Analysis of conflicts in Africa 1997 - 2017

Tuesday. March 06, 2018 - 24 mins

import pandas as pd

Context

The Armed Conflict Location and Event Data Project is designed for disaggregated conflict analysis and crisis mapping. This dataset codes the dates and locations of all reported political violence and protest events in dozens of developing countries in Africa. Political violence and protest includes events that occur within civil wars and periods of instability, public protest and regime breakdown. The project covers all African countries from 1997 to 2017.

Content

These data contain information on:

Dates and locations of conflict events;
Specific types of events including battles, civilian killings, riots, protests and
recruitment activities;
Events by a range of actors, including rebels, governments, militias, armed groups, protesters and civilians;
Changes in territorial control; and Reported fatalities.

Event data are derived from a variety of sources including reports from developing countries and local media, humanitarian agencies, and research publications. Please review the codebook and user guide for additional information: the codebook is for coders and users of ACLED, whereas the brief guide for users reviews important information for downloading, reviewing and using ACLED data. A specific user guide for development and humanitarian practitioners is also available, as is a guide to our sourcing materials.

Acknowledgements:

ACLED is directed by Prof. Clionadh Raleigh (University of Sussex). It is operated by senior research manager Andrea Carboni (University of Sussex) for Africa and Hillary Tanoff for South and South-East Asia. The data collection involves several research analysts, including Charles Vannice, James Moody, Daniel Wigmore-Shepherd, Andrea Carboni, Matt Batten-Carew, Margaux Pinaud, Roudabeh Kishi, Helen Morris, Braden Fuller, Daniel Moody and others. Please cite: Raleigh, Clionadh, Andrew Linke, Håvard Hegre and Joakim Karlsen. 2010. Introducing ACLED-Armed Conflict Location and Event Data. Journal of Peace Research 47(5) 651-660.

Data Source

The Dataset for this analysis can be gotten on Kaggle Website

About The Dataset

ACTOR1: Name of first actor
ACTOR1_ID: Unique numerical ID for first actor
ACTOR2: Name of second actor
ACTOR2_ID: Unique numerical ID for second actor
ACTOR_DYAD_ID: Join of first two actor IDs in format (ACTOR1_ID - ACTOR2_ID)
ADMIN1: The largest sub-national administrative region in which the event took place
ADMIN2: The second-largest sub-national administrative region in which the event took place
ADMIN3: The third-largest sub-national administrative region in which the event took place
ALLY_ACTOR_1: Allied parties to Actor1
ALLY_ACTOR_2: Allied parties to Actor2
COUNTRY: country of conflict
EVENT_DATE: date of conflict, DD/MM/YYYY
EVENT_ID_CNTY: An individual identifier by number and country acronym
EVENT_ID_NO_CNTY: An individual numeric identifier
EVENT_TYPE: Event occurrence, string eg “Riots against police”
FATALITIES: Integer value of fatalities that occurred, as reported by source
GEO_PRECISION: A numeric code indicating the level of certainty of the geocode for the event
GWNO: Gleditsch & Ward (2007) country code
INTER1: A numeric code indicating the type of ACTOR1
INTER2: A numeric code indicating the type of ACTOR2
INTERACTION: A numeric code indicating the interaction between types of ACTOR1 and ACTOR2
LOCATION: The location where event occurred
NOTES: additional notes, string
SOURCE: Source of conflict information
TIME_PRECISION: A numeric code indicating the level of certainty of the date coded for the event
YEAR: Year event occurred

data = pd.read_csv('african_conflicts.csv')

/home/anaconda/.conda/envs/py27/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (21,23) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

numbers of rows and columns in the dataset

data.shape

(165808, 28)

First five rows in the dataset

pandas.set_option('display.max_columns', None)

data.head()

