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Datasets:
electricsheepafrica
/
africa-erbera-district-conflict-and-security-assessment-2015

Tasks:
Tabular Classification
Tabular Regression
Other
Modalities:
Tabular
Text
Formats:
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Languages:
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Tags:
africa
humanitarian
hdx
electric-sheep-africa
complex-emergency-conflict-security
conflict-violence
Libraries:
Datasets
pandas
Polars
License:
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region_name
int64
district_name
int64
village_name
int64
gender_responder
int64
age
int64
marital_status
int64
level_education
int64
police_presense
int64
number_of_stations
float64
number_of_stations_other
string
distance_to_station
float64
reporting_civil
int64
reporting_civil_other
string
reporting_petty_crime
int64
reporting_petty_other
string
reporting_serious_crime
int64
reporting_serious_other
string
trusted_sec_prov
int64
trusted_sec_other
string
reason_for_choice_sec
float64
reason_for_choice_sec_other
string
level_trust_police
int64
police_yearly_trend
int64
court_presense
int64
number_of_courts
float64
number_of_courts_other
string
where_is_court
float64
distance_to_court
float64
legal_clinic_aware
int64
legal_clinic_use
string
legal_clinic_ref
string
legal_clinic_ref_other
string
legal_clinic_issue
string
legal_clinic_issue_other
string
legal_clinic_judgement
string
legal_clinic_enforced
string
court_use
int64
court_ref
string
court_ref_other
string
court_issue
string
court_issue_other
string
court_judgement
string
court_enforced
string
elders_use
int64
elders_ref
string
elders_ref_other
string
elders_issue
string
elders_issue_other
string
elders_judgement
string
elders_enforced
string
religious_use
int64
religious_ref
string
religious_ref_other
string
religious_issue
string
religious_issue_other
string
religious_judgement
string
religious_enforced
string
trusted_just_prov
int64
trusted_just_prov_other
string
reason_for_choice_just
float64
reason_for_choice_just_other
string
conf_formal_just
int64
court_yearly_trend
int64
local_council_aware
int64
aware_of_services
float64
channels_comm
float64
consultation_participation
string
participation_frequency
string
participation_frequency_other
string
elected_opinion
int64
loc_gov_serviceseducation
string
loc_gov_serviceshealth
string
loc_gov_servicessecurity
string
loc_gov_servicesjustice
string
loc_gov_servicesagriculture
string
loc_gov_servicesinfrastructure
string
loc_gov_servicessanitation
string
loc_gov_serviceswater
string
loc_gov_servicesother
string
loc_gov_servicesdont_know
string
loc_gov_servicesrefused_to_answer
string
loc_gov_services_other
string
community_issueslack_of_water
string
community_issuesdrought
string
community_issueslack_of_infrastructure
string
community_issuespoor_sanitation
string
community_issuespoor_health
string
community_issuesunemployment
string
community_issuespoor_education
string
community_issuesshortage_of_electicity_supply
string
community_issuespoor_economy
string
community_issuescharcoal_production_deforestation
string
community_issuesbad_health_centers
string
community_issuesinsecurity
string
community_issuesgender_based_violence
string
community_issuesother
string
community_issuesdont_know
string
community_issuesrefused_to_answer
string
