Internal Displacement Monitoring Centre

Real-Time Displacement Forecast for Natural Hazards: The team developed a complete framework of real-time displacement prediction caused by storm and flood events. It allows access to and combinations of various data sources reflecting hazard intensity, exposed population, vulnerability and the people displaced in the past, benefiting training a machine learning algorithm that allows the forecast of displaced people in future events.

For more info, see the report and for the code gitlab.

Students: Janik Baumer, Vincent Bardenhagen, Daniel Benesch, Mian Zhong, Xiang Li, Gaël Perrin, Nathan Rouff, Anastasia Sycheva

Mentors: Lionel Trebuchon, Pelayo Choya

IMPACT Initiatives: External factors

One approach to providing humanitarian aid is in the form of cash-based assistance. To enable efficient distribution of funds, IMPACT Initiatives conducts a monthly market research in Syria to compute the price of a Survival Minimum Expenditure Basket (SMEB), that “represents the minimum […] items required to support a 6-person household for a month”. These items vary by season and include food- and non-food items. In this work, the influence of external factors on the price of the SMEB in Syria were analyzed

For more info, see the report and for the code gitlab.

Students: Damian Durrer, Dominique Heyn, Heiko Kromer, Jonathan Mendieta

Mentor: Antonios Garas

IMPACT Initiatives: Imputation

Humanitarian aid in the form of cash-based assistance is becoming increasingly popular in regions of conflict. IMPACT Initiatives provides data-driven solutions to better inform humanitarian cash programming in Syria. This assistance relies on the calculation of a Survival Minimum Expenditure Basket (SMEB), corresponding to the minimum amount of cash necessary to purchase items required to support a 6-person household for one month in a specific geographical area. Price data for these items are acquired through informants in the regions of interest. In this project, the team  investigated methods to impute missing price values.

For more info, see the report and for the code gitlab.

Students: Pepa Arán Paredesa, Olivier Dietricha, Mariëlle van Kootena, and Pierre Wintera

Mentor: Mario Tomasello

IMPACT Initiatives: Interpretable indicators

Using the commodity price data collected by IMPACT Initiatives’ Market Monitoring exercise in Syria, the team developed higher-level indicators that characterize price changes over time. The resulting framework is both flexible in which facet of the data to analyze, in addition to being generalizable to similar data sets of other crisis-affected countries. The output is kept in a form that is easy to interpret so that it can clearly help inform humanitarian actions.

For more info, see the report and for the code gitlab.

Students: Peshal Agarwal, Jelena Čuklina, Andrei Kolar, Anna Maddux

Mentor: Amit Gupta

IMPACT Initiatives: Prediction

It is becoming more and more of a common practice for aid organizations and NGOs to deliver cash-based assistance to war-town countries such as Syria. Meanwhile, it is critical to forecast the minimum cost of monthly survival (SMEB, ”survival minimum expenditure basket”) to ensure that the aid delivered is neither too high nor too low. The goal of this project was to develop a model to forecast the SMEB price. It turns out that the baseline model which simply uses the value from the previous month as a prediction for the upcoming month outperformed all other tested models. For a forecast of several months into the future, the team presents a variant of an ARIMA model.

For more info, see the report and for the code gitlab.

Students: Aneesh Dahiya, Jaco Fuchs, Christoph Glanzer, Julia Ortheden

Mentor: Anastasia Pentina