	ACTOR1	ACTOR1_ID	ACTOR2	ACTOR2_ID	ACTOR_DYAD_ID	ADMIN1	ADMIN2	ADMIN3	ALLY_ACTOR_1	ALLY_ACTOR_2	COUNTRY	EVENT_DATE	EVENT_ID_CNTY	EVENT_ID_NO_CNTY	EVENT_TYPE	FATALITIES	GEO_PRECISION	GWNO	INTER1	INTER2	INTERACTION	LATITUDE	LOCATION	LONGITUDE	NOTES	SOURCE	TIME_PRECISION	YEAR
0	Police Forces of Algeria (1999-)	NaN	Civilians (Algeria)	NaN	NaN	Tizi Ouzou	Beni-Douala	NaN	NaN	Berber Ethnic Group (Algeria)	Algeria	18/04/2001	1416RTA	NaN	Violence against civilians	1	1	615	1	7	17	36.61954	Beni Douala	4.08282	A Berber student was shot while in police cust...	Associated Press Online	1	2001
1	Rioters (Algeria)	NaN	Police Forces of Algeria (1999-)	NaN	NaN	Tizi Ouzou	Tizi Ouzou	NaN	Berber Ethnic Group (Algeria)	NaN	Algeria	19/04/2001	2229RTA	NaN	Riots/Protests	0	3	615	5	1	15	36.71183	Tizi Ouzou	4.04591	Riots were reported in numerous villages in Ka...	Kabylie report	1	2001
2	Protesters (Algeria)	NaN	NaN	NaN	NaN	Bejaia	Amizour	NaN	Students (Algeria)	NaN	Algeria	20/04/2001	2230RTA	NaN	Riots/Protests	0	1	615	6	0	60	36.64022	Amizour	4.90131	Students protested in the Amizour area. At lea...	Crisis Group	1	2001
3	Rioters (Algeria)	NaN	Police Forces of Algeria (1999-)	NaN	NaN	Bejaia	Amizour	NaN	Berber Ethnic Group (Algeria)	NaN	Algeria	21/04/2001	2231RTA	NaN	Riots/Protests	0	1	615	5	1	15	36.64022	Amizour	4.90131	Rioters threw molotov cocktails, rocks and bur...	Kabylie report	1	2001
4	Rioters (Algeria)	NaN	Police Forces of Algeria (1999-)	NaN	NaN	Tizi Ouzou	Beni-Douala	NaN	Berber Ethnic Group (Algeria)	NaN	Algeria	21/04/2001	2232RTA	NaN	Riots/Protests	0	1	615	5	1	15	36.61954	Beni Douala	4.08282	Rioters threw molotov cocktails, rocks and bur...	Kabylie report	1	2001

the dataset contains more than 165 thousands rows and 28 columns. we will extract columns of interest for this analysis

we extract columns of interests

cols = ['ACTOR1','ACTOR2','ALLY_ACTOR_1','ALLY_ACTOR_2','COUNTRY','EVENT_DATE','EVENT_TYPE','LOCATION','NOTES','SOURCE']

len(cols) ### we will extract ten columns for analysis

data = data[cols]

data.columns ## now we have 10 columns to work with

Index([u'ACTOR1', u'ACTOR2', u'ALLY_ACTOR_1', u'ALLY_ACTOR_2', u'COUNTRY',
       u'EVENT_DATE', u'EVENT_TYPE', u'LOCATION', u'NOTES', u'SOURCE'],
      dtype='object')

we display first five rows again

data.head()

	ACTOR1	ACTOR2	ALLY_ACTOR_1	ALLY_ACTOR_2	COUNTRY	EVENT_DATE	EVENT_TYPE	LOCATION	NOTES	SOURCE
0	Police Forces of Algeria (1999-)	Civilians (Algeria)	NaN	Berber Ethnic Group (Algeria)	Algeria	18/04/2001	Violence against civilians	Beni Douala	A Berber student was shot while in police cust...	Associated Press Online
1	Rioters (Algeria)	Police Forces of Algeria (1999-)	Berber Ethnic Group (Algeria)	NaN	Algeria	19/04/2001	Riots/Protests	Tizi Ouzou	Riots were reported in numerous villages in Ka...	Kabylie report
2	Protesters (Algeria)	NaN	Students (Algeria)	NaN	Algeria	20/04/2001	Riots/Protests	Amizour	Students protested in the Amizour area. At lea...	Crisis Group
3	Rioters (Algeria)	Police Forces of Algeria (1999-)	Berber Ethnic Group (Algeria)	NaN	Algeria	21/04/2001	Riots/Protests	Amizour	Rioters threw molotov cocktails, rocks and bur...	Kabylie report
4	Rioters (Algeria)	Police Forces of Algeria (1999-)	Berber Ethnic Group (Algeria)	NaN	Algeria	21/04/2001	Riots/Protests	Beni Douala	Rioters threw molotov cocktails, rocks and bur...	Kabylie report

