ACLED
Introduction:
The Conflict and Protest Events dataset displays the dates, actors, types of violence, locations, and fatalities of all reported political violence and protest events across Africa, South and Southeast Asia, the Middle East, and Europe. Political violence and protest includes events that occur in the context of civil wars and periods of instability, public protest, and regime breakdown. This data can be used for immediate and long-term analysis and mapping of political violence and protest across developing countries through the use of historical data, as well as to inform humanitarian and development work in crisis- and conflict-affected contexts through real-time data updates and reports.
This data is produced by the Armed Conflict Location and Event Data Project (ACLED). The project covers all African countries from 1997 to the present, and select countries in the Middle East, Asia, and Europe from 2010 or 2018.
Tags: Weekly, Event Record, Risk, Logistics and Supply Chain, GeoJson
Event Record, Risk - map_coordinates, Identifier, value, country_code, country, location_level1, location_level2
GeoJson - map_coordinates
Modules:
Scrapping:
ACLED scrapper gets the data of violence types, and numbers for a particular location(country, state, city, province etc). Data is scrapped using API call requests and handled using the pandas library.
Cleaning:
Column names are rectified, extra spaces, special characters etc are removed. Columns with irrelevant data; ["geom", "gid_1", "gid_2"] are dropped. Location names are rectified and country names are formatted correctly.
Geocoder:
Coordinates are added to the metadata for the country/state/city/subdivision level location. Geocoder library is used for getting coordinates.
Standardisation:
Additional information like region, region_code, subdivision level, subdivision code/ income level , name, domain, subdomain, source and description are included in the metadata. Function for fetching ISO country code and appending it is present in standardisation. Predefined domains and subdomains are added in this step.
Ingest:
Metadata and event record data is ingested in the mongoDB and latest timestamp id (mongoDB id for latest timestamp) is appended to metadata for decreasing search for latest data point.
LocationRisk:
Three different files are available in the LocationRisk. Model.py is used for validating data in the mongoDB and risk_model.py is used to calculate risk score for the data. Risk for ACLED is calculated using z-score and z-score is classified into the risk categories. Pipeline.py fetches data from the mongoDB and implements model.py and risk_model.py. Risk data is ingested into the location risk database.
Data Format:
Time series Data:
| Attributes | Descriptions |
|---|---|
| ts_ref_id | Id used to connect time series data to the metadata |
| value | Time Series information stored for ACLED |
| timestamp | standard timestamp used for the timeseries |
MetaData:
| Short Name | Long Name | Description |
|---|---|---|
| ts_ref_id | Time series reference Id | Id used to connect metadata to the timeseries |
| map_coordinates | Map coordinates | Latitude and Longitude of the location (geojson format) |
| country | Country Name | Country name for which the conflict events are recorded |
| country_code | Country code | ISO 3622 letter country code |
| date_of_sampling | Date in "%d/%m/%Y" | Date on which data was collected |
|---|---|---|
| domain | Domain | Predefined domain by Taiyo |
| subdomain | Subdomain | Predefined subdomain by Taiyo |
| Identifier | Identifier / Indicator | 6 types of identifiers are stored for conflict and protest events - battles - protests - riots - explosions_remote_violence - strategic_developments - violence_against_civilians |
| location_level_1 | Location level | Location of the event |
| location_level_2 | Location level 2 | Granular location of the event |
| name | name | Name of the Source |
| objectid | Objectid as supplied by the ACLED | Location-specific unique id assigned by the ACLED |
| region | Rgion | Region for a country according to World Bank Standards |
| region_code | Region Code | Region code for a region according to World Bank Standards |
| value | Value of Identifiers | Number of identifier events happening at that location |
| sub_division_name | Sub Division | State/Country/Province ISO 3622 sub division name etc |
| sub_division_code | Sub Division Code | ISO 3622 sub division code |
| url | Data Source URL | Url to access the datasource |
| income_level | Income level | Income level of the region in context |
| sample_frequency | Frequency | Frequency of data being collected/updated |
| time_of_sampling | Time of data collection | Time of data collection recorded in "%I:%M:%S %p" |
| timestamp | timestamp | UTC standard time of data sampling |
| shape_length | Shape length | Shape length of the region |
| shape_area | Shape Area | Area of the region |
|---|---|---|
| subdivision_level | Subdivision type meta | Subdivision (state/province/territory etc) meta data |
| city_level | City | City metadata |
Data Flow:
The above data pipeline runs on Argo and it will be executed on a periodic frequency.
Taiyo Data Format:
| Entity | ACLED geo political |
|---|---|
| Frequency | Weekly |
| Updated On | 21-04-2022 UTC 06:16:09 PM |
| Coverage | City and state level |
| Uncertainties | Calculated risk is not absolute but dependent on data collected & according to the standard formula used by Taiyo. It can be subjective depending on the business use case. |
Scope for Improvement
Following can be improved in the next version of the data product:
- Bulk data ingestion lacks logic of including previous data in the current
dataset. Hence, MetaData and Ingest step needs to be updated for the ACLED data product.
- Data can be more granular.
Useful Links:
- https://resourcewatch.org/data/
- https://www.wri.org/data/
Standard Fields for Front-End
Data Product Name: ACLED
Image:
Dataset Availability: 2021-04-01 to current
Dataset Provider: The Armed Conflict Location & Event Data Project (ACLED) and Resource Watch
Tags:Weekly, Time-series, Risk, Logistics and Supply Chain
Last update:
Type: Event Record
Frequency:Weekly
Category: Risk
Domain: Geopolitics
Sub-domain: Geopolitical Threats, Conflict and Protest
Measurement Use Cases: political violence and protest events, fatalities, crisis mapping, Battles, protests, riots, remote explosions, violence against civilians