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Determining the poverty levels of various regions throughout the world is crucial in identifying interventions for poverty reduction initiatives and directing resources fairly. However, reliable data on global economic livelihoods is hard…
In recent years, there has been growing interest in leveraging machine learning for homeless service assignment. However, the categorical nature of administrative data recorded for homeless individuals hinders the development of accurate…
Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints. Existing algorithms almost always allow for…
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the…
Information and communication technologies (ICTs) have increasingly become vital for people on the move including the nearly 80 million displaced due to conflict, violence, and human right violations globally. However, existing research on…
Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improve emerging infectious disease forecasting under operational…
Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private…
This work contributes to the discussion on how innovative data can support a fast crisis response. We use operational data from Facebook to gain useful insights on where people fleeing Ukraine following the Russian invasion are likely to be…
Understanding temporary migration is crucial for addressing various socio-economic and environmental challenges in developing countries. However, traditional surveys often fail to capture such movements effectively, leading to a scarcity of…
Hunger crises are critical global issues affecting millions, particularly in low-income and developing countries. This research investigates how machine learning can be utilized to predict and inform decisions regarding famine and hunger…
Regularization of control policies using entropy can be instrumental in adjusting predictability of real-world systems. Applications benefiting from such approaches range from, e.g., cybersecurity, which aims at maximal unpredictability, to…
Fine-grained migration data illuminate demographic, environmental, and health phenomena. However, United States migration data have serious drawbacks: public data lack spatial granularity, and higher-resolution proprietary data suffer from…
Knowledge of population distribution is critical for building infrastructure, distributing resources, and monitoring the progress of sustainable development goals. Although censuses can provide this information, they are typically conducted…
More than a billion people around the world experience intermittence in their water supply, posing challenges for urban households in Global South cities. An intermittent water supply (IWS) system prompts water users to adapt to service…
Violence and armed conflicts have emerged as prominent factors driving food crises. However, the extent of their impact remains largely unexplored. This paper provides an in-depth analysis of the impact of violent conflicts on food security…
This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of…
Modelling migration is complicated, as people move for many reasons. Some leave their country for the first time, others return to places they once called home, or move on to new destinations. However, most models focus only on who arrives,…
We propose a phenomenological non-equilibrium framework for modelling the evolution of cities which describes the intra-urban resettlement as an irreversible diffusive process. We validate this framework using the actual migration data for…
To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain…
Housing instability is a persistent challenge faced by households in cities across the United States. In worst-case scenarios, households are displaced from their residences and forced to start anew. In an effort to mitigate the harms of…