lets first check the number of countries embroiled in conflict across Africa

num_conflict = data.COUNTRY.value_counts()

num_conflict.count()

we check how each countries are affected

num_conflict

Somalia                         25232
Democratic Republic of Congo    12294
Nigeria                         12253
Sudan                           12018
South Africa                    10887
Egypt                            9290
Burundi                          7396
Libya                            6532
Zimbabwe                         6001
Kenya                            5812
Algeria                          5309
Uganda                           5185
South Sudan                      5144
Ethiopia                         5035
Sierra Leone                     4669
Tunisia                          4493
Central African Republic         3973
Angola                           3181
Ivory Coast                      1804
Mali                             1753
Liberia                          1369
Morocco                          1355
Zambia                           1289
Senegal                          1132
Madagascar                       1064
Burkina Faso                     1032
Cameroon                          937
Guinea                            894
Mozambique                        886
Namibia                           754
Tanzania                          746
Chad                              732
Ghana                             671
Niger                             660
Rwanda                            633
Republic of Congo                 473
Mauritania                        467
Malawi                            434
Eritrea                           408
Togo                              258
Guinea-Bissau                     233
Gabon                             215
Swaziland                         198
Gambia                            190
Benin                             187
Lesotho                           118
Djibouti                          112
Botswana                           53
Equatorial Guinea                  46
Mozambique                          1
Name: COUNTRY, dtype: int64

import matplotlib.pyplot as plt
%matplotlib inline

ax = num_conflict.plot(kind='bar',figsize=(35,17),fontsize=(30))
plt.title("conflicts in Africa (1997-2017)", fontname='Ubuntu', fontsize=38,
          fontstyle='italic', fontweight='normal',color='green')

<matplotlib.text.Text at 0xac7b90ec>

we inspect event type to see kind of conflict that is common

cm_conflict = data.EVENT_TYPE.value_counts()

cm_conflict

Riots/Protests                                47224
Violence against civilians                    45021
Battle-No change of territory                 44005
Strategic development                         10923
Remote violence                               10456
Battle-Government regains territory            2632
Non-violent transfer of territory              2566
Battle-Non-state actor overtakes territory     2135
Headquarters or base established                771
Remote Violence                                  30
Violence Against Civilians                       21
Battle-no change of territory                    16
Strategic Development                             7
Strategic development                             1
Name: EVENT_TYPE, dtype: int64

Some event type are not unique!!! lets convert the whole string to lower and then count again, we will also strip white space as well to avoid duplicate

import string

a = data.EVENT_TYPE.apply(string.lower).apply(string.strip).value_counts()

riots/protests                                47224
violence against civilians                    45042
battle-no change of territory                 44021
strategic development                         10931
remote violence                               10486
battle-government regains territory            2632
non-violent transfer of territory              2566
battle-non-state actor overtakes territory     2135
headquarters or base established                771
Name: EVENT_TYPE, dtype: int64

now we can visualize event type

ax = a.plot(kind='bar',figsize=(16,6),fontsize=(20))
plt.xticks(rotation=70,fontsize=(17))
plt.title("conflicts Type in Africa (1997-2017)", fontname='Ubuntu', fontsize=20,
          fontstyle='italic', fontweight='normal',color='green')

<matplotlib.text.Text at 0xac53ac8c>

which year recorded highest conflict

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 165808 entries, 0 to 165807
Data columns (total 10 columns):
ACTOR1          165808 non-null object
ACTOR2          122255 non-null object
ALLY_ACTOR_1    28144 non-null object
ALLY_ACTOR_2    19651 non-null object
COUNTRY         165808 non-null object
EVENT_DATE      165808 non-null object
EVENT_TYPE      165808 non-null object
LOCATION        165805 non-null object
NOTES           155581 non-null object
SOURCE          165635 non-null object
dtypes: object(10)
memory usage: 6.3+ MB

