	Sector could not be confirmed*	Unspecified	Asia	Philippines	Quezon City	1
2016-08-29	Diego Alfredo Chirán Nastacuas	Male	24.000000	Indigenous peoples	Sector could not be confirmed*	Unspecified	Americas	Colombia	Nariño	1
2014-04-21	Luis Javier Campo Méndez	Male	22.000000	Indigenous peoples	Sector could not be confirmed*	Unspecified	Americas	Colombia	nan	1
2016-12-30	Yaneth Alejandra Calvache Viveros	Female	nan	Small-scale farmer	Sector could not be confirmed*	Hitmen	Americas	Colombia	Cauca	1
2017-10-24	Aulio Isarama Forastero	Male	nan	Indigenous peoples	Sector could not be confirmed*	Private military actors	Americas	Colombia	Chocó	1
2016-05-20	Manuel Chimá Pérez	Male	21.000000	Indigenous peoples	Sector could not be confirmed*	Private military actors	Americas	Colombia	Antioquia	1

#gw_data.number_of_victims.unique()
# Making sure each row records one attack/victim

Note: each row records one attack victim

About the victims

gw_data["gender"].value_counts()

gender
Male       1545
Female      183
Unknown       1
Name: count, dtype: int64

gender_data = {
    "gender": [ "Male", "Female", "unknown"],
    "count": [1545, 183, 1]
}
gender_df = pd.DataFrame(gender_data)

treemap_gender = px.treemap(gender_df,
                            path=['gender'], values='count', color='gender', hover_name='count',
                            color_discrete_sequence=["#81a1c1", "#a3be8c", "#b48ead", "#b48ead"])

scatter_df = gw_data[["age", "gender", "person_characteristics", "number_of_victims", "industry_driver", "continent"]]
#scatter_df.head()

# Age data error: 4145
#scatter_df[scatter_df.age > 90]

# I assume it's a typo and should be 41
fixed_df = scatter_df.replace(to_replace=4145.0, value=41)

hist_age_gender = px.histogram(fixed_df, x="age", nbins=20,
                               color_discrete_sequence=["#81a1c1", "#a3be8c", "#b48ead"], color="gender")

bar_characteristic = px.bar(fixed_df, x='person_characteristics', y='number_of_victims',
                            color="gender", color_discrete_sequence=["#0354A4", "#0A9623", "#f08c00"], 
                            hover_data="age", hover_name="person_characteristics")

treemap_gender.update_traces(textinfo="label+percent root")
hist_age_gender.update_traces(legendgroup="1")
bar_characteristic.update_traces(showlegend=False)

fig1_traces, fig2_traces, fig3_traces = [], [], []

for trace in range(len(treemap_gender["data"])):
    fig1_traces.append(treemap_gender["data"][trace])
for trace in range(len(hist_age_gender["data"])):
    fig2_traces.append(hist_age_gender["data"][trace])
for trace in range(len(bar_characteristic["data"])):
    fig3_traces.append(bar_characteristic["data"][trace])
    
fig_gender = make_subplots(specs=[[{"type": "domain"}, None], [{"type": "xy"}, {"type": "xy"}]], 
                           cols=2, rows=2, column_widths=[0.5, 0.5], row_heights=[0.35, 0.65],
                           subplot_titles=('Attacks victims are men in majority',  'Victims are mostly adults between 20-60', 'Indigenous peoples overrepresented among victims'))

for traces in fig1_traces:
    fig_gender.add_trace(traces, row=1, col=1)
for traces in fig2_traces:
    fig_gender.add_trace(traces, row=2, col=1)
for traces in fig3_traces:
    fig_gender.add_trace(traces, row=2, col=2)

fig_gender.update_layout(
    template="plotly_white",
    title="Victims distributed by gender, age and personal characteristic (data source: www.globalwitness.org)",
    xaxis= {"title": 'Age'}, yaxis= {"title": 'Number of victims'},
    showlegend=True, 
    legend={"title": "Gender", "yanchor":"bottom", "y":0.3, "xanchor":"right", "x":0.45}, 
    legend_tracegroupgap = 50,
    barmode='relative',
    margin=dict(r=5, t=80, b=5, l=5))

fig_gender.update_layout(
    autosize=False,
    width=1200,
    height=700)
        
fig_gender.show()