community_issues_other
string
council_yearly_trend
float64
witnessed_conflict
float64
number_of_conflicts
string
number_conf_violence
string
number_casualties
string
conflict_reasonresources
string
conflict_reasonfamily_disputes
string
conflict_reasoncrime
string
conflict_reasonpower
string
conflict_reasonrevenge
string
conflict_reasonbusiness_disputes
string
conflict_reasonrape
string
conflict_reasonlack_of_justice
string
conflict_reasonother
string
conflict_reasondont_know
string
conflict_reasonrefused_to_answer
string
conflict_reason_other
string
witnessed_crimes
float64
how_safe
float64
safety_yearly_trend
float64
nspc
float64
njpc
string
esa_source
string
esa_processed
string
1
1
1
1
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2
4
1
2
1
5
5
5
5
4
4
1
1
1
1
1
2
1
2
1
1
1
2
2
1
6
3
3
1
1
1
1
3
1
1
1
1
1
1
2
2
4
1
4
6
HDX
2026-04-11
1
1
2
1
6
2
1
1
1
1
4
5
5
5
1
4
1
1
2
1
1
2
1
2
3
1
1
1
3
3
2
2
1
6
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
4
2
1
1
1
1
3
1
1
6
HDX
2026-04-11
1
1
1
2
2
2
3
1
3
1
5
5
5
5
1
3
777
1
1
1
1
2
2
2
2
2
3
777
3
1
2
2
1
1
1
1
1
3
2
2
4
1
1
HDX
2026-04-11
1
1
1
1
2
2
1
1
3
1
5
5
5
5
1
3
1
1
2
1
1
2
2
2
2
1
1
1
1
1
2
2
1
1
1
1
1
2
2
2
2
1
1
HDX
2026-04-11
1
1
3
1
3
2
3
1
1
1
5
5
5
5
2
3
3
1
3
1
1
2
2
2
2
2
2
2
3
1
2
2
2
1
1
3
2
2
3
1
2
HDX
2026-04-11
1
1
2
1
6
2
1
1
1
1
1
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
3
1
1
2
2
1
1
1
1
3
2
2
1
1
1
1
HDX
2026-04-11
1
1
1
2
2
2
7
2
null
null
5
5
5
5
2
2
3
1
1
1
3
2
2
2
2
3
3
3
777
1
2
2
2
1
1
1
2
2
2
3
1
2
HDX
2026-04-11
1
1
1
1
2
2
4
1
3
1
5
5
5
5
1
3
2
1
1
1
1
2
2
2
2
1
1
2
3
1
2
2
1
1
1
1
2
2
4
3
1
1
HDX
2026-04-11
1
1
1
2
4
2
1
1
3
1
5
5
5
5
1
4
1
1
2
1
1
2
2
2
2
1
1
1
1
1
2
2
1
1
1
1
2
2
3
1
1
1
HDX
2026-04-11
1
1
1
2
3
1
3
1
1
1
4
5
5
5
1
4
1
1
1
1
1
1
2
2
2
2
1
2
1
1
1
1
1
2
1
1
1
1
1
3
2
2
4
1
1
2
HDX
2026-04-11
1
1
2
2
3
2
3
1
3
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
2
2
2
1
1
HDX
2026-04-11
1
1
4
1
4
1
3
1
5
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
2
2
1
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
1
2
1
1
1
1
3
1
5
5
5
5
1
3
1
1
1
1
2
2
2
2
2
1
1
1
1
1
777
1
2
1
1
1
1
2
2
2
3
1
1
HDX
2026-04-11
1
1
4
1
3
2
4
1
3
1
5
5
5
5
1
3
1
1
1
1
2
2
2
2
2
1
2
1
1
1
1
2
1
1
1
1
1
1
1
1
2
2
4
1
1
2
HDX
2026-04-11
1
1
3
1
3
2
3
1
1
1
5
5
5
5
1
3
3
1
3
1
1
2
2
2
2
3
1
2
1
1
1
2
1
1
1
1
3
2
2
4
1
1
HDX
2026-04-11
1
1
4
1
2
2
4
1
1
1
5
5
5
5
1
1
1
1
2
1
1
2
1
2
2
1
1
1
3
2
1
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
1
2
4
2
7
2
null
null
5
5
5
5
1
3
1
1
3
1
2
2
2
2
2
1
6
1
777
2
null
null
1
1
null
2
2
4
3
1
6
HDX
2026-04-11
1
1
2
1
3
2
3
1
2
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
2
2
1
1
1
1
2
2
2
4
1
1
1
HDX
2026-04-11
1
1
4
1
4
2
2
1
3
1
5
5
5
5
1
3
1
1
2
1
2
2
2
2
2
1
1
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
2
1
3
2
2
1
3
1
5
5
5
5
4
2
1
1
3
1
1
2
2
2
2
1
6
1
1
1
1
2
1
1
1
1
1
1
2
2
3
1
4
6
HDX
2026-04-11
1
1
3
1
4
2
5
1
2
1
1
5
5
5
1
4
1
1
2
1
1
2
1
2
1
1
1
2
2
1
4
1
1
1
1
1
2
1
1
1
1
1
1
2
2
4
1
1
4
HDX
2026-04-11
1
1
2
1
3
2
5
1
3
1
5
5
5
5
1
3
2
1
2
1
1
2
2
2
2
2
3
2
1
1
2
2
1
1
1
1
2
2
4
2
1
HDX
2026-04-11
1
1
1
2
2
2
7
2
null
null
5
5
5
5
5
Amaan buuxa ayaan ku helaynaa
3
1
1
1
1
2
2
2
2
2
3
2
2
777
2
null
null
1
1
1
null
2
2
4
777
5
HDX
2026-04-11
1
1
2
1
1
1
1
1
2
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
