Data related to the quality of life and overall well-being of human populations,  including poverty, conflict, access to energy, housing conditions, and sanitation.

human-well-being

Human Well-being

Data related to water scarcity, water quality, flood risk, and water stress.

water-risks

Water Risks

### Overview  
  
This forecast categorizes areas by the modeled risk of conflict over the following 12 months. Each district is labeled with one of the following: emerging conflict, ongoing conflict, or peace.    
  
 Conflict is defined by this subnational (ADM-2) district, forecasted to have at least 10 fatalities in the next 12 months using a random forest machine model that learns from past records and optimizes its predictive power.  The forecast incorporates frequently updated environmental variables, including water indicators, in order to capture the many stressors that may lead to conflict.   
  
  It was produced by researchers at the World Resources Institute (WRI) and the Hague Center for Strategic Studies (HCSS) under the Water, Peace, and Security Partnership (WPS), and updated forecasts are released on a quarterly basis.    
  
### Methodology  
  
This model forecasts conflict (defined as organized violence resulting in at least 10 fatalities (measured with ACLED data) over a 12-month period) up to a year in advance using a random forest machine learning model.  Machine learning techniques are adept at detecting subtle or complex patterns within large volumes of data without requiring a priori instructions or a theoretical framework. Since conflict literature does not offer a clear consensus on what factors contribute to violence, nor on the causal pathways to conflict, machine learning was identified as a viable means of establishing quantitative linkages between local conditions and the subsequent emergence (or continuation, or cessation) of conflict. 
  
  

The data used as indicators in the model capture a wide range of factors—from socioeconomics to governance to biophysical phenomena—and are correspondingly collected at a variety of geographic and temporal resolutions.
In order to utilize and analyze them in concert, they reconciled all data to a common unit of analysis the district (ADM-2)-month.  reconciling all data to a common unit of analysis.  Only districts with at least three years of recorded organized violence within the selected conflict database were eligible for inclusion in the modeling process.  Currently the geographic scope is limited to the following regions: Africa, Asia, and the Middle East.
  
  

The model used this historical data to learn to “predict” past conflict outcomes based on past conditions. The model was then applied to a separate tranche of more recent historical data and used its performance on this validation data to estimate the model’s predictive power on “true” forecasts made based on current conditions.  When applied to test data, the model captures 86 percent of future conflicts. The model’s conflict signal is noisy, with half of conflict predictions representing false positives.  
  
### Additional Information  
  
Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.  
  
### Disclaimer  
  
Excerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.

Water, Peace and Security Global Early Warning Tool

wps-001-conflict-forecast-archived-rw

Conflict Forecast October 2019 - September 2020

September 2020 - August  2021 Conflict Forecast

June 2020 - May  2021 Conflict Forecast

December 2018 - December 2019 Risk of Conflict Liz API

A forecast for risk of emerging or ongoing conflict with at least 10 fatalities, or peace over the next 12 months. 

No Team

Conflict Forecast

Conflict Forecast October 2019 - September 2020

September 2020 - August 2021 Conflict Forecast

June 2020 - May 2021 Conflict Forecast

December 2018 - December 2019 Risk of Conflict Liz API