we first convert the date and year to datatime

import datetime

data['EVENT_DATE'] = pd.to_datetime(data['EVENT_DATE'],dayfirst=True)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 165808 entries, 0 to 165807
Data columns (total 10 columns):
ACTOR1          165808 non-null object
ACTOR2          122255 non-null object
ALLY_ACTOR_1    28144 non-null object
ALLY_ACTOR_2    19651 non-null object
COUNTRY         165808 non-null object
EVENT_DATE      165808 non-null datetime64[ns]
EVENT_TYPE      165808 non-null object
LOCATION        165805 non-null object
NOTES           155581 non-null object
SOURCE          165635 non-null object
dtypes: datetime64[ns](1), object(9)
memory usage: 7.0+ MB

data_n = data.set_index(data.EVENT_DATE)

data_n['counter'] = 0 #### we create new column to hold number of conflict per year

data_n = data_n['counter']

data_n = pd.DataFrame(data_n)

con_yr = data_n.resample('Y').count()

t = (range(1997,2018))

t = tuple(t)

ax = con_yr.plot(kind='bar',figsize=(15,6),fontsize=(18))
plt.title('Annual conflict 1997-2017',fontsize=(16))
ax.set_xticklabels(t,rotation=30)
ax.grid()

2016 was the most troubled year

Conflict according to region of Africa

data.COUNTRY.unique()

array(['Algeria', 'Angola', 'Benin', 'Burkina Faso', 'Burundi',
       'Cameroon', 'Central African Republic', 'Chad',
       'Democratic Republic of Congo', 'Egypt', 'Equatorial Guinea',
       'Eritrea', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea',
       'Guinea-Bissau', 'Ivory Coast', 'Kenya', 'Lesotho', 'Liberia',
       'Libya', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Morocco',
       'Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Republic of Congo',
       'Rwanda', 'Senegal', 'Sierra Leone', 'Somalia', 'South Africa',
       'South Sudan', 'Sudan', 'Swaziland', 'Tanzania', 'Togo', 'Tunisia',
       'Uganda', 'Zambia', 'Zimbabwe', 'Botswana', 'Djibouti',
       'Mozambique '], dtype=object)

region = {
    'north':['Algeria','Egypt','Morocco','Sudan','Tunisia','Western Sahara','Libya','Mauritania'],
    'east':['Burundi','Comoros','Djibouti','Eritrea','Ethiopia','Kenya','Zambia',
            'Madagascar','Malawi','Mauritius','Mayotte','Mozambique','Uganda',
            'Reunion','Rwanda','Seychelles','Somalia','South Sudan','Tanzania','Zimbabwe'],
    'west':['Benin','Burkina Faso','Cape Verde','Ivory Coast','Gambia',
            'Ghana','Guinea','Guinea-Bissau','Liberia','Mali',
            'Mauritania','Niger','Nigeria','Saint Helena','Senegal','Sierra Leone',
            'Togo'],
    'central':['Angola','Cameroon','Central African Republic','Chad',
               'Democratic Republic of Congo','Republic of Congo',
               'Equatorial Guinea','Gabon'],

    'south':['Botswana','Lesotho','Namibia','South Africa''Swaziland','Angola','Zambia']
}

def mapper(country):
    if string.strip(country) in region['north'] or string.capitalize(country) in region['north']:
        return 1
    if string.strip(country) in region['east'] or string.capitalize(country) in region['east']:
        return 2
    if string.strip(country) in region['west'] or string.capitalize(country) in region['west']:
        return 3
    if string.strip(country) in region['central'] or string.capitalize(country) in region['central']:
        return 4
    if string.strip(country) in region['south'] or string.capitalize(country) in region['south']:
        return 5
    return 5 ### South africa and Swaziland here too

data_region = data

data_region.COUNTRY = data.COUNTRY.apply(mapper)

data_region = data_region.COUNTRY.value_counts()

data_region

  65378
  39464
  27105
  21851
  12010
Name: COUNTRY, dtype: int64

data_region = pd.DataFrame(data_region)

data_region['val'] = [2,1,3,4,5]

def mapper(val):
    if val == 1:
        return 'North Africa'
    if val == 2:
        return 'East Africa'
    if val == 3:
        return 'West Africa'
    if val == 4 :
        return 'Central Africa'
    return 'South Africa' 

data_region.val = data_region.val.apply(mapper)