1
2
2
2
1
1
2
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
2
2
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
3
1
3
1
4
1
1
1
5
5
5
5
3
3
3
1
2
1
1
2
2
2
2
2
1
2
3
1
2
2
1
1
1
3
2
2
3
1
3
HDX
2026-04-11
1
1
2
1
2
2
2
1
1
1
5
5
5
5
4
2
3
1
3
1
2
777
2
2
2
2
4
2
3
1
2
2
1
1
2
777
2
4
1
4
HDX
2026-04-11
1
1
3
1
5
2
6
1
1
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5
5
5
1
3
3
1
3
1
1
2
2
2
2
2
3
3
3
1
2
2
1
1
1
3
2
2
3
1
1
HDX
2026-04-11
1
1
1
2
3
2
3
1
4
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
2
1
1
1
2
2
1
1
1
1
1
2
2
2
2
1
2
HDX
2026-04-11
1
1
1
1
4
2
4
1
2
1
5
5
5
5
1
4
1
1
2
1
1
2
1
2
3
1
1
2
2
1
2
1
1
1
2
2
1
1
1
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4
1
1
2
HDX
2026-04-11
1
1
2
1
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1
5
1
1
1
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5
5
5
1
4
1
1
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1
1
2
2
2
2
1
1
1
1
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1
2
2
1
1
1
1
1
1
2
2
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1
1
1
HDX
2026-04-11
1
1
4
1
3
2
4
1
2
1
5
5
5
5
1
4
1
1
1
1
1
2
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
4
1
1
1
HDX
2026-04-11
1
1
3
1
4
2
7
1
2
1
5
5
5
5
2
3
3
1
2
1
1
777
2
2
2
3
2
2
3
1
1
1
2
1
1
1
1
1
2
2
3
1
2
HDX
2026-04-11
1
1
2
1
2
1
5
1
3
1
4
5
5
5
4
4
1
1
2
1
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2
2
1
1
1
1
1
1
1
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1
1
1
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4
1
4
1
HDX
2026-04-11
1
1
3
1
3
2
3
1
1
1
5
5
5
5
3
2
3
1
3
1
1
2
2
2
2
3
2
2
3
1
2
2
1
1
1
3
2
2
3
1
3
HDX
2026-04-11
1
1
2
2
3
2
4
1
2
1
1
5
5
5
4
4
1
1
2
1
1
2
1
2
1
1
1
2
2
1
1
1
1
1
1
777
1
1
1
1
1
1
2
2
4
1
4
1
HDX
2026-04-11
1
1
1
1
2
2
4
1
4
1
5
5
5
5
1
4
1
1
2
1
1
2
2
2
2
1
1
2
3
1
777
2
1
1
1
1
2
1
4
1
1
1
HDX
2026-04-11
1
1
1
2
4
2
7
2
null
null
5
5
5
5
1
3
3
2
null
null
null
777
777
2
2
777
null
2
777
1
2
777
1
1
1
3
2
1
4
3
1
HDX
2026-04-11
1
1
2
2
3
2
1
1
1
1
5
5
5
5
2
3
3
1
1
1
1
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2
2
2
3
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1
2
1
1
1
2
2
4
1
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HDX
2026-04-11
1
1
2
2
3
2
3
1
2
1
5
5
5
5
1
4
1
1
1
1
1
2
1
1
1
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
0
1
1
4
1
1
1
HDX
2026-04-11
1
1
1
1
1
1
4
1
3
1
5
5
5
5
1
4
1
1
1
1
1
1
2
2
2
2
2
4
2
3
1
1
1
2
1
1
1
1
1
1
1
1
1
2
2
4
3
1
HDX
2026-04-11
1
1
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1
2
1
4
1
3
1
5
5
5
5
1
3
1
1
1
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1
2
2
2
2
1
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3
1
1
2
1
1
1
1
1
1
1
1
1
1
1
2
2
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1
1
2
HDX
2026-04-11
1
1
2
1
2
2
4
1
2
1
5
5
5
5
1
3
1
1
1
1
1
2
2
2
2
3
2
2
777
1
2
2
1
1
1
1
2
2
4
1
1
HDX
2026-04-11
1
1
2
2
5
2
7
1
2
1
4
5
5
5
1
4
3
1
2
1
1
2
1
2
1
1
1
2
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
2
2
4
3
1
1
HDX
2026-04-11
1
1
4
2
5
2
1
1
2
1
5
5
5
5
1
3
1
1
1
1
1
2
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
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2
2
4
1
1
1
HDX
2026-04-11
1
1
1
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End of preview. Expand in Data Studio

