In [2]:
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)¶

In [3]:
# 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:

In [4]:
gw_data.columns
Out[4]:
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:

In [5]:
gw_data.shape
Out[5]:
(1733, 13)

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

In [6]:
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)')]}]
     )
Out[6]:
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
In [9]:
#gw_data.number_of_victims.unique()
# Making sure each row records one attack/victim

Note: each row records one attack victim

About the victims¶

In [10]:
gw_data["gender"].value_counts()
Out[10]:
gender
Male       1545
Female      183
Unknown       1
Name: count, dtype: int64
In [11]:
gender_data = {
    "gender": [ "Male", "Female", "unknown"],
    "count": [1545, 183, 1]
}
gender_df = pd.DataFrame(gender_data)
In [12]:
treemap_gender = px.treemap(gender_df,
                            path=['gender'], values='count', color='gender', hover_name='count',
                            color_discrete_sequence=["#81a1c1", "#a3be8c", "#b48ead", "#b48ead"])
In [13]:
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)
In [14]:
hist_age_gender = px.histogram(fixed_df, x="age", nbins=20,
                               color_discrete_sequence=["#81a1c1", "#a3be8c", "#b48ead"], color="gender")
In [15]:
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")
In [16]:
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()