What about the children?

bar_child_characteristic = px.bar(fixed_df[fixed_df['age'] < 18], x='person_characteristics', y='number_of_victims',
                            color="industry_driver", color_discrete_sequence=["#6A3E75", "#40c057", "#f08c00", "#3bc9db", "#ff6b6b", "#9775fa"], 
                            hover_data="age", hover_name="person_characteristics", barmode="relative")

bar_child_characteristic2 = px.bar(fixed_df[fixed_df['age'] < 18], x='person_characteristics', y='number_of_victims',
                            color="continent", color_discrete_sequence=["#b48ead", "#a3be8c"],
                            hover_data="age", hover_name="person_characteristics", barmode="relative")

hist_age_gender.update_traces(legendgroup="1")
hist_age_gender.update_traces(legendgroup="2")

fig4_traces, fig5_traces = [], []

for trace in range(len(bar_child_characteristic["data"])):
    fig4_traces.append(bar_child_characteristic["data"][trace])
for trace in range(len(bar_child_characteristic2["data"])):
    fig5_traces.append(bar_child_characteristic2["data"][trace])
    
fig_child = make_subplots(specs=[[{"type": "xy"}, {"type": "xy"}]], cols=2, rows=1)

for traces in fig4_traces:
    fig_child.add_trace(traces, row=1, col=1)
for traces in fig5_traces:
    fig_child.add_trace(traces, row=1, col=2)

fig_child.update_layout(
    template="plotly_white",
    title="Most children victims are indigenous of (the) America(s) close to mining activities (data source: www.globalwitness.org)",
    yaxis= {"title": 'Number of victims'},
    showlegend=True, 
    legend={"title": "Legend", "yanchor":"top", "y":1.2, "xanchor":"right", "x":1.3}, 
    legend_tracegroupgap = 4,
    barmode='relative',
    margin=dict(r=5, t=80, b=5, l=5)
)

fig_child.update_layout(
    autosize=False,
    width=1200,
    height=400)
        
fig_child.show()

What about the elderly?

bar_elder_characteristic = px.bar(fixed_df[fixed_df['age'] > 60], x='person_characteristics', y='number_of_victims',
                            color="industry_driver", hover_data="age", hover_name="person_characteristics", barmode="relative",
                            color_discrete_sequence=["#6A3E75", "#40c057", "#f08c00", "#3bc9db", "#ff6b6b", "#9775fa", "#f06595", "#ffd43b"])

bar_elder_characteristic2 = px.bar(fixed_df[fixed_df['age'] > 60], x='person_characteristics', y='number_of_victims',
                            color="continent", color_discrete_sequence=["#a3be8c", "#b48ead", "#81a1c1", "#d08770"], 
                            hover_data="age", hover_name="person_characteristics", barmode="relative")

fig6_traces, fig7_traces = [], []

for trace in range(len(bar_elder_characteristic["data"])):
    fig6_traces.append(bar_elder_characteristic["data"][trace])
for trace in range(len(bar_elder_characteristic2["data"])):
    fig7_traces.append(bar_elder_characteristic2["data"][trace])
    
fig_elder = make_subplots(specs=[[{"type": "xy"}, {"type": "xy"}]], 
                           cols=2, rows=1)

for traces in fig6_traces:
    fig_elder.add_trace(traces, row=1, col=1)
for traces in fig7_traces:
    fig_elder.add_trace(traces, row=1, col=2)

fig_elder.update_layout(
    template="plotly_white",
    title="Most elderly victims are indigenous people and/or small-scale farmers of (the) America(s) close to mining activities (data source: www.globalwitness.org)",
    yaxis= {"title": 'Number of victims'},
    showlegend=True, 
    legend={"title": "Legend", "yanchor":"top", "y":1.0, "xanchor":"right", "x":1.3}, 
    legend_tracegroupgap = 4,
    barmode='relative',
    margin=dict(r=5, t=80, b=5, l=5),)

fig_elder.update_layout(
    autosize=False,
    width=1200,
    height=500)
        
fig_elder.show()

Where in the world?

num_attacks = gw_data.shape[0]
continents_attacks = ", ".join(gw_data["continent"].unique())

print(f'There were {num_attacks} attacks across {continents_attacks} between 2012 and 2021, according to the NGO Global Witness.')