Berbera District Conflict and Security Assessment - 2015

Publisher: Observatory of Conflict and Violence Prevention (inactive) · Source: HDX · License: cc-by-igo · Updated: 2023-03-03

Abstract

As part of its continual assessment of issues directly affecting community security and safety, OCVP conducted an extensive collection of primary data in the BERBERA District- the regional administration of the Sahil region of Somaliland. Further details @ http://www.ocvp.org/ocvp5/index.php/publications/dcsa/51-berbera-district-conflict-and-security-assessment-report-2015

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-03-03. Geographic scope: SOM.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics

Domain Public health
Unit of observation Subnational administrative unit observations
Rows (total) 200
Columns 123 (39 numeric, 84 categorical, 0 datetime)
Train split 160 rows
Test split 40 rows
Geographic scope SOM
Publisher Observatory of Conflict and Violence Prevention (inactive)
HDX last updated 2023-03-03

Variables

Geographic — region_name (range 1.0–1.0), district_name (range 1.0–1.0), reporting_petty_crime (range 1.0–5.0), reporting_petty_other ( ), police_yearly_trend (range 1.0–777.0) and 24 others.

Demographic — village_name (range 1.0–4.0), gender_responder (range 1.0–2.0), age (range 1.0–6.0).

Outcome / Measurement — number_of_stations (range 1.0–5.0), number_of_stations_other ( ), number_of_courts (range 1.0–777.0), number_of_courts_other ( ), number_of_conflicts and 2 others.

Identifier / Metadata — legal_clinic_ref ( ), legal_clinic_ref_other ( ), court_ref, court_ref_other, elders_ref and 8 others.