data_region

	val	REGION
2	65378	East Africa
1	39464	North Africa
3	27105	West Africa
4	21851	Central Africa
5	12010	South Africa

we visulize conflicts in terms of region

data_region.set_index(data_region.val,inplace=True)

del data_region['val']

data_region

	val
REGION
East Africa	65378
North Africa	39464
West Africa	27105
Central Africa	21851
South Africa	12010

ax = data_region.COUNTRY.plot(kind='bar',figsize=(16,6))
plt.xticks(fontsize=(24),rotation=30)
plt.tight_layout()
plt.yticks(fontsize=(20))
plt.title('Conflicts in Africa according to region',fontsize=(28))
plt.legend()
plt.grid()

Analysis of conflicts that has something to do with Students

we will inspect column Note and we extract any rows that mention student

stud_con = data[(data.NOTES.str.find('students') >=0 ) | (data.NOTES.str.find('Students') >=0 )]

lets count the number of rows returned

stud_con.shape

(5474, 28)

## more than five thousand

stud_con.head()

	ACTOR1	ACTOR1_ID	ACTOR2	ACTOR2_ID	ACTOR_DYAD_ID	ADMIN1	ADMIN2	ADMIN3	ALLY_ACTOR_1	ALLY_ACTOR_2	COUNTRY	EVENT_DATE	EVENT_ID_CNTY	EVENT_ID_NO_CNTY	EVENT_TYPE	GEO_PRECISION	GWNO	INTER1	INTER2	INTERACTION	LATITUDE	LOCATION	LONGITUDE	NOTES	SOURCE	TIME_PRECISION	YEAR
2	Protesters (Algeria)	NaN	NaN	NaN	NaN	Bejaia	Amizour	NaN	Students (Algeria)	NaN	Algeria	20/04/2001	2230RTA	NaN	Riots/Protests	1	615	6	0	60	36.64022	Amizour	4.90131	Students protested in the Amizour area. At lea...	Crisis Group	1	2001
27	Protesters (Algeria)	NaN	Police Forces of Algeria (1999-)	NaN	NaN	Oran	Oran	NaN	Students (Algeria)	NaN	Algeria	3/05/2001	2243RTA	NaN	Riots/Protests	1	615	6	1	16	35.6911	Oran	-0.6417	Students demonstrated in Oran despite signific...	Kabylie report	1	2001
28	Protesters (Algeria)	NaN	NaN	NaN	NaN	Alger	Sidi MHamed	NaN	Students (Algeria)	NaN	Algeria	4/05/2001	2244RTA	NaN	Riots/Protests	1	615	6	0	60	36.7525	Algiers	3.04197	As many as 10,000 students protested at the go...	Kabylie report	1	2001
52	Protesters (Algeria)	NaN	NaN	NaN	NaN	Alger	Sidi MHamed	NaN	Students (Algeria)	NaN	Algeria	29/05/2001	1450RTA	NaN	Riots/Protests	1	615	6	0	60	36.7525	Algiers	3.04197	Students protested in the Algerian capital to ...	Associated Press Online	1	2001
155	Protesters (Algeria)	NaN	NaN	NaN	NaN	Tizi Ouzou	Tizi Ouzou	NaN	Berber Ethnic Group (Algeria); Students (Algeria)	NaN	Algeria	15/04/2002	2622RTA	NaN	Riots/Protests	3	615	6	0	60	36.71183	Tizi Ouzou	4.04591	Students protested and boycotted classes to de...	Kabylie report	3	2002

which country has highest number of student conflict?

stud_con.COUNTRY.value_counts()

South Africa                    1167
Egypt                            521
Ethiopia                         497
Sudan                            481
Nigeria                          461
Kenya                            318
Uganda                           202
Tunisia                          177
Democratic Republic of Congo     140
Algeria                          126
Burkina Faso                     115
Burundi                          106
Senegal                          102
Zambia                            96
Zimbabwe                          80
Ivory Coast                       79
Somalia                           75
Malawi                            71
Madagascar                        66
Morocco                           61
Liberia                           60
Central African Republic          53
Niger                             41
Guinea                            32
Gabon                             32
Swaziland                         31
South Sudan                       31
Namibia                           29
Togo                              29
Chad                              27
Sierra Leone                      23
Libya                             18
Ghana                             17
Tanzania                          17
Mauritania                        15
Gambia                             9
Rwanda                             9
Cameroon                           9
Mali                               8
Benin                              8
Republic of Congo                  8
Angola                             7
Botswana                           7
Mozambique                         4
Guinea-Bissau                      3
Equatorial Guinea                  2
Djibouti                           2
Eritrea                            1
Lesotho                            1
Name: COUNTRY, dtype: int64