There were 1733 attacks across Americas, Asia, Africa, Oceania, Europe between 2012 and 2021, according to the NGO Global Witness.

countries_attacks = gw_data["country"].unique()
most_attacks = gw_data["country"].value_counts()[:3]

print(f'Attacks occured in {len(countries_attacks)} countries.' )
print(f'The highest number of attacks happened in {most_attacks.index[0]} ({most_attacks[0]} attacks), {most_attacks.index[1]} ({most_attacks[1]} attacks) \
and the {most_attacks.index[2]} ({most_attacks[2]} attacks).')

Attacks occured in 61 countries.
The highest number of attacks happened in Brazil (342 attacks), Colombia (322 attacks) and the Philippines (270 attacks).

victims_by_country = gw_data[["country", "number_of_victims"]].groupby(by="country", as_index=False).sum()

map_victims = px.choropleth(
                            victims_by_country, locations="country", 
                            locationmode="country names",
                            color='number_of_victims', 
                            color_continuous_scale="sunsetdark",
                            title="Lethal attacks of land defenders worlwide between 2012 and 2021 (data source: www.globalwitness.org)",
                            projection="natural earth",
                            width=1200, height=500
                           )
map_victims.update_layout(margin={"r":5,"t":80,"l":0,"b":5})
map_victims.show()

Attacks have happened on all continents, but areas with tropical rainforests seem to suffer more attacks (especially in Latin America).

About the causes & perpetrators

#gw_data[["perpetrator_type", "continent", "number_of_victims"]].value_counts()

df_victims_industries = pd.read_csv("victims_by_industry.csv")

df_victims_perp = pd.read_csv("victims_by_perpetrator.csv")

treemap_industries = px.treemap(df_victims_industries, 
                            path=['industry_driver'], values='count', color='industry_driver',
                            color_discrete_sequence=["#c47f5d", "#817568", "#80958e", "#b4874f", "#7c9a52"],
                            width=800, height=600)

treemap_perpetrators = px.treemap(df_victims_perp, 
                            path=['perpetrator_type'], values='count', color='perpetrator_type',
                            color_discrete_sequence=["#004e6f", "#00523e", "#6a5d2c", "#008e64", "#36873c", "#004e6f", "#6a5d2c"],
                            width=800, height=600)

fig8_traces, fig9_traces = [], []

for trace in range(len(treemap_industries["data"])):
    fig8_traces.append(treemap_industries["data"][trace])
for trace in range(len(treemap_perpetrators["data"])):
    fig9_traces.append(treemap_perpetrators["data"][trace])
    
fig_causes = make_subplots(specs=[[{"type": "domain"}, {"type": "domain"}]], 
                           cols=2, rows=1, 
                           column_widths=[0.5, 0.5])

for traces in fig8_traces:
    fig_causes.add_trace(traces, row=1, col=1)
for traces in fig9_traces:
    fig_causes.add_trace(traces, row=1, col=2)

fig_causes.update_layout(
    template="plotly_white",
    title="Indentified perpetrators are mostly hitmen in America(s), armed forces in Asia, private military actors in Africa (data source: www.globalwitness.org)",
    xaxis= {"title": ''},
    showlegend=True, 
    margin=dict(r=25, t=40, b=0, l=0)
)

fig_causes.update_layout(
    autosize=False,
    width=1300,
    height=500)
        
fig_causes.show()

Attacks over time

df_victims_year_continents = pd.read_csv("victims_by_year_continent.csv")

yearly_continent = px.area(df_victims_year_continents, template="plotly_white",
                           x='year', y='count', color="continent",
                           color_discrete_sequence=["#b48ead", "#a3be8c", "#ebcb8b", "#81a1c1", "#d08770"],
                           title="Between 2012 and 2021 attacks increased, with Americas suffering the most attacks.",
                           width=1100, height=600)

yearly_continent.show()

The lethal attacks against land defenders are either getting more frequent or better documented (or both). 2013-2014 shows the start of an increase for attacks in Asia and 2014-2015 shows the start of an increase for attacks in Africa.

At a time where the consequences of the climate emergency get even more visible, some corporations double down on resources extraction (fossil-fuel, minerals),

deforestation for large scale agriculture... The land defenders (mostly indigenous people in Brazil, Colombia and The Philippines) are at increased risk for their lives.