Other — marital_status (range 1.0–4.0), level_education (range 1.0–7.0), police_presense (range 1.0–2.0), distance_to_station (range 1.0–2.0), reporting_civil (range 1.0–6.0) and 66 others.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-erbera-district-conflict-and-security-assessment-2015")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
region_name int64 0.0% 1.0 – 1.0 (mean 1.0)
district_name int64 0.0% 1.0 – 1.0 (mean 1.0)
village_name int64 0.0% 1.0 – 4.0 (mean 2.1)
gender_responder int64 0.0% 1.0 – 2.0 (mean 1.44)
age int64 0.0% 1.0 – 6.0 (mean 3.01)
marital_status int64 0.0% 1.0 – 4.0 (mean 1.81)
level_education int64 0.0% 1.0 – 7.0 (mean 3.84)
police_presense int64 0.0% 1.0 – 2.0 (mean 1.07)
number_of_stations float64 7.0% 1.0 – 5.0 (mean 1.8871)
number_of_stations_other object 0.0%
distance_to_station float64 7.0% 1.0 – 2.0 (mean 1.0269)
reporting_civil int64 0.0% 1.0 – 6.0 (mean 4.6)
reporting_civil_other object 0.0% , Mayarka
reporting_petty_crime int64 0.0% 1.0 – 5.0 (mean 4.925)
reporting_petty_other object 0.0%
reporting_serious_crime int64 0.0% 1.0 – 6.0 (mean 4.955)
reporting_serious_other object 0.0% , Kuf
trusted_sec_prov int64 0.0% 1.0 – 6.0 (mean 4.76)
trusted_sec_other object 0.0% , Non
reason_for_choice_sec float64 0.5% 1.0 – 5.0 (mean 1.6683)
reason_for_choice_sec_other object 0.0% , Amaan buuxa ayaan ku helaynaa, Waa amnigii qaranka
level_trust_police int64 0.0% 1.0 – 4.0 (mean 3.26)
police_yearly_trend int64 0.0% 1.0 – 777.0 (mean 24.86)
court_presense int64 0.0% 1.0 – 2.0 (mean 1.025)
number_of_courts float64 2.5% 1.0 – 777.0 (mean 9.4923)
number_of_courts_other object 0.0%
where_is_court float64 2.5% 1.0 – 777.0 (mean 5.0)
distance_to_court float64 5.0%
legal_clinic_aware int64 0.0%
legal_clinic_use object 0.0% , 2
legal_clinic_ref object 0.0%
legal_clinic_ref_other object 0.0%
legal_clinic_issue object 0.0%
legal_clinic_issue_other object 0.0%
legal_clinic_judgement object 0.0%
legal_clinic_enforced object 0.0%
court_use int64 0.0%
court_ref object 0.0%
court_ref_other object 0.0%
court_issue object 0.0%
court_issue_other object 0.0%
court_judgement object 0.0%
court_enforced object 0.0%
elders_use int64 0.0%
elders_ref object 0.0%
elders_ref_other object 0.0%
elders_issue object 0.0%
elders_issue_other object 0.0%
elders_judgement object 0.0%
elders_enforced object 0.0%
religious_use int64 0.0%
religious_ref object 0.0%
religious_ref_other object 0.0%
religious_issue object 0.0%
religious_issue_other object 0.0%
religious_judgement object 0.0%
religious_enforced object 0.0%
trusted_just_prov int64 0.0%
trusted_just_prov_other object 0.0%
reason_for_choice_just float64 0.5%
reason_for_choice_just_other object 0.0%
conf_formal_just int64 0.0%
court_yearly_trend int64 0.0%
local_council_aware int64 0.0%
aware_of_services float64 4.0%
channels_comm float64 4.0%
consultation_participation object 0.0%
participation_frequency object 0.0%
participation_frequency_other object 0.0%
elected_opinion int64 0.0%
loc_gov_serviceseducation object 0.0%
loc_gov_serviceshealth object 0.0%
loc_gov_servicessecurity object 0.0%
loc_gov_servicesjustice object 0.0%
loc_gov_servicesagriculture object 0.0%
loc_gov_servicesinfrastructure object 0.0%
loc_gov_servicessanitation object 0.0%
loc_gov_serviceswater object 0.0%
loc_gov_servicesother object 0.0%
loc_gov_servicesdont_know object 0.0%
loc_gov_servicesrefused_to_answer object 0.0%
loc_gov_services_other object 0.0%
community_issueslack_of_water object 0.0%
community_issuesdrought object 0.0%
community_issueslack_of_infrastructure object 0.0%
community_issuespoor_sanitation object 0.0%
community_issuespoor_health object 0.0%
community_issuesunemployment object 0.0%
community_issuespoor_education object 0.0%
community_issuesshortage_of_electicity_supply object 0.0%
community_issuespoor_economy object 0.0%
community_issuescharcoal_production_deforestation object 0.0%
community_issuesbad_health_centers object 0.0%
community_issuesinsecurity object 0.0%
community_issuesgender_based_violence object 0.0%
community_issuesother object 0.0%
community_issuesdont_know object 0.0%
community_issuesrefused_to_answer object 0.0%
community_issues_other object 0.0%
council_yearly_trend float64 4.5%
witnessed_conflict float64 0.5%
number_of_conflicts object 0.0%
number_conf_violence object 0.0%
number_casualties object 0.0%
conflict_reasonresources object 0.0%
conflict_reasonfamily_disputes object 0.0%
conflict_reasoncrime object 0.0%
conflict_reasonpower object 0.0%
conflict_reasonrevenge object 0.0%
conflict_reasonbusiness_disputes object 0.0%
conflict_reasonrape object 0.0%
conflict_reasonlack_of_justice object 0.0%
conflict_reasonother object 0.0%
conflict_reasondont_know object 0.0%
conflict_reasonrefused_to_answer object 0.0%
conflict_reason_other object 0.0%
witnessed_crimes float64 0.5%
how_safe float64 0.5%
safety_yearly_trend float64 0.5%
nspc float64 8.0%
njpc object 0.0%
esa_source object 0.0%
esa_processed object 0.0%

Numeric Summary

Column Min Max Mean Median
region_name 1.0 1.0 1.0 1.0
district_name 1.0 1.0 1.0 1.0
village_name 1.0 4.0 2.1 2.0
gender_responder 1.0 2.0 1.44 1.0
age 1.0 6.0 3.01 3.0
marital_status 1.0 4.0 1.81 2.0
level_education 1.0 7.0 3.84 4.0
police_presense 1.0 2.0 1.07 1.0
number_of_stations 1.0 5.0 1.8871 2.0
distance_to_station 1.0 2.0 1.0269 1.0
reporting_civil 1.0 6.0 4.6 5.0
reporting_petty_crime 1.0 5.0 4.925 5.0
reporting_serious_crime 1.0 6.0 4.955 5.0
trusted_sec_prov 1.0 6.0 4.76 5.0
reason_for_choice_sec 1.0 5.0 1.6683 1.0

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 15 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

  • Data originates from Observatory of Conflict and Violence Prevention (inactive) and has not been independently validated by ESA.
  • Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

@dataset{hdx_africa_erbera_district_conflict_and_security_assessment_2015,
  title     = {Berbera District Conflict and Security Assessment - 2015},
  author    = {Observatory of Conflict and Violence Prevention (inactive)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/erbera-district-conflict-and-security-assessment-2015},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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