## south africa has the highest number of student conflict or conflict that has something to
## do with student

std_con = stud_con.COUNTRY.value_counts()

ax = std_con.plot(kind='bar',figsize=(25,10),fontsize=(30))
plt.title('student conflict---countries wise',fontsize=(30))

<matplotlib.text.Text at 0xa7fde38c>

conflict involving women

wn = data[(data.NOTES.str.find('women') >=0 ) | (data.NOTES.str.find('Women') >=0 )|(data.NOTES.str.find('Woman') >=0 )|(data.NOTES.str.find('woman') >=0 )]

len(wn) # it return more than 3000 rows

wn.head(2)

	ACTOR1	ACTOR1_ID	ACTOR2	ACTOR2_ID	ACTOR_DYAD_ID	ADMIN1	ADMIN2	ADMIN3	ALLY_ACTOR_1	ALLY_ACTOR_2	COUNTRY	EVENT_DATE	EVENT_ID_CNTY	EVENT_ID_NO_CNTY	EVENT_TYPE	FATALITIES	GEO_PRECISION	GWNO	INTER1	INTER2	INTERACTION	LATITUDE	LOCATION	LONGITUDE	NOTES	SOURCE	TIME_PRECISION	YEAR
78	Protesters (Algeria)	NaN	NaN	NaN	NaN	Alger	Cheraga	NaN	NaN	NaN	Algeria	19/06/2001	2601RTA	NaN	Riots/Protests	0	1	615	6	0	60	36.80277	Ain Benian	2.92185	100 women gathered in Ain Benian, west of Algi...	Kabylie report	1	2001
410	Protesters (Algeria)	NaN	Police Forces of Algeria (1999-)	NaN	NaN	Bejaia	Bejaia	NaN	SNATEGZ: National Autonomous Union of Sonelgaz...	NaN	Algeria	22/03/2017	3605RTA	NaN	Violence against civilians	0	1	615	6	1	16	36.75	Bejaia	5.0833	Security forces attacked the participants of t...	Arab Trade Union Confederation; El Watan	1	2017

wn.COUNTRY.value_counts()

Sudan                           651
Somalia                         492
Democratic Republic of Congo    416
Nigeria                         318
South Africa                    289
South Sudan                     223
Central African Republic        179
Burundi                         160
Egypt                           140
Kenya                           138
Zimbabwe                        137
Tunisia                         112
Uganda                           88
Algeria                          87
Libya                            70
Morocco                          44
Ivory Coast                      32
Ghana                            31
Ethiopia                         26
Senegal                          25
Namibia                          24
Mali                             21
Cameroon                         20
Liberia                          18
Angola                           17
Guinea                           17
Mauritania                       16
Mozambique                       16
Zambia                           16
Burkina Faso                     15
Madagascar                       14
Rwanda                           13
Malawi                           12
Sierra Leone                     10
Tanzania                         10
Niger                             9
Republic of Congo                 7
Swaziland                         6
Chad                              5
Eritrea                           4
Togo                              4
Botswana                          4
Djibouti                          2
Gambia                            2
Lesotho                           1
Gabon                             1
Guinea-Bissau                     1
Equatorial Guinea                 1
Name: COUNTRY, dtype: int64

ax = wn.COUNTRY.value_counts().plot(kind='bar',figsize=(20,8),fontsize=(20))

SUMMARIES BASED ON THE DATASET

the dataset contains more than 165,000 rows
East Africa is the most volatile region in Africa
South Africa is the least voltile region in Africa
Somalia is the most affected countries by conflict according to this report
Mozambique is the most peaceful country in Africa
2016 is the most troubled Year in Africa within the period of 1997 to 2017
Student involvement in conflict is most common in South Africa

Mustapha Omotosho

constant learner,machine learning enthusiast,huge Barcelona fan