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Project

Emergence

Copyright

© MIT City Science

MIT City Science

Emergence brings together more than a decade of CityScope research into a practical emergency-response platform for cities under stress. The approach integrates incomplete and heterogeneous data, makes assumptions visible, tests scenarios rapidly, and translates analysis into accountable operational recommendations.

At its core is a Situational Urban Digital Twin (SUDT): a purpose-specific digital twin assembled in days or weeks around a bounded emergency question. The goal is not to reproduce every detail of a city, but to provide the right representation, at the right time, to support action.

Chile | Wildfire Data Viewer

Negev | The Right to Shelter

Case studies

Hamburg FindingPlaces

During the refugee housing emergency of 2015–2018, FindingPlaces demonstrated how this methodology could support rapid public decision-making under political and social pressure. The platform combined citywide GIS data, planning constraints, and a tangible interface to help residents and officials evaluate potential housing locations. Public workshops identified hundreds of candidate sites, several of which advanced into formal planning channels.

Andorra COVID-19 Monitoring

In Andorra, a national mobility observatory originally developed for tourism and urban analytics was rapidly repurposed during the COVID-19 pandemic. High-resolution mobility data revealed how confinement measures affected movement, social interaction, and economic activity, showing how modular data pipelines can pivot from long-term analysis to crisis interpretation.

Kharkiv Redevelopment Observatory

In Kharkiv, the group contributed to conflict-related reconstruction planning through a lightweight, web-based variant of its urban simulation platform. The work integrated spatial data, proposed interventions, stakeholder feedback, and scenario visualization for local agencies and international partners, creating a shared analytical environment for a city operating under conflict and uncertainty.

Planning for sheltering during conflict

In the Negev and Beit-Shemesh, rapid web-based platforms combined incomplete municipal records, open building data, local knowledge, shelter inventories, land ownership, and planning assumptions into actionable spatial models. These tools diagnosed shelter gaps, tested access scenarios, and produced ranked placement recommendations—translating fragmented data into a single operational picture and a phased list of high-impact interventions.

Wildfire response in Chile

During fires affecting the Biobío and Ñuble regions, City Lab Biobío supported the Biobío Regional Government with daily satellite-based territorial analysis and launched a public wildfire viewer with data partners. The viewer integrates fire-detection data, demographics, housing, schools, hospitals, road closures, disrupted routes, and sources including NASA FIRMS, INE, the 2024 Census, IDE Chile, and Waze. The case shows how a City Science Network platform can support government decision-making and public transparency during a fast-moving climate emergency.

Together, these cases show how City Science tools can combine open data, municipal records, mobility data, remote sensing, field reports, and community knowledge into interactive systems for emergency decision support. They help agencies identify exposed populations, vulnerable assets, priority interventions, critical assumptions, and changing conditions.