fatalities001

What data informs the model?

Input data (predictors)

The fatalities001 model was informed by data on hundreds of variables from data providers such as the Uppsala Conflict Data Program (UCDP), ACLED, PRIO-GRID, the World Bank, IMF, FAO, Mapsdam, SPEI and MIRCA. Based on these raw data variables, VIEWS also created a suite of additional variables by applying data transformations such as time and space lags, imputations to fill in for missing data, and other common data processing techniques.

Together, the raw- and processed data variables informing the various VIEWS models are referred to as  features, which are grouped into feature sets based on the overall theme they relate to and/or the data provider(s) from which they are derived.

The feature sets that informed the sub-models and model ensembles in the fatalities001  model are documented in the GitHub repository FCDO_predicing_fatalities.

Key themes of predictors in the model

• Conflict history:
  • A suite of features capturing the history of conflict in each country and sub-national grid cell, e.g. the number of battle-related deaths per unit and level of analysis, and measures of the temporal and spatial distance to recent conflict events. 
  • Data providers: Uppsala Conflict Data Program (UCDP), The Armed Conflict Location & Event Data Project (ACLED).
• Political institutions, democracy:
  • Features that capture democracy indices and the strength of political institutions in each country, such as liberal democracy, rule of law, equality, and the level of exclusion of social groups in politics. 
  • Data providers: Varieties of Democracy (V-Dem)
• Development:
  • Measures of development as provided by the World Bank Indicators, e.g. GDP per capita, infant mortality rate, and school enrollment. 
  • Data providers: The World Bank (Word Development Indicators, WDI)
• Economic growth:
  • A feature set focusing specifically on historic and future economic growth, e.g. real GDP growth per year and growth forecasts for the coming years. 
  • Data providers: The International Monetary Fund World Economic Outlook (IMF WEO)
• Climate & societal vulnerability:
  • Feature sets capturing climate extremes and societal vulnerability to climate hazards and other external shocks, e.g. climate extreme indices, reliance on agriculture, crop yields, precipitation, freshwater withdrawal, water management efficiency, and access to renewable resources. 
  • Data providers: United Nations Food and Agriculture Organisation (FAO), FAO AQUASTAT, PRIO-GRID, MIRCA, MAPSPAM, SPEI Global Drought Monitor
• News monitoring:
  • A feature set based on the Mueller & Rauh (2018) topic model, which captures conflict risks as drawn from a topic analysis of news media. 
  • Data providers:  Mueller & Rauh (2018)
• Natural and social geography:
  • A feature set capturing terrain type, distance to natural resources, demography, proximity to cities and country borders.
  • Data providers: PRIO-GRID
• Food security and access to basic needs:
  • Feature sets capturing staple food prices along with measures of food security and access to basic human needs, such as mean food prices, food price inflation, undernourishment, access to clean water, and basic sanitation. 
  • Data providers: United Nations Food and Agriculture Organisation (FAO), FAOSTAT
    

How does the model work?

Sub-models: combinations of feature sets and algorithms

As a first step when training the fatalities001 model, each feature set was paired with an advanced machine learning algorithm.

The fatalities001 model employed four such algorithms, which you can read more about in our technical reports on the model: random forests, gradient boosting, markov models, and hurdle models. 

The result was a series of sub-models, or constituent models are they are more commonly called, that used patterns in their respective subsets of historic data to generate predictions for future conflict.

Ensemble models: groups of sub-models using “the wisdom of the crowd”

Much like a crowd tends to be wiser than the individuals composing it, prediction models that are informed by a number of smaller and specialized sub-models are known to be more robust and generate stronger predictions than single models.

As a second step in the model training procedures, the sub-models above were therefore combined into two groups or ensembles of models – one ensemble for each level of analysis. 

Two different ensembling techniques were used for this purpose:

• The country-level ensemble model combined the predictions from each of the sub-models using a genetic algorithm that assigned different weights to the contribution from each model in order to maximise predictive performance.
  
• The sub-national ensemble model, in turn, used a simple unweighted average of the sub-model results.

The ensembling techniques above are motivated and described at length in the technical report on the fatalities001 model. 

Data infrastructure

The fatalities models are built in a rigorous and sophisticated data infrastructure called VIEWS3 – the third iteration of the back-end system and database supporting the VIEWS system. While parts of the database are restricted in order to comply with the user licenses applied by our data providers, the codebase – much like our model documentation – is available under an open-source license in a series of GitHub repositories that ensure full transparency of our work. The VIEWS3 infrastructure is provided alongside the web-based CLI viewser, which allows users to interact with the VIEWS3 back-end system directly from the browser.

VIEWS3 and viewser are documented in a suite of open-source GitHub repositories that users are welcome to consult for detailed information.